AlgorithmicResearchGroup/arxiv_python_research_code

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gecco-2022	gecco-2022-main/AND-XOR/DynamicalMatrix.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Tue Oct 10 22:09:09 2017 @author: Hightoutou """ def DM_mass(N, x0, y0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] M = np.zeros((2N, 2N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly rijsq = dx2+dy2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij m_sqrt = np.zeros((2N, 2N)) m_inv = np.zeros((2N, 2N)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_3D(N, x0, y0, z0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] Lz = L[2] M = np.zeros((3N, 3N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly dz = z0[i]-z0[j] dz = dz-round(dz/Lz)Lz rijsq = dx2+dy2+dz*2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy, dxdz], [dydx, dydy, dydz], [dzdx, dzdy, dzdz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3i:3i+3,3j:3j+3] = Mij M[3j:3j+3,3i:3i+3] = Mij M[3i:3i+3,3i:3i+3] = M[3i:3i+3,3i:3i+3] - Mij M[3j:3j+3,3j:3j+3] = M[3j:3j+3,3j:3j+3] - Mij m_sqrt = np.zeros((3N, 3N)) m_inv = np.zeros((3N, 3N)) for i in range(N): m_sqrt[3i, 3i] = 1/np.sqrt(m0[i]) m_sqrt[3i+1, 3i+1] = 1/np.sqrt(m0[i]) m_sqrt[3i+2, 3i+2] = 1/np.sqrt(m0[i]) m_inv[3i, 3i] = 1/m0[i] m_inv[3i+1, 3i+1] = 1/m0[i] m_inv[3i+2, 3i+2] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_Yfixed(N, x0, y0, D0, m0, Lx, y_bot, y_top, k): import numpy as np M = np.zeros((2N, 2N)) contactNum = 0 for i in range(N): r_now = 0.5D0[i] if y0[i]-y_bot<r_now or y_top-y0[i]<r_now: M[2i+1, 2i+1] = M[2i+1, 2i+1]+1/r_now/r_now for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] rijsq = dx2+dy2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij M = kM m_sqrt = np.zeros((2N, 2N)) m_inv = np.zeros((2N, 2N)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_Xfixed(N, x0, y0, D0, m0, Ly): import numpy as np M = np.zeros((2N, 2N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly rijsq = dx2+dy2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij m_sqrt = np.zeros((2N, 2N)) m_inv = np.zeros((2N, 2N)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_DiffK_Yfixed(N, x0, y0, D0, m0, Lx, y_bot, y_top, k_list, k_type): import numpy as np M = np.zeros((2N, 2N)) contactNum = 0 for i in range(N): r_now = 0.5D0[i] if y0[i]-y_bot<r_now or y_top-y0[i]<r_now: M[2i+1, 2i+1] = M[2i+1, 2i+1] + k_list[k_type[i]] / r_now / r_now for j in range(i): dij = 0.5 * (D0[i] + D0[j]) dijsq = dij*2 dx = x0[i] - x0[j] dx = dx - round(dx / Lx) Lx dy = y0[i] - y0[j] rijsq = dx2 + dy2 if rijsq < dijsq: contactNum += 1 k = k_list[(k_type[i] ^ k_type[j]) + np.maximum(k_type[i], k_type[j])] rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -k * rijmat / rijsq / dijsq Mij2 = -k * (1.0 - rij / dij) * (rijmat / rijsq - [[1,0],[0,1]]) / rij / dij Mij = Mij1 + Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij m_sqrt = np.zeros((2N, 2N)) m_inv = np.zeros((2N, 2N)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) w,v = np.linalg.eig(M) return w,v def DM_mass_Zfixed_3D(N, x0, y0, z0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] Lz = L[2] M = np.zeros((3N, 3N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly dz = z0[i]-z0[j] rijsq = dx2+dy2+dz2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy, dxdz], [dydx, dydy, dydz], [dzdz, dzdy, dzdz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3i:3i+3,3j:3j+3] = Mij M[3j:3j+3,3i:3i+3] = Mij M[3i:3i+3,3i:3i+3] = M[3i:3i+3,3i:3i+3] - Mij M[3j:3j+3,3j:3j+3] = M[3j:3j+3,3j:3j+3] - Mij m_sqrt = np.zeros((3N, 3N)) m_inv = np.zeros((3N, 3N)) for i in range(N): m_sqrt[3i, 3i] = 1/np.sqrt(m0[i]) m_sqrt[3i+1, 3i+1] = 1/np.sqrt(m0[i]) m_sqrt[3i+2, 3i+2] = 1/np.sqrt(m0[i]) m_inv[3i, 3i] = 1/m0[i] m_inv[3i+1, 3i+1] = 1/m0[i] m_inv[3i+2, 3i+2] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_UpPlate(N, x0, y0, D0, m0, Lx, y_up, m_up): import numpy as np M = np.zeros((2N+1, 2N+1)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij*2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] rijsq = dx2+dy2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij for i in range(N): r_now = 0.5D0[i] if y0[i]<r_now: M[2i+1, 2i+1] = M[2i+1, 2i+1]+1/r_now*2 if y_up-y0[i]<r_now: M[2i+1, 2i+1] = M[2i+1, 2i+1]+1/r_now2 M[2N, 2N] = M[2N, 2N]+1/r_now2 M[2i+1, 2N] = M[2i+1, 2N]-1/r_now2 M[2N, 2i+1] = M[2N, 2i+1]-1/r_now2 m_sqrt = np.zeros((2N+1, 2N+1)) m_inv = np.zeros((2N+1, 2N+1)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] m_sqrt[2N, 2N] = 1/np.sqrt(m_up) m_inv[2N, 2N] = 1/m_up #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_UpPlate_3D(N, x0, y0, z0, D0, m0, Lx, Ly, z_up, m_up): import numpy as np M = np.zeros((3N+1, 3N+1)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij*2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly dz = z0[i]-z0[j] rijsq = dx2+dy2+dz2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy, dxdz], [dydx, dydy, dydz], [dzdz, dzdy, dzdz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3i:3i+3,3j:3j+3] = Mij M[3j:3j+3,3i:3i+3] = Mij M[3i:3i+3,3i:3i+3] = M[3i:3i+3,3i:3i+3] - Mij M[3j:3j+3,3j:3j+3] = M[3j:3j+3,3j:3j+3] - Mij for i in range(N): r_now = 0.5D0[i] if z0[i]<r_now: M[3i+2, 3i+2] = M[3i+2, 3i+2]+1/r_now*2 if z_up-z0[i]<r_now: M[3i+2, 3i+2] = M[3i+2, 3i+2]+1/r_now2 M[3N, 3N] = M[3N, 3N]+1/r_now2 M[3i+2, 3N] = M[3i+2, 3N]-1/r_now2 M[3N, 3i+2] = M[3N, 3i+2]-1/r_now2 m_sqrt = np.zeros((3N+1, 3N+1)) m_inv = np.zeros((3N+1, 3N+1)) for i in range(N): m_sqrt[3i, 3i] = 1/np.sqrt(m0[i]) m_sqrt[3i+1, 3i+1] = 1/np.sqrt(m0[i]) m_sqrt[3i+2, 3i+2] = 1/np.sqrt(m0[i]) m_inv[3i, 3i] = 1/m0[i] m_inv[3i+1, 3i+1] = 1/m0[i] m_inv[3i+2, 3i+2] = 1/m0[i] m_sqrt[3N, 3N] = 1/np.sqrt(m_up) m_inv[3N, 3*N] = 1/m_up #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v	12,537	30.423559	104	py
gecco-2022	gecco-2022-main/AND-XOR/ConfigPlot.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Tue Oct 10 21:01:26 2017 @author: Hightoutou """ import numpy as np def ConfigPlot_DiffSize(N, x, y, D, L, mark_print): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse Dmin = min(D) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] D_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) D_all.append(D[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_alpha(0.3) if D_all[i] > Dmin: e.set_facecolor('C1') else: e.set_facecolor('C0') i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass(N, x, y, D, L, m, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffMass2(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness(N, x, y, D, L, m, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C2') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness2(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C2') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness3(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='^', s=80, color=(0, 1, 0, 1)) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='s', s=80, color=(0, 0, 1, 1)) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='', s=100, color=(1, 0, 0, 1)) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('k') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) import matplotlib.lines as mlines red_star = mlines.Line2D([], [], color=(1, 0, 0), marker='', linestyle='None', markersize=10, label='Output') blue_square = mlines.Line2D([], [], color=(0, 0, 1), marker='s', linestyle='None', markersize=10, label='Input 2') green_triangle = mlines.Line2D([], [], color=(0, 1, 0), marker='^', linestyle='None', markersize=10, label='Input 1') plt.legend(handles=[red_star, green_triangle, blue_square], bbox_to_anchor=(1.215, 1)) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffMass_3D(N, x, y, z, D, L, m, mark_print): import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D m_min = min(m) m_max = max(m) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.set_aspect('equal') sphes = [] m_all = [] for i in range(int(N/2)): x_now = x[i]%L[0] y_now = y[i]%L[1] z_now = z[i]%L[2] r_now = 0.5D[i] #alpha_now = 0.2+(m_all[i]-m_min)/(m_max-m_min)0.3 alpha_now = 0.3 pos1 = 0 pos2 = 1 for j in range(pos1, pos2): for k in range(pos1, pos2): for l in range(pos1, pos2): u = np.linspace(0, 2 * np.pi, 100) v = np.linspace(0, np.pi, 100) x_plot = x_now+jL[0]+r_now np.outer(np.cos(u), np.sin(v)) y_plot = y_now+kL[1]+r_now np.outer(np.sin(u), np.sin(v)) z_plot = z_now+lL[2]+r_now np.outer(np.ones(np.size(u)), np.cos(v)) ymin = y_plot[y_plot>0].min() ymax = y_plot[y_plot>0].max() print (i, ymin, ymax) ax.plot_surface(x_plot,y_plot,z_plot,rstride=4,cstride=4, color='C0',linewidth=0,alpha=alpha_now) #sphes.append(e) #m_all.append(m[i]) # i = 0 # for e in sphes: # ax.add_artist(e) # e.set_clip_box(ax.bbox) # e.set_facecolor('C0') # e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) # i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) ax.set_zlim(0, L[2]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_YFixed_rec(N, x, y, D, Lx, y_top, y_bot, m, mark_order): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_print = 0 m_min = min(m) m_max = max(m) if m_min == m_max: m_max = 1.001 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) for k in range(-1, 2): e = Ellipse((x_now+kLx, y_now), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.3+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 #rect = Rectangle([0, y_top], Lx, 0.2D[0], color='C0') #ax.add_patch(rect) for nn in np.arange(N): x1 = x[nn]%Lx d_up = y_top-y[nn] d_bot = y[nn]-y_bot r_now = 0.5D[nn] if d_up<r_now: ax.plot([x1, x1], [y[nn], y[nn]+r_now], '-', color='w') if d_bot<r_now: ax.plot([x1, x1], [y[nn], y[nn]-r_now], '-', color='w') for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < Dmn: x2 = x[mm]%Lx if x2>x1: xl = x1 xr = x2 yl = y[nn] yr = y[mm] else: xl = x2 xr = x1 yl = y[mm] yr = y[nn] dx0 = xr-xl dx = dx0-round(dx0/Lx)Lx dmn = np.sqrt(dx2+dy2) if dmn < Dmn: if dx0<Dmn: ax.plot([xl, xr], [yl, yr], '-', color='w') else: ax.plot([xl, xr-Lx], [yl, yr], '-', color='w') ax.plot([xl+Lx, xr], [yl, yr], '-', color='w') ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/plot_test/fig'+str(int(ind_nt+1e4))+'.png', dpi = 150) def ConfigPlot_DiffMass_SP(N, x, y, D, L, m, mark_print, ind_in, ind_out, ind_fix): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)0.3) if i == ind_in: e.set_edgecolor('r') e.set_linewidth(width) if i == ind_out: e.set_edgecolor('b') e.set_linewidth(width) if i == ind_fix: e.set_edgecolor('k') e.set_linewidth(width) ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass_FixLx(N, x, y, D, L, m, mark_print, ind_wall): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i] y_now = y[i]%L[1] for l in range(-1, 2): e = Ellipse((x_now, y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) if ind_wall[i] > 0: e.set_edgecolor('k') e.set_linewidth(width) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass_SP_rec(N, x, y, D, L, m, mark_print, ind_in, ind_out, ind_fix): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)0.3) if i == ind_in: e.set_edgecolor('r') e.set_linewidth(width) if i == ind_out: e.set_edgecolor('b') e.set_linewidth(width) if i == ind_fix: e.set_edgecolor('k') e.set_linewidth(width) Lx = L[0] Ly = L[1] for nn in np.arange(N): x1 = x[nn]%Lx y1 = y[nn]%Ly for mm in np.arange(nn+1, N): x2 = x[mm]%Lx y2 = y[mm]%Ly if x2>x1: xl = x1 xr = x2 yl = y1 yr = y2 else: xl = x2 xr = x1 yl = y2 yr = y1 dx0 = xr-xl dx = dx0-round(dx0/Lx)Lx if y2>y1: xd = x1 xu = x2 yd = y1 yu = y2 else: xd = x2 xu = x1 yd = y2 yu = y1 dy0 = yu-yd dy = dy0-round(dy0/Ly)Ly Dmn = 0.5(D[mm]+D[nn]) dmn = np.sqrt(dx2+dy2) if dmn < Dmn: if dx0<Dmn and dy0<Dmn: ax.plot([xl, xr], [yl, yr], '-', color='w') else: if dx0>Dmn and dy0>Dmn: if yr>yl: ax.plot([xl, xr-Lx], [yl, yr-Ly], '-', color='w') ax.plot([xl+Lx, xr], [yl+Ly, yr], '-', color='w') else: ax.plot([xl, xr-Lx], [yl, yr+Ly], '-', color='w') ax.plot([xl+Lx, xr], [yl-Ly, yr], '-', color='w') else: if dx0>Dmn: ax.plot([xl, xr-Lx], [yl, yr], '-', color='w') ax.plot([xl+Lx, xr], [yl, yr], '-', color='w') if dy0>Dmn: ax.plot([xd, xu], [yd, yu-Ly], '-', color='w') ax.plot([xd, xu], [yd+Ly, yu], '-', color='w') ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) return fig def ConfigPlot_EigenMode_DiffMass(N, x, y, D, L, m, em, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) if m_min == m_max: m_max = 1.001 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 r_now = D[0]0.5 dr = np.zeros(N) for i in range(N): dr[i] = np.sqrt(em[2i]2+em[2i+1]*2) dr_max = max(dr) for i in range(N): ratio = dr[i]/dr_maxr_now/dr_max plt.arrow(x[i], y[i],em[2i]ratio, em[2i+1]ratio, head_width=0.005) ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_YFixed_SelfAssembly(N, Nl, x, y, theta, n, d1, d2, Lx, y_top, y_bot): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_order = 0 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] alpha_all = [] alpha1 = 0.6 alpha2 = 0.3 for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) alpha = alpha1 if i < Nl else alpha2 for k in range(-1, 2): e = Ellipse((x_now+kLx, y_now), d1,d1,0) ells.append(e) alpha_all.append(alpha) if i >= Nl: for ind in range(n): x_i = x_now+kLx+0.5(d1+d2)np.cos(theta[i]+ind2np.pi/n) y_i = y_now+0.5(d1+d2)np.sin(theta[i]+ind2np.pi/n) e = Ellipse((x_i, y_i), d2,d2,0) ells.append(e) alpha_all.append(alpha) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(alpha_all[i]) i += 1 ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show() def ConfigPlot_YFixed_SelfAssembly_BumpyBd(N, n_col, Nl, x, y, theta, n, d0, d1, d2, Lx, y_top, y_bot): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_order = 0 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] alpha_all = [] alpha1 = 0.6 alpha2 = 0.3 for i in range(n_col+1): x_now = id0 e1 = Ellipse((x_now, y_bot), d0,d0,0) e2 = Ellipse((x_now, y_top), d0,d0,0) ells.append(e1) alpha_all.append(alpha1) ells.append(e2) alpha_all.append(alpha1) for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) alpha = alpha1 if i < Nl else alpha2 for k in range(-1, 2): e = Ellipse((x_now+kLx, y_now), d1,d1,0) ells.append(e) alpha_all.append(alpha) if i >= Nl: for ind in range(n): x_i = x_now+kLx+0.5(d1+d2)np.cos(theta[i]+ind2np.pi/n) y_i = y_now+0.5(d1+d2)np.sin(theta[i]+ind2np.pi/n) e = Ellipse((x_i, y_i), d2,d2,0) ells.append(e) alpha_all.append(alpha) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(alpha_all[i]) i += 1 ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show()	22,835	29.488652	117	py
gecco-2022	gecco-2022-main/AND-XOR/MOO2.py	# multi-objective optimization to evolve AND and XOR gates in the same material # imports for DEAP import time, array, random, copy, math import numpy as np from deap import algorithms, base, benchmarks, tools, creator import matplotlib.pyplot as plt import seaborn #seaborn.set(style='whitegrid') import pandas as pd # imports for the simulator from ConfigPlot import ConfigPlot_EigenMode_DiffMass, ConfigPlot_YFixed_rec, ConfigPlot_DiffMass_SP from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK from MD_functions import FIRE_YFixed_ConstV_DiffK from DynamicalMatrix import DM_mass_DiffK_Yfixed from plot_functions import Line_single, Line_multi from ConfigPlot import ConfigPlot_DiffStiffness import random import matplotlib.pyplot as plt import pickle from os.path import exists from scoop import futures import multiprocessing import os def evaluate(indices): #%% Initial Configuration m1 = 1 m2 = 10 k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem #w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) #w = np.real(w) #v = np.real(v) #freq = np.sqrt(np.absolute(w)) #ind_sort = np.argsort(freq) #freq = freq[ind_sort] #v = v[:, ind_sort] #ind = freq > 1e-4 #eigen_freq = freq[ind] #eigen_mode = v[:, ind] #w_delta = eigen_freq[1:] - eigen_freq[0:-1] #index = np.argmax(w_delta) #F_low_exp = eigen_freq[index] #F_high_exp = eigen_freq[index+1] #print("specs:") #print(F_low_exp) #print(F_high_exp) #print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) andness = 2gain1/(gain2+gain3) # we are designing an and gait at this frequency Freq_Vibr = 10 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) XOR = (gain2+gain3)/(2gain1) print("done eval", flush=True) return andness, XOR #cleaning up the data files try: os.remove("results.pickle") except OSError: pass try: os.remove("logs.pickle") except OSError: pass try: os.remove("hofs.pickle") except OSError: pass try: os.remove("hostfile") except OSError: pass try: os.remove("scoop-python.sh") except OSError: pass # start of the optimization: random.seed(a=42) creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 30) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evaluate) toolbox.register("mate", tools.cxOnePoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selNSGA2) # parallelization? toolbox.register("map", futures.map) stats = tools.Statistics() #stats.register("avg", np.mean, axis=0) #stats.register("std", np.std, axis=0) #stats.register("min", np.min, axis=0) #stats.register("max", np.max, axis=0) # also save the population of each generation stats.register("pop", copy.deepcopy) def main(): toolbox.pop_size = 50 toolbox.max_gen = 250 toolbox.mut_prob = 0.8 logbook = tools.Logbook() logbook.header = ["gen", "evals"] + stats.fields hof = tools.HallOfFame(1, similar=np.array_equal) #can change the size def run_ea(toolbox, stats=stats, verbose=True, hof=hof): pop = toolbox.population(n=toolbox.pop_size) pop = toolbox.select(pop, len(pop)) return algorithms.eaMuPlusLambda(pop, toolbox, mu=toolbox.pop_size, lambda_=toolbox.pop_size, cxpb=1-toolbox.mut_prob, #: no cross-over? mutpb=toolbox.mut_prob, stats=stats, ngen=toolbox.max_gen, verbose=verbose, halloffame=hof) res,log = run_ea(toolbox, stats=stats, verbose=True, hof=hof) return res, log, hof if __name__ == '__main__': print("starting") res, log, hof = main() print("done") pickle.dump(res, open('results.pickle', 'wb')) pickle.dump(log, open('logs.pickle', 'wb')) pickle.dump(hof, open('hofs.pickle', 'wb'))	12,452	35.626471	248	py
gecco-2022	gecco-2022-main/AND-XOR/MOO.py	# multi-objective optimization to evolve AND and XOR gates in the same material # imports for DEAP import time, array, random, copy, math import numpy as np from deap import algorithms, base, benchmarks, tools, creator import matplotlib.pyplot as plt import seaborn #seaborn.set(style='whitegrid') import pandas as pd # imports for the simulator from ConfigPlot import ConfigPlot_EigenMode_DiffMass, ConfigPlot_YFixed_rec, ConfigPlot_DiffMass_SP from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK from MD_functions import FIRE_YFixed_ConstV_DiffK from DynamicalMatrix import DM_mass_DiffK_Yfixed from plot_functions import Line_single, Line_multi from ConfigPlot import ConfigPlot_DiffStiffness import random import matplotlib.pyplot as plt import pickle from os.path import exists from scoop import futures import multiprocessing import os def evaluate(indices): #%% Initial Configuration m1 = 1 m2 = 10 k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem #w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) #w = np.real(w) #v = np.real(v) #freq = np.sqrt(np.absolute(w)) #ind_sort = np.argsort(freq) #freq = freq[ind_sort] #v = v[:, ind_sort] #ind = freq > 1e-4 #eigen_freq = freq[ind] #eigen_mode = v[:, ind] #w_delta = eigen_freq[1:] - eigen_freq[0:-1] #index = np.argmax(w_delta) #F_low_exp = eigen_freq[index] #F_high_exp = eigen_freq[index+1] #print("specs:") #print(F_low_exp) #print(F_high_exp) #print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) andness = 2gain1/(gain2+gain3) # we are designing an and gait at this frequency Freq_Vibr = 10 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) XOR = (gain2+gain3)/(2gain1) print("done eval", flush=True) return andness, XOR #cleaning up the data files try: os.remove("results.pickle") except OSError: pass try: os.remove("logs.pickle") except OSError: pass try: os.remove("hofs.pickle") except OSError: pass try: os.remove("hostfile") except OSError: pass try: os.remove("scoop-python.sh") except OSError: pass # start of the optimization: random.seed(a=42) creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 30) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evaluate) toolbox.register("mate", tools.cxOnePoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selNSGA2) # parallelization? toolbox.register("map", futures.map) stats = tools.Statistics(key=lambda ind: ind.fitness.values) stats.register("avg", np.mean, axis=0) stats.register("std", np.std, axis=0) stats.register("min", np.min, axis=0) stats.register("max", np.max, axis=0) # also save the population of each generation stats.register("pop", copy.deepcopy) def main(): toolbox.pop_size = 50 toolbox.max_gen = 250 toolbox.mut_prob = 0.8 logbook = tools.Logbook() logbook.header = ["gen", "evals"] + stats.fields hof = tools.HallOfFame(1, similar=np.array_equal) #can change the size def run_ea(toolbox, stats=stats, verbose=True, hof=hof): pop = toolbox.population(n=toolbox.pop_size) pop = toolbox.select(pop, len(pop)) return algorithms.eaMuPlusLambda(pop, toolbox, mu=toolbox.pop_size, lambda_=toolbox.pop_size, cxpb=1-toolbox.mut_prob, #: no cross-over? mutpb=toolbox.mut_prob, stats=stats, ngen=toolbox.max_gen, verbose=verbose, halloffame=hof) res,log = run_ea(toolbox, stats=stats, verbose=True, hof=hof) return res, log, hof if __name__ == '__main__': print("starting") res, log, hof = main() print("done") pickle.dump(res, open('results.pickle', 'wb')) pickle.dump(log, open('logs.pickle', 'wb')) pickle.dump(hof, open('hofs.pickle', 'wb')) # print info for best solution found: #print("-----") #print(len(hof)) #best = hof.items[0] #print("-- Best Individual = ", best) #print("-- Best Fitness = ", best.fitness.values) # print hall of fame members info: #print("- Best solutions are:") # for i in range(HALL_OF_FAME_SIZE): # print(i, ": ", hof.items[i].fitness.values[0], " -> ", hof.items[i]) #print("Hall of Fame Individuals = ", hof.items, sep="\n") #fronts = tools.emo.sortLogNondominated(res, len(res)) #for i,inds in enumerate(fronts): # counter = 0 # for ind in inds: # counter += 1 # if counter == 1 or counter == 15 or counter==30: # print("####") # evaluateAndPlot(ind) #logbook.record(gen=0, evals=30, *record) avg = log.select("avg") std = log.select("std") avg_stack = np.stack(avg, axis=0) avg_f1 = avg_stack[:, 0] avg_f2 = avg_stack[:, 1] std_stack = np.stack(std, axis=0) std_f1 = std_stack[:, 0] std_f2 = std_stack[:, 1] plt.figure(figsize=(6.4,4.8)) plt.plot(avg_f1, color='blue') plt.fill_between(list(range(0, toolbox.max_gen+1)), avg_f1-std_f1, avg_f1+std_f1, color='cornflowerblue', alpha=0.2) plt.xlabel("Generations") plt.ylabel("Average Fitness") plt.title("Average Fitness of Individuals in the Population - F1", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() plt.show() plt.savefig("avg_F1.jpg", dpi = 300) plt.figure(figsize=(6.4,4.8)) plt.plot(avg_f2, color='blue') plt.fill_between(list(range(0, toolbox.max_gen+1)), avg_f2-std_f2, avg_f2+std_f2, color='cornflowerblue', alpha=0.2) plt.xlabel("Generations") plt.ylabel("Average Fitness") plt.title("Average Fitness of Individuals in the Population - F2", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() plt.show() plt.savefig("avg_F2.jpg", dpi = 300) seaborn.set(style='whitegrid') plot_colors = seaborn.color_palette("Set1", n_colors=10) fig, ax = plt.subplots(1, figsize=(4,4)) for i,inds in enumerate(fronts): par = [toolbox.evaluate(ind) for ind in inds] print("fronts:") print(par) df = pd.DataFrame(par) df.plot(ax=ax, kind='scatter', x=df.columns[0], y=df.columns[1], color=plot_colors[i]) plt.xlabel('$f_1(\mathbf{x})$');plt.ylabel('$f_2(\mathbf{x})$'); plt.title("Pareto Front", fontsize='small') plt.show() plt.savefig("paretoFront.jpg", dpi = 300)	15,147	35.501205	248	py
gecco-2022	gecco-2022-main/AND-XOR/plotMOO3.py	## plot stuff after loading everything from the pickled files for MOO import time, array, random, copy, math import numpy as np from deap import algorithms, base, benchmarks, tools, creator import matplotlib.pyplot as plt import seaborn import pandas as pd import random import pickle from os.path import exists import os from ConfigPlot import ConfigPlot_DiffStiffness2, ConfigPlot_DiffStiffness3 from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK, FIRE_YFixed_ConstV_DiffK, MD_VibrSP_ConstV_Yfixed_DiffK2 from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK_Freqs, MD_VibrSP_ConstV_Yfixed_DiffK2_Freqs from DynamicalMatrix import DM_mass_DiffK_Yfixed from joblib import Parallel, delayed import multiprocessing plt.rcParams['pdf.fonttype'] = 42 plt.rcParams['ps.fonttype'] = 42 def evaluate(indices): #%% Initial Configuration m1 = 1 m2 = 10 k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem #w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) #w = np.real(w) #v = np.real(v) #freq = np.sqrt(np.absolute(w)) #ind_sort = np.argsort(freq) #freq = freq[ind_sort] #v = v[:, ind_sort] #ind = freq > 1e-4 #eigen_freq = freq[ind] #eigen_mode = v[:, ind] #w_delta = eigen_freq[1:] - eigen_freq[0:-1] #index = np.argmax(w_delta) #F_low_exp = eigen_freq[index] #F_high_exp = eigen_freq[index+1] #print("specs:") #print(F_low_exp) #print(F_high_exp) #print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) andness = 2gain1/(gain2+gain3) # we are designing an and gait at this frequency Freq_Vibr = 10 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) XOR = (gain2+gain3)/(2gain1) print("done eval", flush=True) return andness, XOR random.seed(a=42) creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 30) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evaluate) toolbox.register("mate", tools.cxOnePoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selNSGA2) # parallelization? #toolbox.register("map", futures.map) stats = tools.Statistics(key=lambda ind: ind.fitness.values) stats.register("avg", np.mean, axis=0) stats.register("std", np.std, axis=0) stats.register("min", np.min, axis=0) stats.register("max", np.max, axis=0) toolbox.pop_size = 50 toolbox.max_gen = 250 toolbox.mut_prob = 0.8 logbook = tools.Logbook() logbook.header = ["gen", "evals"] + stats.fields hof = tools.HallOfFame(1, similar=np.array_equal) #can change the size inds = pickle.load(open('test.pickle', 'rb')) indices = [] for i in inds: indices.append(np.array(i)) # plot the pareto front plt.figure(figsize=(4,4)) num_cores = multiprocessing.cpu_count() outputs = Parallel(n_jobs=num_cores)(delayed(evaluate)(ind) for ind in indices) print(type(outputs)) print(outputs) print(inds) for point in outputs: plt.scatter(x=point[0], y=point[1], color='blue', marker='o', alpha=0.4) plt.scatter(x=point[0], y=point[1], color='blue', marker='o', alpha=0.4, label='front') plt.xlabel('$f_1(\mathbf{x})$'+' = AND-ness', fontsize=16) plt.ylabel('$f_2(\mathbf{x})$'+' = XOR-ness', fontsize=16) plt.title("Pareto Front", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.legend(bbox_to_anchor=(1.215, 1)) plt.tight_layout() plt.show()	11,771	36.730769	248	py
gecco-2022	gecco-2022-main/AND-XOR/plot_functions.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Sun Oct 22 14:48:04 2017 @author: Hightoutou """ import matplotlib.pyplot as plt #import matplotlib #matplotlib.use('TkAgg') def Line_single(xdata, ydata, line_spec, xlabel, ylabel, mark_print, fn = '', xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.53/4) pos1 = ax1.get_position() pos2 = [pos1.x0 + 0.12, pos1.y0 + 0.05, pos1.width-0.1, pos1.height] ax1.set_position(pos2) #ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) plt.ylabel(ylabel, fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') ax1.plot(xdata, ydata, line_spec) if mark_print == 1: fig.savefig(fn, dpi = 300) fig.show() def Line_multi(xdata, ydata, line_spec, xlabel, ylabel, xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.53/4) ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) ax1.set_ylabel(ylabel, fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') for ii in range(len(xdata)): ax1.plot(xdata[ii], ydata[ii], line_spec[ii]) plt.show() def Line_yy(xdata, ydata, line_spec, xlabel, ylabel, xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.5*3/4) ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) ax1.set_ylabel(ylabel[0], fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') ax1.plot(xdata[0], ydata[0], line_spec[0]) ax2 = ax1.twinx() ax2.set_ylabel(ylabel[1], fontsize=12) ax2.plot(xdata[1], ydata[1], line_spec[1]) plt.show()	2,062	33.966102	112	py
gecco-2022	gecco-2022-main/AND-XOR/FFT_functions.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Tue Oct 17 15:10:21 2017 @author: Hightoutou """ import numpy as np import matplotlib.pyplot as plt from plot_functions import Line_multi, Line_single #from numba import jit def FFT_Fup(Nt, F, dt, Freq_Vibr): sampling_rate = 1/dt t = np.arange(Nt)dt fft_size = Nt xs = F[:fft_size] xf = np.absolute(np.fft.rfft(xs)/fft_size) freqs = (2np.pi)np.linspace(0, sampling_rate/2, fft_size//2+1) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 0: Line_multi([freqs[1:], [Freq_Vibr, Freq_Vibr]], [xf[1:], [min(xf[1:]), max(xf[1:])]], ['o', 'r--'], 'Frequency', 'FFT', 'linear', 'log') return freqs[1:], xf[1:] def FFT_Fup_RealImag(Nt, F, dt, Freq_Vibr): sampling_rate = 1/dt t = np.arange(Nt)dt fft_size = Nt xs = F[:fft_size] xf = np.fft.rfft(xs)/fft_size freqs = (2np.pi)np.linspace(0, sampling_rate/2, fft_size/2+1) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] xf_real = xf.real xf_imag = xf.imag if 1 == 0: Line_multi([freqs[1:], [Freq_Vibr, Freq_Vibr]], [xf[1:], [min(xf[1:]), max(xf[1:])]], ['o', 'r--'], 'Frequency', 'FFT') return freqs[1:], xf_real[1:], xf_imag[1:] #@jit def vCorr_Cal(fft_size, Nt, y_raw): y_fft = np.zeros(fft_size) for jj in np.arange(fft_size): sum_vcf = 0 sum_tt = 0 count = 0 for kk in np.arange(Nt-jj): count = count+1 sum_vcf += y_raw[kk]y_raw[kk+jj]; sum_tt = sum_tt+y_raw[kk]y_raw[kk]; y_fft[jj] = sum_vcf/sum_tt; return y_fft def FFT_vCorr(Nt, N, vx_rec, vy_rec, dt): sampling_rate = 1/dt fft_size = Nt-1 freqs = (2np.pi)np.linspace(0, sampling_rate/2, fft_size/2+1) for ii in np.arange(2N): #for ii in [0,4]: if np.mod(ii, 10) == 0: print('ii=%d\n' % (ii)) if ii >= N: y_raw = vy_rec[:, ii-N] else: y_raw = vx_rec[:, ii] y_fft = vCorr_Cal(fft_size, Nt, y_raw) if ii == 0: xf = np.absolute(np.fft.rfft(y_fft)/fft_size) else: xf += np.absolute(np.fft.rfft(y_fft)/fft_size) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 1: Line_single(freqs[1:], xf[1:], 'o', 'Frequency', 'FFT') return freqs[1:], xf[1:] def FFT_vCorr_3D(Nt, N, vx_rec, vy_rec, vz_rec, dt): sampling_rate = 1/dt fft_size = Nt-1 freqs = (2np.pi)np.linspace(0, sampling_rate/2, fft_size/2+1) for ii in np.arange(3N): #for ii in [0,4]: if np.mod(ii, 10) == 0: print('ii=%d\n' % (ii)) if ii >= 2N: y_raw = vz_rec[:, ii-2N] elif ii < N: y_raw = vx_rec[:, ii] else: y_raw = vy_rec[:, ii-N] y_fft = vCorr_Cal(fft_size, Nt, y_raw) if ii == 0: xf = np.absolute(np.fft.rfft(y_fft)/fft_size) else: xf += np.absolute(np.fft.rfft(y_fft)/fft_size) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 1: Line_single(freqs[1:], xf[1:], 'o', 'Frequency', 'FFT') return freqs[1:], xf[1:]	3,487	25.225564	152	py
gecco-2022	gecco-2022-main/AND-XOR/plotMOO.py	## plot stuff after loading everything from the pickled files for MOO import time, array, random, copy, math import numpy as np from deap import algorithms, base, benchmarks, tools, creator import matplotlib.pyplot as plt import seaborn import pandas as pd import random import pickle from os.path import exists import os from ConfigPlot import ConfigPlot_DiffStiffness2, ConfigPlot_DiffStiffness3 from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK, FIRE_YFixed_ConstV_DiffK, MD_VibrSP_ConstV_Yfixed_DiffK2 from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK_Freqs, MD_VibrSP_ConstV_Yfixed_DiffK2_Freqs from DynamicalMatrix import DM_mass_DiffK_Yfixed plt.rcParams['pdf.fonttype'] = 42 plt.rcParams['ps.fonttype'] = 42 def evaluate(indices): #%% Initial Configuration m1 = 1 m2 = 10 k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem #w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) #w = np.real(w) #v = np.real(v) #freq = np.sqrt(np.absolute(w)) #ind_sort = np.argsort(freq) #freq = freq[ind_sort] #v = v[:, ind_sort] #ind = freq > 1e-4 #eigen_freq = freq[ind] #eigen_mode = v[:, ind] #w_delta = eigen_freq[1:] - eigen_freq[0:-1] #index = np.argmax(w_delta) #F_low_exp = eigen_freq[index] #F_high_exp = eigen_freq[index+1] #print("specs:") #print(F_low_exp) #print(F_high_exp) #print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) andness = 2gain1/(gain2+gain3) # we are designing an and gait at this frequency Freq_Vibr = 10 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) XOR = (gain2+gain3)/(2gain1) print("done eval", flush=True) return andness, XOR def showPacking(indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row m1=1 m2=10 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) # show packing ConfigPlot_DiffStiffness3(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf', ind_in1, ind_in2, ind_out) def plotInOut_and(indices): #%% Initial Configuration k1 = 1. k2 = 10. m1 = 1 m2 = 10 n_col = 6 n_row = 5 N = n_coln_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) w = np.real(w) v = np.real(v) freq = np.sqrt(np.absolute(w)) ind_sort = np.argsort(freq) freq = freq[ind_sort] v = v[:, ind_sort] ind = freq > 1e-4 eigen_freq = freq[ind] eigen_mode = v[:, ind] w_delta = eigen_freq[1:] - eigen_freq[0:-1] index = np.argmax(w_delta) F_low_exp = eigen_freq[index] F_high_exp = eigen_freq[index+1] plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.scatter(np.arange(0, len(eigen_freq)), eigen_freq, marker='x', color='blue') plt.xlabel(r"Index $(k)$", fontsize=16) plt.ylabel(r"Frequency $(\omega)$", fontsize=16) plt.title("Frequency Spectrum", fontsize=16, fontweight="bold") plt.grid(color='skyblue', linestyle=':', linewidth=0.5) props = dict(facecolor='green', alpha=0.1) myText = r'$\omega_{low}=$'+"{:.2f}".format(F_low_exp)+"\n"+r'$\omega_{high}=$'+"{:.2f}".format(F_high_exp)+"\n"+r'$\Delta \omega=$'+"{:.2f}".format(max(w_delta)) #plt.text(0.78, 0.15, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16, bbox=props) plt.text(0.2, 0.8, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16, bbox=props) plt.hlines(y=7, xmin=0, xmax=50, linewidth=1, linestyle='dashdot', color='limegreen', alpha=0.9) plt.hlines(y=10, xmin=0, xmax=50, linewidth=1, linestyle='dotted', color='brown', alpha=0.9) plt.text(51, 5, '$\omega=7$', fontsize=12, color='limegreen', alpha=0.9) plt.text(51, 12, '$\omega=10$', fontsize=12, color='brown', alpha=0.9) plt.xticks(fontsize=16) plt.yticks(fontsize=16) plt.tight_layout() plt.show() print("specs:") print(F_low_exp) print(F_high_exp) print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 0, input [0, 0] fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.hlines(y=0, xmin=0, xmax=30, color='green', label='Input1', linestyle='dotted') plt.hlines(y=0, xmin=0, xmax=30, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.hlines(y=0, xmin=0, xmax=30, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 00", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.ylim(0, 0.005) plt.tight_layout() plt.show() fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.hlines(y=0, xmin=0, xmax=10000, color='green', label='Input1', linestyle='solid') plt.hlines(y=0, xmin=0, xmax=10000, color='blue', label='Input2', linestyle='dotted') plt.hlines(y=0, xmin=0, xmax=10000, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 00", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.ylim(-0.0100, 0.0100) plt.tight_layout() plt.show() # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 11", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain1) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 11", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 10", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain2) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 10", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 01", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain3) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 01", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() print("gain1:") print(gain1) print("gain2:") print(gain2) print("gain3:") print(gain3) andness = 2gain1/(gain2+gain3) return andness def plotInOut_xor(indices): #%% Initial Configuration k1 = 1. k2 = 10. m1 = 1 m2 = 10 n_col = 6 n_row = 5 N = n_coln_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) w = np.real(w) v = np.real(v) freq = np.sqrt(np.absolute(w)) ind_sort = np.argsort(freq) freq = freq[ind_sort] v = v[:, ind_sort] ind = freq > 1e-4 eigen_freq = freq[ind] eigen_mode = v[:, ind] w_delta = eigen_freq[1:] - eigen_freq[0:-1] index = np.argmax(w_delta) F_low_exp = eigen_freq[index] F_high_exp = eigen_freq[index+1] plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.scatter(np.arange(0, len(eigen_freq)), eigen_freq, marker='x', color='blue') plt.xlabel(r"Index $(k)$", fontsize=16) plt.ylabel(r"Frequency $(\omega)$", fontsize=16) plt.title("Frequency Spectrum", fontsize=16, fontweight="bold") plt.grid(color='skyblue', linestyle=':', linewidth=0.5) props = dict(facecolor='green', alpha=0.1) myText = r'$\omega_{low}=$'+"{:.2f}".format(F_low_exp)+"\n"+r'$\omega_{high}=$'+"{:.2f}".format(F_high_exp)+"\n"+r'$\Delta \omega=$'+"{:.2f}".format(max(w_delta)) plt.text(0.78, 0.15, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16, bbox=props) plt.xticks(fontsize=16) plt.yticks(fontsize=16) plt.tight_layout() plt.show() print("specs:") print(F_low_exp) print(F_high_exp) print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 10 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 11", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain1) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 11", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 10", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain2) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 10", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 01", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain3) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 01", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() print("gain1:") print(gain1) print("gain2:") print(gain2) print("gain3:") print(gain3) XOR = (gain2+gain3)/(2gain1) return XOR def plotInOut_adder(indices): #%% Initial Configuration k1 = 1. k2 = 10. m1 = 1 m2 = 10 n_col = 6 n_row = 5 N = n_coln_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr1 = 7 Freq_Vibr2 = 10 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK_Freqs(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr1, Freq_Vibr2, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='A', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='B', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='C/S', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 11", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) #myText = 'Gain='+"{:.3f}".format(gain1) #plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2_Freqs(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr1, Freq_Vibr2, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) print(np.mean(x_out, axis=0)) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='A', linestyle='solid') plt.plot(x_in2, color='blue', label='B', linestyle='dotted') plt.plot(x_out, color='red', label='C/S', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 11", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() B=1 # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK_Freqs(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr1, Freq_Vibr2, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='A', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='B', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='C/S', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 10", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) #myText = 'Gain='+"{:.3f}".format(gain2) #plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2_Freqs(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr1, Freq_Vibr2, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='A', linestyle='solid') plt.plot(x_in2, color='blue', label='B', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='C/S', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 10", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK_Freqs(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr1, Freq_Vibr2, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='A', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='B', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='C/S', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 01", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) #myText = 'Gain='+"{:.3f}".format(gain3) #plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2_Freqs(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr1, Freq_Vibr2, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='A', linestyle='solid') plt.plot(x_in2, color='blue', label='B', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='C/S', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 01", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() return 0 #cleaning up the data files #try: # os.remove("indices.pickle") #except OSError: # pass #try: # os.remove("outputs.pickle") #except OSError: # pass # deap setup: random.seed(a=42) creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 30) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evaluate) toolbox.register("mate", tools.cxOnePoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selNSGA2) # parallelization? #toolbox.register("map", futures.map) stats = tools.Statistics(key=lambda ind: ind.fitness.values) stats.register("avg", np.mean, axis=0) stats.register("std", np.std, axis=0) stats.register("min", np.min, axis=0) stats.register("max", np.max, axis=0) toolbox.pop_size = 50 toolbox.max_gen = 250 toolbox.mut_prob = 0.8 logbook = tools.Logbook() logbook.header = ["gen", "evals"] + stats.fields hof = tools.HallOfFame(1, similar=np.array_equal) #can change the size # load the results from the files res = pickle.load(open('results.pickle', 'rb')) hof = pickle.load(open('hofs.pickle', 'rb')) log = pickle.load(open('logs.pickle', 'rb')) # evaluate and plot an individual #[0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0] #showPacking([0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0]) #plotInOut_and([0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0]) #plotInOut_xor([0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0]) plotInOut_adder([0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0]) # plot average fitness vs generations avg = log.select("avg") std = log.select("std") max_ = log.select("max") min_ = log.select("min") avg_stack = np.stack(avg, axis=0) avg_f1 = avg_stack[:, 0] avg_f2 = avg_stack[:, 1] std_stack = np.stack(std, axis=0) std_f1 = std_stack[:, 0] std_f2 = std_stack[:, 1] max_stack = np.stack(max_, axis=0) max_f1 = max_stack[:, 0] max_f2 = max_stack[:, 1] min_stack = np.stack(min_, axis=0) min_f1 = min_stack[:, 0] min_f2 = min_stack[:, 1] plt.figure(figsize=(6.4,4.8)) plt.plot(avg_f1, color='blue', label='Average', linestyle='solid') plt.plot(max_f1, color='red', label='Maximum', linestyle='dashed') plt.plot(min_f1, color='green', label='Minimum', linestyle='dashed') plt.fill_between(list(range(0, toolbox.max_gen+1)), avg_f1-std_f1, avg_f1+std_f1, color='cornflowerblue', alpha=0.2, linestyle='dotted', label='STD') plt.xlabel("Generations", fontsize=16) plt.ylabel("Fitness", fontsize=16) plt.title("Multi-objective Optimization, Fitness 1 = AND-ness", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper left', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() plt.figure(figsize=(6.4,4.8)) plt.plot(avg_f2, color='blue', label='Average', linestyle='solid') plt.plot(max_f2, color='red', label='Maximum', linestyle='dashed') plt.plot(min_f2, color='green', label='Minimum', linestyle='dashed') plt.fill_between(list(range(0, toolbox.max_gen+1)), avg_f2-std_f2, avg_f2+std_f2, color='cornflowerblue', alpha=0.2, linestyle='dotted', label='STD') plt.xlabel("Generations", fontsize=16) plt.ylabel("Fitness", fontsize=16) plt.title("Multi-objective Optimization, Fitness 2 = XOR-ness", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper left', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() # print info for best solution found: #print("-----") #print(len(hof)) #best = hof.items[0] #print("-- Best Individual = ", best) #print("-- Best Fitness = ", best.fitness.values) # print hall of fame members info: #print("- Best solutions are:") # for i in range(HALL_OF_FAME_SIZE): # print(i, ": ", hof.items[i].fitness.values[0], " -> ", hof.items[i]) #print("Hall of Fame Individuals = ", *hof.items, sep="\n") # get the pareto front from the results fronts = tools.emo.sortLogNondominated(res, len(res)) # print(fronts[0][0]) # 50 indvs in fronts[0] # plot the pareto front plt.figure(figsize=(4,4)) counter = 1 outputs = [] indices = [] for i,inds in enumerate(fronts): for ind in inds: indices.append(ind) print(str(counter)+': '+str(ind)) output = toolbox.evaluate(ind) outputs.append(output) print(output) plt.scatter(x=output[0], y=output[1], color='blue') plt.annotate(str(counter), (output[0]+0.1, output[1]+0.1)) counter = counter + 1 plt.xlabel('$f_1(\mathbf{x})$'+' = AND-ness', fontsize=16) plt.ylabel('$f_2(\mathbf{x})$'+' = XOR-ness', fontsize=16) plt.title("Pareto Front", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() pickle.dump(indices, open('indices.pickle', 'wb')) pickle.dump(outputs, open('outputs.pickle', 'wb')) # plot the pareto front again without annotation plt.figure(figsize=(4,4)) for output in outputs: plt.scatter(x=output[0], y=output[1], color='blue') plt.xlabel('$f_1(\mathbf{x})$'+' = AND-ness', fontsize=16) plt.ylabel('$f_2(\mathbf{x})$'+' = XOR-ness', fontsize=16) plt.title("Pareto Front", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() #seaborn.set(style='whitegrid') #plot_colors = seaborn.color_palette("Set1", n_colors=10) #fig, ax = plt.subplots(1, figsize=(4,4)) #for i,inds in enumerate(fronts): # par = [toolbox.evaluate(ind) for ind in inds] # print("fronts:") # print(par) # df = pd.DataFrame(par) # df.plot(ax=ax, kind='scatter', # x=df.columns[0], y=df.columns[1], # color=plot_colors[i]) #plt.xlabel('$f_1(\mathbf{x})$');plt.ylabel('$f_2(\mathbf{x})$'); #plt.title("Pareto Front", fontsize='small') #plt.show() # simulate and plot all of the fronts #for i,inds in enumerate(fronts): # counter = 0 # for ind in inds: # counter += 1 # if counter == 1 or counter == 15 or counter==30: # print("####") # plotInOut_and(ind) # plotInOut_xor(ind)	51,850	38.281061	267	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/switch_binary.py	import numpy as np from ConfigPlot import ConfigPlot_EigenMode_DiffMass, ConfigPlot_YFixed_rec, ConfigPlot_DiffMass_SP from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK from MD_functions import FIRE_YFixed_ConstV_DiffK from DynamicalMatrix import DM_mass_DiffK_Yfixed from plot_functions import Line_single, Line_multi from ConfigPlot import ConfigPlot_DiffStiffness import random import matplotlib.pyplot as plt import pickle from os.path import exists class switch(): def evaluate(m1, m2, N_light, indices): #%% Initial Configuration k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem #w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) #w = np.real(w) #v = np.real(v) #freq = np.sqrt(np.absolute(w)) #ind_sort = np.argsort(freq) #freq = freq[ind_sort] #v = v[:, ind_sort] #ind = freq > 1e-4 #eigen_freq = freq[ind] #eigen_mode = v[:, ind] #w_delta = eigen_freq[1:] - eigen_freq[0:-1] #index = np.argmax(w_delta) #F_low_exp = eigen_freq[index] #F_high_exp = eigen_freq[index+1] #print("specs:") #print(F_low_exp) #print(F_high_exp) #print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) andness = 2gain1/(gain2+gain3) return andness def showPacking(m1, m2, N_light, indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row dphi_index = -1 dphi = 10dphi_index d0 = 0.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # show packing ConfigPlot_DiffStiffness(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf') def evaluateAndPlot(m1, m2, N_light, indices): #%% Initial Configuration k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) w = np.real(w) v = np.real(v) freq = np.sqrt(np.absolute(w)) ind_sort = np.argsort(freq) freq = freq[ind_sort] v = v[:, ind_sort] ind = freq > 1e-4 eigen_freq = freq[ind] eigen_mode = v[:, ind] w_delta = eigen_freq[1:] - eigen_freq[0:-1] index = np.argmax(w_delta) F_low_exp = eigen_freq[index] F_high_exp = eigen_freq[index+1] plt.figure(figsize=(6.4,4.8)) plt.scatter(np.arange(0, len(eigen_freq)), eigen_freq, marker='x', color='blue') plt.xlabel("Number") plt.ylabel("Frequency") plt.title("Vibrational Reponse", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() plt.show() print("specs:") print(F_low_exp) print(F_high_exp) print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) print("gain1:") print(gain1) print("gain2:") print(gain2) print("gain3:") print(gain3) andness = 2gain1/(gain2+gain3) return andness	14,736	36.594388	252	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/MD_functions.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Fri Oct 13 13:09:27 2017 @author: Hightoutou """ import numpy as np import time #from numba import jit from FFT_functions import FFT_Fup, FFT_vCorr from plot_functions import Line_multi, Line_yy, Line_single from ConfigPlot import ConfigPlot_YFixed_rec import matplotlib.pyplot as plt #import IPython.core.debugger #dbg = IPython.core.debugger.Pdb() #@jit def force_YFixed(Fx, Fy, N, x, y, D, Lx, y_bot, y_up): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5D[nn] if d_up<r_now: F = -(1-d_up/r_now)/(r_now) Fup -= F Fy[nn] += F Ep += (1/2)(1-d_up/r_now)*2 cont_up += 1 cont += 1 #dbg.set_trace() if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fbot += F Fy[nn] -= F Ep += (1/2)(1-d_bot/r_now)*2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy Ep += (1/2)(1-dmn/Dmn)*2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up def force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D, Lx, y_bot, y_up, k_list, k_type, VL_list, VL_counter): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 for nn in np.arange(N): d_up = y_up - y[nn] d_bot = y[nn] - y_bot r_now = 0.5 * D[nn] if d_up < r_now: F = -k_list[k_type[nn]] * (1 - d_up / r_now) / (r_now) Fup -= F Fy[nn] += F Ep += 0.5 * k_list[k_type[nn]] * (1 - d_up / r_now)*2 cont_up += 1 cont += 1 #dbg.set_trace() if d_bot < r_now: F = -k_list[k_type[nn]] (1 - d_bot / r_now) / (r_now) Fbot += F Fy[nn] -= F Ep += 0.5 * k_list[k_type[nn]] * (1 - d_bot / r_now)*2 cont += 1 for vl_idx in np.arange(VL_counter): nn = VL_list[vl_idx][0] mm = VL_list[vl_idx][1] dy = y[mm] - y[nn] Dmn = 0.5 (D[mm] + D[nn]) if abs(dy) < Dmn: dx = x[mm] - x[nn] dx = dx - round(dx / Lx) * Lx if abs(dx) < Dmn: dmn = np.sqrt(dx2 + dy2) if dmn < Dmn: k = k_list[(k_type[nn] ^ k_type[mm]) + np.maximum(k_type[nn], k_type[mm])] F = -k * (1 - dmn / Dmn) / Dmn / dmn Fx[nn] += F * dx Fx[mm] -= F * dx Fy[nn] += F * dy Fy[mm] -= F * dy Ep += 0.5 * k * (1 - dmn / Dmn)*2 cont += 1 p_now += (-F) (dx2 + dy2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up def force_YFixed_upDS(Fx, Fy, N, x, y, D, Lx, y_bot, y_up, ind_up): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5D[nn] if ind_up[nn] == 1: F = -(1-d_up/r_now)/(r_now) Fup -= F Fy[nn] += F Ep += (1/2)(1-d_up/r_now)*2 #dbg.set_trace() if d_up<r_now: cont_up = cont_up+1 cont += 1 if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fbot += F Fy[nn] -= F Ep += (1/2)(1-d_bot/r_now)*2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy Ep += (1/2)(1-dmn/Dmn)*2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up #@jit def force_Regular(Fx, Fy, N, x, y, D, Lx, Ly): Ep = 0 cont = 0 p_now = 0 for nn in np.arange(N): for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] dy = dy-round(dy/Ly)Ly Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy Ep += (1/2)(1-dmn/Dmn)*2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, Ep, cont, p_now def MD_UpDownFixed_SD(N, x0, y0, D0, m0, L): dt = min(D0)/40 Nt = int(1e4) Ep = np.zeros(Nt) F_up = np.zeros(Nt) F_bot = np.zeros(Nt) F_tot = np.zeros(Nt) Fup_now = 0 vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) #Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) F_up[nt] = Fup_now F_bot[nt] = Fbot_now Ep[nt] = Ep_now vx = np.divide(Fx, m0) vy = np.divide(Fy, m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) t_end = time.time() print ("time=%.3e" %(t_end-t_start)) if 1 == 0: # Plot the amplitide of F Line_single(range(Nt), F_tot[0:Nt], '-', 't', 'Ftot', 'log', yscale='log') return x, y def MD_VibrBot_ForceUp(N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr): dt = min(D0)/40 Nt = int(5e4) Ep = np.zeros(Nt) Ek = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr #y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)) vx = np.zeros(N) vy = np.zeros(N) if 1 == 0: y_bot = np.zeros(Nt) vx = np.random.rand(N) vy = np.random.rand(N) T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) T_set = 1e-6 vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now = force_YFixed(Fx, Fy, N, x, y, D0, L[0], y_bot[nt], L[1]) #Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) F_up[nt] = Fup_now Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) freq_now, fft_now = FFT_Fup(Nt, F_up[:Nt], dt, Freq_Vibr) # Plot the amplitide of F if 1 == 1: Line_yy([dtrange(Nt), dtrange(Nt)], [F_up[0:Nt],y_bot[0:Nt]], ['-', ':'], 't', ['$F_{up}$', '$y_{bottom}$']) Etot = Ep[1:Nt]+Ek[1:Nt] xdata = [dtrange(Nt), dtrange(Nt), dtrange(Nt-1)] ydata = [Ep[0:Nt], Ek[0:Nt], Etot] line_spec = ['--', ':', 'r-'] Line_multi(xdata, ydata, line_spec, 't', '$E$', 'log') print("std(Etot)=%e\n" %(np.std(Etot))) #dt2 = 1e-3 #xx = np.arange(0, 5, dt2) #yy = np.sin(50xx)+np.sin(125xx) #print("dt=%e, w=%f\n" % (dt, Freq_Vibr)) FFT_Fup(Nt, F_up[:Nt], dt, Freq_Vibr) #FFT_Fup(yy.size, yy, dt2, 50) return freq_now, fft_now, np.mean(cont) def MD_Periodic_equi(Nt, N, x0, y0, D0, m0, L, T_set, V_em, n_em): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) vx_rec = np.zeros([Nt, N]) vy_rec = np.zeros([Nt, N]) vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): #for ii in [60]: ind1 = 2np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx_rec[nt] = vx vy_rec[nt] = vy t_end = time.time() print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) #Etot = Ep[1:Nt]+Ek[1:Nt] #xdata = [dtrange(Nt), dtrange(Nt), dtrange(Nt-1)] #ydata = [Ep[0:Nt], Ek[0:Nt], Etot] #line_spec = ['--', ':', 'r-'] #Line_multi(xdata, ydata, line_spec, 't', '$E$', 'log', 'log') freq_now, fft_now = FFT_vCorr(Nt, N, vx_rec, vy_rec, dt) return freq_now, fft_now, np.mean(cont) def MD_YFixed_ConstP_SD(Nt, N, x0, y0, D0, m0, L, F0_up): dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e6) #Nt = int(5e2) Ep = np.zeros(Nt) F_up = np.zeros(Nt) F_bot = np.zeros(Nt) F_tot = np.zeros(Nt) Fup_now = 0 y_up = y0[N] vx = np.zeros(N+1) vy = np.zeros(N+1) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], 0, y[N]) F_up[nt] = Fup_now+F0_up F_bot[nt] = Fbot_now Ep[nt] = Ep_now+(y_up-y[N])F0_up vx = 0.1np.divide(np.append(Fx,0), m0) vy = 0.1np.divide(np.append(Fy, F_up[nt]), m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #print("nt=%d, Fup=%e, Fup_tot=%e\n" % (nt, Fup_now, F_up[nt])) #dbg.set_trace() t_end = time.time() print ("F_tot=%.3e\n" %(F_tot[nt])) print ("time=%.3e" %(t_end-t_start)) if 1 == 0: # Plot the amplitide of F Line_single(range(Nt), F_tot[0:Nt], '-', 't', 'Ftot', 'log', yscale='log') #Line_single(range(Nt), -F_up[0:Nt], '-', 't', 'Fup', 'log', yscale='log') #Line_single(range(Nt), Ep[0:Nt], '-', 't', 'Ep', 'log', yscale='linear') return x, y, p_now def MD_VibrBot_DispUp_ConstP(mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up): dt = D0[0]/40 B = 0.1 # damping coefficient Nt = int(5e7) #Nt = int(5e2) Ep = np.zeros(Nt) Ek = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr #y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)) vx = np.zeros(N+1) vy = np.zeros(N+1) # for test if 1 == 0: y_bot = np.zeros(Nt) vx = np.random.rand(N+1) vx[N] = 0 vy = np.random.rand(N+1) vy[N] = 0 T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) T_set = 1e-6 vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) if mark_upDS == 0: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) elif mark_upDS == 1: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_upDS(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N], ind_up) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0)-Bvx Fy_all = np.append(Fy, F_up[nt])-Bvy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-y_up0 freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) #freq_y, fft_y_real, fft_y_imag = FFT_Fup_RealImag(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) # plot the energy to see when the system reaches steady state if 1 == 0: Etot = Ep+Ek nt_start = int(1e3) xdata = [range(nt_start, Nt), range(nt_start, Nt), range(Nt)] ydata = [Ep[nt_start:Nt], Ek[nt_start:Nt], Etot] line_spec = [':', ':', 'r-'] Line_multi(xdata, ydata, line_spec, 't', '$E$', 'linear', 'log') # Plot the amplitide of F if 1 == 0: Line_yy([dtrange(Nt), dtrange(Nt)], [F_up[0:Nt],y_bot[0:Nt]], ['-', ':'], 't', ['$F_{up}$', '$y_{bottom}$']) Line_yy([dtrange(Nt), dtrange(Nt)], [y_up[0:Nt],y_bot[0:Nt]], ['-', ':'], 't', ['$y_{up}$', '$y_{bottom}$']) Line_single(range(Nt), p[0:Nt], '-', 't', 'p', 'log', 'linear') Etot = Ep[1:Nt]+Ek[1:Nt] xdata = [dtrange(Nt), dtrange(Nt), dtrange(Nt-1)] ydata = [Ep[0:Nt], Ek[0:Nt], Etot] line_spec = ['--', ':', 'r-'] #Line_multi(xdata, ydata, line_spec, 't', '$E$', 'log') print("std(Etot)=%e\n" %(np.std(Etot))) return freq_y, fft_y, freq_bot, fft_bot, np.mean(cont), np.mean(cont_up) #return freq_y, fft_y_real, fft_y_imag, freq_bot, fft_bot, np.mean(cont) def MD_VibrBot_DispUp_ConstP_ConfigRec(N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up, fn): dt = D0[0]/40 B = 0.1 # damping coefficient Nt = int(5e6) nt_rec = np.linspace(Nt-5e4, Nt, 500) #Nt = int(1e4) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) ind_nt = 0 Ep = np.zeros(Nt) Ek = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr #y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): if nt == nt_rec[ind_nt]: ConfigPlot_YFixed_rec(N, x[0:N], y[0:N], D0[0:N], L[0], y[N], y_bot[nt], m0[0:N], ind_nt, fn) ind_nt += 1 x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0)-Bvx Fy_all = np.append(Fy, F_up[nt])-Bvy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-y_up0 freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, freq_bot, fft_bot, np.mean(cont), np.mean(cont_up) def MD_VibrBot_DispUp_ConstP_EkCheck(Nt, mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up): dt = D0[0]/40 B = 0.1 # damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ek_up_now = np.array(0) Ep_now = np.array(0) Ep_up_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ep_up = np.zeros(Nt) Ek = np.zeros(Nt) Ek_up = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) if mark_upDS == 0: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) elif mark_upDS == 1: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_upDS(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N], ind_up) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])F0_up Ep_up[nt] = (y_up0-y[N])F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0)-Bvx Fy_all = np.append(Fy, F_up[nt])-Bvy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_up[nt] = 0.5m0[N](vx[N]2+vy[N]2) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ek_up_now = np.append(Ek_up_now, np.mean(Ek_up[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) Ep_up_now = np.append(Ep_up_now, np.mean(Ep_up[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-np.mean(y_up) y_up = y_up/np.mean(np.absolute(y_up)) freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, freq_bot, fft_bot, np.mean(cont), np.mean(cont_up) #@jit def force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D, Lx, y_bot, v_bot, y_up): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 #betta = 1 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5D[nn] if d_up<r_now: F = -(1-d_up/r_now)/(r_now) Fup -= F Fy[nn] += F dvy = vy[N]-vy[nn] FD = betadvy #FD = np.absolute(FD) Fy[nn] += FD Fup -= FD Ep += (1/2)(1-d_up/r_now)2 cont_up += 1 cont += 1 #dbg.set_trace() if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fbot += F Fy[nn] -= F dvy = v_bot-vy[nn] FD = betadvy Fy[nn] += FD Ep += (1/2)(1-d_bot/r_now)2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy dvx = vx[mm]-vx[nn] dvy = vy[mm]-vy[nn] FD = beta(dvxdx+dvydy)/dmn #FD = np.absolute(FD) Fx[nn] += FDdx/dmn Fx[mm] -= FDdx/dmn Fy[nn] += FDdy/dmn Fy[mm] -= FDdy/dmn Ep += (1/2)(1-dmn/Dmn)2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up def MD_VibrBot_DispUp_ConstP_EkCheck_Collision(beta, Nt, mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up, mark_norm): dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ek_up_now = np.array(0) Ep_now = np.array(0) Ep_up_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ep_up = np.zeros(Nt) Ek = np.zeros(Nt) Ek_up = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vy_bot = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)+1.5np.pi) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D0[0:N], L[0], y_bot[nt], vy_bot[nt], y[N]) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])F0_up Ep_up[nt] = (y_up0-y[N])F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0) Fy_all = np.append(Fy, F_up[nt]) ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_up[nt] = 0.5m0[N](vx[N]2+vy[N]2) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ek_up_now = np.append(Ek_up_now, np.mean(Ek_up[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) Ep_up_now = np.append(Ep_up_now, np.mean(Ep_up[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-np.mean(y_up) if mark_norm == 1: y_up = y_up/np.mean(np.absolute(y_up)) freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, fft_bot, np.mean(cont), np.mean(cont_up), nt_rec[1:], Ek_now[1:],Ek_up_now[1:],Ep_now[1:],Ep_up_now[1:] def MD_YFixed_ConstP_Gravity_SD(N, x0, y0, D0, m0, L, F0_up): g = 1e-5 dt = D0[0]/40 Nt = int(5e6) #Nt = int(1e4) Ep = np.zeros(Nt) F_up = np.zeros(Nt) F_bot = np.zeros(Nt) F_tot = np.zeros(Nt) Fup_now = 0 y_up = y0[N] vx = np.zeros(N+1) vy = np.zeros(N+1) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], 0, y[N]) Fy -= gm0[0:N] F_up[nt] = Fup_now+F0_up-gm0[N] F_bot[nt] = Fbot_now Ep[nt] = Ep_now+(y_up-y[N])F0_up+sum(gnp.multiply(m0, y-y0)) vx = 0.1np.divide(np.append(Fx,0), m0) vy = 0.1np.divide(np.append(Fy, F_up[nt]), m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #print("nt=%d, Fup=%e, Fup_tot=%e\n" % (nt, Fup_now, F_up[nt])) #dbg.set_trace() t_end = time.time() print ("F_tot=%.3e\n" %(F_tot[nt])) print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def MD_VibrBot_DispUp_ConstP_EkCheck_Gravity(Nt, mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up): dt = D0[0]/40 #B = 0.1 # damping coefficient g = 1e-5 Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ek_up_now = np.array(0) Ep_now = np.array(0) Ep_up_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ep_up = np.zeros(Nt) Ek = np.zeros(Nt) Ek_up = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vy_bot = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)+1.5np.pi) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) #Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) beta = 1 Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D0[0:N], L[0], y_bot[nt], vy_bot[nt], y[N]) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])F0_up+sum(gnp.multiply(m0, y-y_ini)) Ep_up[nt] = (y_up0-y[N])F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now #Fx_all = np.append(Fx,0)-Bvx #Fy_all = np.append(Fy, F_up[nt])-Bvy-gm0 Fx_all = np.append(Fx,0) Fy_all = np.append(Fy, F_up[nt])-gm0 ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_up[nt] = 0.5m0[N](vx[N]2+vy[N]2) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ek_up_now = np.append(Ek_up_now, np.mean(Ek_up[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) Ep_up_now = np.append(Ep_up_now, np.mean(Ep_up[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-np.mean(y_up) #y_up = y_up/np.mean(np.absolute(y_up)) freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, fft_bot, np.mean(cont), np.mean(cont_up), nt_rec[1:], Ek_now[1:],Ek_up_now[1:],Ep_now[1:],Ep_up_now[1:] def MD_YFixed_ConstV_SP_SD(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now vx = 0.1np.divide(Fx, m0) vy = 0.1np.divide(Fy, m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) t_end = time.time() print ("F_tot=%.3e" %(F_tot[nt])) print ("time=%.3e" %(t_end-t_start)) plt.figure(figsize=(6.4,4.8)) plt.plot(range(Nt), F_tot[0:Nt], color='blue') ax = plt.gca() ax.set_yscale('log') plt.xlabel("t") plt.ylabel("F_total") plt.title("Finding the Equilibrium", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() plt.show() return x, y, p_now def MD_YFixed_ConstV_SP_SD_2(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now vx = 0.1np.divide(Fx, m0) vy = 0.1np.divide(Fy, m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) # putting a threshold on total force if (F_tot[nt]<1e-11): break t_end = time.time() #print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now #@jit def force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D, Lx, y_bot, y_up): Ep = 0 cont = 0 p_now = 0 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5D[nn] if d_up<r_now: F = -(1-d_up/r_now)/(r_now) Fy[nn] += F dvy = -vy[nn] FD = betadvy Fy[nn] += FD Ep += (1/2)(1-d_up/r_now)*2 cont += 1 #dbg.set_trace() if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fy[nn] -= F dvy = -vy[nn] FD = betadvy Fy[nn] += FD Ep += (1/2)(1-d_bot/r_now)2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy dvx = vx[mm]-vx[nn] dvy = vy[mm]-vy[nn] FD = beta(dvxdx+dvydy)/dmn #FD = np.absolute(FD) Fx[nn] += FDdx/dmn Fx[mm] -= FDdx/dmn Fy[nn] += FDdy/dmn Fy[mm] -= FDdy/dmn Ep += (1/2)(1-dmn/Dmn)2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, Ep, cont, p_now def MD_VibrSP_ConstV_Collision(beta, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, mark_vibrY): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in] vx_in = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)) elif mark_vibrY == 1: y_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in] vy_in = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] vx[ind_in] = vx_in[nt] vy[ind_in] = 0 elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] vx[ind_in] = 0 vy[ind_in] = vy_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx Fy_all = Fy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, mark_vibrY, mark_resonator): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in] elif mark_vibrY == 1: y_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-Bvx Fy_all = Fy-Bvy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) if Nt == 5e5: print(x[ind_out], y[ind_out]) print(fft_x_out[100], fft_y_out[100]) print(fft_in[100]) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_Periodic_ConstV_SP_SD(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, Lx, Ly) Ep[nt] = Ep_now vx = 0.1np.divide(Fx, m0) vy = 0.1np.divide(Fy, m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) t_end = time.time() print ("F_tot=%.3e\n" %(F_tot[nt])) print ("nt=%e, time=%.3e" %(nt, t_end-t_start)) return x, y, p_now def MD_Periodic_equi_Ekcheck(Nt, N, x0, y0, D0, m0, L, T_set, V_em, n_em): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) vx_rec = np.zeros([Nt, N]) vy_rec = np.zeros([Nt, N]) nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) #nt_rec = np.linspace(0, Nt, int(Nt/1e2)+1) nt_rec = nt_rec.astype(int) Ek_now = np.array(0) Ep_now = np.array(0) vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx_rec[nt] = vx vy_rec[nt] = vy for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) freq_now, fft_now = FFT_vCorr(int(Nt/2), N, vx_rec[int(Nt/2):Nt], vy_rec[int(Nt/2):Nt], dt) return freq_now, fft_now, np.mean(cont), nt_rec, Ek_now, Ep_now #@jit def force_Xfixed(Fx, Fy, N, x, y, D, x_l, x_r, Ly, ind_wall): F_l = 0 F_r = 0 Ep = 0 cont = 0 p_now = 0 for nn in np.arange(N): d_l = x[nn]-x_l d_r = x_r-x[nn] r_now = 0.5D[nn] if (ind_wall[nn]==0) and (d_r<r_now): F = -(1-d_r/r_now)/(r_now) F_r -= F Fx[nn] += F Ep += (1/2)(1-d_r/r_now)*2 cont += 1 #dbg.set_trace() if (ind_wall[nn]==0) and (d_l<r_now): F = -(1-d_l/r_now)/(r_now) F_l += F Fx[nn] -= F Ep += (1/2)(1-d_l/r_now)*2 cont += 1 for mm in np.arange(nn+1, N): dx = x[mm]-x[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dx) < Dmn: dy = y[mm]-y[nn] dy = dy-round(dy/Ly)Ly dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy Ep += (1/2)(1-dmn/Dmn)*2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, F_l, F_r, Ep, cont, p_now def MD_Xfixed_SD(Nt, N, x0, y0, D0, m0, Lx, Ly, ind_wall): wall = np.where(ind_wall>0) dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e6) #Nt = int(5e2) Ep = np.zeros(Nt) F_l = np.zeros(Nt) F_r = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_Xfixed(Fx, Fy, N, x, y, D0, 0, Lx, Ly, ind_wall) F_l[nt] = Fl_now F_r[nt] = Fr_now Ep[nt] = Ep_now Fx[wall] = 0 Fy[wall] = 0 vx = 0.1np.divide(Fx, m0) vy = 0.1np.divide(Fy, m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #print("nt=%d, Fup=%e, Fup_tot=%e\n" % (nt, Fup_now, F_up[nt])) #dbg.set_trace() t_end = time.time() print ("F_tot=%.3e" %(F_tot[nt])) #print ("Ep_tot=%.3e\n" %(Ep[nt])) print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def MD_VibrWall_DiffP_Xfixed(Nt, N, x_ini, y_ini,D0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_wall, B): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_Xfixed(Fx, Fy, N, x, y, D0, x_l[nt], Lx, Ly, ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-Bvx Fy_all = Fy-Bvy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #for ii in np.arange(len(nt_rec)-1): # Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) # Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) #CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) freq_fft, fft_receive = FFT_Fup(int(Nt/2), F_r[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_drive = FFT_Fup(int(Nt/2), x_l[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_receive, fft_drive, cont_now, nt_rec, Ek_now, Ep_now #@jit def force_XFixed_collision_VibrLx(beta, Fx, Fy, N, x, y, vx, vy, D, x_l, Lx, Ly, vx_l, ind_wall): Fr = 0 Fl = 0 Ep = 0 cont = 0 p_now = 0 #betta = 1 for nn in np.arange(N): if ind_wall[nn] == 0: d_r = Lx-x[nn] d_l = x[nn]-x_l r_now = 0.5D[nn] if d_r<r_now: F = -(1-d_r/r_now)/(r_now) Fr -= F Fx[nn] += F dvx = -vx[nn] FD = betadvx Fx[nn] += FD Fr -= FD Ep += (1/2)(1-d_r/r_now)2 cont += 1 #dbg.set_trace() if d_l<r_now: F = -(1-d_l/r_now)/(r_now) Fl += F Fx[nn] -= F dvx = vx_l-vx[nn] FD = betadvx Fx[nn] += FD Ep += (1/2)(1-d_l/r_now)2 cont += 1 for mm in np.arange(nn+1, N): dx = x[mm]-x[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dx) < Dmn: dy = y[mm]-y[nn] dy = dy-round(dy/Ly)Ly dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy dvx = vx[mm]-vx[nn] dvy = vy[mm]-vy[nn] FD = beta(dvxdx+dvydy)/dmn #FD = np.absolute(FD) Fx[nn] += FDdx/dmn Fx[mm] -= FDdx/dmn Fy[nn] += FDdy/dmn Fy[mm] -= FDdy/dmn Ep += (1/2)(1-dmn/Dmn)2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, Fl, Fr, Ep, cont, p_now def MD_VibrWall_DiffP_Xfixed_Collision(Nt, N, x_ini, y_ini,D0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_wall, beta): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vx_l = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)+1.5np.pi) wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] vx[wall_l] = vx_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_XFixed_collision_VibrLx(beta, Fx, Fy, N, x, y, vx, vy, D0, x_l[nt], Lx, Ly, vx_l[nt], ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx Fy_all = Fy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) freq_fft, fft_receive = FFT_Fup(int(Nt/2), F_r[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_drive = FFT_Fup(int(Nt/2), x_l[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_receive, fft_drive, cont_now, nt_rec, Ek_now, Ep_now def MD_VibrWall_LySignal_Collision(Nt, N, x_ini, y_ini,D0, m0, Lx0, Ly0, Freq_Vibr, Amp_Vibr, ind_wall, beta, dLy_scheme, num_gap): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) dLy_max = 0.1 nt_transition = int(Nt/num_gap/20) dLy_inc = np.linspace(0, dLy_max, nt_transition) dLy_dec = np.linspace(dLy_max, 0, nt_transition) if dLy_scheme == 0: dLy_all = np.zeros(Nt) elif dLy_scheme == 1: dLy_all = np.ones(Nt)dLy_max dLy_all[0:nt_transition] = dLy_inc elif dLy_scheme == 2: dLy_all = np.zeros(Nt) nt_Ly = np.linspace(0, Nt, num_gap+1) nt_Ly = nt_Ly.astype(int) for ii in np.arange(1, num_gap): nt1 = nt_Ly[ii]-int(nt_transition/2) nt2 = nt_Ly[ii]+int(nt_transition/2) if ii%2 == 1: dLy_all[nt_Ly[ii]:nt_Ly[ii+1]] = dLy_max dLy_all[nt1:nt2] = dLy_inc else: dLy_all[nt1:nt2] = dLy_dec nt_rec = np.linspace(0, Nt, int(Nt/5e4num_gap/5)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vx_l = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)+1.5np.pi) wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): Ly = Ly0+dLy_all[nt] x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] vx[wall_l] = vx_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_XFixed_collision_VibrLx(beta, Fx, Fy, N, x, y, vx, vy, D0, x_l[nt], Lx0, Ly, vx_l[nt], ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx Fy_all = Fy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) nt_dLy = np.arange(0, Nt, 100) return nt_dLy, dLy_all[nt_dLy], F_r, nt_rec, Ek_now, Ep_now def MD_VibrWall_LySignal(Nt, N, x_ini, y_ini,D0, m0, Lx0, Ly0, Freq_Vibr, Amp_Vibr, ind_wall, B, dLy_scheme, num_gap): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) dLy_max = 0.1 nt_transition = int(Nt/num_gap/20) dLy_inc = np.linspace(0, dLy_max, nt_transition) dLy_dec = np.linspace(dLy_max, 0, nt_transition) if dLy_scheme == 0: dLy_all = np.zeros(Nt) elif dLy_scheme == 1: dLy_all = np.ones(Nt)dLy_max dLy_all[0:nt_transition] = dLy_inc elif dLy_scheme == 2: dLy_all = np.zeros(Nt) nt_Ly = np.linspace(0, Nt, num_gap+1) nt_Ly = nt_Ly.astype(int) for ii in np.arange(1, num_gap): nt1 = nt_Ly[ii]-int(nt_transition/2) nt2 = nt_Ly[ii]+int(nt_transition/2) if ii%2 == 1: dLy_all[nt_Ly[ii]:nt_Ly[ii+1]] = dLy_max dLy_all[nt1:nt2] = dLy_inc else: dLy_all[nt1:nt2] = dLy_dec nt_rec = np.linspace(0, Nt, int(Nt/5e4num_gap/5)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vx_l = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)+1.5np.pi) wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): Ly = Ly0+dLy_all[nt] x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] vx[wall_l] = vx_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_Xfixed(Fx, Fy, N, x, y, D0, x_l[nt], Lx0, Ly, ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-Bvx Fy_all = Fy-Bvy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) nt_dLy = np.arange(0, Nt, 100) return nt_dLy, dLy_all[nt_dLy], F_r, nt_rec, Ek_now, Ep_now def MD_VibrBot_FSignal_Collision(beta, Nt, N, x_ini, y_ini, D0, m0, Lx, Freq_Vibr, Amp_Vibr, F_scheme, num_gap): dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) F_max = 0.01 F_min = 1e-8 nt_transition = int(Nt/num_gap/20) F_inc = np.linspace(F_min, F_max, nt_transition) F_dec = np.linspace(F_max, F_min, nt_transition) if F_scheme == 1: F_all = np.ones(Nt)F_max elif F_scheme == 0: F_all = np.ones(Nt)F_min F_all[0:nt_transition] = F_dec elif F_scheme == 2: F_all = np.ones(Nt)F_max nt_F = np.linspace(0, Nt, num_gap+1) nt_F = nt_F.astype(int) for ii in np.arange(1, num_gap): nt1 = nt_F[ii]-int(nt_transition/2) nt2 = nt_F[ii]+int(nt_transition/2) if ii%2 == 1: F_all[nt_F[ii]:nt_F[ii+1]] = F_min F_all[nt1:nt2] = F_dec else: F_all[nt1:nt2] = F_inc nt_rec = np.linspace(0, Nt, int(Nt/5e4num_gap/5)+1) Ek_now = np.array(0) Ep_now = np.array(0) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vy_bot = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)+1.5np.pi) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D0[0:N], Lx, y_bot[nt], vy_bot[nt], y[N]) F_up[nt] = Fup_now-F_all[nt] Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = np.append(Fx,0) Fy_all = np.append(Fy, F_up[nt]) ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek_up = 0.5m0[N](vx[N]2+vy[N]2) Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy)))-Ek_up for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) print ("freq=%f, cont_min=%d, cont_max=%d, cont_ave=%f\n" %(Freq_Vibr, min(cont), max(cont), np.mean(cont))) nt_F = np.arange(0, Nt, 100) return nt_F, F_all[nt_F], y_up, nt_rec, Ek_now, Ep_now def MD_SPSignal(mark_collision, beta, Nt, N, x_ini, y_ini,D0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_in, ind_out, ind_fix, dr_scheme, num_gap, mark_vibrY, dr_one, dr_two): dt = D0[0]/40 Nt = int(Nt) d_ini = D0[0] d0 = 0.1 dr_all = np.zeros(Nt)+dr_one if abs(dr_scheme) <= 2: nt_dr = np.linspace(0, Nt, 3) nt_dr = nt_dr.astype(int) dr_all[nt_dr[1]:nt_dr[2]] = dr_two num_gap = 5 elif dr_scheme == 3 or dr_scheme == 4: nt_dr = np.linspace(0, Nt, num_gap+1) nt_dr = nt_dr.astype(int) for ii in np.arange(1, num_gap, 2): dr_all[nt_dr[ii]:nt_dr[ii+1]] = dr_two D_fix = d_ini+dr_alld_ini nt_rec = np.linspace(0, Nt, int(Nt/5e4num_gap/5)+1) Ek_rec = np.array(0) Ep_rec = np.array(0) cont_rec = np.array(0) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 1: y_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in] vy_in = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)) else: x_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in] vx_in = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): D0[ind_fix] = D_fix[nt] x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 1: y[ind_in] = y_in[nt] x[ind_in] = x_ini[ind_in] vy[ind_in] = vy_in[nt] vx[ind_in] = 0 else: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] vx[ind_in] = vx_in[nt] vy[ind_in] = 0 Fx = np.zeros(N) Fy = np.zeros(N) if mark_collision == 1: Fx, Fy, Ep_now, cont_now, p_now = force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D0, Lx, 0, Ly) Fx_all = Fx Fy_all = Fy else: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Fx_all = Fx-betavx Fy_all = Fy-betavy Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if nt % 2000 == 0: print ("nt = %d, Ek = %.2e, cont = %.2e" %(nt, Ek[nt], cont[nt])) for ii in np.arange(len(nt_rec)-1): Ek_rec = np.append(Ek_rec, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec = np.append(Ep_rec, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec = np.append(cont_rec, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) print ("freq=%f, cont_min=%d, cont_max=%d, cont_ave=%f\n" %(Freq_Vibr, min(cont), max(cont), np.mean(cont))) nt_dr = np.arange(0, Nt, 100) if mark_vibrY == 1: xy_out = y_out else: xy_out = x_out return nt_dr, dr_all[nt_dr], xy_out, nt_rec, Ek_rec, Ep_rec, cont_rec def MD_YFixed_equi_SP_modecheck(Nt, N, x0, y0, D0, m0, Lx, Ly, T_set, V_em, n_em, ind_out): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) Freq_Vibr = 0 freq_x, fft_x = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_y, fft_y = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) ind1 = freq_x<30 ind2 = freq_y<30 return freq_x[ind1], freq_y[ind2], fft_x[ind1], fft_y[ind2], np.mean(cont), nt_rec, Ek_rec, Ep_rec, cont_rec def MD_YFixed_SPVibr_SP_modecheck(Nt, N, x0, y0, D0, m0, Lx, Ly, T_set, ind_in, ind_out, mark_vibrY): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ek_rec = np.array(0) Ep_rec = np.array(0) cont_rec = np.array(0) vx = np.zeros(N) vy = np.zeros(N) if mark_vibrY == 1: vy[ind_in] = 1 vy_mc = sum(np.multiply(vy,m0))/sum(m0) vy = vy-vy_mc else: vx[ind_in] = 1 vx_mc = sum(np.multiply(vx,m0))/sum(m0) vx = vx-vx_mc T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_vibrY == 1: vy = vynp.sqrt(NT_set/T_rd) print("\|vy\|_Max=%.3e, \|vy\|_Min=%.3e" %(max(abs(vy)), min(abs(vy)))) else: vx = vxnp.sqrt(NT_set/T_rd) print("\|vx\|_Max=%.3e, \|vx\|_Min=%.3e" %(max(abs(vx)), min(abs(vx)))) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() mark_CB = 0 for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now cont[nt] = cont_now if mark_CB == 0 and cont_now<cont[0]: print("nt_CB=%d" % nt) mark_CB = 1 ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec = np.append(Ek_rec, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec = np.append(Ep_rec, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec = np.append(cont_rec, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) Freq_Vibr = 0 freq_x, fft_x = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_y, fft_y = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) ind1 = freq_x<30 ind2 = freq_y<30 return freq_x[ind1], freq_y[ind2], fft_x[ind1], fft_y[ind2], cont_rec, nt_rec, Ek_rec, Ep_rec #181105 def MD_YFixed_SPVibr_vCorr_modecheck(Nt_MD, Nt_FFT, N, x0, y0, D0, m0, Lx, Ly, T_set, ind_in, ind_out, mark_vibrY): N = int(N) Nt_FFT = int(Nt_FFT) Nt_MD = int(Nt_MD) dt = min(D0)/40 Nt = Nt_MD+Nt_FFT Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) mark_FFT = np.zeros(Nt) mark_FFT[Nt_MD:Nt] = 1 nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) if mark_vibrY == 1: vy[ind_in] = 1 vy_mc = sum(np.multiply(vy,m0))/sum(m0) vy = vy-vy_mc else: vx[ind_in] = 1 vx_mc = sum(np.multiply(vx,m0))/sum(m0) vx = vx-vx_mc T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_vibrY == 1: vy = vynp.sqrt(NT_set/T_rd) print("\|vy\|_Max=%.3e, \|vy\|_Min=%.3e" %(max(abs(vy)), min(abs(vy)))) else: vx = vxnp.sqrt(NT_set/T_rd) print("\|vx\|_Max=%.3e, \|vx\|_Min=%.3e" %(max(abs(vx)), min(abs(vx)))) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_FFT[nt] == 1: if mark_FFT[nt-1] == 0: nt_ref = nt vx_rec = np.zeros([Nt_FFT, N]) vy_rec = np.zeros([Nt_FFT, N]) nt_delta = nt-nt_ref vx_rec[nt_delta] = vx vy_rec[nt_delta] = vy if nt_delta == Nt_FFT-1: freq_now, fft_now = FFT_vCorr(Nt_FFT, N, vx_rec, vy_rec, dt) print ("Nt_End="+str(nt)) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return freq_now, fft_now, (nt_rec[:-1]+nt_rec[1:])/2, Ek_rec, Ep_rec, cont_rec def MD_YFixed_ConstV(B, Nt, N, x0, y0, D0, m0, Lx, Ly): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() mark_CB = 0 for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Fx = Fx-Bvx Fy = Fy-Bvy Ep[nt] = Ep_now cont[nt] = cont_now if mark_CB == 0 and cont_now<cont[0]: print("nt_CB=%d" % nt) mark_CB = 1 ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[0:-1]+nt_rec[1:])/2 CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) print ("Ek_last=%.3e" % Ek[-1]) return x, y, nt_rec, Ek_rec, Ep_rec, cont_rec def MD_Vibr3Part_ConstV(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in_all, ind_out, mark_vibrY, eigen_mode_now): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) num_in = ind_in_all.size Phase_Vibr = np.sin(Freq_Vibrdtnp.arange(Nt)) Amp_Vibr_all = np.zeros(num_in) for i_in in np.arange(num_in): ind_in = ind_in_all[i_in] if mark_vibrY == 0: Amp_Vibr_all[i_in] = eigen_mode_now[2ind_in] elif mark_vibrY == 1: Amp_Vibr_all[i_in] = eigen_mode_now[2ind_in+1] print(ind_in_all) print(Amp_Vibr_all) Amp_Vibr_all = Amp_Vibr_allAmp_Vibr/max(np.abs(Amp_Vibr_all)) print(Amp_Vibr_all) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; for i_in in np.arange(num_in): ind_in = ind_in_all[i_in] if mark_vibrY == 0: x[ind_in] = Phase_Vibr[nt]Amp_Vibr_all[i_in]+x_ini[ind_in] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = Phase_Vibr[nt]Amp_Vibr_all[i_in]+y_ini[ind_in] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-Bvx Fy_all = Fy-Bvy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: x_in = Phase_VibrAmp_Vibr freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: y_in = Phase_VibrAmp_Vibr freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_dPhiSignal(mark_collision, beta, Nt, N, x_ini, y_ini, d0, phi0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_in, ind_out, dphi_scheme, dphi_on, dphi_off, num_gap, mark_vibrY): dt = d0/40 Nt = int(Nt) if dphi_scheme == 1: nt_dphi = np.linspace(0, Nt, 3) nt_dphi = nt_dphi.astype(int) dphi_all = np.zeros(Nt)+dphi_on dphi_all[nt_dphi[1]:nt_dphi[2]] = dphi_off elif dphi_scheme == -1: nt_dphi = np.linspace(0, Nt, 3) nt_dphi = nt_dphi.astype(int) dphi_all = np.zeros(Nt)+dphi_off dphi_all[nt_dphi[1]:nt_dphi[2]] = dphi_on else: dphi_all = np.zeros(Nt)+dphi_on nt_dphi = np.linspace(0, Nt, num_gap+1) nt_dphi = nt_dphi.astype(int) for ii in np.arange(1, num_gap, 2): dphi_all[nt_dphi[ii]:nt_dphi[ii+1]] = dphi_off D_ini = np.zeros(N)+d0 nt_rec = np.linspace(0, Nt, int(Nt/5e4num_gap/5)+1) Ek_rec = np.array(0) Ep_rec = np.array(0) cont_rec = np.array(0) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 1: y_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in] vy_in = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)) else: x_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in] vx_in = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): D0 = D_ininp.sqrt(1+dphi_all[nt]/phi0) #if np.mod(nt,100000) == 0: #print(D0[3]) x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 1: y[ind_in] = y_in[nt] x[ind_in] = x_ini[ind_in] vy[ind_in] = vy_in[nt] vx[ind_in] = 0 else: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] vx[ind_in] = vx_in[nt] vy[ind_in] = 0 Fx = np.zeros(N) Fy = np.zeros(N) if mark_collision == 1: Fx, Fy, Ep_now, cont_now, p_now = force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D0, Lx, 0, Ly) Fx_all = Fx Fy_all = Fy else: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Fx_all = Fx-betavx Fy_all = Fy-betavy Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec = np.append(Ek_rec, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec = np.append(Ep_rec, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec = np.append(cont_rec, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) print ("freq=%f, cont_min=%d, cont_max=%d, cont_ave=%f\n" %(Freq_Vibr, min(cont), max(cont), np.mean(cont))) nt_dphi = np.arange(0, Nt, 100) if mark_vibrY == 1: xy_out = y_out else: xy_out = x_out return nt_dphi, dphi_all[nt_dphi], xy_out, nt_rec[1:], Ek_rec[1:], Ep_rec[1:], cont_rec[1:] def Damping_calc(Damp_scheme, B, N, x, y, vx, vy, Lx, Ly): Fx_damp = np.zeros(N) Fy_damp = np.zeros(N) if Damp_scheme == 1: Fx_damp = -Bvx Fy_damp = -Bvy if Damp_scheme == 2: Fx_damp = -Bvxnp.abs(vx)5e5 Fy_damp = -Bvynp.abs(vy)5e5 if Damp_scheme == 3: Fx_damp = -Bvx/np.sqrt(np.abs(vx))np.sqrt(2e-6) Fy_damp = -Bvy/np.sqrt(np.abs(vy))np.sqrt(2e-6) if Damp_scheme == 4: Fx_damp = -Bvxnp.exp(-5e4np.abs(vx)+1)0.1 Fy_damp = -Bvynp.exp(-5e4np.abs(vy)+1)0.1 if Damp_scheme == 5: Fx_damp = -Bvxnp.exp(-5e5np.abs(vx)+1) Fy_damp = -Bvynp.exp(-5e5np.abs(vy)+1) if Damp_scheme == 6: Fx_damp = -Bvxnp.exp(-5e6np.abs(vx)+1)10 Fy_damp = -Bvynp.exp(-5e6np.abs(vy)+1)10 if Damp_scheme == 7: Fx_damp = -Bvxnp.exp(-5e7np.abs(vx)+1)100 Fy_damp = -Bvynp.exp(-5e7np.abs(vy)+1)100 return Fx_damp, Fy_damp def Force_FixedPos_calc(k, N, x, y, x0, y0, D0, vx, vy, Lx, Ly): Fx_damp = np.zeros(N) Fy_damp = np.zeros(N) Ep = 0 for nn in np.arange(N): dy = y[nn]-y0[nn] dy = dy-round(dy/Ly)Ly Dmn = 0.5D0[nn] dx = x[nn]-x0[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if (dmn > 0): F = -k(dmn/Dmn/Dmn)/dmn Fx_damp[nn] += Fdx Fy_damp[nn] += Fdy Ep += (1/2)k(dmn/Dmn)*2 return Fx_damp, Fy_damp, Ep def MD_FilterCheck_Periodic_Equi_vCorr(Nt_damp, Nt_FFT, num_period, Damp_scheme, B, N, x0, y0, D0, m0, L, T_set, V_em, n_em): if Damp_scheme < 0: return N = int(N) Nt_FFT = int(Nt_FFT) Nt_damp = int(Nt_damp) dt = min(D0)/40 Nt_period = int(2Nt_damp+Nt_FFT) Nt = Nt_periodnum_period Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) mark_damp = np.zeros(Nt) mark_FFT = np.zeros(Nt) for ii in np.arange(num_period): if ii > 0: t1 = iiNt_period t2 = t1+Nt_damp mark_damp[t1:t2] = 1 t3 = iiNt_period+2Nt_damp t4 = t3+Nt_FFT mark_FFT[t3:t4] = 1 nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] num_FFT = 0 vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) if mark_damp[nt] == 1: Fx_damp, Fy_damp = Damping_calc(Damp_scheme, B, N, x, y, vx, vy, L[0], L[1]) Fx = Fx + Fx_damp Fy = Fy + Fy_damp Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_FFT[nt] == 1: if mark_FFT[nt-1] == 0: nt_ref = nt vx_rec = np.zeros([Nt_FFT, N]) vy_rec = np.zeros([Nt_FFT, N]) nt_delta = nt-nt_ref vx_rec[nt_delta] = vx vy_rec[nt_delta] = vy if nt_delta == Nt_FFT-1: num_FFT += 1 freq_now, fft_now = FFT_vCorr(Nt_FFT, N, vx_rec, vy_rec, dt) if num_FFT == 1: fft_all = np.array([fft_now]) freq_all = np.array([freq_now]) len_fft_ref = len(fft_now) len_freq_ref = len(freq_now) else: fft_add = np.zeros(len_fft_ref) freq_add = np.zeros(len_freq_ref) len_fft_now = len(fft_now) len_freq_now = len(freq_now) if len_fft_now >= len_fft_ref: fft_add[0:len_fft_ref] = fft_now[0:len_fft_ref] else: fft_add[0:len_fft_now] = fft_now[0:len_fft_now] fft_add[len_fft_now:] = fft_now[len_fft_now] if len_freq_now >= len_freq_ref: freq_add[0:len_freq_ref] = freq_now[0:len_freq_ref] else: freq_add[0:len_freq_now] = freq_now[0:len_freq_now] freq_add[len_freq_now:] = freq_now[len_freq_now] fft_all = np.append(fft_all, [fft_add], axis=0) freq_all = np.append(freq_all, [freq_add], axis=0) print("FFT_iteration: %d" % num_FFT) print("Ek_ave: %e" %(np.mean(Ek[nt_ref:nt]))) ind1 = m0>5 ind2 = m0<5 print("\|vx\|_ave(heavy):%e" % np.mean(np.abs(vx[ind1]))) print("\|vx\|_ave(light):%e" % np.mean(np.abs(vx[ind2]))) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return freq_all, fft_all, (nt_rec[:-1]+nt_rec[1:])/2, Ek_rec, Ep_rec, cont_rec def MD_FilterCheck_Periodic_Equi_vCorr_Seperate(Nt_damp, Nt_FFT, num_period, Damp_scheme, k, B, N, x0, y0, D0, m0, L, T_set, V_em, n_em): # for damping scheme = -1 (fixed spring at initial position) if Damp_scheme != -1: return N = int(N) Nt_FFT = int(Nt_FFT) Nt_damp = int(Nt_damp) dt = min(D0)/40 Nt = Nt_dampnum_period+Nt_FFT if num_period == 0: Nt = Nt_damp+Nt_FFT Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) mark_FFT = np.zeros(Nt) t1 = Nt_damp num_period if num_period == 0: t1 = Nt_damp t2 = t1 + Nt_FFT mark_FFT[t1:t2] = 1 nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) # always have damping exceot num_period = 0 if num_period > 0: Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_calc(k, N, x, y, x0, y0, D0, vx, vy, L[0], L[1]) if (B > 0): Fx_damp += -Bvx Fy_damp += -Bvy elif num_period == 0: Fx_damp = 0 Fy_damp = 0 Ep_fix = 0 Ep_now += Ep_fix Fx = Fx + Fx_damp Fy = Fy + Fy_damp Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_FFT[nt] == 1: if mark_FFT[nt-1] == 0: nt_ref = nt vx_rec = np.zeros([Nt_FFT, N]) vy_rec = np.zeros([Nt_FFT, N]) nt_delta = nt-nt_ref vx_rec[nt_delta] = vx vy_rec[nt_delta] = vy if nt_delta == Nt_FFT-1: freq_now, fft_now = FFT_vCorr(Nt_FFT, N, vx_rec, vy_rec, dt) print ("Nt_End="+str(nt)) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return freq_now, fft_now, (nt_rec[:-1]+nt_rec[1:])/2, Ek_rec, Ep_rec, cont_rec def MD_Periodic_Equi_vDistr(Nt_MD, Nt_rec, N, x0, y0, D0, m0, L, T_set, V_em, n_em): N = int(N) Nt_MD = int(Nt_MD) Nt_rec = int(Nt_rec) dt = min(D0)/40 Nt = Nt_MD+Nt_rec Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] ind1 = m0>5 ind2 = m0<5 vx_light = [] vx_heavy = [] vy_light = [] vy_heavy = [] vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if nt >= Nt_MD: vx_light.extend(vx[ind2]) vy_light.extend(vy[ind2]) vx_heavy.extend(vx[ind1]) vy_heavy.extend(vy[ind1]) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return nt_rec, Ek_rec, Ep_rec, cont_rec, vx_light, vx_heavy, vy_light, vy_heavy def Output_resonator_1D(Nt, x_drive, x0, m0, w0, dt): dx = x_drive - x0 k = w0*2m0 x = 0 vx = 0 ax_old = 0 Nt = int(Nt) x_rec = np.zeros(Nt) for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration x_rec[nt] = x Fx = k(dx[nt]-x) ax = Fx/m0; vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration ax_old = ax; freq_fft, fft_x_rec = FFT_Fup(int(Nt/2), x_rec[int(Nt/2):Nt], dt, w0) return freq_fft, fft_x_rec def MD_Periodic_vCorr(Nt, N, x0, y0, D0, m0, vx0, vy0, L, T_set): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) vx_rec = np.zeros([int(Nt/2), N]) vy_rec = np.zeros([int(Nt/2), N]) vx = vx0 vy = vy0 T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if (nt >= Nt/2): vx_rec[int(nt-Nt/2)] = vx vy_rec[int(nt-Nt/2)] = vy CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) freq_now, fft_now = FFT_vCorr(int(Nt/2), N, vx_rec, vy_rec, dt) return freq_now, fft_now, np.mean(cont) def MD_Period_ConstV_SD(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, Lx, Ly) Ep[nt] = Ep_now vx = 0.1np.divide(Fx, m0) vy = 0.1np.divide(Fy, m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #t_end = time.time() print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def Force_FixedPos_YFixed_calc(k, N, x, y, x0, y0, D0, vx, vy, Lx, Ly): Fx_damp = np.zeros(N) Fy_damp = np.zeros(N) Ep = 0 for nn in np.arange(N): dy = y[nn]-y0[nn] Dmn = 0.5D0[nn] dx = x[nn]-x0[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if (dmn > 0): F = -k(dmn/Dmn/Dmn)/dmn Fx_damp[nn] += Fdx Fy_damp[nn] += Fdy Ep += (1/2)k(dmn/Dmn)2 return Fx_damp, Fy_damp, Ep def MD_VibrSP_ConstV_Yfixed_FixSpr(k, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out): mark_vibrY = 0 mark_resonator = 1 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in] elif mark_vibrY == 1: y_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_YFixed_calc(k, N, x, y, x_ini, y_ini, D0, vx, vy, L[0], L[1]) #Fx_damp = 0; Fy_damp = 0; Ep_fix = 0 Fx_damp += -Bvx Fy_damp += -Bvy Ep[nt] = Ep_now + Ep_fix cont[nt] = cont_now p[nt] = p_now Fx_all = Fx+Fx_damp Fy_all = Fy+Fy_damp x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_Yfixed_FixSpr2(k, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out): mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1np.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2np.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1np.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2np.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in2] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_YFixed_calc(k, N, x, y, x_ini, y_ini, D0, vx, vy, L[0], L[1]) #Fx_damp = 0; Fy_damp = 0; Ep_fix = 0 Fx_damp += -Bvx Fy_damp += -Bvy Ep[nt] = Ep_now + Ep_fix cont[nt] = cont_now p[nt] = p_now Fx_all = Fx+Fx_damp Fy_all = Fy+Fy_damp x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_Yfixed_FixSpr3(k, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr1, Amp_Vibr1, ind_in1, Freq_Vibr2, Amp_Vibr2, ind_in2, ind_out): mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1np.sin(Freq_Vibr1dtnp.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2np.sin(Freq_Vibr2dtnp.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1np.sin(Freq_Vibr1dtnp.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2np.sin(Freq_Vibr2dtnp.arange(Nt))+y_ini[ind_in2] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_YFixed_calc(k, N, x, y, x_ini, y_ini, D0, vx, vy, L[0], L[1]) #Fx_damp = 0; Fy_damp = 0; Ep_fix = 0 Fx_damp += -Bvx Fy_damp += -Bvy Ep[nt] = Ep_now + Ep_fix cont[nt] = cont_now p[nt] = p_now Fx_all = Fx+Fx_damp Fy_all = Fy+Fy_damp x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr1, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr1) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr2) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr1) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr2) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr1) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr1) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr1, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr1, dt) return freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_Force_ConstV(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, mark_vibrY, mark_resonator): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) x_in = np.zeros(Nt) y_in = np.zeros(Nt) if mark_vibrY == 0: Fx_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)) elif mark_vibrY == 1: Fy_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; x_in[nt] = x[ind_in] y_in[nt] = y[ind_in] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-Bvx Fy_all = Fy-Bvy if mark_vibrY == 0: Fx_all[ind_in] += Fx_in[nt] elif mark_vibrY == 1: Fy_all[ind_in] += Fy_in[nt] x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) if Nt == 5e5: print(x[ind_out], y[ind_out]) print(fft_x_out[100], fft_y_out[100]) print(fft_in[100]) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_ConfigCB(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, Nt_rec): mark_vibrY = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in] elif mark_vibrY == 1: y_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): if nt == Nt_rec: x_rec = x[:] y_rec = y[:] x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-Bvx Fy_all = Fy-Bvy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) return x_rec, y_rec def VL_YFixed_ConstV(N, x, y, D, Lx, VL_list, VL_counter_old, x_save, y_save, first_call): r_factor = 1.2 r_cut = np.amax(D) r_list = r_factor * r_cut r_list_sq = r_list*2 r_skin_sq = ((r_factor - 1.0) r_cut)*2 if first_call == 0: dr_sq_max = 0.0 for nn in np.arange(N): dy = y[nn] - y_save[nn] dx = x[nn] - x_save[nn] dx = dx - round(dx / Lx) Lx dr_sq = dx2 + dy2 if dr_sq > dr_sq_max: dr_sq_max = dr_sq if dr_sq_max < r_skin_sq: return VL_list, VL_counter_old, x_save, y_save VL_counter = 0 for nn in np.arange(N): r_now = 0.5D[nn] for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < r_list: dx = x[mm]-x[nn] dx = dx - round(dx / Lx) * Lx if abs(dx) < r_list: dmn_sq = dx2 + dy2 if dmn_sq < r_list_sq: VL_list[VL_counter][0] = nn VL_list[VL_counter][1] = mm VL_counter += 1 return VL_list, VL_counter, x, y def MD_YFixed_ConstV_SP_SD_DiffK(Nt, N, x0, y0, D0, m0, Lx, Ly, k_list, k_type): dt = D0[0] * np.sqrt(k_list[2]) / 20.0 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) x_save = np.array(x0) y_save = np.array(y0) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now vx = 0.1 * Fx vy = 0.1 * Fy x += vx * dt y += vy * dt F_tot[nt] = sum(np.absolute(Fx) + np.absolute(Fy)) # putting a threshold on total force if (F_tot[nt] < 1e-11): break print(nt) print(F_tot[nt]) t_end = time.time() #print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def FIRE_YFixed_ConstV_DiffK(Nt, N, x0, y0, D0, m0, Lx, Ly, k_list, k_type): dt_md = 0.01 * D0[0] * np.sqrt(k_list[2]) N_delay = 20 N_pn_max = 2000 f_inc = 1.1 f_dec = 0.5 a_start = 0.15 f_a = 0.99 dt_max = 10.0 * dt_md dt_min = 0.05 * dt_md initialdelay = 1 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) x_save = np.array(x0) y_save = np.array(y0) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) a_fire = a_start delta_a_fire = 1.0 - a_fire dt = dt_md dt_half = dt / 2.0 N_pp = 0 # number of P being positive N_pn = 0 # number of P being negative ## FIRE for nt in np.arange(Nt): # FIRE update P = np.dot(vx, Fx) + np.dot(vy, Fy) if P > 0.0: N_pp += 1 N_pn = 0 if N_pp > N_delay: dt = min(f_inc * dt, dt_max) dt_half = dt / 2.0 a_fire = f_a * a_fire delta_a_fire = 1.0 - a_fire else: N_pp = 0 N_pn += 1 if N_pn > N_pn_max: break if (initialdelay < 0.5) or (nt >= N_delay): if f_dec * dt > dt_min: dt = f_dec * dt dt_half = dt / 2.0 a_fire = a_start delta_a_fire = 1.0 - a_fire x -= vx * dt_half y -= vy * dt_half vx = np.zeros(N) vy = np.zeros(N) # MD using Verlet method vx += Fx * dt_half vy += Fy * dt_half rsc_fire = np.sqrt(np.sum(vx2 + vy2)) / np.sqrt(np.sum(Fx2 + Fy2)) vx = delta_a_fire * vx + a_fire * rsc_fire * Fx vy = delta_a_fire * vy + a_fire * rsc_fire * Fy x += vx * dt y += vy * dt Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now F_tot[nt] = sum(np.absolute(Fx) + np.absolute(Fy)) # putting a threshold on total force if (F_tot[nt] < 1e-11): break vx += Fx * dt_half vy += Fy * dt_half #print(nt) #print(F_tot[nt]) t_end = time.time() #print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out): Lx = L[0] Ly = L[1] mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) F_tot = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1np.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2np.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1np.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2np.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in2] vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) x_save = np.array(x_ini) y_save = np.array(y_ini) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2 # first step in Verlet integration y = y+vydt+ay_olddt2/2 if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx - Bvx Fy_all = Fy - Bvy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2 # second step in Verlet integration vy = vy+(ay_old+ay)dt/2 ax_old = ax ay_old = ay Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out): Lx = L[0] Ly = L[1] mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) F_tot = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1np.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2np.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1np.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2np.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in2] vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) x_save = np.array(x_ini) y_save = np.array(y_ini) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2 # first step in Verlet integration y = y+vydt+ay_olddt2/2 if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx - Bvx Fy_all = Fy - Bvy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2 # second step in Verlet integration vy = vy+(ay_old+ay)dt/2 ax_old = ax ay_old = ay Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return x_in1-x_ini[ind_in1], x_in2-x_ini[ind_in2], x_out-x_ini[ind_out]	133,858	30.675106	178	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/constants.py	# number of runs RUNS = 3 worstFitness = +1000000 # population size popSize = 50 # number of generations numGenerations = 200	129	10.818182	23	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/afpo_seed_binary.py	import constants as c import copy import numpy as np import operator import statistics import pickle from joblib import Parallel, delayed import multiprocessing from genome_seed_binary import GENOME class AFPO: def __init__(self,randomSeed): self.randomSeed = randomSeed self.currentGeneration = 0 self.nextAvailableID = 0 self.genomes = {} for populationPosition in range(c.popSize): self.genomes[populationPosition] = GENOME(self.nextAvailableID) self.nextAvailableID = self.nextAvailableID + 1 def Evolve(self): self.Perform_First_Generation() for self.currentGeneration in range(1,c.numGenerations): self.Perform_One_Generation() self.SaveLastGen() # -------------------------- Private methods ---------------------- def Age(self): for genome in self.genomes: self.genomes[genome].Age() def Aggressor_Dominates_Defender(self,aggressor,defender): return self.genomes[aggressor].Dominates(self.genomes[defender]) def Choose_Aggressor(self): return np.random.randint(c.popSize) def Choose_Defender(self,aggressor): defender = np.random.randint(c.popSize) while defender == aggressor: defender = np.random.randint(c.popSize) return defender def Contract(self): while len(self.genomes) > c.popSize: aggressorDominatesDefender = False while not aggressorDominatesDefender: aggressor = self.Choose_Aggressor() defender = self.Choose_Defender(aggressor) aggressorDominatesDefender = self.Aggressor_Dominates_Defender(aggressor,defender) for genomeToMove in range(defender,len(self.genomes)-1): self.genomes[genomeToMove] = self.genomes.pop(genomeToMove+1) def process(self, i): return ii def Evaluate_Genomes(self): num_cores = multiprocessing.cpu_count() print("we have") print(num_cores) print("cores") outs = Parallel(n_jobs=num_cores)(delayed(self.genomes[genome].Evaluate)() for genome in self.genomes) #print("done") #print(type(outs)) #self.genomes = copy.deepcopy(np.array(outs)) #self.genomes = dict(zip(list(range(0, c.popSize)), outs)) #print(type(self.genomes)) for genome in self.genomes: # print(genome) #print(self.genomes[genome].indv.fitness) # print(outs[genome]) self.genomes[genome].fitness = outs[genome] self.genomes[genome].indv.fitness = -outs[genome] def Expand(self): popSize = len(self.genomes) for newGenome in range( popSize , 2 popSize - 1 ): spawner = self.Choose_Aggressor() self.genomes[newGenome] = copy.deepcopy(self.genomes[spawner]) self.genomes[newGenome].Set_ID(self.nextAvailableID) self.nextAvailableID = self.nextAvailableID + 1 self.genomes[newGenome].Mutate() def Find_Best_Genome(self): genomesSortedByFitness = sorted(self.genomes.values(), key=operator.attrgetter('fitness'),reverse=False) return genomesSortedByFitness[0] def Find_Avg_Fitness(self): add = 0 for g in self.genomes: add += self.genomes[g].fitness return add/c.popSize def Inject(self): popSize = len(self.genomes) self.genomes[popSize-1] = GENOME(self.nextAvailableID) self.nextAvailableID = self.nextAvailableID + 1 def Perform_First_Generation(self): self.Evaluate_Genomes() self.Print() self.Save_Best() self.Save_Avg() def Perform_One_Generation(self): self.Expand() self.Age() self.Inject() self.Evaluate_Genomes() self.Contract() self.Print() self.Save_Best() self.Save_Avg() def Print(self): print('Generation ', end='', flush=True) print(self.currentGeneration, end='', flush=True) print(' of ', end='', flush=True) print(str(c.numGenerations), end='', flush=True) print(': ', end='', flush=True) bestGenome = self.Find_Best_Genome() bestGenome.Print() def Save_Best(self): bestGenome = self.Find_Best_Genome() bestGenome.Save(self.randomSeed) def SaveLastGen(self): genomesSortedByFitness = sorted(self.genomes.values(), key=operator.attrgetter('fitness'),reverse=False) f = open('savedRobotsLastGenAfpoSeed.dat', 'ab') pickle.dump(genomesSortedByFitness, f) f.close() def Save_Avg(self): f = open('avgFitnessAfpoSeed.dat', 'ab') avg = self.Find_Avg_Fitness() print('Average ' + str(avg)) print() #f.write("%.3f\n" % avg) pickle.dump(avg, f) f.close() def Show_Best_Genome(self): bestGenome = self.Find_Best_Genome() bestGenome.Show()	5,048	30.955696	112	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/DynamicalMatrix.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Tue Oct 10 22:09:09 2017 @author: Hightoutou """ def DM_mass(N, x0, y0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] M = np.zeros((2N, 2N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly rijsq = dx2+dy2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij m_sqrt = np.zeros((2N, 2N)) m_inv = np.zeros((2N, 2N)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_3D(N, x0, y0, z0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] Lz = L[2] M = np.zeros((3N, 3N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly dz = z0[i]-z0[j] dz = dz-round(dz/Lz)Lz rijsq = dx2+dy2+dz*2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy, dxdz], [dydx, dydy, dydz], [dzdx, dzdy, dzdz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3i:3i+3,3j:3j+3] = Mij M[3j:3j+3,3i:3i+3] = Mij M[3i:3i+3,3i:3i+3] = M[3i:3i+3,3i:3i+3] - Mij M[3j:3j+3,3j:3j+3] = M[3j:3j+3,3j:3j+3] - Mij m_sqrt = np.zeros((3N, 3N)) m_inv = np.zeros((3N, 3N)) for i in range(N): m_sqrt[3i, 3i] = 1/np.sqrt(m0[i]) m_sqrt[3i+1, 3i+1] = 1/np.sqrt(m0[i]) m_sqrt[3i+2, 3i+2] = 1/np.sqrt(m0[i]) m_inv[3i, 3i] = 1/m0[i] m_inv[3i+1, 3i+1] = 1/m0[i] m_inv[3i+2, 3i+2] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_Yfixed(N, x0, y0, D0, m0, Lx, y_bot, y_top, k): import numpy as np M = np.zeros((2N, 2N)) contactNum = 0 for i in range(N): r_now = 0.5D0[i] if y0[i]-y_bot<r_now or y_top-y0[i]<r_now: M[2i+1, 2i+1] = M[2i+1, 2i+1]+1/r_now/r_now for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] rijsq = dx2+dy2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij M = kM m_sqrt = np.zeros((2N, 2N)) m_inv = np.zeros((2N, 2N)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_Xfixed(N, x0, y0, D0, m0, Ly): import numpy as np M = np.zeros((2N, 2N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly rijsq = dx2+dy2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij m_sqrt = np.zeros((2N, 2N)) m_inv = np.zeros((2N, 2N)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_DiffK_Yfixed(N, x0, y0, D0, m0, Lx, y_bot, y_top, k_list, k_type): import numpy as np M = np.zeros((2N, 2N)) contactNum = 0 for i in range(N): r_now = 0.5D0[i] if y0[i]-y_bot<r_now or y_top-y0[i]<r_now: M[2i+1, 2i+1] = M[2i+1, 2i+1] + k_list[k_type[i]] / r_now / r_now for j in range(i): dij = 0.5 * (D0[i] + D0[j]) dijsq = dij*2 dx = x0[i] - x0[j] dx = dx - round(dx / Lx) Lx dy = y0[i] - y0[j] rijsq = dx2 + dy2 if rijsq < dijsq: contactNum += 1 k = k_list[(k_type[i] ^ k_type[j]) + np.maximum(k_type[i], k_type[j])] rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -k * rijmat / rijsq / dijsq Mij2 = -k * (1.0 - rij / dij) * (rijmat / rijsq - [[1,0],[0,1]]) / rij / dij Mij = Mij1 + Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij m_sqrt = np.zeros((2N, 2N)) m_inv = np.zeros((2N, 2N)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) w,v = np.linalg.eig(M) return w,v def DM_mass_Zfixed_3D(N, x0, y0, z0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] Lz = L[2] M = np.zeros((3N, 3N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly dz = z0[i]-z0[j] rijsq = dx2+dy2+dz2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy, dxdz], [dydx, dydy, dydz], [dzdz, dzdy, dzdz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3i:3i+3,3j:3j+3] = Mij M[3j:3j+3,3i:3i+3] = Mij M[3i:3i+3,3i:3i+3] = M[3i:3i+3,3i:3i+3] - Mij M[3j:3j+3,3j:3j+3] = M[3j:3j+3,3j:3j+3] - Mij m_sqrt = np.zeros((3N, 3N)) m_inv = np.zeros((3N, 3N)) for i in range(N): m_sqrt[3i, 3i] = 1/np.sqrt(m0[i]) m_sqrt[3i+1, 3i+1] = 1/np.sqrt(m0[i]) m_sqrt[3i+2, 3i+2] = 1/np.sqrt(m0[i]) m_inv[3i, 3i] = 1/m0[i] m_inv[3i+1, 3i+1] = 1/m0[i] m_inv[3i+2, 3i+2] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_UpPlate(N, x0, y0, D0, m0, Lx, y_up, m_up): import numpy as np M = np.zeros((2N+1, 2N+1)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij*2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] rijsq = dx2+dy2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij for i in range(N): r_now = 0.5D0[i] if y0[i]<r_now: M[2i+1, 2i+1] = M[2i+1, 2i+1]+1/r_now*2 if y_up-y0[i]<r_now: M[2i+1, 2i+1] = M[2i+1, 2i+1]+1/r_now2 M[2N, 2N] = M[2N, 2N]+1/r_now2 M[2i+1, 2N] = M[2i+1, 2N]-1/r_now2 M[2N, 2i+1] = M[2N, 2i+1]-1/r_now2 m_sqrt = np.zeros((2N+1, 2N+1)) m_inv = np.zeros((2N+1, 2N+1)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] m_sqrt[2N, 2N] = 1/np.sqrt(m_up) m_inv[2N, 2N] = 1/m_up #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_UpPlate_3D(N, x0, y0, z0, D0, m0, Lx, Ly, z_up, m_up): import numpy as np M = np.zeros((3N+1, 3N+1)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij*2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly dz = z0[i]-z0[j] rijsq = dx2+dy2+dz2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy, dxdz], [dydx, dydy, dydz], [dzdz, dzdy, dzdz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3i:3i+3,3j:3j+3] = Mij M[3j:3j+3,3i:3i+3] = Mij M[3i:3i+3,3i:3i+3] = M[3i:3i+3,3i:3i+3] - Mij M[3j:3j+3,3j:3j+3] = M[3j:3j+3,3j:3j+3] - Mij for i in range(N): r_now = 0.5D0[i] if z0[i]<r_now: M[3i+2, 3i+2] = M[3i+2, 3i+2]+1/r_now*2 if z_up-z0[i]<r_now: M[3i+2, 3i+2] = M[3i+2, 3i+2]+1/r_now2 M[3N, 3N] = M[3N, 3N]+1/r_now2 M[3i+2, 3N] = M[3i+2, 3N]-1/r_now2 M[3N, 3i+2] = M[3N, 3i+2]-1/r_now2 m_sqrt = np.zeros((3N+1, 3N+1)) m_inv = np.zeros((3N+1, 3N+1)) for i in range(N): m_sqrt[3i, 3i] = 1/np.sqrt(m0[i]) m_sqrt[3i+1, 3i+1] = 1/np.sqrt(m0[i]) m_sqrt[3i+2, 3i+2] = 1/np.sqrt(m0[i]) m_inv[3i, 3i] = 1/m0[i] m_inv[3i+1, 3i+1] = 1/m0[i] m_inv[3i+2, 3i+2] = 1/m0[i] m_sqrt[3N, 3N] = 1/np.sqrt(m_up) m_inv[3N, 3*N] = 1/m_up #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v	12,537	30.423559	104	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/ConfigPlot.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Tue Oct 10 21:01:26 2017 @author: Hightoutou """ import numpy as np def ConfigPlot_DiffSize(N, x, y, D, L, mark_print): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse Dmin = min(D) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] D_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) D_all.append(D[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_alpha(0.3) if D_all[i] > Dmin: e.set_facecolor('C1') else: e.set_facecolor('C0') i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass(N, x, y, D, L, m, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffMass2(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness(N, x, y, D, L, m, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C2') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness2(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C2') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness3(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='^', s=80, color=(0, 1, 0, 1)) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='s', s=80, color=(0, 0, 1, 1)) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='', s=100, color=(1, 0, 0, 1)) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('k') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) import matplotlib.lines as mlines red_star = mlines.Line2D([], [], color=(1, 0, 0), marker='', linestyle='None', markersize=10, label='Output') blue_square = mlines.Line2D([], [], color=(0, 0, 1), marker='s', linestyle='None', markersize=10, label='Input 2') green_triangle = mlines.Line2D([], [], color=(0, 1, 0), marker='^', linestyle='None', markersize=10, label='Input 1') plt.legend(handles=[red_star, green_triangle, blue_square], bbox_to_anchor=(1.215, 1)) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffMass_3D(N, x, y, z, D, L, m, mark_print): import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D m_min = min(m) m_max = max(m) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.set_aspect('equal') sphes = [] m_all = [] for i in range(int(N/2)): x_now = x[i]%L[0] y_now = y[i]%L[1] z_now = z[i]%L[2] r_now = 0.5D[i] #alpha_now = 0.2+(m_all[i]-m_min)/(m_max-m_min)0.3 alpha_now = 0.3 pos1 = 0 pos2 = 1 for j in range(pos1, pos2): for k in range(pos1, pos2): for l in range(pos1, pos2): u = np.linspace(0, 2 * np.pi, 100) v = np.linspace(0, np.pi, 100) x_plot = x_now+jL[0]+r_now np.outer(np.cos(u), np.sin(v)) y_plot = y_now+kL[1]+r_now np.outer(np.sin(u), np.sin(v)) z_plot = z_now+lL[2]+r_now np.outer(np.ones(np.size(u)), np.cos(v)) ymin = y_plot[y_plot>0].min() ymax = y_plot[y_plot>0].max() print (i, ymin, ymax) ax.plot_surface(x_plot,y_plot,z_plot,rstride=4,cstride=4, color='C0',linewidth=0,alpha=alpha_now) #sphes.append(e) #m_all.append(m[i]) # i = 0 # for e in sphes: # ax.add_artist(e) # e.set_clip_box(ax.bbox) # e.set_facecolor('C0') # e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) # i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) ax.set_zlim(0, L[2]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_YFixed_rec(N, x, y, D, Lx, y_top, y_bot, m, mark_order): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_print = 0 m_min = min(m) m_max = max(m) if m_min == m_max: m_max = 1.001 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) for k in range(-1, 2): e = Ellipse((x_now+kLx, y_now), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.3+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 #rect = Rectangle([0, y_top], Lx, 0.2D[0], color='C0') #ax.add_patch(rect) for nn in np.arange(N): x1 = x[nn]%Lx d_up = y_top-y[nn] d_bot = y[nn]-y_bot r_now = 0.5D[nn] if d_up<r_now: ax.plot([x1, x1], [y[nn], y[nn]+r_now], '-', color='w') if d_bot<r_now: ax.plot([x1, x1], [y[nn], y[nn]-r_now], '-', color='w') for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < Dmn: x2 = x[mm]%Lx if x2>x1: xl = x1 xr = x2 yl = y[nn] yr = y[mm] else: xl = x2 xr = x1 yl = y[mm] yr = y[nn] dx0 = xr-xl dx = dx0-round(dx0/Lx)Lx dmn = np.sqrt(dx2+dy2) if dmn < Dmn: if dx0<Dmn: ax.plot([xl, xr], [yl, yr], '-', color='w') else: ax.plot([xl, xr-Lx], [yl, yr], '-', color='w') ax.plot([xl+Lx, xr], [yl, yr], '-', color='w') ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/plot_test/fig'+str(int(ind_nt+1e4))+'.png', dpi = 150) def ConfigPlot_DiffMass_SP(N, x, y, D, L, m, mark_print, ind_in, ind_out, ind_fix): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)0.3) if i == ind_in: e.set_edgecolor('r') e.set_linewidth(width) if i == ind_out: e.set_edgecolor('b') e.set_linewidth(width) if i == ind_fix: e.set_edgecolor('k') e.set_linewidth(width) ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass_FixLx(N, x, y, D, L, m, mark_print, ind_wall): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i] y_now = y[i]%L[1] for l in range(-1, 2): e = Ellipse((x_now, y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) if ind_wall[i] > 0: e.set_edgecolor('k') e.set_linewidth(width) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass_SP_rec(N, x, y, D, L, m, mark_print, ind_in, ind_out, ind_fix): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)0.3) if i == ind_in: e.set_edgecolor('r') e.set_linewidth(width) if i == ind_out: e.set_edgecolor('b') e.set_linewidth(width) if i == ind_fix: e.set_edgecolor('k') e.set_linewidth(width) Lx = L[0] Ly = L[1] for nn in np.arange(N): x1 = x[nn]%Lx y1 = y[nn]%Ly for mm in np.arange(nn+1, N): x2 = x[mm]%Lx y2 = y[mm]%Ly if x2>x1: xl = x1 xr = x2 yl = y1 yr = y2 else: xl = x2 xr = x1 yl = y2 yr = y1 dx0 = xr-xl dx = dx0-round(dx0/Lx)Lx if y2>y1: xd = x1 xu = x2 yd = y1 yu = y2 else: xd = x2 xu = x1 yd = y2 yu = y1 dy0 = yu-yd dy = dy0-round(dy0/Ly)Ly Dmn = 0.5(D[mm]+D[nn]) dmn = np.sqrt(dx2+dy2) if dmn < Dmn: if dx0<Dmn and dy0<Dmn: ax.plot([xl, xr], [yl, yr], '-', color='w') else: if dx0>Dmn and dy0>Dmn: if yr>yl: ax.plot([xl, xr-Lx], [yl, yr-Ly], '-', color='w') ax.plot([xl+Lx, xr], [yl+Ly, yr], '-', color='w') else: ax.plot([xl, xr-Lx], [yl, yr+Ly], '-', color='w') ax.plot([xl+Lx, xr], [yl-Ly, yr], '-', color='w') else: if dx0>Dmn: ax.plot([xl, xr-Lx], [yl, yr], '-', color='w') ax.plot([xl+Lx, xr], [yl, yr], '-', color='w') if dy0>Dmn: ax.plot([xd, xu], [yd, yu-Ly], '-', color='w') ax.plot([xd, xu], [yd+Ly, yu], '-', color='w') ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) return fig def ConfigPlot_EigenMode_DiffMass(N, x, y, D, L, m, em, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) if m_min == m_max: m_max = 1.001 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 r_now = D[0]0.5 dr = np.zeros(N) for i in range(N): dr[i] = np.sqrt(em[2i]2+em[2i+1]*2) dr_max = max(dr) for i in range(N): ratio = dr[i]/dr_maxr_now/dr_max plt.arrow(x[i], y[i],em[2i]ratio, em[2i+1]ratio, head_width=0.005) ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_YFixed_SelfAssembly(N, Nl, x, y, theta, n, d1, d2, Lx, y_top, y_bot): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_order = 0 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] alpha_all = [] alpha1 = 0.6 alpha2 = 0.3 for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) alpha = alpha1 if i < Nl else alpha2 for k in range(-1, 2): e = Ellipse((x_now+kLx, y_now), d1,d1,0) ells.append(e) alpha_all.append(alpha) if i >= Nl: for ind in range(n): x_i = x_now+kLx+0.5(d1+d2)np.cos(theta[i]+ind2np.pi/n) y_i = y_now+0.5(d1+d2)np.sin(theta[i]+ind2np.pi/n) e = Ellipse((x_i, y_i), d2,d2,0) ells.append(e) alpha_all.append(alpha) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(alpha_all[i]) i += 1 ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show() def ConfigPlot_YFixed_SelfAssembly_BumpyBd(N, n_col, Nl, x, y, theta, n, d0, d1, d2, Lx, y_top, y_bot): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_order = 0 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] alpha_all = [] alpha1 = 0.6 alpha2 = 0.3 for i in range(n_col+1): x_now = id0 e1 = Ellipse((x_now, y_bot), d0,d0,0) e2 = Ellipse((x_now, y_top), d0,d0,0) ells.append(e1) alpha_all.append(alpha1) ells.append(e2) alpha_all.append(alpha1) for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) alpha = alpha1 if i < Nl else alpha2 for k in range(-1, 2): e = Ellipse((x_now+kLx, y_now), d1,d1,0) ells.append(e) alpha_all.append(alpha) if i >= Nl: for ind in range(n): x_i = x_now+kLx+0.5(d1+d2)np.cos(theta[i]+ind2np.pi/n) y_i = y_now+0.5(d1+d2)np.sin(theta[i]+ind2np.pi/n) e = Ellipse((x_i, y_i), d2,d2,0) ells.append(e) alpha_all.append(alpha) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(alpha_all[i]) i += 1 ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show()	22,835	29.488652	117	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/genome_seed_binary.py	import constants as c from individual_seed_binary import INDIVIDUAL import pickle class GENOME: def __init__(self,ID,fitness=c.worstFitness): self.Set_ID(ID) self.indv = INDIVIDUAL(ID) self.age = 0 self.fitness = fitness def Age(self): self.age = self.age + 1 def Dominates(self,other): if self.Get_Fitness() <= other.Get_Fitness(): if self.Get_Age() <= other.Get_Age(): equalFitnesses = self.Get_Fitness() == other.Get_Fitness() equalAges = self.Get_Age() == other.Get_Age() if not equalFitnesses and equalAges: return True else: return self.Is_Newer_Than(other) else: return False else: return False def Evaluate(self): #self.indv.Start_Evaluation(True) f = self.indv.Compute_Fitness() # if f < 0: # self.fitness = c.worstFitness # else: # self.fitness = 1/(1+f) self.fitness = -f #print(f) #print(self.indv.genome) return self.fitness def Get_Age(self): return self.age def Get_Fitness(self): return self.fitness def Mutate(self): self.indv.Mutate() def Print(self): print(' fitness: ' , end = '' ) print(self.fitness , end = '' ) print(' age: ' , end = '' ) print(self.age , end = '' ) print() def Save(self,randomSeed): f = open('savedRobotsAfpoSeed.dat', 'ab') pickle.dump(self.indv , f) f.close() pass def Set_ID(self,ID): self.ID = ID def Show(self): #self.indv.Start_Evaluation(False, 40) self.indv.Compute_Fitness(True) # def __add__(self, other): # total_fitness = self.fitness + other.fitness # print("I've been called") # return GENOME(1, total_fitness) # def __radd__(self, other): # if other == 0: # return self # else: # return self.__add__(other) # -------------------- Private methods ---------------------- def Get_ID(self): return self.ID def Is_Newer_Than(self,other): return self.Get_ID() > other.Get_ID()	2,336	24.681319	74	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/afpoPlots_seed_binary.py	import pickle import matplotlib.pyplot as plt from switch_binary import switch import constants as c import numpy runs = c.RUNS gens = c.numGenerations fitnesses = numpy.zeros([runs, gens]) temp = [] individuals = [] with open('savedRobotsAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): for g in range(1, gens+1): try: temp.append(pickle.load(f).fitness) except EOFError: break fitnesses[r-1] = temp temp = [] f.close() mean_f = numpy.mean(fitnesses, axis=0) std_f = numpy.std(fitnesses, axis=0) plt.figure(figsize=(6.4,4.8)) plt.plot(list(range(1, gens+1)), mean_f, color='blue') plt.fill_between(list(range(1, gens+1)), mean_f-std_f, mean_f+std_f, color='cornflowerblue', alpha=0.2) plt.xlabel("Generations") plt.ylabel("Best Fitness") plt.title("Fitness of the Best Individual in the Population - AFPO", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() #plt.legend(['two robot', 'three robots'], loc='upper left') #plt.savefig("compare.pdf") plt.show() # running the best individuals m1 = 1 m2 = 10 N_light = 9 N = 30 bests = numpy.zeros([runs, gens]) temp = [] rubish = [] with open('savedRobotsLastGenAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): # population of the last generation temp = pickle.load(f) # best individual of last generation best = temp[0] switch.showPacking(m1, m2, N_light, best.indv.genome) print(switch.evaluate(m1, m2, N_light, best.indv.genome)) print(switch.evaluateAndPlot(m1, m2, N_light, best.indv.genome)) temp = [] f.close() # running all of the individuals of the last generation of each of the runs bests = numpy.zeros([runs, gens]) temp = [] rubish = [] with open('savedRobotsLastGenAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): print("run:") print(r) # population of the last generation temp = pickle.load(f) for g in range(0, gens): switch.showPacking(m1, m2, N_light, temp[g].indv.genome) print(switch.evaluateAndPlot(m1, m2, N_light, temp[g].indv.genome)) temp = [] f.close()	2,217	28.972973	103	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/evolveAfpo_seed_binary.py	from afpo_seed_binary import AFPO import os import constants as c import random #cleaning up the data files try: os.remove("savedRobotsLastGenAfpoSeed.dat") except OSError: pass try: os.remove("avgFitnessAfpoSeed.dat") except OSError: pass try: os.remove("savedRobotsAfpoSeed.dat") except OSError: pass runs = c.RUNS for r in range(1, runs+1): print("*********************************************************", flush=True) print("run: "+str(r), flush=True) randomSeed = r random.seed(r) afpo = AFPO(randomSeed) afpo.Evolve() #afpo.Show_Best_Genome()	616	18.28125	82	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/individual_seed_binary.py	from switch_binary import switch import constants as c import random import math import numpy as np import sys import pickle class INDIVIDUAL: def __init__(self, i): # assuming curves have one control point, [Sx, Ex, Cx, Cy] for each fiber # assuming we have two planes, each with c.FIBERS of fibers on them self.m1 = 1 self.m2 = 10 #[2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000] #10 is what is in fig2 of the paper self.N_light = 9 #[25, 50, 75] #[9, 15, 21] self.N = 30 #self.low = 7 #self.high = 12 #indices = random.sample(range(0, N), N_light) self.genome = np.random.randint(low=0, high=2, size=self.N)#random.sample(range(0, self.N), self.N_light) #np.random.randint(0, high=c.GRID_SIZE-1, size=(c.FIBERS*2, 4), dtype='int') self.fitness = 0 self.ID = i def Compute_Fitness(self, show=False): # wait for the simulation to end and get the fitness self.fitness = switch.evaluate(self.m1, self.m2, self.N_light, self.genome)#, self.low, self.high) if show: switch.showPacking(self.m1, self.m2, self.N_light, self.genome)#, self.low, self.high) print("fitness is:") print(self.fitness) return self.fitness def Mutate(self): mutationRate = 0.05 probToMutate = np.random.choice([False, True], size=self.genome.shape, p=[1-mutationRate, mutationRate]) candidate = np.where(probToMutate, 1-self.genome, self.genome) self.genome = candidate def Print(self): print('[', self.ID, self.fitness, ']', end=' ') def Save(self): f = open('savedFitnessSeed.dat', 'ab') pickle.dump(self.fitness , f) f.close() def SaveBest(self): f = open('savedBestsSeed.dat', 'ab') pickle.dump(self.genome , f) f.close()	1,936	35.54717	190	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/randomSearch2.py	from switch_binary import switch import matplotlib.pyplot as plt import random import numpy as np from joblib import Parallel, delayed import multiprocessing import pickle from scipy.stats import norm import operator from ConfigPlot import ConfigPlot_DiffStiffness3 with open('outs.dat', "rb") as f: outs = pickle.load(f) f.close() with open('samples.dat', "rb") as f: samples = pickle.load(f) f.close() # compute the cumulative sum #Nk_cum = np.cumsum(Nk) # go to log scale #log_Nk_cum = np.log10(Nk_cum) #log_k = np.log10(k) # plot the original data #fig = plt.figure() #ax = plt.gca() #ax.scatter(log_k, log_Nk_cum, s=5, alpha=0.3) #ax.set_title('CCDF in log-log scale') #ax.set_xlabel('$Log_{10}(k)$') #ax.set_ylabel('$Log_{10}(Nk_{>k})$') def showPacking(indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row m1=1 m2=10 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) # show packing ConfigPlot_DiffStiffness3(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf', ind_in1, ind_in2, ind_out) print("done", flush=True) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() n, bins, patches = plt.hist(x=outs, bins='auto', color='#0504aa', alpha=0.7, cumulative=False)#, grid=True) # fitting a normal distribution mu, std = norm.fit(outs) xmin, xmax = plt.xlim() x = np.linspace(xmin, xmax, 1000) p = norm.pdf(x, mu, std) #plt.plot(x, p, linewidth=2) myText = "Mean={:.3f}, STD={:.3f}".format(mu, std) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium', color='g') plt.xlabel('AND-Ness') plt.ylabel('Counts') plt.title('Random Search', fontsize='medium') #plt.xlim([0, 8]) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) #plt.show() plt.savefig("histogram2.jpg", dpi=300) sortedList = list(zip(*sorted(zip(samples,outs), key=operator.itemgetter(1)))) showPacking(sortedList[0][0]) print(sortedList[1][0]) showPacking(sortedList[0][-1]) print(sortedList[1][-1])	3,115	24.966667	138	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/plot_functions.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Sun Oct 22 14:48:04 2017 @author: Hightoutou """ import matplotlib.pyplot as plt #import matplotlib #matplotlib.use('TkAgg') def Line_single(xdata, ydata, line_spec, xlabel, ylabel, mark_print, fn = '', xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.53/4) pos1 = ax1.get_position() pos2 = [pos1.x0 + 0.12, pos1.y0 + 0.05, pos1.width-0.1, pos1.height] ax1.set_position(pos2) #ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) plt.ylabel(ylabel, fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') ax1.plot(xdata, ydata, line_spec) if mark_print == 1: fig.savefig(fn, dpi = 300) fig.show() def Line_multi(xdata, ydata, line_spec, xlabel, ylabel, xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.53/4) ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) ax1.set_ylabel(ylabel, fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') for ii in range(len(xdata)): ax1.plot(xdata[ii], ydata[ii], line_spec[ii]) plt.show() def Line_yy(xdata, ydata, line_spec, xlabel, ylabel, xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.5*3/4) ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) ax1.set_ylabel(ylabel[0], fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') ax1.plot(xdata[0], ydata[0], line_spec[0]) ax2 = ax1.twinx() ax2.set_ylabel(ylabel[1], fontsize=12) ax2.plot(xdata[1], ydata[1], line_spec[1]) plt.show()	2,062	33.966102	112	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/FFT_functions.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Tue Oct 17 15:10:21 2017 @author: Hightoutou """ import numpy as np import matplotlib.pyplot as plt from plot_functions import Line_multi, Line_single #from numba import jit def FFT_Fup(Nt, F, dt, Freq_Vibr): sampling_rate = 1/dt t = np.arange(Nt)dt fft_size = Nt xs = F[:fft_size] xf = np.absolute(np.fft.rfft(xs)/fft_size) freqs = (2np.pi)np.linspace(0, sampling_rate/2, fft_size//2+1) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 0: Line_multi([freqs[1:], [Freq_Vibr, Freq_Vibr]], [xf[1:], [min(xf[1:]), max(xf[1:])]], ['o', 'r--'], 'Frequency', 'FFT', 'linear', 'log') return freqs[1:], xf[1:] def FFT_Fup_RealImag(Nt, F, dt, Freq_Vibr): sampling_rate = 1/dt t = np.arange(Nt)dt fft_size = Nt xs = F[:fft_size] xf = np.fft.rfft(xs)/fft_size freqs = (2np.pi)np.linspace(0, sampling_rate/2, fft_size/2+1) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] xf_real = xf.real xf_imag = xf.imag if 1 == 0: Line_multi([freqs[1:], [Freq_Vibr, Freq_Vibr]], [xf[1:], [min(xf[1:]), max(xf[1:])]], ['o', 'r--'], 'Frequency', 'FFT') return freqs[1:], xf_real[1:], xf_imag[1:] #@jit def vCorr_Cal(fft_size, Nt, y_raw): y_fft = np.zeros(fft_size) for jj in np.arange(fft_size): sum_vcf = 0 sum_tt = 0 count = 0 for kk in np.arange(Nt-jj): count = count+1 sum_vcf += y_raw[kk]y_raw[kk+jj]; sum_tt = sum_tt+y_raw[kk]y_raw[kk]; y_fft[jj] = sum_vcf/sum_tt; return y_fft def FFT_vCorr(Nt, N, vx_rec, vy_rec, dt): sampling_rate = 1/dt fft_size = Nt-1 freqs = (2np.pi)np.linspace(0, sampling_rate/2, fft_size/2+1) for ii in np.arange(2N): #for ii in [0,4]: if np.mod(ii, 10) == 0: print('ii=%d\n' % (ii)) if ii >= N: y_raw = vy_rec[:, ii-N] else: y_raw = vx_rec[:, ii] y_fft = vCorr_Cal(fft_size, Nt, y_raw) if ii == 0: xf = np.absolute(np.fft.rfft(y_fft)/fft_size) else: xf += np.absolute(np.fft.rfft(y_fft)/fft_size) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 1: Line_single(freqs[1:], xf[1:], 'o', 'Frequency', 'FFT') return freqs[1:], xf[1:] def FFT_vCorr_3D(Nt, N, vx_rec, vy_rec, vz_rec, dt): sampling_rate = 1/dt fft_size = Nt-1 freqs = (2np.pi)np.linspace(0, sampling_rate/2, fft_size/2+1) for ii in np.arange(3N): #for ii in [0,4]: if np.mod(ii, 10) == 0: print('ii=%d\n' % (ii)) if ii >= 2N: y_raw = vz_rec[:, ii-2N] elif ii < N: y_raw = vx_rec[:, ii] else: y_raw = vy_rec[:, ii-N] y_fft = vCorr_Cal(fft_size, Nt, y_raw) if ii == 0: xf = np.absolute(np.fft.rfft(y_fft)/fft_size) else: xf += np.absolute(np.fft.rfft(y_fft)/fft_size) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 1: Line_single(freqs[1:], xf[1:], 'o', 'Frequency', 'FFT') return freqs[1:], xf[1:]	3,487	25.225564	152	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/randomSearch.py	from switch_binary import switch import matplotlib.pyplot as plt import random import numpy as np from joblib import Parallel, delayed import multiprocessing import pickle m1 = 1 m2 = 10 N = 30 N_light = 9 samples = [] print("sampling", flush=True) for i in range(0, 5001): samples.append(np.random.randint(low=0, high=2, size=N)) print("sampling done", flush=True) num_cores = multiprocessing.cpu_count() outs = Parallel(n_jobs=num_cores)(delayed(switch.evaluate)(m1, m2, N_light, samples[i]) for i in range(0, 5001)) print("done", flush=True) f = open('outs.dat', 'ab') pickle.dump(outs , f) f.close() f = open('samples.dat', 'ab') pickle.dump(samples , f) f.close() n, bins, patches = plt.hist(x=outs, bins='auto', color='#0504aa', alpha=0.7, rwidth=0.85)#, grid=True) plt.xlabel('Andness') plt.ylabel('Counts') plt.title('Random Search') #plt.xlim([0, 8]) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.show() plt.savefig("histogram.jpg", dpi=300)	978	21.767442	112	py
gecco-2022	gecco-2022-main/RandomSearch/AND-NESS/plotInOut.py	import constants as c import numpy as np from ConfigPlot import ConfigPlot_DiffStiffness2 from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK, FIRE_YFixed_ConstV_DiffK, MD_VibrSP_ConstV_Yfixed_DiffK2 from DynamicalMatrix import DM_mass_DiffK_Yfixed import random import matplotlib.pyplot as plt import pickle from os.path import exists from switch_binary import switch def showPacking(indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row m1=1 m2=10 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) # show packing ConfigPlot_DiffStiffness2(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf', ind_in1, ind_in2, ind_out) def plotInOut(indices): #%% Initial Configuration k1 = 1. k2 = 10. m1 = 1 m2 = 10 n_col = 6 n_row = 5 N = n_coln_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) w = np.real(w) v = np.real(v) freq = np.sqrt(np.absolute(w)) ind_sort = np.argsort(freq) freq = freq[ind_sort] v = v[:, ind_sort] ind = freq > 1e-4 eigen_freq = freq[ind] eigen_mode = v[:, ind] w_delta = eigen_freq[1:] - eigen_freq[0:-1] index = np.argmax(w_delta) F_low_exp = eigen_freq[index] F_high_exp = eigen_freq[index+1] plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.scatter(np.arange(0, len(eigen_freq)), eigen_freq, marker='x', color='blue') plt.xlabel("Number") plt.ylabel("Frequency") plt.title("Vibrational Response", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) props = dict(facecolor='green', alpha=0.1) myText = 'f_low='+"{:.2f}".format(F_low_exp)+"\n"+'f_high='+"{:.2f}".format(F_high_exp)+"\n"+'band gap='+"{:.2f}".format(max(w_delta)) plt.text(0.85, 0.1, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='large', bbox=props) plt.tight_layout() plt.show() print("specs:") print(F_low_exp) print(F_high_exp) print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency") plt.ylabel("Amplitude of FFT") plt.title("Logic Gate Response - input = 11", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain1) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium') plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='solid') plt.plot(x_out, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps") plt.ylabel("Amplitude of Displacement") plt.title("Logic Gate Response - input = 11", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() plt.show() # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency") plt.ylabel("Amplitude of FFT") plt.title("Logic Gate Response - input = 10", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain2) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium') plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='solid') plt.plot(x_out, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps") plt.ylabel("Amplitude of Displacement") plt.title("Logic Gate Response - input = 10", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() plt.show() # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency") plt.ylabel("Amplitude of FFT") plt.title("Logic Gate Response - input = 01", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain3) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium') plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='solid') plt.plot(x_out, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps") plt.ylabel("Amplitude of Displacement") plt.title("Logic Gate Response - input = 01", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() plt.show() print("gain1:") print(gain1) print("gain2:") print(gain2) print("gain3:") print(gain3) andness = 2gain1/(gain2+gain3) return andness runs = c.RUNS gens = c.numGenerations # running the best individuals temp = [] rubish = [] with open('savedRobotsLastGenAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): # population of the last generation temp = pickle.load(f) # best individual of last generation best = temp[0] showPacking(best.indv.genome) print(plotInOut(best.indv.genome)) temp = [] f.close()	13,392	35.997238	248	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/switch_binary.py	import numpy as np from ConfigPlot import ConfigPlot_EigenMode_DiffMass, ConfigPlot_YFixed_rec, ConfigPlot_DiffMass_SP from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK from MD_functions import FIRE_YFixed_ConstV_DiffK from DynamicalMatrix import DM_mass_DiffK_Yfixed from plot_functions import Line_single, Line_multi from ConfigPlot import ConfigPlot_DiffStiffness import random import matplotlib.pyplot as plt import pickle from os.path import exists class switch(): def evaluate(m1, m2, N_light, indices): #%% Initial Configuration k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem #w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) #w = np.real(w) #v = np.real(v) #freq = np.sqrt(np.absolute(w)) #ind_sort = np.argsort(freq) #freq = freq[ind_sort] #v = v[:, ind_sort] #ind = freq > 1e-4 #eigen_freq = freq[ind] #eigen_mode = v[:, ind] #w_delta = eigen_freq[1:] - eigen_freq[0:-1] #index = np.argmax(w_delta) #F_low_exp = eigen_freq[index] #F_high_exp = eigen_freq[index+1] #print("specs:") #print(F_low_exp) #print(F_high_exp) #print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) XOR = (gain2+gain3)/(2gain1) return XOR def showPacking(m1, m2, N_light, indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row dphi_index = -1 dphi = 10dphi_index d0 = 0.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # show packing ConfigPlot_DiffStiffness(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf') def evaluateAndPlot(m1, m2, N_light, indices): #%% Initial Configuration k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) w = np.real(w) v = np.real(v) freq = np.sqrt(np.absolute(w)) ind_sort = np.argsort(freq) freq = freq[ind_sort] v = v[:, ind_sort] ind = freq > 1e-4 eigen_freq = freq[ind] eigen_mode = v[:, ind] w_delta = eigen_freq[1:] - eigen_freq[0:-1] index = np.argmax(w_delta) F_low_exp = eigen_freq[index] F_high_exp = eigen_freq[index+1] plt.figure(figsize=(6.4,4.8)) plt.scatter(np.arange(0, len(eigen_freq)), eigen_freq, marker='x', color='blue') plt.xlabel("Number") plt.ylabel("Frequency") plt.title("Vibrational Reponse", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() plt.show() print("specs:") print(F_low_exp) print(F_high_exp) print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) print("gain1:") print(gain1) print("gain2:") print(gain2) print("gain3:") print(gain3) XOR = (gain2+gain3)/(2gain1) return XOR	14,724	36.563776	252	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/MD_functions.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Fri Oct 13 13:09:27 2017 @author: Hightoutou """ import numpy as np import time #from numba import jit from FFT_functions import FFT_Fup, FFT_vCorr from plot_functions import Line_multi, Line_yy, Line_single from ConfigPlot import ConfigPlot_YFixed_rec import matplotlib.pyplot as plt #import IPython.core.debugger #dbg = IPython.core.debugger.Pdb() #@jit def force_YFixed(Fx, Fy, N, x, y, D, Lx, y_bot, y_up): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5D[nn] if d_up<r_now: F = -(1-d_up/r_now)/(r_now) Fup -= F Fy[nn] += F Ep += (1/2)(1-d_up/r_now)*2 cont_up += 1 cont += 1 #dbg.set_trace() if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fbot += F Fy[nn] -= F Ep += (1/2)(1-d_bot/r_now)*2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy Ep += (1/2)(1-dmn/Dmn)*2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up def force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D, Lx, y_bot, y_up, k_list, k_type, VL_list, VL_counter): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 for nn in np.arange(N): d_up = y_up - y[nn] d_bot = y[nn] - y_bot r_now = 0.5 * D[nn] if d_up < r_now: F = -k_list[k_type[nn]] * (1 - d_up / r_now) / (r_now) Fup -= F Fy[nn] += F Ep += 0.5 * k_list[k_type[nn]] * (1 - d_up / r_now)*2 cont_up += 1 cont += 1 #dbg.set_trace() if d_bot < r_now: F = -k_list[k_type[nn]] (1 - d_bot / r_now) / (r_now) Fbot += F Fy[nn] -= F Ep += 0.5 * k_list[k_type[nn]] * (1 - d_bot / r_now)*2 cont += 1 for vl_idx in np.arange(VL_counter): nn = VL_list[vl_idx][0] mm = VL_list[vl_idx][1] dy = y[mm] - y[nn] Dmn = 0.5 (D[mm] + D[nn]) if abs(dy) < Dmn: dx = x[mm] - x[nn] dx = dx - round(dx / Lx) * Lx if abs(dx) < Dmn: dmn = np.sqrt(dx2 + dy2) if dmn < Dmn: k = k_list[(k_type[nn] ^ k_type[mm]) + np.maximum(k_type[nn], k_type[mm])] F = -k * (1 - dmn / Dmn) / Dmn / dmn Fx[nn] += F * dx Fx[mm] -= F * dx Fy[nn] += F * dy Fy[mm] -= F * dy Ep += 0.5 * k * (1 - dmn / Dmn)*2 cont += 1 p_now += (-F) (dx2 + dy2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up def force_YFixed_upDS(Fx, Fy, N, x, y, D, Lx, y_bot, y_up, ind_up): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5D[nn] if ind_up[nn] == 1: F = -(1-d_up/r_now)/(r_now) Fup -= F Fy[nn] += F Ep += (1/2)(1-d_up/r_now)*2 #dbg.set_trace() if d_up<r_now: cont_up = cont_up+1 cont += 1 if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fbot += F Fy[nn] -= F Ep += (1/2)(1-d_bot/r_now)*2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy Ep += (1/2)(1-dmn/Dmn)*2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up #@jit def force_Regular(Fx, Fy, N, x, y, D, Lx, Ly): Ep = 0 cont = 0 p_now = 0 for nn in np.arange(N): for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] dy = dy-round(dy/Ly)Ly Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy Ep += (1/2)(1-dmn/Dmn)*2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, Ep, cont, p_now def MD_UpDownFixed_SD(N, x0, y0, D0, m0, L): dt = min(D0)/40 Nt = int(1e4) Ep = np.zeros(Nt) F_up = np.zeros(Nt) F_bot = np.zeros(Nt) F_tot = np.zeros(Nt) Fup_now = 0 vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) #Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) F_up[nt] = Fup_now F_bot[nt] = Fbot_now Ep[nt] = Ep_now vx = np.divide(Fx, m0) vy = np.divide(Fy, m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) t_end = time.time() print ("time=%.3e" %(t_end-t_start)) if 1 == 0: # Plot the amplitide of F Line_single(range(Nt), F_tot[0:Nt], '-', 't', 'Ftot', 'log', yscale='log') return x, y def MD_VibrBot_ForceUp(N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr): dt = min(D0)/40 Nt = int(5e4) Ep = np.zeros(Nt) Ek = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr #y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)) vx = np.zeros(N) vy = np.zeros(N) if 1 == 0: y_bot = np.zeros(Nt) vx = np.random.rand(N) vy = np.random.rand(N) T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) T_set = 1e-6 vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now = force_YFixed(Fx, Fy, N, x, y, D0, L[0], y_bot[nt], L[1]) #Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) F_up[nt] = Fup_now Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) freq_now, fft_now = FFT_Fup(Nt, F_up[:Nt], dt, Freq_Vibr) # Plot the amplitide of F if 1 == 1: Line_yy([dtrange(Nt), dtrange(Nt)], [F_up[0:Nt],y_bot[0:Nt]], ['-', ':'], 't', ['$F_{up}$', '$y_{bottom}$']) Etot = Ep[1:Nt]+Ek[1:Nt] xdata = [dtrange(Nt), dtrange(Nt), dtrange(Nt-1)] ydata = [Ep[0:Nt], Ek[0:Nt], Etot] line_spec = ['--', ':', 'r-'] Line_multi(xdata, ydata, line_spec, 't', '$E$', 'log') print("std(Etot)=%e\n" %(np.std(Etot))) #dt2 = 1e-3 #xx = np.arange(0, 5, dt2) #yy = np.sin(50xx)+np.sin(125xx) #print("dt=%e, w=%f\n" % (dt, Freq_Vibr)) FFT_Fup(Nt, F_up[:Nt], dt, Freq_Vibr) #FFT_Fup(yy.size, yy, dt2, 50) return freq_now, fft_now, np.mean(cont) def MD_Periodic_equi(Nt, N, x0, y0, D0, m0, L, T_set, V_em, n_em): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) vx_rec = np.zeros([Nt, N]) vy_rec = np.zeros([Nt, N]) vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): #for ii in [60]: ind1 = 2np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx_rec[nt] = vx vy_rec[nt] = vy t_end = time.time() print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) #Etot = Ep[1:Nt]+Ek[1:Nt] #xdata = [dtrange(Nt), dtrange(Nt), dtrange(Nt-1)] #ydata = [Ep[0:Nt], Ek[0:Nt], Etot] #line_spec = ['--', ':', 'r-'] #Line_multi(xdata, ydata, line_spec, 't', '$E$', 'log', 'log') freq_now, fft_now = FFT_vCorr(Nt, N, vx_rec, vy_rec, dt) return freq_now, fft_now, np.mean(cont) def MD_YFixed_ConstP_SD(Nt, N, x0, y0, D0, m0, L, F0_up): dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e6) #Nt = int(5e2) Ep = np.zeros(Nt) F_up = np.zeros(Nt) F_bot = np.zeros(Nt) F_tot = np.zeros(Nt) Fup_now = 0 y_up = y0[N] vx = np.zeros(N+1) vy = np.zeros(N+1) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], 0, y[N]) F_up[nt] = Fup_now+F0_up F_bot[nt] = Fbot_now Ep[nt] = Ep_now+(y_up-y[N])F0_up vx = 0.1np.divide(np.append(Fx,0), m0) vy = 0.1np.divide(np.append(Fy, F_up[nt]), m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #print("nt=%d, Fup=%e, Fup_tot=%e\n" % (nt, Fup_now, F_up[nt])) #dbg.set_trace() t_end = time.time() print ("F_tot=%.3e\n" %(F_tot[nt])) print ("time=%.3e" %(t_end-t_start)) if 1 == 0: # Plot the amplitide of F Line_single(range(Nt), F_tot[0:Nt], '-', 't', 'Ftot', 'log', yscale='log') #Line_single(range(Nt), -F_up[0:Nt], '-', 't', 'Fup', 'log', yscale='log') #Line_single(range(Nt), Ep[0:Nt], '-', 't', 'Ep', 'log', yscale='linear') return x, y, p_now def MD_VibrBot_DispUp_ConstP(mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up): dt = D0[0]/40 B = 0.1 # damping coefficient Nt = int(5e7) #Nt = int(5e2) Ep = np.zeros(Nt) Ek = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr #y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)) vx = np.zeros(N+1) vy = np.zeros(N+1) # for test if 1 == 0: y_bot = np.zeros(Nt) vx = np.random.rand(N+1) vx[N] = 0 vy = np.random.rand(N+1) vy[N] = 0 T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) T_set = 1e-6 vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) if mark_upDS == 0: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) elif mark_upDS == 1: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_upDS(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N], ind_up) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0)-Bvx Fy_all = np.append(Fy, F_up[nt])-Bvy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-y_up0 freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) #freq_y, fft_y_real, fft_y_imag = FFT_Fup_RealImag(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) # plot the energy to see when the system reaches steady state if 1 == 0: Etot = Ep+Ek nt_start = int(1e3) xdata = [range(nt_start, Nt), range(nt_start, Nt), range(Nt)] ydata = [Ep[nt_start:Nt], Ek[nt_start:Nt], Etot] line_spec = [':', ':', 'r-'] Line_multi(xdata, ydata, line_spec, 't', '$E$', 'linear', 'log') # Plot the amplitide of F if 1 == 0: Line_yy([dtrange(Nt), dtrange(Nt)], [F_up[0:Nt],y_bot[0:Nt]], ['-', ':'], 't', ['$F_{up}$', '$y_{bottom}$']) Line_yy([dtrange(Nt), dtrange(Nt)], [y_up[0:Nt],y_bot[0:Nt]], ['-', ':'], 't', ['$y_{up}$', '$y_{bottom}$']) Line_single(range(Nt), p[0:Nt], '-', 't', 'p', 'log', 'linear') Etot = Ep[1:Nt]+Ek[1:Nt] xdata = [dtrange(Nt), dtrange(Nt), dtrange(Nt-1)] ydata = [Ep[0:Nt], Ek[0:Nt], Etot] line_spec = ['--', ':', 'r-'] #Line_multi(xdata, ydata, line_spec, 't', '$E$', 'log') print("std(Etot)=%e\n" %(np.std(Etot))) return freq_y, fft_y, freq_bot, fft_bot, np.mean(cont), np.mean(cont_up) #return freq_y, fft_y_real, fft_y_imag, freq_bot, fft_bot, np.mean(cont) def MD_VibrBot_DispUp_ConstP_ConfigRec(N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up, fn): dt = D0[0]/40 B = 0.1 # damping coefficient Nt = int(5e6) nt_rec = np.linspace(Nt-5e4, Nt, 500) #Nt = int(1e4) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) ind_nt = 0 Ep = np.zeros(Nt) Ek = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr #y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): if nt == nt_rec[ind_nt]: ConfigPlot_YFixed_rec(N, x[0:N], y[0:N], D0[0:N], L[0], y[N], y_bot[nt], m0[0:N], ind_nt, fn) ind_nt += 1 x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0)-Bvx Fy_all = np.append(Fy, F_up[nt])-Bvy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-y_up0 freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, freq_bot, fft_bot, np.mean(cont), np.mean(cont_up) def MD_VibrBot_DispUp_ConstP_EkCheck(Nt, mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up): dt = D0[0]/40 B = 0.1 # damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ek_up_now = np.array(0) Ep_now = np.array(0) Ep_up_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ep_up = np.zeros(Nt) Ek = np.zeros(Nt) Ek_up = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) if mark_upDS == 0: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) elif mark_upDS == 1: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_upDS(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N], ind_up) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])F0_up Ep_up[nt] = (y_up0-y[N])F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0)-Bvx Fy_all = np.append(Fy, F_up[nt])-Bvy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_up[nt] = 0.5m0[N](vx[N]2+vy[N]2) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ek_up_now = np.append(Ek_up_now, np.mean(Ek_up[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) Ep_up_now = np.append(Ep_up_now, np.mean(Ep_up[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-np.mean(y_up) y_up = y_up/np.mean(np.absolute(y_up)) freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, freq_bot, fft_bot, np.mean(cont), np.mean(cont_up) #@jit def force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D, Lx, y_bot, v_bot, y_up): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 #betta = 1 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5D[nn] if d_up<r_now: F = -(1-d_up/r_now)/(r_now) Fup -= F Fy[nn] += F dvy = vy[N]-vy[nn] FD = betadvy #FD = np.absolute(FD) Fy[nn] += FD Fup -= FD Ep += (1/2)(1-d_up/r_now)2 cont_up += 1 cont += 1 #dbg.set_trace() if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fbot += F Fy[nn] -= F dvy = v_bot-vy[nn] FD = betadvy Fy[nn] += FD Ep += (1/2)(1-d_bot/r_now)2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy dvx = vx[mm]-vx[nn] dvy = vy[mm]-vy[nn] FD = beta(dvxdx+dvydy)/dmn #FD = np.absolute(FD) Fx[nn] += FDdx/dmn Fx[mm] -= FDdx/dmn Fy[nn] += FDdy/dmn Fy[mm] -= FDdy/dmn Ep += (1/2)(1-dmn/Dmn)2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up def MD_VibrBot_DispUp_ConstP_EkCheck_Collision(beta, Nt, mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up, mark_norm): dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ek_up_now = np.array(0) Ep_now = np.array(0) Ep_up_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ep_up = np.zeros(Nt) Ek = np.zeros(Nt) Ek_up = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vy_bot = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)+1.5np.pi) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D0[0:N], L[0], y_bot[nt], vy_bot[nt], y[N]) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])F0_up Ep_up[nt] = (y_up0-y[N])F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0) Fy_all = np.append(Fy, F_up[nt]) ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_up[nt] = 0.5m0[N](vx[N]2+vy[N]2) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ek_up_now = np.append(Ek_up_now, np.mean(Ek_up[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) Ep_up_now = np.append(Ep_up_now, np.mean(Ep_up[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-np.mean(y_up) if mark_norm == 1: y_up = y_up/np.mean(np.absolute(y_up)) freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, fft_bot, np.mean(cont), np.mean(cont_up), nt_rec[1:], Ek_now[1:],Ek_up_now[1:],Ep_now[1:],Ep_up_now[1:] def MD_YFixed_ConstP_Gravity_SD(N, x0, y0, D0, m0, L, F0_up): g = 1e-5 dt = D0[0]/40 Nt = int(5e6) #Nt = int(1e4) Ep = np.zeros(Nt) F_up = np.zeros(Nt) F_bot = np.zeros(Nt) F_tot = np.zeros(Nt) Fup_now = 0 y_up = y0[N] vx = np.zeros(N+1) vy = np.zeros(N+1) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], 0, y[N]) Fy -= gm0[0:N] F_up[nt] = Fup_now+F0_up-gm0[N] F_bot[nt] = Fbot_now Ep[nt] = Ep_now+(y_up-y[N])F0_up+sum(gnp.multiply(m0, y-y0)) vx = 0.1np.divide(np.append(Fx,0), m0) vy = 0.1np.divide(np.append(Fy, F_up[nt]), m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #print("nt=%d, Fup=%e, Fup_tot=%e\n" % (nt, Fup_now, F_up[nt])) #dbg.set_trace() t_end = time.time() print ("F_tot=%.3e\n" %(F_tot[nt])) print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def MD_VibrBot_DispUp_ConstP_EkCheck_Gravity(Nt, mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up): dt = D0[0]/40 #B = 0.1 # damping coefficient g = 1e-5 Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ek_up_now = np.array(0) Ep_now = np.array(0) Ep_up_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ep_up = np.zeros(Nt) Ek = np.zeros(Nt) Ek_up = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vy_bot = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)+1.5np.pi) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) #Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) beta = 1 Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D0[0:N], L[0], y_bot[nt], vy_bot[nt], y[N]) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])F0_up+sum(gnp.multiply(m0, y-y_ini)) Ep_up[nt] = (y_up0-y[N])F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now #Fx_all = np.append(Fx,0)-Bvx #Fy_all = np.append(Fy, F_up[nt])-Bvy-gm0 Fx_all = np.append(Fx,0) Fy_all = np.append(Fy, F_up[nt])-gm0 ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_up[nt] = 0.5m0[N](vx[N]2+vy[N]2) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ek_up_now = np.append(Ek_up_now, np.mean(Ek_up[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) Ep_up_now = np.append(Ep_up_now, np.mean(Ep_up[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-np.mean(y_up) #y_up = y_up/np.mean(np.absolute(y_up)) freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, fft_bot, np.mean(cont), np.mean(cont_up), nt_rec[1:], Ek_now[1:],Ek_up_now[1:],Ep_now[1:],Ep_up_now[1:] def MD_YFixed_ConstV_SP_SD(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now vx = 0.1np.divide(Fx, m0) vy = 0.1np.divide(Fy, m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) t_end = time.time() print ("F_tot=%.3e" %(F_tot[nt])) print ("time=%.3e" %(t_end-t_start)) plt.figure(figsize=(6.4,4.8)) plt.plot(range(Nt), F_tot[0:Nt], color='blue') ax = plt.gca() ax.set_yscale('log') plt.xlabel("t") plt.ylabel("F_total") plt.title("Finding the Equilibrium", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() plt.show() return x, y, p_now def MD_YFixed_ConstV_SP_SD_2(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now vx = 0.1np.divide(Fx, m0) vy = 0.1np.divide(Fy, m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) # putting a threshold on total force if (F_tot[nt]<1e-11): break t_end = time.time() #print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now #@jit def force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D, Lx, y_bot, y_up): Ep = 0 cont = 0 p_now = 0 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5D[nn] if d_up<r_now: F = -(1-d_up/r_now)/(r_now) Fy[nn] += F dvy = -vy[nn] FD = betadvy Fy[nn] += FD Ep += (1/2)(1-d_up/r_now)*2 cont += 1 #dbg.set_trace() if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fy[nn] -= F dvy = -vy[nn] FD = betadvy Fy[nn] += FD Ep += (1/2)(1-d_bot/r_now)2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy dvx = vx[mm]-vx[nn] dvy = vy[mm]-vy[nn] FD = beta(dvxdx+dvydy)/dmn #FD = np.absolute(FD) Fx[nn] += FDdx/dmn Fx[mm] -= FDdx/dmn Fy[nn] += FDdy/dmn Fy[mm] -= FDdy/dmn Ep += (1/2)(1-dmn/Dmn)2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, Ep, cont, p_now def MD_VibrSP_ConstV_Collision(beta, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, mark_vibrY): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in] vx_in = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)) elif mark_vibrY == 1: y_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in] vy_in = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] vx[ind_in] = vx_in[nt] vy[ind_in] = 0 elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] vx[ind_in] = 0 vy[ind_in] = vy_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx Fy_all = Fy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, mark_vibrY, mark_resonator): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in] elif mark_vibrY == 1: y_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-Bvx Fy_all = Fy-Bvy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) if Nt == 5e5: print(x[ind_out], y[ind_out]) print(fft_x_out[100], fft_y_out[100]) print(fft_in[100]) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_Periodic_ConstV_SP_SD(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, Lx, Ly) Ep[nt] = Ep_now vx = 0.1np.divide(Fx, m0) vy = 0.1np.divide(Fy, m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) t_end = time.time() print ("F_tot=%.3e\n" %(F_tot[nt])) print ("nt=%e, time=%.3e" %(nt, t_end-t_start)) return x, y, p_now def MD_Periodic_equi_Ekcheck(Nt, N, x0, y0, D0, m0, L, T_set, V_em, n_em): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) vx_rec = np.zeros([Nt, N]) vy_rec = np.zeros([Nt, N]) nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) #nt_rec = np.linspace(0, Nt, int(Nt/1e2)+1) nt_rec = nt_rec.astype(int) Ek_now = np.array(0) Ep_now = np.array(0) vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx_rec[nt] = vx vy_rec[nt] = vy for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) freq_now, fft_now = FFT_vCorr(int(Nt/2), N, vx_rec[int(Nt/2):Nt], vy_rec[int(Nt/2):Nt], dt) return freq_now, fft_now, np.mean(cont), nt_rec, Ek_now, Ep_now #@jit def force_Xfixed(Fx, Fy, N, x, y, D, x_l, x_r, Ly, ind_wall): F_l = 0 F_r = 0 Ep = 0 cont = 0 p_now = 0 for nn in np.arange(N): d_l = x[nn]-x_l d_r = x_r-x[nn] r_now = 0.5D[nn] if (ind_wall[nn]==0) and (d_r<r_now): F = -(1-d_r/r_now)/(r_now) F_r -= F Fx[nn] += F Ep += (1/2)(1-d_r/r_now)*2 cont += 1 #dbg.set_trace() if (ind_wall[nn]==0) and (d_l<r_now): F = -(1-d_l/r_now)/(r_now) F_l += F Fx[nn] -= F Ep += (1/2)(1-d_l/r_now)*2 cont += 1 for mm in np.arange(nn+1, N): dx = x[mm]-x[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dx) < Dmn: dy = y[mm]-y[nn] dy = dy-round(dy/Ly)Ly dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy Ep += (1/2)(1-dmn/Dmn)*2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, F_l, F_r, Ep, cont, p_now def MD_Xfixed_SD(Nt, N, x0, y0, D0, m0, Lx, Ly, ind_wall): wall = np.where(ind_wall>0) dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e6) #Nt = int(5e2) Ep = np.zeros(Nt) F_l = np.zeros(Nt) F_r = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_Xfixed(Fx, Fy, N, x, y, D0, 0, Lx, Ly, ind_wall) F_l[nt] = Fl_now F_r[nt] = Fr_now Ep[nt] = Ep_now Fx[wall] = 0 Fy[wall] = 0 vx = 0.1np.divide(Fx, m0) vy = 0.1np.divide(Fy, m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #print("nt=%d, Fup=%e, Fup_tot=%e\n" % (nt, Fup_now, F_up[nt])) #dbg.set_trace() t_end = time.time() print ("F_tot=%.3e" %(F_tot[nt])) #print ("Ep_tot=%.3e\n" %(Ep[nt])) print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def MD_VibrWall_DiffP_Xfixed(Nt, N, x_ini, y_ini,D0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_wall, B): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_Xfixed(Fx, Fy, N, x, y, D0, x_l[nt], Lx, Ly, ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-Bvx Fy_all = Fy-Bvy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #for ii in np.arange(len(nt_rec)-1): # Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) # Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) #CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) freq_fft, fft_receive = FFT_Fup(int(Nt/2), F_r[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_drive = FFT_Fup(int(Nt/2), x_l[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_receive, fft_drive, cont_now, nt_rec, Ek_now, Ep_now #@jit def force_XFixed_collision_VibrLx(beta, Fx, Fy, N, x, y, vx, vy, D, x_l, Lx, Ly, vx_l, ind_wall): Fr = 0 Fl = 0 Ep = 0 cont = 0 p_now = 0 #betta = 1 for nn in np.arange(N): if ind_wall[nn] == 0: d_r = Lx-x[nn] d_l = x[nn]-x_l r_now = 0.5D[nn] if d_r<r_now: F = -(1-d_r/r_now)/(r_now) Fr -= F Fx[nn] += F dvx = -vx[nn] FD = betadvx Fx[nn] += FD Fr -= FD Ep += (1/2)(1-d_r/r_now)2 cont += 1 #dbg.set_trace() if d_l<r_now: F = -(1-d_l/r_now)/(r_now) Fl += F Fx[nn] -= F dvx = vx_l-vx[nn] FD = betadvx Fx[nn] += FD Ep += (1/2)(1-d_l/r_now)2 cont += 1 for mm in np.arange(nn+1, N): dx = x[mm]-x[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dx) < Dmn: dy = y[mm]-y[nn] dy = dy-round(dy/Ly)Ly dmn = np.sqrt(dx2+dy2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += Fdx Fx[mm] -= Fdx Fy[nn] += Fdy Fy[mm] -= Fdy dvx = vx[mm]-vx[nn] dvy = vy[mm]-vy[nn] FD = beta(dvxdx+dvydy)/dmn #FD = np.absolute(FD) Fx[nn] += FDdx/dmn Fx[mm] -= FDdx/dmn Fy[nn] += FDdy/dmn Fy[mm] -= FDdy/dmn Ep += (1/2)(1-dmn/Dmn)2 cont += 1 p_now += (-F)(dx2+dy2) return Fx, Fy, Fl, Fr, Ep, cont, p_now def MD_VibrWall_DiffP_Xfixed_Collision(Nt, N, x_ini, y_ini,D0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_wall, beta): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vx_l = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)+1.5np.pi) wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] vx[wall_l] = vx_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_XFixed_collision_VibrLx(beta, Fx, Fy, N, x, y, vx, vy, D0, x_l[nt], Lx, Ly, vx_l[nt], ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx Fy_all = Fy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) freq_fft, fft_receive = FFT_Fup(int(Nt/2), F_r[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_drive = FFT_Fup(int(Nt/2), x_l[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_receive, fft_drive, cont_now, nt_rec, Ek_now, Ep_now def MD_VibrWall_LySignal_Collision(Nt, N, x_ini, y_ini,D0, m0, Lx0, Ly0, Freq_Vibr, Amp_Vibr, ind_wall, beta, dLy_scheme, num_gap): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) dLy_max = 0.1 nt_transition = int(Nt/num_gap/20) dLy_inc = np.linspace(0, dLy_max, nt_transition) dLy_dec = np.linspace(dLy_max, 0, nt_transition) if dLy_scheme == 0: dLy_all = np.zeros(Nt) elif dLy_scheme == 1: dLy_all = np.ones(Nt)dLy_max dLy_all[0:nt_transition] = dLy_inc elif dLy_scheme == 2: dLy_all = np.zeros(Nt) nt_Ly = np.linspace(0, Nt, num_gap+1) nt_Ly = nt_Ly.astype(int) for ii in np.arange(1, num_gap): nt1 = nt_Ly[ii]-int(nt_transition/2) nt2 = nt_Ly[ii]+int(nt_transition/2) if ii%2 == 1: dLy_all[nt_Ly[ii]:nt_Ly[ii+1]] = dLy_max dLy_all[nt1:nt2] = dLy_inc else: dLy_all[nt1:nt2] = dLy_dec nt_rec = np.linspace(0, Nt, int(Nt/5e4num_gap/5)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vx_l = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)+1.5np.pi) wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): Ly = Ly0+dLy_all[nt] x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] vx[wall_l] = vx_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_XFixed_collision_VibrLx(beta, Fx, Fy, N, x, y, vx, vy, D0, x_l[nt], Lx0, Ly, vx_l[nt], ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx Fy_all = Fy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) nt_dLy = np.arange(0, Nt, 100) return nt_dLy, dLy_all[nt_dLy], F_r, nt_rec, Ek_now, Ep_now def MD_VibrWall_LySignal(Nt, N, x_ini, y_ini,D0, m0, Lx0, Ly0, Freq_Vibr, Amp_Vibr, ind_wall, B, dLy_scheme, num_gap): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) dLy_max = 0.1 nt_transition = int(Nt/num_gap/20) dLy_inc = np.linspace(0, dLy_max, nt_transition) dLy_dec = np.linspace(dLy_max, 0, nt_transition) if dLy_scheme == 0: dLy_all = np.zeros(Nt) elif dLy_scheme == 1: dLy_all = np.ones(Nt)dLy_max dLy_all[0:nt_transition] = dLy_inc elif dLy_scheme == 2: dLy_all = np.zeros(Nt) nt_Ly = np.linspace(0, Nt, num_gap+1) nt_Ly = nt_Ly.astype(int) for ii in np.arange(1, num_gap): nt1 = nt_Ly[ii]-int(nt_transition/2) nt2 = nt_Ly[ii]+int(nt_transition/2) if ii%2 == 1: dLy_all[nt_Ly[ii]:nt_Ly[ii+1]] = dLy_max dLy_all[nt1:nt2] = dLy_inc else: dLy_all[nt1:nt2] = dLy_dec nt_rec = np.linspace(0, Nt, int(Nt/5e4num_gap/5)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vx_l = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)+1.5np.pi) wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): Ly = Ly0+dLy_all[nt] x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] vx[wall_l] = vx_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_Xfixed(Fx, Fy, N, x, y, D0, x_l[nt], Lx0, Ly, ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-Bvx Fy_all = Fy-Bvy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) nt_dLy = np.arange(0, Nt, 100) return nt_dLy, dLy_all[nt_dLy], F_r, nt_rec, Ek_now, Ep_now def MD_VibrBot_FSignal_Collision(beta, Nt, N, x_ini, y_ini, D0, m0, Lx, Freq_Vibr, Amp_Vibr, F_scheme, num_gap): dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) F_max = 0.01 F_min = 1e-8 nt_transition = int(Nt/num_gap/20) F_inc = np.linspace(F_min, F_max, nt_transition) F_dec = np.linspace(F_max, F_min, nt_transition) if F_scheme == 1: F_all = np.ones(Nt)F_max elif F_scheme == 0: F_all = np.ones(Nt)F_min F_all[0:nt_transition] = F_dec elif F_scheme == 2: F_all = np.ones(Nt)F_max nt_F = np.linspace(0, Nt, num_gap+1) nt_F = nt_F.astype(int) for ii in np.arange(1, num_gap): nt1 = nt_F[ii]-int(nt_transition/2) nt2 = nt_F[ii]+int(nt_transition/2) if ii%2 == 1: F_all[nt_F[ii]:nt_F[ii+1]] = F_min F_all[nt1:nt2] = F_dec else: F_all[nt1:nt2] = F_inc nt_rec = np.linspace(0, Nt, int(Nt/5e4num_gap/5)+1) Ek_now = np.array(0) Ep_now = np.array(0) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)+1.5np.pi)+Amp_Vibr vy_bot = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)+1.5np.pi) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D0[0:N], Lx, y_bot[nt], vy_bot[nt], y[N]) F_up[nt] = Fup_now-F_all[nt] Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = np.append(Fx,0) Fy_all = np.append(Fy, F_up[nt]) ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek_up = 0.5m0[N](vx[N]2+vy[N]2) Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy)))-Ek_up for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) print ("freq=%f, cont_min=%d, cont_max=%d, cont_ave=%f\n" %(Freq_Vibr, min(cont), max(cont), np.mean(cont))) nt_F = np.arange(0, Nt, 100) return nt_F, F_all[nt_F], y_up, nt_rec, Ek_now, Ep_now def MD_SPSignal(mark_collision, beta, Nt, N, x_ini, y_ini,D0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_in, ind_out, ind_fix, dr_scheme, num_gap, mark_vibrY, dr_one, dr_two): dt = D0[0]/40 Nt = int(Nt) d_ini = D0[0] d0 = 0.1 dr_all = np.zeros(Nt)+dr_one if abs(dr_scheme) <= 2: nt_dr = np.linspace(0, Nt, 3) nt_dr = nt_dr.astype(int) dr_all[nt_dr[1]:nt_dr[2]] = dr_two num_gap = 5 elif dr_scheme == 3 or dr_scheme == 4: nt_dr = np.linspace(0, Nt, num_gap+1) nt_dr = nt_dr.astype(int) for ii in np.arange(1, num_gap, 2): dr_all[nt_dr[ii]:nt_dr[ii+1]] = dr_two D_fix = d_ini+dr_alld_ini nt_rec = np.linspace(0, Nt, int(Nt/5e4num_gap/5)+1) Ek_rec = np.array(0) Ep_rec = np.array(0) cont_rec = np.array(0) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 1: y_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in] vy_in = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)) else: x_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in] vx_in = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): D0[ind_fix] = D_fix[nt] x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 1: y[ind_in] = y_in[nt] x[ind_in] = x_ini[ind_in] vy[ind_in] = vy_in[nt] vx[ind_in] = 0 else: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] vx[ind_in] = vx_in[nt] vy[ind_in] = 0 Fx = np.zeros(N) Fy = np.zeros(N) if mark_collision == 1: Fx, Fy, Ep_now, cont_now, p_now = force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D0, Lx, 0, Ly) Fx_all = Fx Fy_all = Fy else: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Fx_all = Fx-betavx Fy_all = Fy-betavy Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if nt % 2000 == 0: print ("nt = %d, Ek = %.2e, cont = %.2e" %(nt, Ek[nt], cont[nt])) for ii in np.arange(len(nt_rec)-1): Ek_rec = np.append(Ek_rec, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec = np.append(Ep_rec, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec = np.append(cont_rec, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) print ("freq=%f, cont_min=%d, cont_max=%d, cont_ave=%f\n" %(Freq_Vibr, min(cont), max(cont), np.mean(cont))) nt_dr = np.arange(0, Nt, 100) if mark_vibrY == 1: xy_out = y_out else: xy_out = x_out return nt_dr, dr_all[nt_dr], xy_out, nt_rec, Ek_rec, Ep_rec, cont_rec def MD_YFixed_equi_SP_modecheck(Nt, N, x0, y0, D0, m0, Lx, Ly, T_set, V_em, n_em, ind_out): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) Freq_Vibr = 0 freq_x, fft_x = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_y, fft_y = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) ind1 = freq_x<30 ind2 = freq_y<30 return freq_x[ind1], freq_y[ind2], fft_x[ind1], fft_y[ind2], np.mean(cont), nt_rec, Ek_rec, Ep_rec, cont_rec def MD_YFixed_SPVibr_SP_modecheck(Nt, N, x0, y0, D0, m0, Lx, Ly, T_set, ind_in, ind_out, mark_vibrY): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ek_rec = np.array(0) Ep_rec = np.array(0) cont_rec = np.array(0) vx = np.zeros(N) vy = np.zeros(N) if mark_vibrY == 1: vy[ind_in] = 1 vy_mc = sum(np.multiply(vy,m0))/sum(m0) vy = vy-vy_mc else: vx[ind_in] = 1 vx_mc = sum(np.multiply(vx,m0))/sum(m0) vx = vx-vx_mc T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_vibrY == 1: vy = vynp.sqrt(NT_set/T_rd) print("\|vy\|_Max=%.3e, \|vy\|_Min=%.3e" %(max(abs(vy)), min(abs(vy)))) else: vx = vxnp.sqrt(NT_set/T_rd) print("\|vx\|_Max=%.3e, \|vx\|_Min=%.3e" %(max(abs(vx)), min(abs(vx)))) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() mark_CB = 0 for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now cont[nt] = cont_now if mark_CB == 0 and cont_now<cont[0]: print("nt_CB=%d" % nt) mark_CB = 1 ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec = np.append(Ek_rec, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec = np.append(Ep_rec, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec = np.append(cont_rec, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) Freq_Vibr = 0 freq_x, fft_x = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_y, fft_y = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) ind1 = freq_x<30 ind2 = freq_y<30 return freq_x[ind1], freq_y[ind2], fft_x[ind1], fft_y[ind2], cont_rec, nt_rec, Ek_rec, Ep_rec #181105 def MD_YFixed_SPVibr_vCorr_modecheck(Nt_MD, Nt_FFT, N, x0, y0, D0, m0, Lx, Ly, T_set, ind_in, ind_out, mark_vibrY): N = int(N) Nt_FFT = int(Nt_FFT) Nt_MD = int(Nt_MD) dt = min(D0)/40 Nt = Nt_MD+Nt_FFT Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) mark_FFT = np.zeros(Nt) mark_FFT[Nt_MD:Nt] = 1 nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) if mark_vibrY == 1: vy[ind_in] = 1 vy_mc = sum(np.multiply(vy,m0))/sum(m0) vy = vy-vy_mc else: vx[ind_in] = 1 vx_mc = sum(np.multiply(vx,m0))/sum(m0) vx = vx-vx_mc T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_vibrY == 1: vy = vynp.sqrt(NT_set/T_rd) print("\|vy\|_Max=%.3e, \|vy\|_Min=%.3e" %(max(abs(vy)), min(abs(vy)))) else: vx = vxnp.sqrt(NT_set/T_rd) print("\|vx\|_Max=%.3e, \|vx\|_Min=%.3e" %(max(abs(vx)), min(abs(vx)))) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_FFT[nt] == 1: if mark_FFT[nt-1] == 0: nt_ref = nt vx_rec = np.zeros([Nt_FFT, N]) vy_rec = np.zeros([Nt_FFT, N]) nt_delta = nt-nt_ref vx_rec[nt_delta] = vx vy_rec[nt_delta] = vy if nt_delta == Nt_FFT-1: freq_now, fft_now = FFT_vCorr(Nt_FFT, N, vx_rec, vy_rec, dt) print ("Nt_End="+str(nt)) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return freq_now, fft_now, (nt_rec[:-1]+nt_rec[1:])/2, Ek_rec, Ep_rec, cont_rec def MD_YFixed_ConstV(B, Nt, N, x0, y0, D0, m0, Lx, Ly): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() mark_CB = 0 for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Fx = Fx-Bvx Fy = Fy-Bvy Ep[nt] = Ep_now cont[nt] = cont_now if mark_CB == 0 and cont_now<cont[0]: print("nt_CB=%d" % nt) mark_CB = 1 ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[0:-1]+nt_rec[1:])/2 CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) print ("Ek_last=%.3e" % Ek[-1]) return x, y, nt_rec, Ek_rec, Ep_rec, cont_rec def MD_Vibr3Part_ConstV(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in_all, ind_out, mark_vibrY, eigen_mode_now): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) num_in = ind_in_all.size Phase_Vibr = np.sin(Freq_Vibrdtnp.arange(Nt)) Amp_Vibr_all = np.zeros(num_in) for i_in in np.arange(num_in): ind_in = ind_in_all[i_in] if mark_vibrY == 0: Amp_Vibr_all[i_in] = eigen_mode_now[2ind_in] elif mark_vibrY == 1: Amp_Vibr_all[i_in] = eigen_mode_now[2ind_in+1] print(ind_in_all) print(Amp_Vibr_all) Amp_Vibr_all = Amp_Vibr_allAmp_Vibr/max(np.abs(Amp_Vibr_all)) print(Amp_Vibr_all) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; for i_in in np.arange(num_in): ind_in = ind_in_all[i_in] if mark_vibrY == 0: x[ind_in] = Phase_Vibr[nt]Amp_Vibr_all[i_in]+x_ini[ind_in] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = Phase_Vibr[nt]Amp_Vibr_all[i_in]+y_ini[ind_in] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-Bvx Fy_all = Fy-Bvy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: x_in = Phase_VibrAmp_Vibr freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: y_in = Phase_VibrAmp_Vibr freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_dPhiSignal(mark_collision, beta, Nt, N, x_ini, y_ini, d0, phi0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_in, ind_out, dphi_scheme, dphi_on, dphi_off, num_gap, mark_vibrY): dt = d0/40 Nt = int(Nt) if dphi_scheme == 1: nt_dphi = np.linspace(0, Nt, 3) nt_dphi = nt_dphi.astype(int) dphi_all = np.zeros(Nt)+dphi_on dphi_all[nt_dphi[1]:nt_dphi[2]] = dphi_off elif dphi_scheme == -1: nt_dphi = np.linspace(0, Nt, 3) nt_dphi = nt_dphi.astype(int) dphi_all = np.zeros(Nt)+dphi_off dphi_all[nt_dphi[1]:nt_dphi[2]] = dphi_on else: dphi_all = np.zeros(Nt)+dphi_on nt_dphi = np.linspace(0, Nt, num_gap+1) nt_dphi = nt_dphi.astype(int) for ii in np.arange(1, num_gap, 2): dphi_all[nt_dphi[ii]:nt_dphi[ii+1]] = dphi_off D_ini = np.zeros(N)+d0 nt_rec = np.linspace(0, Nt, int(Nt/5e4num_gap/5)+1) Ek_rec = np.array(0) Ep_rec = np.array(0) cont_rec = np.array(0) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 1: y_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in] vy_in = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)) else: x_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in] vx_in = Amp_VibrFreq_Vibrnp.cos(Freq_Vibrdtnp.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): D0 = D_ininp.sqrt(1+dphi_all[nt]/phi0) #if np.mod(nt,100000) == 0: #print(D0[3]) x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 1: y[ind_in] = y_in[nt] x[ind_in] = x_ini[ind_in] vy[ind_in] = vy_in[nt] vx[ind_in] = 0 else: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] vx[ind_in] = vx_in[nt] vy[ind_in] = 0 Fx = np.zeros(N) Fy = np.zeros(N) if mark_collision == 1: Fx, Fy, Ep_now, cont_now, p_now = force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D0, Lx, 0, Ly) Fx_all = Fx Fy_all = Fy else: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Fx_all = Fx-betavx Fy_all = Fy-betavy Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec = np.append(Ek_rec, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec = np.append(Ep_rec, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec = np.append(cont_rec, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) print ("freq=%f, cont_min=%d, cont_max=%d, cont_ave=%f\n" %(Freq_Vibr, min(cont), max(cont), np.mean(cont))) nt_dphi = np.arange(0, Nt, 100) if mark_vibrY == 1: xy_out = y_out else: xy_out = x_out return nt_dphi, dphi_all[nt_dphi], xy_out, nt_rec[1:], Ek_rec[1:], Ep_rec[1:], cont_rec[1:] def Damping_calc(Damp_scheme, B, N, x, y, vx, vy, Lx, Ly): Fx_damp = np.zeros(N) Fy_damp = np.zeros(N) if Damp_scheme == 1: Fx_damp = -Bvx Fy_damp = -Bvy if Damp_scheme == 2: Fx_damp = -Bvxnp.abs(vx)5e5 Fy_damp = -Bvynp.abs(vy)5e5 if Damp_scheme == 3: Fx_damp = -Bvx/np.sqrt(np.abs(vx))np.sqrt(2e-6) Fy_damp = -Bvy/np.sqrt(np.abs(vy))np.sqrt(2e-6) if Damp_scheme == 4: Fx_damp = -Bvxnp.exp(-5e4np.abs(vx)+1)0.1 Fy_damp = -Bvynp.exp(-5e4np.abs(vy)+1)0.1 if Damp_scheme == 5: Fx_damp = -Bvxnp.exp(-5e5np.abs(vx)+1) Fy_damp = -Bvynp.exp(-5e5np.abs(vy)+1) if Damp_scheme == 6: Fx_damp = -Bvxnp.exp(-5e6np.abs(vx)+1)10 Fy_damp = -Bvynp.exp(-5e6np.abs(vy)+1)10 if Damp_scheme == 7: Fx_damp = -Bvxnp.exp(-5e7np.abs(vx)+1)100 Fy_damp = -Bvynp.exp(-5e7np.abs(vy)+1)100 return Fx_damp, Fy_damp def Force_FixedPos_calc(k, N, x, y, x0, y0, D0, vx, vy, Lx, Ly): Fx_damp = np.zeros(N) Fy_damp = np.zeros(N) Ep = 0 for nn in np.arange(N): dy = y[nn]-y0[nn] dy = dy-round(dy/Ly)Ly Dmn = 0.5D0[nn] dx = x[nn]-x0[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if (dmn > 0): F = -k(dmn/Dmn/Dmn)/dmn Fx_damp[nn] += Fdx Fy_damp[nn] += Fdy Ep += (1/2)k(dmn/Dmn)*2 return Fx_damp, Fy_damp, Ep def MD_FilterCheck_Periodic_Equi_vCorr(Nt_damp, Nt_FFT, num_period, Damp_scheme, B, N, x0, y0, D0, m0, L, T_set, V_em, n_em): if Damp_scheme < 0: return N = int(N) Nt_FFT = int(Nt_FFT) Nt_damp = int(Nt_damp) dt = min(D0)/40 Nt_period = int(2Nt_damp+Nt_FFT) Nt = Nt_periodnum_period Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) mark_damp = np.zeros(Nt) mark_FFT = np.zeros(Nt) for ii in np.arange(num_period): if ii > 0: t1 = iiNt_period t2 = t1+Nt_damp mark_damp[t1:t2] = 1 t3 = iiNt_period+2Nt_damp t4 = t3+Nt_FFT mark_FFT[t3:t4] = 1 nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] num_FFT = 0 vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) if mark_damp[nt] == 1: Fx_damp, Fy_damp = Damping_calc(Damp_scheme, B, N, x, y, vx, vy, L[0], L[1]) Fx = Fx + Fx_damp Fy = Fy + Fy_damp Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_FFT[nt] == 1: if mark_FFT[nt-1] == 0: nt_ref = nt vx_rec = np.zeros([Nt_FFT, N]) vy_rec = np.zeros([Nt_FFT, N]) nt_delta = nt-nt_ref vx_rec[nt_delta] = vx vy_rec[nt_delta] = vy if nt_delta == Nt_FFT-1: num_FFT += 1 freq_now, fft_now = FFT_vCorr(Nt_FFT, N, vx_rec, vy_rec, dt) if num_FFT == 1: fft_all = np.array([fft_now]) freq_all = np.array([freq_now]) len_fft_ref = len(fft_now) len_freq_ref = len(freq_now) else: fft_add = np.zeros(len_fft_ref) freq_add = np.zeros(len_freq_ref) len_fft_now = len(fft_now) len_freq_now = len(freq_now) if len_fft_now >= len_fft_ref: fft_add[0:len_fft_ref] = fft_now[0:len_fft_ref] else: fft_add[0:len_fft_now] = fft_now[0:len_fft_now] fft_add[len_fft_now:] = fft_now[len_fft_now] if len_freq_now >= len_freq_ref: freq_add[0:len_freq_ref] = freq_now[0:len_freq_ref] else: freq_add[0:len_freq_now] = freq_now[0:len_freq_now] freq_add[len_freq_now:] = freq_now[len_freq_now] fft_all = np.append(fft_all, [fft_add], axis=0) freq_all = np.append(freq_all, [freq_add], axis=0) print("FFT_iteration: %d" % num_FFT) print("Ek_ave: %e" %(np.mean(Ek[nt_ref:nt]))) ind1 = m0>5 ind2 = m0<5 print("\|vx\|_ave(heavy):%e" % np.mean(np.abs(vx[ind1]))) print("\|vx\|_ave(light):%e" % np.mean(np.abs(vx[ind2]))) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return freq_all, fft_all, (nt_rec[:-1]+nt_rec[1:])/2, Ek_rec, Ep_rec, cont_rec def MD_FilterCheck_Periodic_Equi_vCorr_Seperate(Nt_damp, Nt_FFT, num_period, Damp_scheme, k, B, N, x0, y0, D0, m0, L, T_set, V_em, n_em): # for damping scheme = -1 (fixed spring at initial position) if Damp_scheme != -1: return N = int(N) Nt_FFT = int(Nt_FFT) Nt_damp = int(Nt_damp) dt = min(D0)/40 Nt = Nt_dampnum_period+Nt_FFT if num_period == 0: Nt = Nt_damp+Nt_FFT Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) mark_FFT = np.zeros(Nt) t1 = Nt_damp num_period if num_period == 0: t1 = Nt_damp t2 = t1 + Nt_FFT mark_FFT[t1:t2] = 1 nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) # always have damping exceot num_period = 0 if num_period > 0: Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_calc(k, N, x, y, x0, y0, D0, vx, vy, L[0], L[1]) if (B > 0): Fx_damp += -Bvx Fy_damp += -Bvy elif num_period == 0: Fx_damp = 0 Fy_damp = 0 Ep_fix = 0 Ep_now += Ep_fix Fx = Fx + Fx_damp Fy = Fy + Fy_damp Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_FFT[nt] == 1: if mark_FFT[nt-1] == 0: nt_ref = nt vx_rec = np.zeros([Nt_FFT, N]) vy_rec = np.zeros([Nt_FFT, N]) nt_delta = nt-nt_ref vx_rec[nt_delta] = vx vy_rec[nt_delta] = vy if nt_delta == Nt_FFT-1: freq_now, fft_now = FFT_vCorr(Nt_FFT, N, vx_rec, vy_rec, dt) print ("Nt_End="+str(nt)) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return freq_now, fft_now, (nt_rec[:-1]+nt_rec[1:])/2, Ek_rec, Ep_rec, cont_rec def MD_Periodic_Equi_vDistr(Nt_MD, Nt_rec, N, x0, y0, D0, m0, L, T_set, V_em, n_em): N = int(N) Nt_MD = int(Nt_MD) Nt_rec = int(Nt_rec) dt = min(D0)/40 Nt = Nt_MD+Nt_rec Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] ind1 = m0>5 ind2 = m0<5 vx_light = [] vx_heavy = [] vy_light = [] vy_heavy = [] vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if nt >= Nt_MD: vx_light.extend(vx[ind2]) vy_light.extend(vy[ind2]) vx_heavy.extend(vx[ind1]) vy_heavy.extend(vy[ind1]) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return nt_rec, Ek_rec, Ep_rec, cont_rec, vx_light, vx_heavy, vy_light, vy_heavy def Output_resonator_1D(Nt, x_drive, x0, m0, w0, dt): dx = x_drive - x0 k = w0*2m0 x = 0 vx = 0 ax_old = 0 Nt = int(Nt) x_rec = np.zeros(Nt) for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration x_rec[nt] = x Fx = k(dx[nt]-x) ax = Fx/m0; vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration ax_old = ax; freq_fft, fft_x_rec = FFT_Fup(int(Nt/2), x_rec[int(Nt/2):Nt], dt, w0) return freq_fft, fft_x_rec def MD_Periodic_vCorr(Nt, N, x0, y0, D0, m0, vx0, vy0, L, T_set): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) vx_rec = np.zeros([int(Nt/2), N]) vy_rec = np.zeros([int(Nt/2), N]) vx = vx0 vy = vy0 T_rd = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vxnp.sqrt(NT_set/T_rd) vy = vynp.sqrt(NT_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if (nt >= Nt/2): vx_rec[int(nt-Nt/2)] = vx vy_rec[int(nt-Nt/2)] = vy CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) freq_now, fft_now = FFT_vCorr(int(Nt/2), N, vx_rec, vy_rec, dt) return freq_now, fft_now, np.mean(cont) def MD_Period_ConstV_SD(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, Lx, Ly) Ep[nt] = Ep_now vx = 0.1np.divide(Fx, m0) vy = 0.1np.divide(Fy, m0) x += vxdt y += vydt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #t_end = time.time() print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def Force_FixedPos_YFixed_calc(k, N, x, y, x0, y0, D0, vx, vy, Lx, Ly): Fx_damp = np.zeros(N) Fy_damp = np.zeros(N) Ep = 0 for nn in np.arange(N): dy = y[nn]-y0[nn] Dmn = 0.5D0[nn] dx = x[nn]-x0[nn] dx = dx-round(dx/Lx)Lx dmn = np.sqrt(dx2+dy2) if (dmn > 0): F = -k(dmn/Dmn/Dmn)/dmn Fx_damp[nn] += Fdx Fy_damp[nn] += Fdy Ep += (1/2)k(dmn/Dmn)2 return Fx_damp, Fy_damp, Ep def MD_VibrSP_ConstV_Yfixed_FixSpr(k, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out): mark_vibrY = 0 mark_resonator = 1 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in] elif mark_vibrY == 1: y_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_YFixed_calc(k, N, x, y, x_ini, y_ini, D0, vx, vy, L[0], L[1]) #Fx_damp = 0; Fy_damp = 0; Ep_fix = 0 Fx_damp += -Bvx Fy_damp += -Bvy Ep[nt] = Ep_now + Ep_fix cont[nt] = cont_now p[nt] = p_now Fx_all = Fx+Fx_damp Fy_all = Fy+Fy_damp x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_Yfixed_FixSpr2(k, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out): mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1np.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2np.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1np.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2np.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in2] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_YFixed_calc(k, N, x, y, x_ini, y_ini, D0, vx, vy, L[0], L[1]) #Fx_damp = 0; Fy_damp = 0; Ep_fix = 0 Fx_damp += -Bvx Fy_damp += -Bvy Ep[nt] = Ep_now + Ep_fix cont[nt] = cont_now p[nt] = p_now Fx_all = Fx+Fx_damp Fy_all = Fy+Fy_damp x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_Yfixed_FixSpr3(k, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr1, Amp_Vibr1, ind_in1, Freq_Vibr2, Amp_Vibr2, ind_in2, ind_out): mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1np.sin(Freq_Vibr1dtnp.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2np.sin(Freq_Vibr2dtnp.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1np.sin(Freq_Vibr1dtnp.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2np.sin(Freq_Vibr2dtnp.arange(Nt))+y_ini[ind_in2] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_YFixed_calc(k, N, x, y, x_ini, y_ini, D0, vx, vy, L[0], L[1]) #Fx_damp = 0; Fy_damp = 0; Ep_fix = 0 Fx_damp += -Bvx Fy_damp += -Bvy Ep[nt] = Ep_now + Ep_fix cont[nt] = cont_now p[nt] = p_now Fx_all = Fx+Fx_damp Fy_all = Fy+Fy_damp x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr1, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr1) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr2) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr1) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr2) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr1) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr1) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr1, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr1, dt) return freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_Force_ConstV(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, mark_vibrY, mark_resonator): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) x_in = np.zeros(Nt) y_in = np.zeros(Nt) if mark_vibrY == 0: Fx_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)) elif mark_vibrY == 1: Fy_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; x_in[nt] = x[ind_in] y_in[nt] = y[ind_in] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-Bvx Fy_all = Fy-Bvy if mark_vibrY == 0: Fx_all[ind_in] += Fx_in[nt] elif mark_vibrY == 1: Fy_all[ind_in] += Fy_in[nt] x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) if Nt == 5e5: print(x[ind_out], y[ind_out]) print(fft_x_out[100], fft_y_out[100]) print(fft_in[100]) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_ConfigCB(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, Nt_rec): mark_vibrY = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in] elif mark_vibrY == 1: y_in = Amp_Vibrnp.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): if nt == Nt_rec: x_rec = x[:] y_rec = y[:] x = x+vxdt+ax_olddt*2/2; # first step in Verlet integration y = y+vydt+ay_olddt2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-Bvx Fy_all = Fy-Bvy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) return x_rec, y_rec def VL_YFixed_ConstV(N, x, y, D, Lx, VL_list, VL_counter_old, x_save, y_save, first_call): r_factor = 1.2 r_cut = np.amax(D) r_list = r_factor * r_cut r_list_sq = r_list*2 r_skin_sq = ((r_factor - 1.0) r_cut)*2 if first_call == 0: dr_sq_max = 0.0 for nn in np.arange(N): dy = y[nn] - y_save[nn] dx = x[nn] - x_save[nn] dx = dx - round(dx / Lx) Lx dr_sq = dx2 + dy2 if dr_sq > dr_sq_max: dr_sq_max = dr_sq if dr_sq_max < r_skin_sq: return VL_list, VL_counter_old, x_save, y_save VL_counter = 0 for nn in np.arange(N): r_now = 0.5D[nn] for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < r_list: dx = x[mm]-x[nn] dx = dx - round(dx / Lx) * Lx if abs(dx) < r_list: dmn_sq = dx2 + dy2 if dmn_sq < r_list_sq: VL_list[VL_counter][0] = nn VL_list[VL_counter][1] = mm VL_counter += 1 return VL_list, VL_counter, x, y def MD_YFixed_ConstV_SP_SD_DiffK(Nt, N, x0, y0, D0, m0, Lx, Ly, k_list, k_type): dt = D0[0] * np.sqrt(k_list[2]) / 20.0 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) x_save = np.array(x0) y_save = np.array(y0) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now vx = 0.1 * Fx vy = 0.1 * Fy x += vx * dt y += vy * dt F_tot[nt] = sum(np.absolute(Fx) + np.absolute(Fy)) # putting a threshold on total force if (F_tot[nt] < 1e-11): break print(nt) print(F_tot[nt]) t_end = time.time() #print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def FIRE_YFixed_ConstV_DiffK(Nt, N, x0, y0, D0, m0, Lx, Ly, k_list, k_type): dt_md = 0.01 * D0[0] * np.sqrt(k_list[2]) N_delay = 20 N_pn_max = 2000 f_inc = 1.1 f_dec = 0.5 a_start = 0.15 f_a = 0.99 dt_max = 10.0 * dt_md dt_min = 0.05 * dt_md initialdelay = 1 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) x_save = np.array(x0) y_save = np.array(y0) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) a_fire = a_start delta_a_fire = 1.0 - a_fire dt = dt_md dt_half = dt / 2.0 N_pp = 0 # number of P being positive N_pn = 0 # number of P being negative ## FIRE for nt in np.arange(Nt): # FIRE update P = np.dot(vx, Fx) + np.dot(vy, Fy) if P > 0.0: N_pp += 1 N_pn = 0 if N_pp > N_delay: dt = min(f_inc * dt, dt_max) dt_half = dt / 2.0 a_fire = f_a * a_fire delta_a_fire = 1.0 - a_fire else: N_pp = 0 N_pn += 1 if N_pn > N_pn_max: break if (initialdelay < 0.5) or (nt >= N_delay): if f_dec * dt > dt_min: dt = f_dec * dt dt_half = dt / 2.0 a_fire = a_start delta_a_fire = 1.0 - a_fire x -= vx * dt_half y -= vy * dt_half vx = np.zeros(N) vy = np.zeros(N) # MD using Verlet method vx += Fx * dt_half vy += Fy * dt_half rsc_fire = np.sqrt(np.sum(vx2 + vy2)) / np.sqrt(np.sum(Fx2 + Fy2)) vx = delta_a_fire * vx + a_fire * rsc_fire * Fx vy = delta_a_fire * vy + a_fire * rsc_fire * Fy x += vx * dt y += vy * dt Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now F_tot[nt] = sum(np.absolute(Fx) + np.absolute(Fy)) # putting a threshold on total force if (F_tot[nt] < 1e-11): break vx += Fx * dt_half vy += Fy * dt_half #print(nt) #print(F_tot[nt]) t_end = time.time() #print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out): Lx = L[0] Ly = L[1] mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) F_tot = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1np.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2np.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1np.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2np.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in2] vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) x_save = np.array(x_ini) y_save = np.array(y_ini) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2 # first step in Verlet integration y = y+vydt+ay_olddt2/2 if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx - Bvx Fy_all = Fy - Bvy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2 # second step in Verlet integration vy = vy+(ay_old+ay)dt/2 ax_old = ax ay_old = ay Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out): Lx = L[0] Ly = L[1] mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) F_tot = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1np.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2np.sin(Freq_Vibrdtnp.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1np.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2np.sin(Freq_Vibrdtnp.arange(Nt))+y_ini[ind_in2] vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) x_save = np.array(x_ini) y_save = np.array(y_ini) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) for nt in np.arange(Nt): x = x+vxdt+ax_olddt*2/2 # first step in Verlet integration y = y+vydt+ay_olddt2/2 if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx - Bvx Fy_all = Fy - Bvy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)dt/2 # second step in Verlet integration vy = vy+(ay_old+ay)dt/2 ax_old = ax ay_old = ay Ek[nt] = sum(0.5np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return x_in1-x_ini[ind_in1], x_in2-x_ini[ind_in2], x_out-x_ini[ind_out]	133,858	30.675106	178	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/constants.py	# number of runs RUNS = 3 worstFitness = +1000000 # population size popSize = 50 # number of generations numGenerations = 200	129	10.818182	23	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/afpo_seed_binary.py	import constants as c import copy import numpy as np import operator import statistics import pickle from joblib import Parallel, delayed import multiprocessing from genome_seed_binary import GENOME class AFPO: def __init__(self,randomSeed): self.randomSeed = randomSeed self.currentGeneration = 0 self.nextAvailableID = 0 self.genomes = {} for populationPosition in range(c.popSize): self.genomes[populationPosition] = GENOME(self.nextAvailableID) self.nextAvailableID = self.nextAvailableID + 1 def Evolve(self): self.Perform_First_Generation() for self.currentGeneration in range(1,c.numGenerations): self.Perform_One_Generation() self.SaveLastGen() # -------------------------- Private methods ---------------------- def Age(self): for genome in self.genomes: self.genomes[genome].Age() def Aggressor_Dominates_Defender(self,aggressor,defender): return self.genomes[aggressor].Dominates(self.genomes[defender]) def Choose_Aggressor(self): return np.random.randint(c.popSize) def Choose_Defender(self,aggressor): defender = np.random.randint(c.popSize) while defender == aggressor: defender = np.random.randint(c.popSize) return defender def Contract(self): while len(self.genomes) > c.popSize: aggressorDominatesDefender = False while not aggressorDominatesDefender: aggressor = self.Choose_Aggressor() defender = self.Choose_Defender(aggressor) aggressorDominatesDefender = self.Aggressor_Dominates_Defender(aggressor,defender) for genomeToMove in range(defender,len(self.genomes)-1): self.genomes[genomeToMove] = self.genomes.pop(genomeToMove+1) def process(self, i): return ii def Evaluate_Genomes(self): num_cores = multiprocessing.cpu_count() print("we have") print(num_cores) print("cores") outs = Parallel(n_jobs=num_cores)(delayed(self.genomes[genome].Evaluate)() for genome in self.genomes) #print("done") #print(type(outs)) #self.genomes = copy.deepcopy(np.array(outs)) #self.genomes = dict(zip(list(range(0, c.popSize)), outs)) #print(type(self.genomes)) for genome in self.genomes: # print(genome) #print(self.genomes[genome].indv.fitness) # print(outs[genome]) self.genomes[genome].fitness = outs[genome] self.genomes[genome].indv.fitness = -outs[genome] def Expand(self): popSize = len(self.genomes) for newGenome in range( popSize , 2 popSize - 1 ): spawner = self.Choose_Aggressor() self.genomes[newGenome] = copy.deepcopy(self.genomes[spawner]) self.genomes[newGenome].Set_ID(self.nextAvailableID) self.nextAvailableID = self.nextAvailableID + 1 self.genomes[newGenome].Mutate() def Find_Best_Genome(self): genomesSortedByFitness = sorted(self.genomes.values(), key=operator.attrgetter('fitness'),reverse=False) return genomesSortedByFitness[0] def Find_Avg_Fitness(self): add = 0 for g in self.genomes: add += self.genomes[g].fitness return add/c.popSize def Inject(self): popSize = len(self.genomes) self.genomes[popSize-1] = GENOME(self.nextAvailableID) self.nextAvailableID = self.nextAvailableID + 1 def Perform_First_Generation(self): self.Evaluate_Genomes() self.Print() self.Save_Best() self.Save_Avg() def Perform_One_Generation(self): self.Expand() self.Age() self.Inject() self.Evaluate_Genomes() self.Contract() self.Print() self.Save_Best() self.Save_Avg() def Print(self): print('Generation ', end='', flush=True) print(self.currentGeneration, end='', flush=True) print(' of ', end='', flush=True) print(str(c.numGenerations), end='', flush=True) print(': ', end='', flush=True) bestGenome = self.Find_Best_Genome() bestGenome.Print() def Save_Best(self): bestGenome = self.Find_Best_Genome() bestGenome.Save(self.randomSeed) def SaveLastGen(self): genomesSortedByFitness = sorted(self.genomes.values(), key=operator.attrgetter('fitness'),reverse=False) f = open('savedRobotsLastGenAfpoSeed.dat', 'ab') pickle.dump(genomesSortedByFitness, f) f.close() def Save_Avg(self): f = open('avgFitnessAfpoSeed.dat', 'ab') avg = self.Find_Avg_Fitness() print('Average ' + str(avg)) print() #f.write("%.3f\n" % avg) pickle.dump(avg, f) f.close() def Show_Best_Genome(self): bestGenome = self.Find_Best_Genome() bestGenome.Show()	5,048	30.955696	112	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/DynamicalMatrix.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Tue Oct 10 22:09:09 2017 @author: Hightoutou """ def DM_mass(N, x0, y0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] M = np.zeros((2N, 2N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly rijsq = dx2+dy2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij m_sqrt = np.zeros((2N, 2N)) m_inv = np.zeros((2N, 2N)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_3D(N, x0, y0, z0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] Lz = L[2] M = np.zeros((3N, 3N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly dz = z0[i]-z0[j] dz = dz-round(dz/Lz)Lz rijsq = dx2+dy2+dz*2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy, dxdz], [dydx, dydy, dydz], [dzdx, dzdy, dzdz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3i:3i+3,3j:3j+3] = Mij M[3j:3j+3,3i:3i+3] = Mij M[3i:3i+3,3i:3i+3] = M[3i:3i+3,3i:3i+3] - Mij M[3j:3j+3,3j:3j+3] = M[3j:3j+3,3j:3j+3] - Mij m_sqrt = np.zeros((3N, 3N)) m_inv = np.zeros((3N, 3N)) for i in range(N): m_sqrt[3i, 3i] = 1/np.sqrt(m0[i]) m_sqrt[3i+1, 3i+1] = 1/np.sqrt(m0[i]) m_sqrt[3i+2, 3i+2] = 1/np.sqrt(m0[i]) m_inv[3i, 3i] = 1/m0[i] m_inv[3i+1, 3i+1] = 1/m0[i] m_inv[3i+2, 3i+2] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_Yfixed(N, x0, y0, D0, m0, Lx, y_bot, y_top, k): import numpy as np M = np.zeros((2N, 2N)) contactNum = 0 for i in range(N): r_now = 0.5D0[i] if y0[i]-y_bot<r_now or y_top-y0[i]<r_now: M[2i+1, 2i+1] = M[2i+1, 2i+1]+1/r_now/r_now for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] rijsq = dx2+dy2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij M = kM m_sqrt = np.zeros((2N, 2N)) m_inv = np.zeros((2N, 2N)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_Xfixed(N, x0, y0, D0, m0, Ly): import numpy as np M = np.zeros((2N, 2N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly rijsq = dx2+dy2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij m_sqrt = np.zeros((2N, 2N)) m_inv = np.zeros((2N, 2N)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_DiffK_Yfixed(N, x0, y0, D0, m0, Lx, y_bot, y_top, k_list, k_type): import numpy as np M = np.zeros((2N, 2N)) contactNum = 0 for i in range(N): r_now = 0.5D0[i] if y0[i]-y_bot<r_now or y_top-y0[i]<r_now: M[2i+1, 2i+1] = M[2i+1, 2i+1] + k_list[k_type[i]] / r_now / r_now for j in range(i): dij = 0.5 * (D0[i] + D0[j]) dijsq = dij*2 dx = x0[i] - x0[j] dx = dx - round(dx / Lx) Lx dy = y0[i] - y0[j] rijsq = dx2 + dy2 if rijsq < dijsq: contactNum += 1 k = k_list[(k_type[i] ^ k_type[j]) + np.maximum(k_type[i], k_type[j])] rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -k * rijmat / rijsq / dijsq Mij2 = -k * (1.0 - rij / dij) * (rijmat / rijsq - [[1,0],[0,1]]) / rij / dij Mij = Mij1 + Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij m_sqrt = np.zeros((2N, 2N)) m_inv = np.zeros((2N, 2N)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) w,v = np.linalg.eig(M) return w,v def DM_mass_Zfixed_3D(N, x0, y0, z0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] Lz = L[2] M = np.zeros((3N, 3N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly dz = z0[i]-z0[j] rijsq = dx2+dy2+dz2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy, dxdz], [dydx, dydy, dydz], [dzdz, dzdy, dzdz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3i:3i+3,3j:3j+3] = Mij M[3j:3j+3,3i:3i+3] = Mij M[3i:3i+3,3i:3i+3] = M[3i:3i+3,3i:3i+3] - Mij M[3j:3j+3,3j:3j+3] = M[3j:3j+3,3j:3j+3] - Mij m_sqrt = np.zeros((3N, 3N)) m_inv = np.zeros((3N, 3N)) for i in range(N): m_sqrt[3i, 3i] = 1/np.sqrt(m0[i]) m_sqrt[3i+1, 3i+1] = 1/np.sqrt(m0[i]) m_sqrt[3i+2, 3i+2] = 1/np.sqrt(m0[i]) m_inv[3i, 3i] = 1/m0[i] m_inv[3i+1, 3i+1] = 1/m0[i] m_inv[3i+2, 3i+2] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_UpPlate(N, x0, y0, D0, m0, Lx, y_up, m_up): import numpy as np M = np.zeros((2N+1, 2N+1)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij*2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] rijsq = dx2+dy2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy], [dxdy, dydy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2i:2i+2,2j:2j+2] = Mij M[2j:2j+2,2i:2i+2] = Mij M[2i:2i+2,2i:2i+2] = M[2i:2i+2,2i:2i+2] - Mij M[2j:2j+2,2j:2j+2] = M[2j:2j+2,2j:2j+2] - Mij for i in range(N): r_now = 0.5D0[i] if y0[i]<r_now: M[2i+1, 2i+1] = M[2i+1, 2i+1]+1/r_now*2 if y_up-y0[i]<r_now: M[2i+1, 2i+1] = M[2i+1, 2i+1]+1/r_now2 M[2N, 2N] = M[2N, 2N]+1/r_now2 M[2i+1, 2N] = M[2i+1, 2N]-1/r_now2 M[2N, 2i+1] = M[2N, 2i+1]-1/r_now2 m_sqrt = np.zeros((2N+1, 2N+1)) m_inv = np.zeros((2N+1, 2N+1)) for i in range(N): m_sqrt[2i, 2i] = 1/np.sqrt(m0[i]) m_sqrt[2i+1, 2i+1] = 1/np.sqrt(m0[i]) m_inv[2i, 2i] = 1/m0[i] m_inv[2i+1, 2i+1] = 1/m0[i] m_sqrt[2N, 2N] = 1/np.sqrt(m_up) m_inv[2N, 2N] = 1/m_up #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_UpPlate_3D(N, x0, y0, z0, D0, m0, Lx, Ly, z_up, m_up): import numpy as np M = np.zeros((3N+1, 3N+1)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5(D0[i]+D0[j]) dijsq = dij*2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)Ly dz = z0[i]-z0[j] rijsq = dx2+dy2+dz2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dxdx, dxdy, dxdz], [dydx, dydy, dydz], [dzdz, dzdy, dzdz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3i:3i+3,3j:3j+3] = Mij M[3j:3j+3,3i:3i+3] = Mij M[3i:3i+3,3i:3i+3] = M[3i:3i+3,3i:3i+3] - Mij M[3j:3j+3,3j:3j+3] = M[3j:3j+3,3j:3j+3] - Mij for i in range(N): r_now = 0.5D0[i] if z0[i]<r_now: M[3i+2, 3i+2] = M[3i+2, 3i+2]+1/r_now*2 if z_up-z0[i]<r_now: M[3i+2, 3i+2] = M[3i+2, 3i+2]+1/r_now2 M[3N, 3N] = M[3N, 3N]+1/r_now2 M[3i+2, 3N] = M[3i+2, 3N]-1/r_now2 M[3N, 3i+2] = M[3N, 3i+2]-1/r_now2 m_sqrt = np.zeros((3N+1, 3N+1)) m_inv = np.zeros((3N+1, 3N+1)) for i in range(N): m_sqrt[3i, 3i] = 1/np.sqrt(m0[i]) m_sqrt[3i+1, 3i+1] = 1/np.sqrt(m0[i]) m_sqrt[3i+2, 3i+2] = 1/np.sqrt(m0[i]) m_inv[3i, 3i] = 1/m0[i] m_inv[3i+1, 3i+1] = 1/m0[i] m_inv[3i+2, 3i+2] = 1/m0[i] m_sqrt[3N, 3N] = 1/np.sqrt(m_up) m_inv[3N, 3*N] = 1/m_up #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v	12,537	30.423559	104	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/ConfigPlot.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Tue Oct 10 21:01:26 2017 @author: Hightoutou """ import numpy as np def ConfigPlot_DiffSize(N, x, y, D, L, mark_print): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse Dmin = min(D) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] D_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) D_all.append(D[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_alpha(0.3) if D_all[i] > Dmin: e.set_facecolor('C1') else: e.set_facecolor('C0') i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass(N, x, y, D, L, m, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffMass2(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness(N, x, y, D, L, m, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C2') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness2(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C2') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness3(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='^', s=80, color=(0, 1, 0, 1)) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='s', s=80, color=(0, 0, 1, 1)) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='', s=100, color=(1, 0, 0, 1)) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('k') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) import matplotlib.lines as mlines red_star = mlines.Line2D([], [], color=(1, 0, 0), marker='', linestyle='None', markersize=10, label='Output') blue_square = mlines.Line2D([], [], color=(0, 0, 1), marker='s', linestyle='None', markersize=10, label='Input 2') green_triangle = mlines.Line2D([], [], color=(0, 1, 0), marker='^', linestyle='None', markersize=10, label='Input 1') plt.legend(handles=[red_star, green_triangle, blue_square], bbox_to_anchor=(1.215, 1)) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffMass_3D(N, x, y, z, D, L, m, mark_print): import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D m_min = min(m) m_max = max(m) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.set_aspect('equal') sphes = [] m_all = [] for i in range(int(N/2)): x_now = x[i]%L[0] y_now = y[i]%L[1] z_now = z[i]%L[2] r_now = 0.5D[i] #alpha_now = 0.2+(m_all[i]-m_min)/(m_max-m_min)0.3 alpha_now = 0.3 pos1 = 0 pos2 = 1 for j in range(pos1, pos2): for k in range(pos1, pos2): for l in range(pos1, pos2): u = np.linspace(0, 2 * np.pi, 100) v = np.linspace(0, np.pi, 100) x_plot = x_now+jL[0]+r_now np.outer(np.cos(u), np.sin(v)) y_plot = y_now+kL[1]+r_now np.outer(np.sin(u), np.sin(v)) z_plot = z_now+lL[2]+r_now np.outer(np.ones(np.size(u)), np.cos(v)) ymin = y_plot[y_plot>0].min() ymax = y_plot[y_plot>0].max() print (i, ymin, ymax) ax.plot_surface(x_plot,y_plot,z_plot,rstride=4,cstride=4, color='C0',linewidth=0,alpha=alpha_now) #sphes.append(e) #m_all.append(m[i]) # i = 0 # for e in sphes: # ax.add_artist(e) # e.set_clip_box(ax.bbox) # e.set_facecolor('C0') # e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) # i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) ax.set_zlim(0, L[2]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_YFixed_rec(N, x, y, D, Lx, y_top, y_bot, m, mark_order): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_print = 0 m_min = min(m) m_max = max(m) if m_min == m_max: m_max = 1.001 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) for k in range(-1, 2): e = Ellipse((x_now+kLx, y_now), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.3+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 #rect = Rectangle([0, y_top], Lx, 0.2D[0], color='C0') #ax.add_patch(rect) for nn in np.arange(N): x1 = x[nn]%Lx d_up = y_top-y[nn] d_bot = y[nn]-y_bot r_now = 0.5D[nn] if d_up<r_now: ax.plot([x1, x1], [y[nn], y[nn]+r_now], '-', color='w') if d_bot<r_now: ax.plot([x1, x1], [y[nn], y[nn]-r_now], '-', color='w') for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5(D[mm]+D[nn]) if abs(dy) < Dmn: x2 = x[mm]%Lx if x2>x1: xl = x1 xr = x2 yl = y[nn] yr = y[mm] else: xl = x2 xr = x1 yl = y[mm] yr = y[nn] dx0 = xr-xl dx = dx0-round(dx0/Lx)Lx dmn = np.sqrt(dx2+dy2) if dmn < Dmn: if dx0<Dmn: ax.plot([xl, xr], [yl, yr], '-', color='w') else: ax.plot([xl, xr-Lx], [yl, yr], '-', color='w') ax.plot([xl+Lx, xr], [yl, yr], '-', color='w') ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/plot_test/fig'+str(int(ind_nt+1e4))+'.png', dpi = 150) def ConfigPlot_DiffMass_SP(N, x, y, D, L, m, mark_print, ind_in, ind_out, ind_fix): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)0.3) if i == ind_in: e.set_edgecolor('r') e.set_linewidth(width) if i == ind_out: e.set_edgecolor('b') e.set_linewidth(width) if i == ind_fix: e.set_edgecolor('k') e.set_linewidth(width) ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass_FixLx(N, x, y, D, L, m, mark_print, ind_wall): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i] y_now = y[i]%L[1] for l in range(-1, 2): e = Ellipse((x_now, y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) if ind_wall[i] > 0: e.set_edgecolor('k') e.set_linewidth(width) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass_SP_rec(N, x, y, D, L, m, mark_print, ind_in, ind_out, ind_fix): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)0.3) if i == ind_in: e.set_edgecolor('r') e.set_linewidth(width) if i == ind_out: e.set_edgecolor('b') e.set_linewidth(width) if i == ind_fix: e.set_edgecolor('k') e.set_linewidth(width) Lx = L[0] Ly = L[1] for nn in np.arange(N): x1 = x[nn]%Lx y1 = y[nn]%Ly for mm in np.arange(nn+1, N): x2 = x[mm]%Lx y2 = y[mm]%Ly if x2>x1: xl = x1 xr = x2 yl = y1 yr = y2 else: xl = x2 xr = x1 yl = y2 yr = y1 dx0 = xr-xl dx = dx0-round(dx0/Lx)Lx if y2>y1: xd = x1 xu = x2 yd = y1 yu = y2 else: xd = x2 xu = x1 yd = y2 yu = y1 dy0 = yu-yd dy = dy0-round(dy0/Ly)Ly Dmn = 0.5(D[mm]+D[nn]) dmn = np.sqrt(dx2+dy2) if dmn < Dmn: if dx0<Dmn and dy0<Dmn: ax.plot([xl, xr], [yl, yr], '-', color='w') else: if dx0>Dmn and dy0>Dmn: if yr>yl: ax.plot([xl, xr-Lx], [yl, yr-Ly], '-', color='w') ax.plot([xl+Lx, xr], [yl+Ly, yr], '-', color='w') else: ax.plot([xl, xr-Lx], [yl, yr+Ly], '-', color='w') ax.plot([xl+Lx, xr], [yl-Ly, yr], '-', color='w') else: if dx0>Dmn: ax.plot([xl, xr-Lx], [yl, yr], '-', color='w') ax.plot([xl+Lx, xr], [yl, yr], '-', color='w') if dy0>Dmn: ax.plot([xd, xu], [yd, yu-Ly], '-', color='w') ax.plot([xd, xu], [yd+Ly, yu], '-', color='w') ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) return fig def ConfigPlot_EigenMode_DiffMass(N, x, y, D, L, m, em, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) if m_min == m_max: m_max = 1.001 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+kL[0], y_now+lL[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.3) i += 1 r_now = D[0]0.5 dr = np.zeros(N) for i in range(N): dr[i] = np.sqrt(em[2i]2+em[2i+1]*2) dr_max = max(dr) for i in range(N): ratio = dr[i]/dr_maxr_now/dr_max plt.arrow(x[i], y[i],em[2i]ratio, em[2i+1]ratio, head_width=0.005) ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_YFixed_SelfAssembly(N, Nl, x, y, theta, n, d1, d2, Lx, y_top, y_bot): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_order = 0 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] alpha_all = [] alpha1 = 0.6 alpha2 = 0.3 for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) alpha = alpha1 if i < Nl else alpha2 for k in range(-1, 2): e = Ellipse((x_now+kLx, y_now), d1,d1,0) ells.append(e) alpha_all.append(alpha) if i >= Nl: for ind in range(n): x_i = x_now+kLx+0.5(d1+d2)np.cos(theta[i]+ind2np.pi/n) y_i = y_now+0.5(d1+d2)np.sin(theta[i]+ind2np.pi/n) e = Ellipse((x_i, y_i), d2,d2,0) ells.append(e) alpha_all.append(alpha) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)0.8) e.set_alpha(alpha_all[i]) i += 1 ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show() def ConfigPlot_YFixed_SelfAssembly_BumpyBd(N, n_col, Nl, x, y, theta, n, d0, d1, d2, Lx, y_top, y_bot): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_order = 0 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] alpha_all = [] alpha1 = 0.6 alpha2 = 0.3 for i in range(n_col+1): x_now = id0 e1 = Ellipse((x_now, y_bot), d0,d0,0) e2 = Ellipse((x_now, y_top), d0,d0,0) ells.append(e1) alpha_all.append(alpha1) ells.append(e2) alpha_all.append(alpha1) for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) alpha = alpha1 if i < Nl else alpha2 for k in range(-1, 2): e = Ellipse((x_now+kLx, y_now), d1,d1,0) ells.append(e) alpha_all.append(alpha) if i >= Nl: for ind in range(n): x_i = x_now+kLx+0.5(d1+d2)np.cos(theta[i]+ind2np.pi/n) y_i = y_now+0.5(d1+d2)np.sin(theta[i]+ind2np.pi/n) e = Ellipse((x_i, y_i), d2,d2,0) ells.append(e) alpha_all.append(alpha) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(alpha_all[i]) i += 1 ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show()	22,835	29.488652	117	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/genome_seed_binary.py	import constants as c from individual_seed_binary import INDIVIDUAL import pickle class GENOME: def __init__(self,ID,fitness=c.worstFitness): self.Set_ID(ID) self.indv = INDIVIDUAL(ID) self.age = 0 self.fitness = fitness def Age(self): self.age = self.age + 1 def Dominates(self,other): if self.Get_Fitness() <= other.Get_Fitness(): if self.Get_Age() <= other.Get_Age(): equalFitnesses = self.Get_Fitness() == other.Get_Fitness() equalAges = self.Get_Age() == other.Get_Age() if not equalFitnesses and equalAges: return True else: return self.Is_Newer_Than(other) else: return False else: return False def Evaluate(self): #self.indv.Start_Evaluation(True) f = self.indv.Compute_Fitness() # if f < 0: # self.fitness = c.worstFitness # else: # self.fitness = 1/(1+f) self.fitness = -f #print(f) #print(self.indv.genome) return self.fitness def Get_Age(self): return self.age def Get_Fitness(self): return self.fitness def Mutate(self): self.indv.Mutate() def Print(self): print(' fitness: ' , end = '' ) print(self.fitness , end = '' ) print(' age: ' , end = '' ) print(self.age , end = '' ) print() def Save(self,randomSeed): f = open('savedRobotsAfpoSeed.dat', 'ab') pickle.dump(self.indv , f) f.close() pass def Set_ID(self,ID): self.ID = ID def Show(self): #self.indv.Start_Evaluation(False, 40) self.indv.Compute_Fitness(True) # def __add__(self, other): # total_fitness = self.fitness + other.fitness # print("I've been called") # return GENOME(1, total_fitness) # def __radd__(self, other): # if other == 0: # return self # else: # return self.__add__(other) # -------------------- Private methods ---------------------- def Get_ID(self): return self.ID def Is_Newer_Than(self,other): return self.Get_ID() > other.Get_ID()	2,336	24.681319	74	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/afpoPlots_seed_binary.py	import pickle import matplotlib.pyplot as plt from switch_binary import switch import constants as c import numpy runs = c.RUNS gens = c.numGenerations fitnesses = numpy.zeros([runs, gens]) temp = [] individuals = [] with open('savedRobotsAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): for g in range(1, gens+1): try: temp.append(pickle.load(f).fitness) except EOFError: break fitnesses[r-1] = temp temp = [] f.close() mean_f = numpy.mean(fitnesses, axis=0) std_f = numpy.std(fitnesses, axis=0) plt.figure(figsize=(6.4,4.8)) plt.plot(list(range(1, gens+1)), mean_f, color='blue') plt.fill_between(list(range(1, gens+1)), mean_f-std_f, mean_f+std_f, color='cornflowerblue', alpha=0.2) plt.xlabel("Generations") plt.ylabel("Best Fitness") plt.title("Fitness of the Best Individual in the Population - AFPO", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() #plt.legend(['two robot', 'three robots'], loc='upper left') #plt.savefig("compare.pdf") plt.show() # running the best individuals m1 = 1 m2 = 10 N_light = 9 N = 30 bests = numpy.zeros([runs, gens]) temp = [] rubish = [] with open('savedRobotsLastGenAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): # population of the last generation temp = pickle.load(f) # best individual of last generation best = temp[0] switch.showPacking(m1, m2, N_light, best.indv.genome) print(switch.evaluate(m1, m2, N_light, best.indv.genome)) print(switch.evaluateAndPlot(m1, m2, N_light, best.indv.genome)) temp = [] f.close() # running all of the individuals of the last generation of each of the runs bests = numpy.zeros([runs, gens]) temp = [] rubish = [] with open('savedRobotsLastGenAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): print("run:") print(r) # population of the last generation temp = pickle.load(f) for g in range(0, gens): switch.showPacking(m1, m2, N_light, temp[g].indv.genome) print(switch.evaluateAndPlot(m1, m2, N_light, temp[g].indv.genome)) temp = [] f.close()	2,217	28.972973	103	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/evolveAfpo_seed_binary.py	from afpo_seed_binary import AFPO import os import constants as c import random #cleaning up the data files try: os.remove("savedRobotsLastGenAfpoSeed.dat") except OSError: pass try: os.remove("avgFitnessAfpoSeed.dat") except OSError: pass try: os.remove("savedRobotsAfpoSeed.dat") except OSError: pass runs = c.RUNS for r in range(1, runs+1): print("*********************************************************", flush=True) print("run: "+str(r), flush=True) randomSeed = r random.seed(r) afpo = AFPO(randomSeed) afpo.Evolve() #afpo.Show_Best_Genome()	616	18.28125	82	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/individual_seed_binary.py	from switch_binary import switch import constants as c import random import math import numpy as np import sys import pickle class INDIVIDUAL: def __init__(self, i): # assuming curves have one control point, [Sx, Ex, Cx, Cy] for each fiber # assuming we have two planes, each with c.FIBERS of fibers on them self.m1 = 1 self.m2 = 10 #[2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000] #10 is what is in fig2 of the paper self.N_light = 9 #[25, 50, 75] #[9, 15, 21] self.N = 30 #self.low = 7 #self.high = 12 #indices = random.sample(range(0, N), N_light) self.genome = np.random.randint(low=0, high=2, size=self.N)#random.sample(range(0, self.N), self.N_light) #np.random.randint(0, high=c.GRID_SIZE-1, size=(c.FIBERS*2, 4), dtype='int') self.fitness = 0 self.ID = i def Compute_Fitness(self, show=False): # wait for the simulation to end and get the fitness self.fitness = switch.evaluate(self.m1, self.m2, self.N_light, self.genome)#, self.low, self.high) if show: switch.showPacking(self.m1, self.m2, self.N_light, self.genome)#, self.low, self.high) print("fitness is:") print(self.fitness) return self.fitness def Mutate(self): mutationRate = 0.05 probToMutate = np.random.choice([False, True], size=self.genome.shape, p=[1-mutationRate, mutationRate]) candidate = np.where(probToMutate, 1-self.genome, self.genome) self.genome = candidate def Print(self): print('[', self.ID, self.fitness, ']', end=' ') def Save(self): f = open('savedFitnessSeed.dat', 'ab') pickle.dump(self.fitness , f) f.close() def SaveBest(self): f = open('savedBestsSeed.dat', 'ab') pickle.dump(self.genome , f) f.close()	1,936	35.54717	190	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/randomSearch2.py	from switch_binary import switch import matplotlib.pyplot as plt import random import numpy as np from joblib import Parallel, delayed import multiprocessing import pickle from scipy.stats import norm import operator from ConfigPlot import ConfigPlot_DiffStiffness3 with open('outs.dat', "rb") as f: outs = pickle.load(f) f.close() with open('samples.dat', "rb") as f: samples = pickle.load(f) f.close() # compute the cumulative sum #Nk_cum = np.cumsum(Nk) # go to log scale #log_Nk_cum = np.log10(Nk_cum) #log_k = np.log10(k) # plot the original data #fig = plt.figure() #ax = plt.gca() #ax.scatter(log_k, log_Nk_cum, s=5, alpha=0.3) #ax.set_title('CCDF in log-log scale') #ax.set_xlabel('$Log_{10}(k)$') #ax.set_ylabel('$Log_{10}(Nk_{>k})$') def showPacking(indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row m1=1 m2=10 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) # show packing ConfigPlot_DiffStiffness3(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf', ind_in1, ind_in2, ind_out) print("done", flush=True) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() n, bins, patches = plt.hist(x=outs, bins='auto', color='#0504aa', alpha=0.7, cumulative=False)#, grid=True) # fitting a normal distribution mu, std = norm.fit(outs) xmin, xmax = plt.xlim() x = np.linspace(xmin, xmax, 1000) p = norm.pdf(x, mu, std) #plt.plot(x, p, linewidth=2) myText = "Mean={:.3f}, STD={:.3f}".format(mu, std) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium', color='g') plt.xlabel('XOR-Ness') plt.ylabel('Counts') plt.title('Random Search', fontsize='medium') #plt.xlim([0, 8]) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) #plt.show() plt.savefig("histogram2.jpg", dpi=300) sortedList = list(zip(*sorted(zip(samples,outs), key=operator.itemgetter(1)))) showPacking(sortedList[0][0]) print(sortedList[1][0]) showPacking(sortedList[0][-1]) print(sortedList[1][-1]) showPacking(sortedList[0][-2]) print(sortedList[1][-2])	3,171	25	138	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/plot_functions.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Sun Oct 22 14:48:04 2017 @author: Hightoutou """ import matplotlib.pyplot as plt #import matplotlib #matplotlib.use('TkAgg') def Line_single(xdata, ydata, line_spec, xlabel, ylabel, mark_print, fn = '', xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.53/4) pos1 = ax1.get_position() pos2 = [pos1.x0 + 0.12, pos1.y0 + 0.05, pos1.width-0.1, pos1.height] ax1.set_position(pos2) #ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) plt.ylabel(ylabel, fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') ax1.plot(xdata, ydata, line_spec) if mark_print == 1: fig.savefig(fn, dpi = 300) fig.show() def Line_multi(xdata, ydata, line_spec, xlabel, ylabel, xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.53/4) ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) ax1.set_ylabel(ylabel, fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') for ii in range(len(xdata)): ax1.plot(xdata[ii], ydata[ii], line_spec[ii]) plt.show() def Line_yy(xdata, ydata, line_spec, xlabel, ylabel, xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.5*3/4) ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) ax1.set_ylabel(ylabel[0], fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') ax1.plot(xdata[0], ydata[0], line_spec[0]) ax2 = ax1.twinx() ax2.set_ylabel(ylabel[1], fontsize=12) ax2.plot(xdata[1], ydata[1], line_spec[1]) plt.show()	2,062	33.966102	112	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/FFT_functions.py	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Created on Tue Oct 17 15:10:21 2017 @author: Hightoutou """ import numpy as np import matplotlib.pyplot as plt from plot_functions import Line_multi, Line_single #from numba import jit def FFT_Fup(Nt, F, dt, Freq_Vibr): sampling_rate = 1/dt t = np.arange(Nt)dt fft_size = Nt xs = F[:fft_size] xf = np.absolute(np.fft.rfft(xs)/fft_size) freqs = (2np.pi)np.linspace(0, sampling_rate/2, fft_size//2+1) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 0: Line_multi([freqs[1:], [Freq_Vibr, Freq_Vibr]], [xf[1:], [min(xf[1:]), max(xf[1:])]], ['o', 'r--'], 'Frequency', 'FFT', 'linear', 'log') return freqs[1:], xf[1:] def FFT_Fup_RealImag(Nt, F, dt, Freq_Vibr): sampling_rate = 1/dt t = np.arange(Nt)dt fft_size = Nt xs = F[:fft_size] xf = np.fft.rfft(xs)/fft_size freqs = (2np.pi)np.linspace(0, sampling_rate/2, fft_size/2+1) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] xf_real = xf.real xf_imag = xf.imag if 1 == 0: Line_multi([freqs[1:], [Freq_Vibr, Freq_Vibr]], [xf[1:], [min(xf[1:]), max(xf[1:])]], ['o', 'r--'], 'Frequency', 'FFT') return freqs[1:], xf_real[1:], xf_imag[1:] #@jit def vCorr_Cal(fft_size, Nt, y_raw): y_fft = np.zeros(fft_size) for jj in np.arange(fft_size): sum_vcf = 0 sum_tt = 0 count = 0 for kk in np.arange(Nt-jj): count = count+1 sum_vcf += y_raw[kk]y_raw[kk+jj]; sum_tt = sum_tt+y_raw[kk]y_raw[kk]; y_fft[jj] = sum_vcf/sum_tt; return y_fft def FFT_vCorr(Nt, N, vx_rec, vy_rec, dt): sampling_rate = 1/dt fft_size = Nt-1 freqs = (2np.pi)np.linspace(0, sampling_rate/2, fft_size/2+1) for ii in np.arange(2N): #for ii in [0,4]: if np.mod(ii, 10) == 0: print('ii=%d\n' % (ii)) if ii >= N: y_raw = vy_rec[:, ii-N] else: y_raw = vx_rec[:, ii] y_fft = vCorr_Cal(fft_size, Nt, y_raw) if ii == 0: xf = np.absolute(np.fft.rfft(y_fft)/fft_size) else: xf += np.absolute(np.fft.rfft(y_fft)/fft_size) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 1: Line_single(freqs[1:], xf[1:], 'o', 'Frequency', 'FFT') return freqs[1:], xf[1:] def FFT_vCorr_3D(Nt, N, vx_rec, vy_rec, vz_rec, dt): sampling_rate = 1/dt fft_size = Nt-1 freqs = (2np.pi)np.linspace(0, sampling_rate/2, fft_size/2+1) for ii in np.arange(3N): #for ii in [0,4]: if np.mod(ii, 10) == 0: print('ii=%d\n' % (ii)) if ii >= 2N: y_raw = vz_rec[:, ii-2N] elif ii < N: y_raw = vx_rec[:, ii] else: y_raw = vy_rec[:, ii-N] y_fft = vCorr_Cal(fft_size, Nt, y_raw) if ii == 0: xf = np.absolute(np.fft.rfft(y_fft)/fft_size) else: xf += np.absolute(np.fft.rfft(y_fft)/fft_size) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 1: Line_single(freqs[1:], xf[1:], 'o', 'Frequency', 'FFT') return freqs[1:], xf[1:]	3,487	25.225564	152	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/randomSearch.py	from switch_binary import switch import matplotlib.pyplot as plt import random import numpy as np from joblib import Parallel, delayed import multiprocessing import pickle m1 = 1 m2 = 10 N = 30 N_light = 9 samples = [] print("sampling", flush=True) for i in range(0, 5001): samples.append(np.random.randint(low=0, high=2, size=N)) print("sampling done", flush=True) num_cores = multiprocessing.cpu_count() outs = Parallel(n_jobs=num_cores)(delayed(switch.evaluate)(m1, m2, N_light, samples[i]) for i in range(0, 5001)) print("done", flush=True) f = open('outs.dat', 'ab') pickle.dump(outs , f) f.close() f = open('samples.dat', 'ab') pickle.dump(samples , f) f.close() n, bins, patches = plt.hist(x=outs, bins='auto', color='#0504aa', alpha=0.7, rwidth=0.85)#, grid=True) plt.xlabel('Andness') plt.ylabel('Counts') plt.title('Random Search') #plt.xlim([0, 8]) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.show() plt.savefig("histogram.jpg", dpi=300)	978	21.767442	112	py
gecco-2022	gecco-2022-main/RandomSearch/XOR-NESS/plotInOut.py	import constants as c import numpy as np from ConfigPlot import ConfigPlot_DiffStiffness2 from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK, FIRE_YFixed_ConstV_DiffK, MD_VibrSP_ConstV_Yfixed_DiffK2 from DynamicalMatrix import DM_mass_DiffK_Yfixed import random import matplotlib.pyplot as plt import pickle from os.path import exists from switch_binary import switch def showPacking(indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_coln_row m1=1 m2=10 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) # show packing ConfigPlot_DiffStiffness2(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf', ind_in1, ind_in2, ind_out) def plotInOut(indices): #%% Initial Configuration k1 = 1. k2 = 10. m1 = 1 m2 = 10 n_col = 6 n_row = 5 N = n_coln_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0n_col Ly = (n_row-1)np.sqrt(3)/2d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = Nnp.pid0*2/4/(LxLy) d_ini = d0np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)d0 else: x0[ind] = (i_col-1)d0+0.5d0 y0[ind] = (i_row-1)np.sqrt(3)/2d0 y0 = y0+0.5d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) w = np.real(w) v = np.real(v) freq = np.sqrt(np.absolute(w)) ind_sort = np.argsort(freq) freq = freq[ind_sort] v = v[:, ind_sort] ind = freq > 1e-4 eigen_freq = freq[ind] eigen_mode = v[:, ind] w_delta = eigen_freq[1:] - eigen_freq[0:-1] index = np.argmax(w_delta) F_low_exp = eigen_freq[index] F_high_exp = eigen_freq[index+1] plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.scatter(np.arange(0, len(eigen_freq)), eigen_freq, marker='x', color='blue') plt.xlabel("Number") plt.ylabel("Frequency") plt.title("Vibrational Response", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) props = dict(facecolor='green', alpha=0.1) myText = 'f_low='+"{:.2f}".format(F_low_exp)+"\n"+'f_high='+"{:.2f}".format(F_high_exp)+"\n"+'band gap='+"{:.2f}".format(max(w_delta)) plt.text(0.85, 0.1, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='large', bbox=props) plt.tight_layout() plt.show() print("specs:") print(F_low_exp) print(F_high_exp) print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency") plt.ylabel("Amplitude of FFT") plt.title("Logic Gate Response - input = 11", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain1) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium') plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='solid') plt.plot(x_out, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps") plt.ylabel("Amplitude of Displacement") plt.title("Logic Gate Response - input = 11", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() plt.show() # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency") plt.ylabel("Amplitude of FFT") plt.title("Logic Gate Response - input = 10", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain2) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium') plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='solid') plt.plot(x_out, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps") plt.ylabel("Amplitude of Displacement") plt.title("Logic Gate Response - input = 10", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() plt.show() # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency") plt.ylabel("Amplitude of FFT") plt.title("Logic Gate Response - input = 01", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain3) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium') plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='solid') plt.plot(x_out, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps") plt.ylabel("Amplitude of Displacement") plt.title("Logic Gate Response - input = 01", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() plt.show() print("gain1:") print(gain1) print("gain2:") print(gain2) print("gain3:") print(gain3) andness = 2gain1/(gain2+gain3) return andness runs = c.RUNS gens = c.numGenerations # running the best individuals temp = [] rubish = [] with open('savedRobotsLastGenAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): # population of the last generation temp = pickle.load(f) # best individual of last generation best = temp[0] showPacking(best.indv.genome) print(plotInOut(best.indv.genome)) temp = [] f.close()	13,392	35.997238	248	py
python-urx	python-urx-master/setup.py	from setuptools import setup setup( name="urx", version="0.11.0", description="Python library to control an UR robot", author="Olivier Roulet-Dubonnet", author_email="[email protected]", url='https://github.com/oroulet/python-urx', packages=["urx"], provides=["urx"], install_requires=["numpy", "math3d"], license="GNU Lesser General Public License v3", classifiers=[ "Programming Language :: Python", "Programming Language :: Python :: 3", "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Operating System :: OS Independent", "Topic :: System :: Hardware :: Hardware Drivers", "Topic :: Software Development :: Libraries :: Python Modules", ])	778	32.869565	71	py
python-urx	python-urx-master/make_deb.py	#!/usr/bin/python3 """ hackish file to crreate deb from setup.py """ import subprocess from email.utils import formatdate import urx DEBVERSION = urx.__version__ branch = subprocess.check_output("git rev-parse --abbrev-ref HEAD", shell=True) branch = branch.decode() branch = branch.strip() branch = str(branch).replace("'","") rev = subprocess.check_output("git log -1 --format=\'%ad--%h\' --date=short", shell=True) rev = rev.decode() rev = rev.strip() rev = rev.replace("'","") #rev = rev.replace(" ", "T", 1) #ev = rev.replace(" ", "Z", 1) vcsstring = "git-" + branch + "-" + rev def get_changelog(progname, version, changelog, date): """ return a dummy changelog acceptable by debian script engine """ return """%s (%s) unstable; urgency=low %s -- Olivier R-D <unknown@unknown> %s """ % (progname, version, changelog, date) def check_deb(name): print("checking if %s is installed" % name) subprocess.check_call("dpkg -s %s > /dev/null" % name, shell=True) if __name__ == "__main__": check_deb("build-essential") f = open("debian/changelog", "w") f.write(get_changelog("python-urx", DEBVERSION + vcsstring, "Updated to last changes in repository", formatdate())) f.close() #now build package #subprocess.check_call("dpkg-buildpackage -rfakeroot -uc -us -b", shell=True) subprocess.check_call("fakeroot dh binary --with python3,python2", shell=True) subprocess.check_call("dh clean", shell=True)	1,478	24.947368	119	py
python-urx	python-urx-master/release.py	import re import os def bump_version(): with open("setup.py") as f: s = f.read() m = re.search(r'version="(.)\.(.)\.(.)",', s) v1, v2, v3 = m.groups() oldv = "{0}.{1}.{2}".format(v1, v2, v3) newv = "{0}.{1}.{2}".format(v1, v2, str(int(v3) + 1)) print("Current version is: {0}, write new version, ctrl-c to exit".format(oldv)) ans = input(newv) if ans: newv = ans s = s.replace(oldv, newv) with open("setup.py", "w") as f: f.write(s) return newv def release(): v = bump_version() ans = input("version bumped, commiting?(Y/n)") if ans in ("", "y", "yes"): os.system("git add setup.py") os.system("git commit -m 'new release'") os.system("git tag {0}".format(v)) ans = input("change committed, push to server?(Y/n)") if ans in ("", "y", "yes"): os.system("git push") os.system("git push --tags") ans = input("upload to pip?(Y/n)") if ans in ("", "y", "yes"): os.system("rm -rf dist/") os.system("python setup.py sdist") os.system("twine upload dist/*") if __name__ == "__main__": release()	1,197	27.52381	84	py
python-urx	python-urx-master/tools/find_packet.py	from urx import ursecmon if __name__ == "__main__": f = open("packets.bin", "rb") s = open("packet.bin", "wb") data = f.read(99999) parser = ursecmon.ParserUtils() p, rest = parser.find_first_packet(data) print(len(p)) p, rest = parser.find_first_packet(rest) print(len(p)) s.write(p) p, rest = parser.find_first_packet(rest) print(len(p))	385	24.733333	44	py
python-urx	python-urx-master/tools/grabber.py	import socket import sys if __name__ == "__main__": host, port = "localhost", 30002 host, port = "192.168.1.8", 30002 if len(sys.argv) > 1: host = sys.argv[1] sock = socket.create_connection((host, port)) f = open("packets.bin", "wb") try: # Connect to server and send data for i in range(0, 20): data = sock.recv(1024) f.write(data) print("Got packet: ", i) finally: f.close() sock.close()	500	19.875	49	py
python-urx	python-urx-master/tools/get_rob.py	#!/usr/bin/env python import sys import logging from math import pi from IPython import embed from urx import Robot import math3d if __name__ == "__main__": if len(sys.argv) > 1: host = sys.argv[1] else: host = 'localhost' try: robot = Robot(host) r = robot embed() finally: if "robot" in dir(): robot.close()	389	15.956522	28	py
python-urx	python-urx-master/tools/fakerobot.py	import socket import threading import socketserver import time class RequestHandler(socketserver.BaseRequestHandler): #def __init__(self, args, kwargs): #print(self, args, *kwargs) #print("Got connection from {}".format( self.client_address[0]) ) #socketserver.BaseRequestHandler.__init__(self, args, **kwargs) def handle(self): while True: data = str(self.request.recv(1024), 'ascii') cur_thread = threading.current_thread() print("{} received {} from {}".format(cur_thread.name, data, self.client_address) ) if data == "": return #when this methods returns, the connection to the client closes def setup(self): print("Got new connection from {}".format( self.client_address) ) self.server.handlers.append(self) def finish(self): print("Connection from {} lost".format( self.client_address) ) self.server.handlers.remove(self) class Server(socketserver.ThreadingMixIn, socketserver.TCPServer): def init(self): """ __init__ should not be overriden """ self.handlers = [] def close(self): for handler in self.handlers: handler.request.shutdown(socket.SHUT_RDWR) handler.request.close() self.shutdown() class FakeRobot(object): def run(self): host = "localhost" port = 30002 server = Server((host, port), RequestHandler) server.init() server_thread = threading.Thread(target=server.serve_forever) server_thread.daemon = True server_thread.start() print("Fake Universal robot running at ", host, port) try: f = open("packet.bin", "rb") packet = f.read() f.close() while True: time.sleep(0.09) #The real robot published data 10 times a second for handler in server.handlers: handler.request.sendall(packet) finally: print("Shutting down server") server.close() if __name__ == "__main__": r = FakeRobot() r.run()	2,177	28.432432	95	py
python-urx	python-urx-master/examples/test_movep.py	import time import urx import logging if __name__ == "__main__": rob = urx.Robot("192.168.1.6") try: l = 0.1 v = 0.07 a = 0.1 r = 0.05 pose = rob.getl() pose[2] += l rob.movep(pose, acc=a, vel=v, radius=r, wait=False) while True: p = rob.getl(wait=True) if p[2] > pose[2] - 0.05: break pose[1] += l rob.movep(pose, acc=a, vel=v, radius=r, wait=False) while True: p = rob.getl(wait=True) if p[1] > pose[1] - 0.05: break pose[2] -= l rob.movep(pose, acc=a, vel=v, radius=r, wait=False) while True: p = rob.getl(wait=True) if p[2] < pose[2] + 0.05: break pose[1] -= l rob.movep(pose, acc=a, vel=v, radius=0, wait=True) finally: rob.close()	914	21.317073	59	py
python-urx	python-urx-master/examples/get_robot.py	import urx from IPython import embed import logging if __name__ == "__main__": try: logging.basicConfig(level=logging.INFO) #robot = urx.Robot("192.168.1.6") robot = urx.Robot("192.168.1.100") #robot = urx.Robot("localhost") r = robot print("Robot object is available as robot or r") embed() finally: robot.close()	388	23.3125	56	py
python-urx	python-urx-master/examples/simple.py	import urx import logging if __name__ == "__main__": logging.basicConfig(level=logging.WARN) rob = urx.Robot("192.168.1.100") #rob = urx.Robot("localhost") rob.set_tcp((0,0,0,0,0,0)) rob.set_payload(0.5, (0,0,0)) try: l = 0.05 v = 0.05 a = 0.3 pose = rob.getl() print("robot tcp is at: ", pose) print("absolute move in base coordinate ") pose[2] += l rob.movel(pose, acc=a, vel=v) print("relative move in base coordinate ") rob.translate((0, 0, -l), acc=a, vel=v) print("relative move back and forth in tool coordinate") rob.translate_tool((0, 0, -l), acc=a, vel=v) rob.translate_tool((0, 0, l), acc=a, vel=v) finally: rob.close()	775	25.758621	64	py
python-urx	python-urx-master/examples/get_realtime_data.py	import urx import time import logging r = urx.Robot("192.168.111.134", use_rt=True, urFirm=5.1) if __name__ == "__main__": logging.basicConfig(level=logging.INFO) while 1: try: j_temp = r.get_joint_temperature() j_voltage = r.get_joint_voltage() j_current = r.get_joint_current() main_voltage = r.get_main_voltage() robot_voltage = r.get_robot_voltage() robot_current = r.get_robot_current() print("JOINT TEMPERATURE") print(j_temp) print("JOINT VOLTAGE") print(j_voltage) print("JOINT CURRENT") print(j_current) print("MAIN VOLTAGE") print(main_voltage) print("ROBOT VOLTAGE") print(robot_voltage) print("ROBOT CURRENT") print(robot_current) print("##########\t##########\t##########\t##########") time.sleep(1) except: pass r.close()	1,035	23.093023	67	py
python-urx	python-urx-master/examples/matrices.py	from math import pi import urx import logging if __name__ == "__main__": rob = urx.Robot("192.168.1.100") #rob = urx.Robot("localhost") rob.set_tcp((0,0,0,0,0,0)) rob.set_payload(0.5, (0,0,0)) try: l = 0.05 v = 0.05 a = 0.3 j = rob.getj() print("Initial joint configuration is ", j) t = rob.get_pose() print("Transformation from base to tcp is: ", t) print("Translating in x") rob.translate((l, 0, 0), acc=a, vel=v) pose = rob.getl() print("robot tcp is at: ", pose) print("moving in z") pose[2] += l rob.movel(pose, acc=a, vel=v) print("Translate in -x and rotate") t.orient.rotate_zb(pi/4) t.pos[0] -= l rob.set_pose(t, vel=v, acc=a) print("Sending robot back to original position") rob.movej(j, acc=0.8, vel=0.2) finally: rob.close()	937	23.051282	56	py
python-urx	python-urx-master/examples/spnav_control.py	from __future__ import division import spnav import time import math3d as m3d from math import pi import urx class Cmd(object): def __init__(self): self.reset() def reset(self): self.x = 0 self.y = 0 self.z = 0 self.rx = 0 self.ry = 0 self.rz = 0 self.btn0 = 0 self.btn1 = 0 def get_speeds(self): return [self.x, self.y, self.z, self.rx, self.ry, self.rz] class Service(object): def __init__(self, robot): self.robot = robot self.lin_coef = 5000 self.rot_coef = 5000 def loop(self): ts = 0 btn0_state = 0 btn_event = None cmd = Cmd() while True: time.sleep(0.01) cmd.reset() #spnav.spnav_remove_events(spnav.SPNAV_EVENT_ANY) # seems broken event = spnav.spnav_poll_event() if event: if type(event) is spnav.SpnavButtonEvent: btn_event = event if event.bnum == 0: btn0_state = event.press elif type(event) is spnav.SpnavMotionEvent: if abs(event.translation[0]) > 30: cmd.y = event.translation[0] / self.lin_coef if abs(event.translation[1]) > 30: cmd.z = -1 * event.translation[1] / self.lin_coef if abs(event.translation[2]) > 30: cmd.x = event.translation[2] / self.lin_coef if abs(event.rotation[0]) > 20: cmd.ry = event.rotation[0] / self.lin_coef if abs(event.rotation[1]) > 20: cmd.rz = -1 * event.rotation[1] / self.lin_coef if abs(event.rotation[2]) > 20: cmd.rx = event.rotation[2] / self.lin_coef if (time.time() - ts) > 0.12: ts = time.time() speeds = cmd.get_speeds() if btn0_state: self.robot.speedl_tool(speeds, acc=0.10, min_time=2) else: self.robot.speedl(speeds, acc=0.10, min_time=2) btn_event = None speeds = cmd.get_speeds() #if speeds != [0 for _ in speeds]: print(event) print("Sending", speeds) if __name__ == '__main__': spnav.spnav_open() robot = urx.Robot("192.168.0.90") #robot = urx.Robot("localhost") robot.set_tcp((0, 0, 0.27, 0, 0, 0)) trx = m3d.Transform() trx.orient.rotate_zb(pi/4) robot.set_csys("mycsys", trx) service = Service(robot) try: service.loop() finally: robot.close() spnav.spnav_close()	2,806	28.547368	76	py
python-urx	python-urx-master/examples/joystick_control.py	""" example program to control a universal robot with a joystick All joysticks are differens, so you will need to modify the script to work with your joystick """ import time import pygame import math3d as m3d from math import pi import urx class Cmd(object): def __init__(self): self.reset() def reset(self): self.axis0 = 0 self.axis1 = 0 self.axis2 = 0 self.axis3 = 0 self.axis4 = 0 self.axis5 = 0 self.btn0 = 0 self.btn1 = 0 self.btn2 = 0 self.btn3 = 0 self.btn4 = 0 self.btn5 = 0 self.btn6 = 0 self.btn7 = 0 self.btn8 = 0 self.btn9 = 0 class Service(object): def __init__(self, robot, linear_velocity=0.1, rotational_velocity=0.1, acceleration=0.1): self.joystick = None self.robot = robot #max velocity and acceleration to be send to robot self.linear_velocity = linear_velocity self.rotational_velocity = rotational_velocity self.acceleration = acceleration #one button send the robot to a preprogram position defined by this variable in join space self.init_pose = [-2.0782002408411593, -1.6628931459654561, 2.067930303382134, -1.9172217394630149, 1.5489023943220621, 0.6783171005488982] self.cmd = Cmd() def init_joystick(self): pygame.init() self.joystick = pygame.joystick.Joystick(0) self.joystick.init() print('Initialized Joystick : %s' % self.joystick.get_name()) def loop(self): print("Starting loop") air = False while True: self.cmd.reset() pygame.event.pump()#Seems we need polling in pygame... #get joystick state for i in range(0, self.joystick.get_numaxes()): val = self.joystick.get_axis(i) if i in (2, 5) and val != 0: val += 1 if abs(val) < 0.2: val = 0 tmp = "self.cmd.axis" + str(i) + " = " + str(val) if val != 0: print(tmp) exec(tmp) #get button state for i in range(0, self.joystick.get_numbuttons()): if self.joystick.get_button(i) != 0: tmp = "self.cmd.btn" + str(i) + " = 1" print(tmp) exec(tmp) if self.cmd.btn0: #toggle IO air = not air self.robot.set_digital_out(2, air) if self.cmd.btn9: print("moving to init pose") self.robot.movej(self.init_pose, acc=1, vel=0.1) #initalize speed array to 0 speeds = [0, 0, 0, 0, 0, 0] #get linear speed from joystick speeds[0] = -1 * self.cmd.axis0 * self.linear_velocity speeds[1] = self.cmd.axis1 * self.linear_velocity if self.cmd.btn4 and not self.cmd.axis2: speeds[2] = -self.linear_velocity if self.cmd.axis2 and not self.cmd.btn4: speeds[2] = self.cmd.axis2 * self.linear_velocity #get rotational speed from joystick speeds[4] = -1 * self.cmd.axis3 * self.rotational_velocity speeds[3] = -1 * self.cmd.axis4 * self.rotational_velocity if self.cmd.btn5 and not self.cmd.axis5: speeds[5] = self.rotational_velocity if self.cmd.axis5 and not self.cmd.btn5: speeds[5] = self.cmd.axis5 * -self.rotational_velocity #for some reasons everything is inversed speeds = [-i for i in speeds] #Now sending to robot. tol by default and base csys if btn2 is on if speeds != [0 for _ in speeds]: print("Sending ", speeds) if self.cmd.btn7: self.robot.speedl_tool(speeds, acc=self.acceleration, min_time=2) else: self.robot.speedl(speeds, acc=self.acceleration, min_time=2) def close(self): if self.joystick: self.joystick.quit() if __name__ == "__main__": robot = urx.Robot("192.168.1.100") r = robot #start joystick service with given max speed and acceleration service = Service(robot, linear_velocity=0.1, rotational_velocity=0.1, acceleration=0.1) service.init_joystick() try: service.loop() finally: robot.close() service.close()	4,580	31.721429	147	py
python-urx	python-urx-master/examples/test_all.py	""" Testing script that runs many of the urx methods, while attempting to keep robot pose around its starting pose """ from math import pi import time import sys import urx import logging if sys.version_info[0] < 3: # support python v2 input = raw_input def wait(): if do_wait: print("Click enter to continue") input() if __name__ == "__main__": logging.basicConfig(level=logging.INFO) do_wait = True if len(sys.argv) > 1: do_wait = False rob = urx.Robot("192.168.1.100") #rob = urx.Robot("localhost") rob.set_tcp((0, 0, 0, 0, 0, 0)) rob.set_payload(0.5, (0, 0, 0)) try: l = 0.05 v = 0.05 a = 0.3 r = 0.01 print("Digital out 0 and 1 are: ", rob.get_digital_out(0), rob.get_digital_out(1)) print("Analog inputs are: ", rob.get_analog_inputs()) initj = rob.getj() print("Initial joint configuration is ", initj) t = rob.get_pose() print("Transformation from base to tcp is: ", t) print("Translating in x") wait() rob.translate((l, 0, 0), acc=a, vel=v) pose = rob.getl() print("robot tcp is at: ", pose) print("moving in z") wait() pose[2] += l rob.movel(pose, acc=a, vel=v, wait=False) print("Waiting 2s for end move") time.sleep(2) print("Moving through several points with a radius") wait() pose[0] -= l p1 = pose[:] pose[2] -= l p2 = pose[:] rob.movels([p1, p2], vel=v, acc=a, radius=r) print("rotate tcp around around base z ") wait() t.orient.rotate_zb(pi / 8) rob.set_pose(t, vel=v, acc=a) print("moving in tool z") wait() rob.translate_tool((0, 0, l), vel=v, acc=a) print("moving in tool -z using speed command") wait() rob.speedl_tool((0, 0, -v, 0, 0, 0), acc=a, min_time=3) print("Waiting 2 seconds2") time.sleep(2) print("stop robot") rob.stopj() print("Test movec") wait() pose = rob.get_pose() via = pose.copy() via.pos[0] += l to = via.copy() to.pos[1] += l rob.movec(via, to, acc=a, vel=v) print("Sending robot back to original position") rob.movej(initj, acc=0.8, vel=0.2) finally: rob.close()	2,420	24.484211	110	py
python-urx	python-urx-master/docs/conf.py	# -- coding: utf-8 -- # # Python URx documentation build configuration file, created by # sphinx-quickstart on Mon May 11 21:37:43 2015. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os import shlex # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.coverage', 'sphinx.ext.viewcode', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'Python URx' copyright = u'2015, Olivier Roulet-Dubonnet' author = u'Olivier Roulet-Dubonnet' # The version info for the project you're documenting, acts as replacement for # \|version\| and \|release\|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '1.0' # The full version, including alpha/beta/rc tags. release = '1.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # There are two options for replacing \|today\|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'alabaster' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' #html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # Now only 'ja' uses this config value #html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. #html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = 'PythonURxdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', # Latex figure (float) alignment #'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'PythonURx.tex', u'Python URx Documentation', u'Olivier Roulet-Dubonnet', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'pythonurx', u'Python URx Documentation', [author], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'PythonURx', u'Python URx Documentation', author, 'PythonURx', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False	9,300	31.183391	79	py
python-urx	python-urx-master/urx/urrtmon.py	''' Module for implementing a UR controller real-time monitor over socket port 30003. Confer http://support.universal-robots.com/Technical/RealTimeClientInterface Note: The packet lenght given in the web-page is 740. What is actually received from the controller is 692. It is assumed that the motor currents, the last group of 48 bytes, are not send. Originally Written by Morten Lind Parsing for Firmware 5.9 is added by Byeongdu Lee ''' import logging import socket import struct import time import threading from copy import deepcopy import numpy as np import math3d as m3d __author__ = "Morten Lind, Olivier Roulet-Dubonnet" __copyright__ = "Copyright 2011, NTNU/SINTEF Raufoss Manufacturing AS" __credits__ = ["Morten Lind, Olivier Roulet-Dubonnet"] __license__ = "LGPLv3" class URRTMonitor(threading.Thread): # Struct for revision of the UR controller giving 692 bytes rtstruct692 = struct.Struct('>d6d6d6d6d6d6d6d6d18d6d6d6dQ') # for revision of the UR controller giving 540 byte. Here TCP # pose is not included! rtstruct540 = struct.Struct('>d6d6d6d6d6d6d6d6d18d') rtstruct5_1 = struct.Struct('>d1d6d6d6d6d6d6d6d6d6d6d6d6d6d6d1d6d1d1d1d6d1d6d3d6d1d1d1d1d1d1d1d6d1d1d3d3d') rtstruct5_9 = struct.Struct('>d6d6d6d6d6d6d6d6d6d6d6d6d6d6d1d6d1d1d1d6d1d6d3d6d1d1d1d1d1d1d1d6d1d1d3d3d1d') def __init__(self, urHost, urFirm=None): threading.Thread.__init__(self) self.logger = logging.getLogger(self.__class__.__name__) self.daemon = True self._stop_event = True self._dataEvent = threading.Condition() self._dataAccess = threading.Lock() self._rtSock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self._rtSock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) self._urHost = urHost self.urFirm = urFirm # Package data variables self._timestamp = None self._ctrlTimestamp = None self._qActual = None self._qTarget = None self._tcp = None self._tcp_force = None self._joint_temperature = None self._joint_voltage = None self._joint_current = None self._main_voltage = None self._robot_voltage = None self._robot_current = None self._qdTarget = None self._qddTarget = None self._iTarget = None self._mTarget = None self._qdActual = None self._tcp_speed = None self._tcp = None self._robot_mode = None self._joint_modes = None self._digital_outputs = None self._program_state = None self._safety_status = None self.__recvTime = 0 self._last_ctrl_ts = 0 # self._last_ts = 0 self._buffering = False self._buffer_lock = threading.Lock() self._buffer = [] self._csys = None self._csys_lock = threading.Lock() def set_csys(self, csys): with self._csys_lock: self._csys = csys def __recv_bytes(self, nBytes): ''' Facility method for receiving exactly "nBytes" bytes from the robot connector socket.''' # Record the time of arrival of the first of the stream block recvTime = 0 pkg = b'' while len(pkg) < nBytes: pkg += self._rtSock.recv(nBytes - len(pkg)) if recvTime == 0: recvTime = time.time() self.__recvTime = recvTime return pkg def wait(self): with self._dataEvent: self._dataEvent.wait() def q_actual(self, wait=False, timestamp=False): """ Get the actual joint position vector.""" if wait: self.wait() with self._dataAccess: if timestamp: return self._timestamp, self._qActual else: return self._qActual getActual = q_actual def qd_actual(self, wait=False, timestamp=False): """ Get the actual joint velocity vector.""" if wait: self.wait() with self._dataAccess: if timestamp: return self._timestamp, self._qdActual else: return self._qdActual def q_target(self, wait=False, timestamp=False): """ Get the target joint position vector.""" if wait: self.wait() with self._dataAccess: if timestamp: return self._timestamp, self._qTarget else: return self._qTarget getTarget = q_target def tcf_pose(self, wait=False, timestamp=False, ctrlTimestamp=False): """ Return the tool pose values.""" if wait: self.wait() with self._dataAccess: tcf = self._tcp if ctrlTimestamp or timestamp: ret = [tcf] if timestamp: ret.insert(-1, self._timestamp) if ctrlTimestamp: ret.insert(-1, self._ctrlTimestamp) return ret else: return tcf getTCF = tcf_pose def tcf_force(self, wait=False, timestamp=False): """ Get the tool force. The returned tool force is a six-vector of three forces and three moments.""" if wait: self.wait() with self._dataAccess: # tcf = self._fwkin(self._qActual) tcf_force = self._tcp_force if timestamp: return self._timestamp, tcf_force else: return tcf_force getTCFForce = tcf_force def joint_temperature(self, wait=False, timestamp=False): """ Get the joint temperature.""" if wait: self.wait() with self._dataAccess: joint_temperature = self._joint_temperature if timestamp: return self._timestamp, joint_temperature else: return joint_temperature getJOINTTemperature = joint_temperature def joint_voltage(self, wait=False, timestamp=False): """ Get the joint voltage.""" if wait: self.wait() with self._dataAccess: joint_voltage = self._joint_voltage if timestamp: return self._timestamp, joint_voltage else: return joint_voltage getJOINTVoltage = joint_voltage def joint_current(self, wait=False, timestamp=False): """ Get the joint current.""" if wait: self.wait() with self._dataAccess: joint_current = self._joint_current if timestamp: return self._timestamp, joint_current else: return joint_current getJOINTCurrent = joint_current def main_voltage(self, wait=False, timestamp=False): """ Get the Safety Control Board: Main voltage.""" if wait: self.wait() with self._dataAccess: main_voltage = self._main_voltage if timestamp: return self._timestamp, main_voltage else: return main_voltage getMAINVoltage = main_voltage def robot_voltage(self, wait=False, timestamp=False): """ Get the Safety Control Board: Robot voltage (48V).""" if wait: self.wait() with self._dataAccess: robot_voltage = self._robot_voltage if timestamp: return self._timestamp, robot_voltage else: return robot_voltage getROBOTVoltage = robot_voltage def robot_current(self, wait=False, timestamp=False): """ Get the Safety Control Board: Robot current.""" if wait: self.wait() with self._dataAccess: robot_current = self._robot_current if timestamp: return self._timestamp, robot_current else: return robot_current getROBOTCurrent = robot_current def __recv_rt_data(self): head = self.__recv_bytes(4) # Record the timestamp for this logical package timestamp = self.__recvTime pkgsize = struct.unpack('>i', head)[0] self.logger.debug( 'Received header telling that package is %s bytes long', pkgsize) payload = self.__recv_bytes(pkgsize - 4) if self.urFirm is not None: if self.urFirm == 5.1: unp = self.rtstruct5_1.unpack(payload[:self.rtstruct5_1.size]) if self.urFirm == 5.9: unp = self.rtstruct5_9.unpack(payload[:self.rtstruct5_9.size]) else: if pkgsize >= 692: unp = self.rtstruct692.unpack(payload[:self.rtstruct692.size]) elif pkgsize >= 540: unp = self.rtstruct540.unpack(payload[:self.rtstruct540.size]) else: self.logger.warning( 'Error, Received packet of length smaller than 540: %s ', pkgsize) return with self._dataAccess: self._timestamp = timestamp # it seems that packet often arrives packed as two... maybe TCP_NODELAY is not set on UR controller?? # if (self._timestamp - self._last_ts) > 0.010: # self.logger.warning("Error the we did not receive a packet for {}s ".format( self._timestamp - self._last_ts)) # self._last_ts = self._timestamp self._ctrlTimestamp = np.array(unp[0]) if self._last_ctrl_ts != 0 and ( self._ctrlTimestamp - self._last_ctrl_ts) > 0.010: self.logger.warning( "Error the controller failed to send us a packet: time since last packet %s s ", self._ctrlTimestamp - self._last_ctrl_ts) self._last_ctrl_ts = self._ctrlTimestamp self._qActual = np.array(unp[31:37]) self._qdActual = np.array(unp[37:43]) self._qTarget = np.array(unp[1:7]) self._tcp_force = np.array(unp[67:73]) self._tcp = np.array(unp[73:79]) self._joint_current = np.array(unp[43:49]) if self.urFirm >= 3.1: self._joint_temperature = np.array(unp[86:92]) self._joint_voltage = np.array(unp[124:130]) self._main_voltage = unp[121] self._robot_voltage = unp[122] self._robot_current = unp[123] if self.urFirm>= 5.9: self._qdTarget = np.array(unp[7:13]) self._qddTarget = np.array(unp[13:19]) self._iTarget = np.array(unp[19:25]) self._mTarget = np.array(unp[25:31]) self._tcp_speed = np.array(unp[61:67]) self._joint_current = np.array(unp[49:55]) self._joint_voltage = np.array(unp[124:130]) self._robot_mode = unp[94] self._joint_modes = np.array(unp[95:101]) self._digital_outputs = unp[130] self._program_state = unp[131] self._safety_status = unp[138] if self._csys: with self._csys_lock: # might be a godd idea to remove dependancy on m3d tcp = self._csys.inverse * m3d.Transform(self._tcp) self._tcp = tcp.pose_vector if self._buffering: with self._buffer_lock: self._buffer.append( (self._timestamp, self._ctrlTimestamp, self._tcp, self._qActual)) # FIXME use named arrays of allow to configure what data to buffer with self._dataEvent: self._dataEvent.notifyAll() def start_buffering(self): """ start buffering all data from controller """ self._buffer = [] self._buffering = True def stop_buffering(self): self._buffering = False def try_pop_buffer(self): """ return oldest value in buffer """ with self._buffer_lock: if len(self._buffer) > 0: return self._buffer.pop(0) else: return None def pop_buffer(self): """ return oldest value in buffer """ while True: with self._buffer_lock: if len(self._buffer) > 0: return self._buffer.pop(0) time.sleep(0.001) def get_buffer(self): """ return a copy of the entire buffer """ with self._buffer_lock: return deepcopy(self._buffer) def get_all_data(self, wait=True): """ return all data parsed from robot as a dict """ if wait: self.wait() with self._dataAccess: return dict( timestamp=self._timestamp, ctrltimestamp=self._ctrlTimestamp, qActual=self._qActual, qTarget=self._qTarget, qdActual=self._qdActual, qdTarget=self._qdTarget, tcp=self._tcp, tcp_force=self._tcp_force, tcp_speed=self._tcp_speed, joint_temperature=self._joint_temperature, joint_voltage=self._joint_voltage, joint_current=self._joint_current, joint_modes=self._joint_modes, robot_modes=self._robot_mode, main_voltage=self._main_voltage, robot_voltage=self._robot_voltage, robot_current=self._robot_current, digital_outputs=self._digital_outputs, program_state=self._program_state, safety_status=self._safety_status) getALLData = get_all_data def stop(self): # print(self.__class__.__name__+': Stopping') self._stop_event = True def close(self): self.stop() self.join() def run(self): self._stop_event = False self._rtSock.connect((self._urHost, 30003)) while not self._stop_event: self.__recv_rt_data() self._rtSock.close()	14,305	34.410891	124	py
python-urx	python-urx-master/urx/robot.py	""" Python library to control an UR robot through its TCP/IP interface DOC LINK http://support.universal-robots.com/URRobot/RemoteAccess """ import math3d as m3d import numpy as np from urx.urrobot import URRobot __author__ = "Olivier Roulet-Dubonnet" __copyright__ = "Copyright 2011-2016, Sintef Raufoss Manufacturing" __license__ = "LGPLv3" class Robot(URRobot): """ Generic Python interface to an industrial robot. Compared to the URRobot class, this class adds the possibilty to work directly with matrices and includes support for setting a reference coordinate system """ def __init__(self, host, use_rt=False, urFirm=None): URRobot.__init__(self, host, use_rt, urFirm) self.csys = m3d.Transform() def _get_lin_dist(self, target): pose = URRobot.getl(self, wait=True) target = m3d.Transform(target) pose = m3d.Transform(pose) return pose.dist(target) def set_tcp(self, tcp): """ set robot flange to tool tip transformation """ if isinstance(tcp, m3d.Transform): tcp = tcp.pose_vector URRobot.set_tcp(self, tcp) def set_csys(self, transform): """ Set reference coordinate system to use """ self.csys = transform def set_orientation(self, orient, acc=0.01, vel=0.01, wait=True, threshold=None): """ set tool orientation using a orientation matric from math3d or a orientation vector """ if not isinstance(orient, m3d.Orientation): orient = m3d.Orientation(orient) trans = self.get_pose() trans.orient = orient self.set_pose(trans, acc, vel, wait=wait, threshold=threshold) def translate_tool(self, vect, acc=0.01, vel=0.01, wait=True, threshold=None): """ move tool in tool coordinate, keeping orientation """ t = m3d.Transform() if not isinstance(vect, m3d.Vector): vect = m3d.Vector(vect) t.pos += vect return self.add_pose_tool(t, acc, vel, wait=wait, threshold=threshold) def back(self, z=0.05, acc=0.01, vel=0.01): """ move in z tool """ self.translate_tool((0, 0, -z), acc=acc, vel=vel) def set_pos(self, vect, acc=0.01, vel=0.01, wait=True, threshold=None): """ set tool to given pos, keeping constant orientation """ if not isinstance(vect, m3d.Vector): vect = m3d.Vector(vect) trans = m3d.Transform(self.get_orientation(), m3d.Vector(vect)) return self.set_pose(trans, acc, vel, wait=wait, threshold=threshold) def movec(self, pose_via, pose_to, acc=0.01, vel=0.01, wait=True, threshold=None): """ Move Circular: Move to position (circular in tool-space) see UR documentation """ pose_via = self.csys * m3d.Transform(pose_via) pose_to = self.csys * m3d.Transform(pose_to) pose = URRobot.movec(self, pose_via.pose_vector, pose_to.pose_vector, acc=acc, vel=vel, wait=wait, threshold=threshold) if pose is not None: return self.csys.inverse * m3d.Transform(pose) def set_pose(self, trans, acc=0.01, vel=0.01, wait=True, command="movel", threshold=None): """ move tcp to point and orientation defined by a transformation UR robots have several move commands, by default movel is used but it can be changed using the command argument """ self.logger.debug("Setting pose to %s", trans.pose_vector) t = self.csys * trans pose = URRobot.movex(self, command, t.pose_vector, acc=acc, vel=vel, wait=wait, threshold=threshold) if pose is not None: return self.csys.inverse * m3d.Transform(pose) def add_pose_base(self, trans, acc=0.01, vel=0.01, wait=True, command="movel", threshold=None): """ Add transform expressed in base coordinate """ pose = self.get_pose() return self.set_pose(trans * pose, acc, vel, wait=wait, command=command, threshold=threshold) def add_pose_tool(self, trans, acc=0.01, vel=0.01, wait=True, command="movel", threshold=None): """ Add transform expressed in tool coordinate """ pose = self.get_pose() return self.set_pose(pose * trans, acc, vel, wait=wait, command=command, threshold=threshold) def get_pose(self, wait=False, _log=True): """ get current transform from base to to tcp """ pose = URRobot.getl(self, wait, _log) trans = self.csys.inverse * m3d.Transform(pose) if _log: self.logger.debug("Returning pose to user: %s", trans.pose_vector) return trans def get_orientation(self, wait=False): """ get tool orientation in base coordinate system """ trans = self.get_pose(wait) return trans.orient def get_pos(self, wait=False): """ get tool tip pos(x, y, z) in base coordinate system """ trans = self.get_pose(wait) return trans.pos def speedl(self, velocities, acc, min_time): """ move at given velocities until minimum min_time seconds """ v = self.csys.orient * m3d.Vector(velocities[:3]) w = self.csys.orient * m3d.Vector(velocities[3:]) vels = np.concatenate((v.array, w.array)) return self.speedx("speedl", vels, acc, min_time) def speedj(self, velocities, acc, min_time): """ move at given joint velocities until minimum min_time seconds """ return self.speedx("speedj", velocities, acc, min_time) def speedl_tool(self, velocities, acc, min_time): """ move at given velocities in tool csys until minimum min_time seconds """ pose = self.get_pose() v = pose.orient * m3d.Vector(velocities[:3]) w = pose.orient * m3d.Vector(velocities[3:]) self.speedl(np.concatenate((v.array, w.array)), acc, min_time) def movex(self, command, pose, acc=0.01, vel=0.01, wait=True, relative=False, threshold=None): """ Send a move command to the robot. since UR robotene have several methods this one sends whatever is defined in 'command' string """ t = m3d.Transform(pose) if relative: return self.add_pose_base(t, acc, vel, wait=wait, command=command, threshold=threshold) else: return self.set_pose(t, acc, vel, wait=wait, command=command, threshold=threshold) def movexs(self, command, pose_list, acc=0.01, vel=0.01, radius=0.01, wait=True, threshold=None): """ Concatenate several movex commands and applies a blending radius pose_list is a list of pose. This method is usefull since any new command from python to robot make the robot stop """ new_poses = [] for pose in pose_list: t = self.csys * m3d.Transform(pose) pose = t.pose_vector new_poses.append(pose) return URRobot.movexs(self, command, new_poses, acc, vel, radius, wait=wait, threshold=threshold) def movel_tool(self, pose, acc=0.01, vel=0.01, wait=True, threshold=None): """ move linear to given pose in tool coordinate """ return self.movex_tool("movel", pose, acc=acc, vel=vel, wait=wait, threshold=threshold) def movex_tool(self, command, pose, acc=0.01, vel=0.01, wait=True, threshold=None): t = m3d.Transform(pose) self.add_pose_tool(t, acc, vel, wait=wait, command=command, threshold=threshold) def getl(self, wait=False, _log=True): """ return current transformation from tcp to current csys """ t = self.get_pose(wait, _log) return t.pose_vector.tolist() def set_gravity(self, vector): if isinstance(vector, m3d.Vector): vector = vector.list return URRobot.set_gravity(self, vector) def new_csys_from_xpy(self): """ Restores and returns new coordinate system calculated from three points: X, Origin, Y based on math3d: Transform.new_from_xyp """ # Set coord. sys. to 0 self.csys = m3d.Transform() print("A new coordinate system will be defined from the next three points") print("Firs point is X, second Origin, third Y") print("Set it as a new reference by calling myrobot.set_csys(new_csys)") input("Move to first point and click Enter") # we do not use get_pose so we avoid rounding values pose = URRobot.getl(self) print("Introduced point defining X: {}".format(pose[:3])) px = m3d.Vector(pose[:3]) input("Move to second point and click Enter") pose = URRobot.getl(self) print("Introduced point defining Origo: {}".format(pose[:3])) p0 = m3d.Vector(pose[:3]) input("Move to third point and click Enter") pose = URRobot.getl(self) print("Introduced point defining Y: {}".format(pose[:3])) py = m3d.Vector(pose[:3]) new_csys = m3d.Transform.new_from_xyp(px - p0, py - p0, p0) self.set_csys(new_csys) return new_csys @property def x(self): return self.get_pos().x @x.setter def x(self, val): p = self.get_pos() p.x = val self.set_pos(p) @property def y(self): return self.get_pos().y @y.setter def y(self, val): p = self.get_pos() p.y = val self.set_pos(p) @property def z(self): return self.get_pos().z @z.setter def z(self, val): p = self.get_pos() p.z = val self.set_pos(p) @property def rx(self): return 0 @rx.setter def rx(self, val): p = self.get_pose() p.orient.rotate_xb(val) self.set_pose(p) @property def ry(self): return 0 @ry.setter def ry(self, val): p = self.get_pose() p.orient.rotate_yb(val) self.set_pose(p) @property def rz(self): return 0 @rz.setter def rz(self, val): p = self.get_pose() p.orient.rotate_zb(val) self.set_pose(p) @property def x_t(self): return 0 @x_t.setter def x_t(self, val): t = m3d.Transform() t.pos.x += val self.add_pose_tool(t) @property def y_t(self): return 0 @y_t.setter def y_t(self, val): t = m3d.Transform() t.pos.y += val self.add_pose_tool(t) @property def z_t(self): return 0 @z_t.setter def z_t(self, val): t = m3d.Transform() t.pos.z += val self.add_pose_tool(t) @property def rx_t(self): return 0 @rx_t.setter def rx_t(self, val): t = m3d.Transform() t.orient.rotate_xb(val) self.add_pose_tool(t) @property def ry_t(self): return 0 @ry_t.setter def ry_t(self, val): t = m3d.Transform() t.orient.rotate_yb(val) self.add_pose_tool(t) @property def rz_t(self): return 0 @rz_t.setter def rz_t(self, val): t = m3d.Transform() t.orient.rotate_zb(val) self.add_pose_tool(t)	11,388	29.94837	127	py
python-urx	python-urx-master/urx/robotiq_two_finger_gripper.py	#! /usr/bin/env python """ Python library to control Robotiq Two Finger Gripper connected to UR robot via Python-URX Tested using a UR5 Version CB3 and Robotiq 2-Finger Gripper Version 85 SETUP You must install the driver first and then power on the gripper from the gripper UI. The driver can be found here: http://support.robotiq.com/pages/viewpage.action?pageId=5963876 FAQ Q: Why does this class group all the commands together and run them as a single program as opposed to running each line seperately (like most of URX)? A: The gripper is controlled by connecting to the robot's computer (TCP/IP) and then communicating with the gripper via a socket (127.0.0.1:63352). The scope of the socket is at the program level. It will be automatically closed whenever a program finishes. Therefore it's important that we run all commands as a single program. DOCUMENTATION - This code was developed by downloading the gripper package "DCU-1.0.10" from http://support.robotiq.com/pages/viewpage.action?pageId=5963876. Or more directly from http://support.robotiq.com/download/attachments/5963876/DCU-1.0.10.zip - The file robotiq_2f_gripper_programs_CB3/rq_script.script was referenced to create this class FUTURE FEATURES Though I haven't developed it yet, if you look in robotiq_2f_gripper_programs_CB3/advanced_template_test.script and view function "rq_get_var" there is an example of how to determine the current state of the gripper and if it's holding an object. """ # noqa import logging import os import time from urx.urscript import URScript # Gripper Variables ACT = "ACT" GTO = "GTO" ATR = "ATR" ARD = "ARD" FOR = "FOR" SPE = "SPE" OBJ = "OBJ" STA = "STA" FLT = "FLT" POS = "POS" SOCKET_HOST = "127.0.0.1" SOCKET_PORT = 63352 SOCKET_NAME = "gripper_socket" class RobotiqScript(URScript): def __init__(self, socket_host=SOCKET_HOST, socket_port=SOCKET_PORT, socket_name=SOCKET_NAME): self.socket_host = socket_host self.socket_port = socket_port self.socket_name = socket_name super(RobotiqScript, self).__init__() # Reset connection to gripper self._socket_close(self.socket_name) self._socket_open(self.socket_host, self.socket_port, self.socket_name) def _import_rq_script(self): dir_path = os.path.dirname(os.path.realpath(__file__)) rq_script = os.path.join(dir_path, 'rq_script.script') with open(rq_script, 'rb') as f: rq_script = f.read() self.add_header_to_program(rq_script) def _rq_get_var(self, var_name, nbytes): self._socket_send_string("GET {}".format(var_name)) self._socket_read_byte_list(nbytes) def _get_gripper_fault(self): self._rq_get_var(FLT, 2) def _get_gripper_object(self): self._rq_get_var(OBJ, 1) def _get_gripper_status(self): self._rq_get_var(STA, 1) def _set_gripper_activate(self): self._socket_set_var(GTO, 1, self.socket_name) def _set_gripper_force(self, value): """ FOR is the variable range is 0 - 255 0 is no force 255 is full force """ value = self._constrain_unsigned_char(value) self._socket_set_var(FOR, value, self.socket_name) def _set_gripper_position(self, value): """ SPE is the variable range is 0 - 255 0 is no speed 255 is full speed """ value = self._constrain_unsigned_char(value) self._socket_set_var(POS, value, self.socket_name) def _set_gripper_speed(self, value): """ SPE is the variable range is 0 - 255 0 is no speed 255 is full speed """ value = self._constrain_unsigned_char(value) self._socket_set_var(SPE, value, self.socket_name) def _set_robot_activate(self): self._socket_set_var(ACT, 1, self.socket_name) class Robotiq_Two_Finger_Gripper(object): def __init__(self, robot, payload=0.85, speed=255, force=50, socket_host=SOCKET_HOST, socket_port=SOCKET_PORT, socket_name=SOCKET_NAME): self.robot = robot self.payload = payload self.speed = speed self.force = force self.socket_host = socket_host self.socket_port = socket_port self.socket_name = socket_name self.logger = logging.getLogger(u"robotiq") def _get_new_urscript(self): """ Set up a new URScript to communicate with gripper """ urscript = RobotiqScript(socket_host=self.socket_host, socket_port=self.socket_port, socket_name=self.socket_name) # Set input and output voltage ranges urscript._set_analog_inputrange(0, 0) urscript._set_analog_inputrange(1, 0) urscript._set_analog_inputrange(2, 0) urscript._set_analog_inputrange(3, 0) urscript._set_analog_outputdomain(0, 0) urscript._set_analog_outputdomain(1, 0) urscript._set_tool_voltage(0) urscript._set_runstate_outputs() # Set payload, speed and force urscript._set_payload(self.payload) urscript._set_gripper_speed(self.speed) urscript._set_gripper_force(self.force) # Initialize the gripper urscript._set_robot_activate() urscript._set_gripper_activate() # Wait on activation to avoid USB conflicts urscript._sleep(0.1) return urscript def gripper_action(self, value): """ Activate the gripper to a given value from 0 to 255 0 is open 255 is closed """ urscript = self._get_new_urscript() # Move to the position sleep = 2.0 urscript._set_gripper_position(value) urscript._sleep(sleep) # Send the script self.robot.send_program(urscript()) # sleep the code the same amount as the urscript to ensure that # the action completes time.sleep(sleep) def open_gripper(self): self.gripper_action(0) def close_gripper(self): self.gripper_action(255)	6,377	28.391705	86	py
python-urx	python-urx-master/urx/__init__.py	""" Python library to control an UR robot through its TCP/IP interface """ from urx.urrobot import RobotException, URRobot # noqa __version__ = "0.11.0" try: from urx.robot import Robot except ImportError as ex: print("Exception while importing math3d base robot, disabling use of matrices", ex) Robot = URRobot	327	24.230769	87	py
python-urx	python-urx-master/urx/urscript.py	#! /usr/bin/env python import logging # Controller Settings CONTROLLER_PORTS = [0, 1] CONTROLLER_VOLTAGE = [ 0, # 0-5V 2, # 0-10V ] # Tool Settings TOOL_PORTS = [2, 3] TOOL_VOLTAGE = [ 0, # 0-5V 1, # 0-10V 2, # 4-20mA ] OUTPUT_DOMAIN_VOLTAGE = [ 0, # 4-20mA 1, # 0-10V ] class URScript(object): def __init__(self): self.logger = logging.getLogger(u"urscript") # The header is code that is before and outside the myProg() method self.header = "" # The program is code inside the myProg() method self.program = "" def __call__(self): if(self.program == ""): self.logger.debug(u"urscript program is empty") return "" # Construct the program myprog = """def myProg():{}\nend""".format(self.program) # Construct the full script script = "" if self.header: script = "{}\n\n".format(self.header) script = "{}{}".format(script, myprog) return script def reset(self): self.header = "" self.program = "" def add_header_to_program(self, header_line): self.header = "{}\n{}".format(self.header, header_line) def add_line_to_program(self, new_line): self.program = "{}\n\t{}".format(self.program, new_line) def _constrain_unsigned_char(self, value): """ Ensure that unsigned char values are constrained to between 0 and 255. """ assert(isinstance(value, int)) if value < 0: value = 0 elif value > 255: value = 255 return value def _set_analog_inputrange(self, port, vrange): if port in CONTROLLER_PORTS: assert(vrange in CONTROLLER_VOLTAGE) elif port in TOOL_PORTS: assert(vrange in TOOL_VOLTAGE) msg = "set_analog_inputrange({},{})".format(port, vrange) self.add_line_to_program(msg) def _set_analog_output(self, input_id, signal_level): assert(input_id in [0, 1]) assert(signal_level in [0, 1]) msg = "set_analog_output({}, {})".format(input_id, signal_level) self.add_line_to_program(msg) def _set_analog_outputdomain(self, port, domain): assert(domain in OUTPUT_DOMAIN_VOLTAGE) msg = "set_analog_outputdomain({},{})".format(port, domain) self.add_line_to_program(msg) def _set_payload(self, mass, cog=None): msg = "set_payload({}".format(mass) if cog: assert(len(cog) == 3) msg = "{},{}".format(msg, cog) msg = "{})".format(msg) self.add_line_to_program(msg) def _set_runstate_outputs(self, outputs=None): if not outputs: outputs = [] msg = "set_runstate_outputs({})".format(outputs) self.add_line_to_program(msg) def _set_tool_voltage(self, voltage): assert(voltage in [0, 12, 24]) msg = "set_tool_voltage({})".format(voltage) self.add_line_to_program(msg) def _sleep(self, value): msg = "sleep({})".format(value) self.add_line_to_program(msg) def _socket_close(self, socket_name): msg = "socket_close(\"{}\")".format(socket_name) self.add_line_to_program(msg) def _socket_get_var(self, var, socket_name): msg = "socket_get_var(\"{}\",\"{}\")".format(var, socket_name) self.add_line_to_program(msg) self._sync() def _socket_open(self, socket_host, socket_port, socket_name): msg = "socket_open(\"{}\",{},\"{}\")".format(socket_host, socket_port, socket_name) self.add_line_to_program(msg) def _socket_read_byte_list(self, nbytes, socket_name): msg = "global var_value = socket_read_byte_list({},\"{}\")".format(nbytes, socket_name) # noqa self.add_line_to_program(msg) self._sync() def _socket_send_string(self, message, socket_name): msg = "socket_send_string(\"{}\",\"{}\")".format(message, socket_name) # noqa self.add_line_to_program(msg) self._sync() def _socket_set_var(self, var, value, socket_name): msg = "socket_set_var(\"{}\",{},\"{}\")".format(var, value, socket_name) # noqa self.add_line_to_program(msg) self._sync() def _socket_get_var2var(self, var, varout, socket_name, prefix = ''): msg = "{}{} = socket_get_var(\"{}\",\"{}\")".format(prefix, varout, var, socket_name) self.add_line_to_program(msg) def _socket_send_byte(self, byte, socket_name): msg = "socket_send_byte(\"{}\",\"{}\")".format(str(byte), socket_name) # noqa self.add_line_to_program(msg) self._sync() def _sync(self): msg = "sync()" self.add_line_to_program(msg)	4,900	30.216561	103	py
python-urx	python-urx-master/urx/urrobot.py	""" Python library to control an UR robot through its TCP/IP interface Documentation from universal robots: http://support.universal-robots.com/URRobot/RemoteAccess """ import logging import numbers try: from collections.abc import Sequence except ImportError: from collections import Sequence from urx import urrtmon from urx import ursecmon __author__ = "Olivier Roulet-Dubonnet" __copyright__ = "Copyright 2011-2015, Sintef Raufoss Manufacturing" __license__ = "LGPLv3" class RobotException(Exception): pass class URRobot(object): """ Python interface to socket interface of UR robot. programs are send to port 3002 data is read from secondary interface(10Hz?) and real-time interface(125Hz) (called Matlab interface in documentation) Since parsing the RT interface uses som CPU, and does not support all robots versions, it is disabled by default The RT interfaces is only used for the get_force related methods Rmq: A program sent to the robot i executed immendiatly and any running program is stopped """ def __init__(self, host, use_rt=False, urFirm=None): self.logger = logging.getLogger("urx") self.host = host self.urFirm = urFirm self.csys = None self.logger.debug("Opening secondary monitor socket") self.secmon = ursecmon.SecondaryMonitor(self.host) # data from robot at 10Hz self.rtmon = None if use_rt: self.rtmon = self.get_realtime_monitor() # precision of joint movem used to wait for move completion # the value must be conservative! otherwise we may wait forever self.joinEpsilon = 0.01 # It seems URScript is limited in the character length of floats it accepts self.max_float_length = 6 # FIXME: check max length!!! self.secmon.wait() # make sure we get data from robot before letting clients access our methods def __repr__(self): return "Robot Object (IP=%s, state=%s)" % (self.host, self.secmon.get_all_data()["RobotModeData"]) def __str__(self): return self.__repr__() def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() def is_running(self): """ Return True if robot is running (not necessary running a program, it might be idle) """ return self.secmon.running def is_program_running(self): """ check if program is running. Warning!!!!!: After sending a program it might take several 10th of a second before the robot enters the running state """ return self.secmon.is_program_running() def send_program(self, prog): """ send a complete program using urscript to the robot the program is executed immediatly and any runnning program is interrupted """ self.logger.info("Sending program: " + prog) self.secmon.send_program(prog) def get_tcp_force(self, wait=True): """ return measured force in TCP if wait==True, waits for next packet before returning """ return self.rtmon.getTCFForce(wait) def get_force(self, wait=True): """ length of force vector returned by get_tcp_force if wait==True, waits for next packet before returning """ tcpf = self.get_tcp_force(wait) force = 0 for i in tcpf: force += i2 return force0.5 def get_joint_temperature(self, wait=True): """ return measured joint temperature if wait==True, waits for next packet before returning """ return self.rtmon.getJOINTTemperature(wait) def get_joint_voltage(self, wait=True): """ return measured joint voltage if wait==True, waits for next packet before returning """ return self.rtmon.getJOINTVoltage(wait) def get_joint_current(self, wait=True): """ return measured joint current if wait==True, waits for next packet before returning """ return self.rtmon.getJOINTCurrent(wait) def get_main_voltage(self, wait=True): """ return measured Safety Control Board: Main voltage if wait==True, waits for next packet before returning """ return self.rtmon.getMAINVoltage(wait) def get_robot_voltage(self, wait=True): """ return measured Safety Control Board: Robot voltage (48V) if wait==True, waits for next packet before returning """ return self.rtmon.getROBOTVoltage(wait) def get_robot_current(self, wait=True): """ return measured Safety Control Board: Robot current if wait==True, waits for next packet before returning """ return self.rtmon.getROBOTCurrent(wait) def get_all_rt_data(self, wait=True): """ return all data parsed from robot real-time interace as a dict if wait==True, waits for next packet before returning """ return self.rtmon.getALLData(wait) def set_tcp(self, tcp): """ set robot flange to tool tip transformation """ prog = "set_tcp(p[{}, {}, {}, {}, {}, {}])".format(tcp) self.send_program(prog) def set_payload(self, weight, cog=None): """ set payload in Kg cog is a vector x,y,z if cog is not specified, then tool center point is used """ if cog: cog = list(cog) cog.insert(0, weight) prog = "set_payload({}, ({},{},{}))".format(cog) else: prog = "set_payload(%s)" % weight self.send_program(prog) def set_gravity(self, vector): """ set direction of gravity """ prog = "set_gravity(%s)" % list(vector) self.send_program(prog) def send_message(self, msg): """ send message to the GUI log tab on the robot controller """ prog = "textmsg(%s)" % msg self.send_program(prog) def set_digital_out(self, output, val): """ set digital output. val is a bool """ if val in (True, 1): val = "True" else: val = "False" self.send_program('digital_out[%s]=%s' % (output, val)) def get_analog_inputs(self): """ get analog input """ return self.secmon.get_analog_inputs() def get_analog_in(self, nb, wait=False): """ get analog input """ return self.secmon.get_analog_in(nb, wait=wait) def get_digital_in_bits(self): """ get digital output """ return self.secmon.get_digital_in_bits() def get_digital_in(self, nb, wait=False): """ get digital output """ return self.secmon.get_digital_in(nb, wait) def get_digital_out(self, val, wait=False): """ get digital output """ return self.secmon.get_digital_out(val, wait=wait) def get_digital_out_bits(self, wait=False): """ get digital output as a byte """ return self.secmon.get_digital_out_bits(wait=wait) def set_analog_out(self, output, val): """ set analog output, val is a float """ prog = "set_analog_out(%s, %s)" % (output, val) self.send_program(prog) def set_tool_voltage(self, val): """ set voltage to be delivered to the tool, val is 0, 12 or 24 """ prog = "set_tool_voltage(%s)" % (val) self.send_program(prog) def _wait_for_move(self, target, threshold=None, timeout=5, joints=False): """ wait for a move to complete. Unfortunately there is no good way to know when a move has finished so for every received data from robot we compute a dist equivalent and when it is lower than 'threshold' we return. if threshold is not reached within timeout, an exception is raised """ self.logger.debug("Waiting for move completion using threshold %s and target %s", threshold, target) start_dist = self._get_dist(target, joints) if threshold is None: threshold = start_dist * 0.8 if threshold < 0.001: # roboten precision is limited threshold = 0.001 self.logger.debug("No threshold set, setting it to %s", threshold) count = 0 while True: if not self.is_running(): raise RobotException("Robot stopped") dist = self._get_dist(target, joints) self.logger.debug("distance to target is: %s, target dist is %s", dist, threshold) if not self.secmon.is_program_running(): if dist < threshold: self.logger.debug("we are threshold(%s) close to target, move has ended", threshold) return count += 1 if count > timeout * 10: raise RobotException("Goal not reached but no program has been running for {} seconds. dist is {}, threshold is {}, target is {}, current pose is {}".format(timeout, dist, threshold, target, URRobot.getl(self))) else: count = 0 def _get_dist(self, target, joints=False): if joints: return self._get_joints_dist(target) else: return self._get_lin_dist(target) def _get_lin_dist(self, target): # FIXME: we have an issue here, it seems sometimes the axis angle received from robot pose = URRobot.getl(self, wait=True) dist = 0 for i in range(3): dist += (target[i] - pose[i]) 2 for i in range(3, 6): dist += ((target[i] - pose[i]) / 5) 2 # arbitraty length like return dist 0.5 def _get_joints_dist(self, target): joints = self.getj(wait=True) dist = 0 for i in range(6): dist += (target[i] - joints[i]) 2 return dist ** 0.5 def getj(self, wait=False): """ get joints position """ jts = self.secmon.get_joint_data(wait) return [jts["q_actual0"], jts["q_actual1"], jts["q_actual2"], jts["q_actual3"], jts["q_actual4"], jts["q_actual5"]] def speedx(self, command, velocities, acc, min_time): vels = [round(i, self.max_float_length) for i in velocities] vels.append(acc) vels.append(min_time) prog = "{}([{},{},{},{},{},{}], {}, {})".format(command, vels) self.send_program(prog) def movej(self, joints, acc=0.1, vel=0.05, wait=True, relative=False, threshold=None): """ move in joint space """ if relative: l = self.getj() joints = [v + l[i] for i, v in enumerate(joints)] prog = self._format_move("movej", joints, acc, vel) self.send_program(prog) if wait: self._wait_for_move(joints[:6], threshold=threshold, joints=True) return self.getj() def movel(self, tpose, acc=0.01, vel=0.01, wait=True, relative=False, threshold=None): """ Send a movel command to the robot. See URScript documentation. """ return self.movex("movel", tpose, acc=acc, vel=vel, wait=wait, relative=relative, threshold=threshold) def movep(self, tpose, acc=0.01, vel=0.01, wait=True, relative=False, threshold=None): """ Send a movep command to the robot. See URScript documentation. """ return self.movex("movep", tpose, acc=acc, vel=vel, wait=wait, relative=relative, threshold=threshold) def servoc(self, tpose, acc=0.01, vel=0.01, wait=True, relative=False, threshold=None): """ Send a servoc command to the robot. See URScript documentation. """ return self.movex("servoc", tpose, acc=acc, vel=vel, wait=wait, relative=relative, threshold=threshold) def servoj(self, tjoints, acc=0.01, vel=0.01, t=0.1, lookahead_time=0.2, gain=100, wait=True, relative=False, threshold=None): """ Send a servoj command to the robot. See URScript documentation. """ if relative: l = self.getj() tjoints = [v + l[i] for i, v in enumerate(tjoints)] prog = self._format_servo("servoj", tjoints, acc=acc, vel=vel, t=t, lookahead_time=lookahead_time, gain=gain) self.send_program(prog) if wait: self._wait_for_move(tjoints[:6], threshold=threshold, joints=True) return self.getj() def _format_servo(self, command, tjoints, acc=0.01, vel=0.01, t=0.1, lookahead_time=0.2, gain=100, prefix=""): tjoints = [round(i, self.max_float_length) for i in tjoints] tjoints.append(acc) tjoints.append(vel) tjoints.append(t) tjoints.append(lookahead_time) tjoints.append(gain) return "{}({}[{},{},{},{},{},{}], a={}, v={}, t={}, lookahead_time={}, gain={})".format(command, prefix, tjoints) def _format_move(self, command, tpose, acc, vel, radius=0, prefix=""): tpose = [round(i, self.max_float_length) for i in tpose] tpose.append(acc) tpose.append(vel) tpose.append(radius) return "{}({}[{},{},{},{},{},{}], a={}, v={}, r={})".format(command, prefix, tpose) def movex(self, command, tpose, acc=0.01, vel=0.01, wait=True, relative=False, threshold=None): """ Send a move command to the robot. since UR robotene have several methods this one sends whatever is defined in 'command' string """ if relative: l = self.getl() tpose = [v + l[i] for i, v in enumerate(tpose)] prog = self._format_move(command, tpose, acc, vel, prefix="p") self.send_program(prog) if wait: self._wait_for_move(tpose[:6], threshold=threshold) return self.getl() def getl(self, wait=False, _log=True): """ get TCP position """ pose = self.secmon.get_cartesian_info(wait) if pose: pose = [pose["X"], pose["Y"], pose["Z"], pose["Rx"], pose["Ry"], pose["Rz"]] if _log: self.logger.debug("Received pose from robot: %s", pose) return pose def movec(self, pose_via, pose_to, acc=0.01, vel=0.01, wait=True, threshold=None): """ Move Circular: Move to position (circular in tool-space) see UR documentation """ pose_via = [round(i, self.max_float_length) for i in pose_via] pose_to = [round(i, self.max_float_length) for i in pose_to] prog = "movec(p%s, p%s, a=%s, v=%s, r=%s)" % (pose_via, pose_to, acc, vel, "0") self.send_program(prog) if wait: self._wait_for_move(pose_to, threshold=threshold) return self.getl() def movejs(self, joint_positions_list, acc=0.01, vel=0.01, radius=0.01, wait=True, threshold=None): """ Concatenate several movej commands and applies a blending radius joint_positions_list is a list of joint_positions. This method is usefull since any new command from python to robot make the robot stop """ return URRobot.movexs(self, "movej", joint_positions_list, acc, vel, radius, wait, threshold=threshold) def movels(self, pose_list, acc=0.01, vel=0.01, radius=0.01, wait=True, threshold=None): """ Concatenate several movel commands and applies a blending radius pose_list is a list of pose. This method is usefull since any new command from python to robot make the robot stop """ return self.movexs("movel", pose_list, acc, vel, radius, wait, threshold=threshold) def movexs(self, command, pose_list, acc=0.01, vel=0.01, radius=0.01, wait=True, threshold=None): """ Concatenate several movex commands and applies a blending radius pose_list is a list of pose. This method is usefull since any new command from python to robot make the robot stop """ header = "def myProg():\n" end = "end\n" prog = header # Check if 'vel' is a single number or a sequence. if isinstance(vel, numbers.Number): # Make 'vel' a sequence vel = len(pose_list) [vel] elif not isinstance(vel, Sequence): raise RobotException( 'movexs: "vel" must be a single number or a sequence!') # Check for adequate number of speeds if len(vel) != len(pose_list): raise RobotException( 'movexs: "vel" must be a number or a list ' + 'of numbers the same length as "pose_list"!') # Check if 'radius' is a single number. if isinstance(radius, numbers.Number): # Make 'radius' a sequence radius = len(pose_list) * [radius] elif not isinstance(radius, Sequence): raise RobotException( 'movexs: "radius" must be a single number or a sequence!') # Ensure that last pose a stopping pose. radius[-1] = 0.0 # Require adequate number of radii. if len(radius) != len(pose_list): raise RobotException( 'movexs: "radius" must be a number or a list ' + 'of numbers the same length as "pose_list"!') prefix = '' if command in ['movel', 'movec']: prefix = 'p' for idx, pose in enumerate(pose_list): prog += self._format_move(command, pose, acc, vel[idx], radius[idx], prefix=prefix) + "\n" prog += end self.send_program(prog) if wait: if command == 'movel': self._wait_for_move(target=pose_list[-1], threshold=threshold, joints=False) elif command == 'movej': self._wait_for_move(target=pose_list[-1], threshold=threshold, joints=True) return self.getl() def stopl(self, acc=0.5): self.send_program("stopl(%s)" % acc) def stopj(self, acc=1.5): self.send_program("stopj(%s)" % acc) def stop(self): self.stopj() def close(self): """ close connection to robot and stop internal thread """ self.logger.info("Closing sockets to robot") self.secmon.close() if self.rtmon: self.rtmon.stop() def set_freedrive(self, val, timeout=60): """ set robot in freedrive/backdrive mode where an operator can jog the robot to wished pose. Freedrive will timeout at 60 seconds. """ if val: self.send_program("def myProg():\n\tfreedrive_mode()\n\tsleep({})\nend".format(timeout)) else: # This is a non-existant program, but running it will stop freedrive self.send_program("def myProg():\n\tend_freedrive_mode()\nend") def set_simulation(self, val): if val: self.send_program("set sim") else: self.send_program("set real") def get_realtime_monitor(self): """ return a pointer to the realtime monitor object usefull to track robot position for example """ if not self.rtmon: self.logger.info("Opening real-time monitor socket") self.rtmon = urrtmon.URRTMonitor(self.host, self.urFirm) # som information is only available on rt interface self.rtmon.start() self.rtmon.set_csys(self.csys) return self.rtmon def translate(self, vect, acc=0.01, vel=0.01, wait=True, command="movel"): """ move tool in base coordinate, keeping orientation """ p = self.getl() p[0] += vect[0] p[1] += vect[1] p[2] += vect[2] return self.movex(command, p, vel=vel, acc=acc, wait=wait) def up(self, z=0.05, acc=0.01, vel=0.01): """ Move up in csys z """ p = self.getl() p[2] += z self.movel(p, acc=acc, vel=vel) def down(self, z=0.05, acc=0.01, vel=0.01): """ Move down in csys z """ self.up(-z, acc, vel)	20,532	35.086116	231	py
python-urx	python-urx-master/urx/ursecmon.py	""" This file contains 2 classes: - ParseUtils containing utilies to parse data from UR robot - SecondaryMonitor, a class opening a socket to the robot and with methods to access data and send programs to the robot Both use data from the secondary port of the URRobot. Only the last connected socket on 3001 is the primary client !!!! So do not rely on it unless you know no other client is running (Hint the UR java interface is a client...) http://support.universal-robots.com/Technical/PrimaryAndSecondaryClientInterface """ from threading import Thread, Condition, Lock import logging import struct import socket from copy import copy import time __author__ = "Olivier Roulet-Dubonnet" __copyright__ = "Copyright 2011-2013, Sintef Raufoss Manufacturing" __credits__ = ["Olivier Roulet-Dubonnet"] __license__ = "LGPLv3" class ParsingException(Exception): def __init__(self, args): Exception.__init__(self, args) class Program(object): def __init__(self, prog): self.program = prog self.condition = Condition() def __str__(self): return "Program({})".format(self.program) __repr__ = __str__ class TimeoutException(Exception): def __init__(self, args): Exception.__init__(self, args) class ParserUtils(object): def __init__(self): self.logger = logging.getLogger("ursecmon") self.version = (0, 0) def parse(self, data): """ parse a packet from the UR socket and return a dictionary with the data """ allData = {} # print "Total size ", len(data) while data: psize, ptype, pdata, data = self.analyze_header(data) # print "We got packet with size %i and type %s" % (psize, ptype) if ptype == 16: allData["SecondaryClientData"] = self._get_data(pdata, "!iB", ("size", "type")) data = (pdata + data)[5:] # This is the total size so we resend data to parser elif ptype == 0: # this parses RobotModeData for versions >=3.0 (i.e. 3.0) if psize == 38: self.version = (3, 0) allData['RobotModeData'] = self._get_data(pdata, "!IBQ???????BBdd", ("size", "type", "timestamp", "isRobotConnected", "isRealRobotEnabled", "isPowerOnRobot", "isEmergencyStopped", "isSecurityStopped", "isProgramRunning", "isProgramPaused", "robotMode", "controlMode", "speedFraction", "speedScaling")) elif psize == 46: # It's 46 bytes in 3.2 self.version = (3, 2) allData['RobotModeData'] = self._get_data(pdata, "!IBQ???????BBdd", ("size", "type", "timestamp", "isRobotConnected", "isRealRobotEnabled", "isPowerOnRobot", "isEmergencyStopped", "isSecurityStopped", "isProgramRunning", "isProgramPaused", "robotMode", "controlMode", "speedFraction", "speedScaling", "speedFractionLimit")) elif psize == 47: self.version = (3, 5) allData['RobotModeData'] = self._get_data(pdata, "!IBQ???????BBddc", ("size", "type", "timestamp", "isRobotConnected", "isRealRobotEnabled", "isPowerOnRobot", "isEmergencyStopped", "isSecurityStopped", "isProgramRunning", "isProgramPaused", "robotMode", "controlMode", "speedFraction", "speedScaling", "speedFractionLimit", "reservedByUR")) else: allData["RobotModeData"] = self._get_data(pdata, "!iBQ???????Bd", ("size", "type", "timestamp", "isRobotConnected", "isRealRobotEnabled", "isPowerOnRobot", "isEmergencyStopped", "isSecurityStopped", "isProgramRunning", "isProgramPaused", "robotMode", "speedFraction")) elif ptype == 1: tmpstr = ["size", "type"] for i in range(0, 6): tmpstr += ["q_actual%s" % i, "q_target%s" % i, "qd_actual%s" % i, "I_actual%s" % i, "V_actual%s" % i, "T_motor%s" % i, "T_micro%s" % i, "jointMode%s" % i] allData["JointData"] = self._get_data(pdata, "!iB dddffffB dddffffB dddffffB dddffffB dddffffB dddffffB", tmpstr) elif ptype == 4: if self.version < (3, 2): allData["CartesianInfo"] = self._get_data(pdata, "iBdddddd", ("size", "type", "X", "Y", "Z", "Rx", "Ry", "Rz")) else: allData["CartesianInfo"] = self._get_data(pdata, "iBdddddddddddd", ("size", "type", "X", "Y", "Z", "Rx", "Ry", "Rz", "tcpOffsetX", "tcpOffsetY", "tcpOffsetZ", "tcpOffsetRx", "tcpOffsetRy", "tcpOffsetRz")) elif ptype == 5: allData["LaserPointer(OBSOLETE)"] = self._get_data(pdata, "iBddd", ("size", "type")) elif ptype == 3: if self.version >= (3, 0): fmt = "iBiibbddbbddffffBBb" # firmware >= 3.0 else: fmt = "iBhhbbddbbddffffBBb" # firmware < 3.0 allData["MasterBoardData"] = self._get_data(pdata, fmt, ("size", "type", "digitalInputBits", "digitalOutputBits", "analogInputRange0", "analogInputRange1", "analogInput0", "analogInput1", "analogInputDomain0", "analogInputDomain1", "analogOutput0", "analogOutput1", "masterBoardTemperature", "robotVoltage48V", "robotCurrent", "masterIOCurrent")) # , "masterSafetyState" ,"masterOnOffState", "euromap67InterfaceInstalled" )) elif ptype == 2: allData["ToolData"] = self._get_data(pdata, "iBbbddfBffB", ("size", "type", "analoginputRange2", "analoginputRange3", "analogInput2", "analogInput3", "toolVoltage48V", "toolOutputVoltage", "toolCurrent", "toolTemperature", "toolMode")) elif ptype == 9: continue # This package has a length of 53 bytes. It is used internally by Universal Robots software only and should be skipped. elif ptype == 8 and self.version >= (3, 2): allData["AdditionalInfo"] = self._get_data(pdata, "iB??", ("size", "type", "teachButtonPressed", "teachButtonEnabled")) elif ptype == 7 and self.version >= (3, 2): allData["ForceModeData"] = self._get_data(pdata, "iBddddddd", ("size", "type", "x", "y", "z", "rx", "ry", "rz", "robotDexterity")) # elif ptype == 8: # allData["varMessage"] = self._get_data(pdata, "!iBQbb iiBAcAc", ("size", "type", "timestamp", "source", "robotMessageType", "code", "argument", "titleSize", "messageTitle", "messageText")) # elif ptype == 7: # allData["keyMessage"] = self._get_data(pdata, "!iBQbb iiBAcAc", ("size", "type", "timestamp", "source", "robotMessageType", "code", "argument", "titleSize", "messageTitle", "messageText")) elif ptype == 20: tmp = self._get_data(pdata, "!iB Qbb", ("size", "type", "timestamp", "source", "robotMessageType")) if tmp["robotMessageType"] == 3: allData["VersionMessage"] = self._get_data(pdata, "!iBQbb bAbBBiAb", ("size", "type", "timestamp", "source", "robotMessageType", "projectNameSize", "projectName", "majorVersion", "minorVersion", "svnRevision", "buildDate")) elif tmp["robotMessageType"] == 6: allData["robotCommMessage"] = self._get_data(pdata, "!iBQbb iiAc", ("size", "type", "timestamp", "source", "robotMessageType", "code", "argument", "messageText")) elif tmp["robotMessageType"] == 1: allData["labelMessage"] = self._get_data(pdata, "!iBQbb iAc", ("size", "type", "timestamp", "source", "robotMessageType", "id", "messageText")) elif tmp["robotMessageType"] == 2: allData["popupMessage"] = self._get_data(pdata, "!iBQbb ??BAcAc", ("size", "type", "timestamp", "source", "robotMessageType", "warning", "error", "titleSize", "messageTitle", "messageText")) elif tmp["robotMessageType"] == 0: allData["messageText"] = self._get_data(pdata, "!iBQbb Ac", ("size", "type", "timestamp", "source", "robotMessageType", "messageText")) elif tmp["robotMessageType"] == 8: allData["varMessage"] = self._get_data(pdata, "!iBQbb iiBAcAc", ("size", "type", "timestamp", "source", "robotMessageType", "code", "argument", "titleSize", "messageTitle", "messageText")) elif tmp["robotMessageType"] == 7: allData["keyMessage"] = self._get_data(pdata, "!iBQbb iiBAcAc", ("size", "type", "timestamp", "source", "robotMessageType", "code", "argument", "titleSize", "messageTitle", "messageText")) elif tmp["robotMessageType"] == 5: allData["keyMessage"] = self._get_data(pdata, "!iBQbb iiAc", ("size", "type", "timestamp", "source", "robotMessageType", "code", "argument", "messageText")) else: self.logger.debug("Message type parser not implemented %s", tmp) else: self.logger.debug("Unknown packet type %s with size %s", ptype, psize) return allData def _get_data(self, data, fmt, names): """ fill data into a dictionary data is data from robot packet fmt is struct format, but with added A for arrays and no support for numerical in fmt names args are strings used to store values """ tmpdata = copy(data) fmt = fmt.strip() # space may confuse us d = dict() i = 0 j = 0 while j < len(fmt) and i < len(names): f = fmt[j] if f in (" ", "!", ">", "<"): j += 1 elif f == "A": # we got an array # first we need to find its size if j == len(fmt) - 2: # we are last element, size is the rest of data in packet arraysize = len(tmpdata) else: # size should be given in last element asn = names[i - 1] if not asn.endswith("Size"): raise ParsingException("Error, array without size ! %s %s" % (asn, i)) else: arraysize = d[asn] d[names[i]] = tmpdata[0:arraysize] # print "Array is ", names[i], d[names[i]] tmpdata = tmpdata[arraysize:] j += 2 i += 1 else: fmtsize = struct.calcsize(fmt[j]) # print "reading ", f , i, j, fmtsize, len(tmpdata) if len(tmpdata) < fmtsize: # seems to happen on windows raise ParsingException("Error, length of data smaller than advertized: ", len(tmpdata), fmtsize, "for names ", names, f, i, j) d[names[i]] = struct.unpack("!" + f, tmpdata[0:fmtsize])[0] # print names[i], d[names[i]] tmpdata = tmpdata[fmtsize:] j += 1 i += 1 return d def get_header(self, data): return struct.unpack("!iB", data[0:5]) def analyze_header(self, data): """ read first 5 bytes and return complete packet """ if len(data) < 5: raise ParsingException("Packet size %s smaller than header size (5 bytes)" % len(data)) else: psize, ptype = self.get_header(data) if psize < 5: raise ParsingException("Error, declared length of data smaller than its own header(5): ", psize) elif psize > len(data): raise ParsingException("Error, length of data smaller (%s) than declared (%s)" % (len(data), psize)) return psize, ptype, data[:psize], data[psize:] def find_first_packet(self, data): """ find the first complete packet in a string returns None if none found """ counter = 0 limit = 10 while True: if len(data) >= 5: psize, ptype = self.get_header(data) if psize < 5 or psize > 2000 or ptype != 16: data = data[1:] counter += 1 if counter > limit: self.logger.warning("tried %s times to find a packet in data, advertised packet size: %s, type: %s", counter, psize, ptype) self.logger.warning("Data length: %s", len(data)) limit = limit * 10 elif len(data) >= psize: self.logger.debug("Got packet with size %s and type %s", psize, ptype) if counter: self.logger.info("Remove %s bytes of garbage at begining of packet", counter) # ok we we have somehting which looks like a packet" return (data[:psize], data[psize:]) else: # packet is not complete self.logger.debug("Packet is not complete, advertised size is %s, received size is %s, type is %s", psize, len(data), ptype) return None else: # self.logger.debug("data smaller than 5 bytes") return None class SecondaryMonitor(Thread): """ Monitor data from secondary port and send programs to robot """ def __init__(self, host): Thread.__init__(self) self.logger = logging.getLogger("ursecmon") self._parser = ParserUtils() self._dict = {} self._dictLock = Lock() self.host = host secondary_port = 30002 # Secondary client interface on Universal Robots self._s_secondary = socket.create_connection((self.host, secondary_port), timeout=0.5) self._prog_queue = [] self._prog_queue_lock = Lock() self._dataqueue = bytes() self._trystop = False # to stop thread self.running = False # True when robot is on and listening self._dataEvent = Condition() self.lastpacket_timestamp = 0 self.start() try: self.wait() # make sure we got some data before someone calls us except TimeoutException as ex: self.close() raise ex def send_program(self, prog): """ send program to robot in URRobot format If another program is send while a program is running the first program is aborded. """ prog.strip() self.logger.debug("Enqueueing program: %s", prog) if not isinstance(prog, bytes): prog = prog.encode() data = Program(prog + b"\n") with data.condition: with self._prog_queue_lock: self._prog_queue.append(data) data.condition.wait() self.logger.debug("program sendt: %s", data) def run(self): """ check program execution status in the secondary client data packet we get from the robot This interface uses only data from the secondary client interface (see UR doc) Only the last connected client is the primary client, so this is not guaranted and we cannot rely on information to the primary client. """ while not self._trystop: with self._prog_queue_lock: if len(self._prog_queue) > 0: data = self._prog_queue.pop(0) self._s_secondary.send(data.program) with data.condition: data.condition.notify_all() data = self._get_data() try: tmpdict = self._parser.parse(data) with self._dictLock: self._dict = tmpdict except ParsingException as ex: self.logger.warning("Error parsing one packet from urrobot: %s", ex) continue if "RobotModeData" not in self._dict: self.logger.warning("Got a packet from robot without RobotModeData, strange ...") continue self.lastpacket_timestamp = time.time() rmode = 0 if self._parser.version >= (3, 0): rmode = 7 if self._dict["RobotModeData"]["robotMode"] == rmode \ and self._dict["RobotModeData"]["isRealRobotEnabled"] is True \ and self._dict["RobotModeData"]["isEmergencyStopped"] is False \ and self._dict["RobotModeData"]["isSecurityStopped"] is False \ and self._dict["RobotModeData"]["isRobotConnected"] is True \ and self._dict["RobotModeData"]["isPowerOnRobot"] is True: self.running = True else: if self.running: self.logger.error("Robot not running: " + str(self._dict["RobotModeData"])) self.running = False with self._dataEvent: # print("X: new data") self._dataEvent.notifyAll() def _get_data(self): """ returns something that looks like a packet, nothing is guaranted """ while True: # self.logger.debug("data queue size is: {}".format(len(self._dataqueue))) ans = self._parser.find_first_packet(self._dataqueue[:]) if ans: self._dataqueue = ans[1] # self.logger.debug("found packet of size {}".format(len(ans[0]))) return ans[0] else: # self.logger.debug("Could not find packet in received data") tmp = self._s_secondary.recv(1024) self._dataqueue += tmp def wait(self, timeout=0.5): """ wait for next data packet from robot """ tstamp = self.lastpacket_timestamp with self._dataEvent: self._dataEvent.wait(timeout) if tstamp == self.lastpacket_timestamp: raise TimeoutException("Did not receive a valid data packet from robot in {}".format(timeout)) def get_cartesian_info(self, wait=False): if wait: self.wait() with self._dictLock: if "CartesianInfo" in self._dict: return self._dict["CartesianInfo"] else: return None def get_all_data(self, wait=False): """ return last data obtained from robot in dictionnary format """ if wait: self.wait() with self._dictLock: return self._dict.copy() def get_joint_data(self, wait=False): if wait: self.wait() with self._dictLock: if "JointData" in self._dict: return self._dict["JointData"] else: return None def get_digital_out(self, nb, wait=False): if wait: self.wait() with self._dictLock: output = self._dict["MasterBoardData"]["digitalOutputBits"] mask = 1 << nb if output & mask: return 1 else: return 0 def get_digital_out_bits(self, wait=False): if wait: self.wait() with self._dictLock: return self._dict["MasterBoardData"]["digitalOutputBits"] def get_digital_in(self, nb, wait=False): if wait: self.wait() with self._dictLock: output = self._dict["MasterBoardData"]["digitalInputBits"] mask = 1 << nb if output & mask: return 1 else: return 0 def get_digital_in_bits(self, wait=False): if wait: self.wait() with self._dictLock: return self._dict["MasterBoardData"]["digitalInputBits"] def get_analog_in(self, nb, wait=False): if wait: self.wait() with self._dictLock: return self._dict["MasterBoardData"]["analogInput" + str(nb)] def get_analog_inputs(self, wait=False): if wait: self.wait() with self._dictLock: return self._dict["MasterBoardData"]["analogInput0"], self._dict["MasterBoardData"]["analogInput1"] def is_program_running(self, wait=False): """ return True if robot is executing a program Rmq: The refresh rate is only 10Hz so the information may be outdated """ if wait: self.wait() with self._dictLock: return self._dict["RobotModeData"]["isProgramRunning"] def close(self): self._trystop = True self.join() # with self._dataEvent: #wake up any thread that may be waiting for data before we close. Should we do that? # self._dataEvent.notifyAll() if self._s_secondary: with self._prog_queue_lock: self._s_secondary.close()	20,776	45.795045	443	py
CANTM	CANTM-main/updateTopics_covid.py	import sys from GateMIcateLib import ModelUltiUpdateCAtopic as ModelUlti from GateMIcateLib import BatchIterBert, DictionaryProcess from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor from GateMIcateLib import ScholarPostProcessor as ReaderPostProcessor from GateMIcateLib.readers import WVmisInfoDataIter as dataIter from configobj import ConfigObj from torch.nn import init import argparse import json if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("testReaderargs", help="args for test reader") parser.add_argument("--configFile", help="config files if needed") parser.add_argument("--cachePath", help="save models") parser.add_argument("--randomSeed", type=int, help="randomSeed for reproduction") parser.add_argument("--num_epoches", type=int, default=5, help="num epoches") parser.add_argument("--patient", type=int, default=40, help="early_stop_patient") parser.add_argument("--earlyStopping", default='cls_loss', help="early stopping") parser.add_argument("--corpusType", default='wvmisinfo', help="corpus type, for select reader") parser.add_argument("--x_fields", help="x fileds", default='Claim,Explaination') parser.add_argument("--y_field", help="y filed", default='Label') args = parser.parse_args() testReaderargs = args.testReaderargs.split(',') x_fields = args.x_fields.split(',') config = ConfigObj(args.configFile) mUlti = ModelUlti(load_path=args.cachePath, gpu=True) trainable_weights = ['xy_topics.topic.weight', 'z_y_hidden.hidden1.weight', 'z2y_classifier.layer_output.weight', ] trainable_bias = [ 'xy_topics.topic.bias', 'z_y_hidden.hidden1.bias', 'z2y_classifier.layer_output.bias' ] trainable_no_init = [ 'mu_z2.weight', 'mu_z2.bias', 'log_sigma_z2.weight', 'log_sigma_z2.bias', 'x_y_hidden.hidden1.weight', 'x_y_hidden.hidden1.bias' ] for name, param in mUlti.net.named_parameters(): print(name) if name in trainable_weights: param.requires_grad = True param.data.uniform_(-1.0, 1.0) elif name in trainable_bias: param.requires_grad = True param.data.fill_(0) elif name in trainable_no_init: param.requires_grad = True else: param.requires_grad = False postProcessor = ReaderPostProcessor(config=config, word2id=True, remove_single_list=False, add_spec_tokens=True, x_fields=x_fields, y_field=args.y_field, max_sent_len=300) postProcessor.dictProcess = mUlti.bowdict testDataIter = dataIter(*testReaderargs, label_field=args.y_field, postProcessor=postProcessor, config=config, shuffle=True) testBatchIter = BatchIterBert(testDataIter, filling_last_batch=True, postProcessor=batchPostProcessor, batch_size=32) mUlti.train(testBatchIter, num_epohs=args.num_epoches, cache_path=args.cachePath)	3,093	37.675	175	py
CANTM	CANTM-main/getPerpare.py	import os import torch from transformers import * import nltk from pathlib import Path nltk.download('stopwords') nltk.download('punkt') model = BertModel.from_pretrained('bert-base-uncased') tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') script_path = os.path.abspath(__file__) print(script_path) parent = os.path.dirname(script_path) parent = os.path.join(parent, 'bert-base-uncased') print(parent) model_save_path = os.path.join(parent,'model') path = Path(model_save_path) path.mkdir(parents=True, exist_ok=True) model.save_pretrained(model_save_path) tokenizer_save_path = os.path.join(parent,'tokenizer') path = Path(tokenizer_save_path) path.mkdir(parents=True, exist_ok=True) tokenizer.save_pretrained(tokenizer_save_path)	753	25	62	py
CANTM	CANTM-main/raw2folds.py	import json import os import sys import random import math from pathlib import Path import copy def outputChunk(output_dir, chunkIdx, chunks): output_path = os.path.join(output_dir, str(chunkIdx)) path = Path(output_path) path.mkdir(parents=True, exist_ok=True) for i, item in enumerate(chunks): if i == chunkIdx: json_path = os.path.join(output_path, 'test.jsonlist') else: json_path = os.path.join(output_path, 'train.jsonlist') with open(json_path, 'a') as jsout: for line in item: jsout.write(json.dumps(line) + '\n') input_json = sys.argv[1] num_fold = int(sys.argv[2]) output_dir = sys.argv[3] with open(input_json, 'r') as inf: data = json.load(inf) random.shuffle(data) num_data = len(data) chunkSize = math.ceil(num_data/num_fold) chunks = [] for i in range(0, len(data), chunkSize): chunks.append(copy.deepcopy(data[i:i+chunkSize])) for idx, eachchunk in enumerate(chunks): outputChunk(output_dir, idx, chunks)	1,062	22.108696	67	py
CANTM	CANTM-main/evaluation.py	import argparse import json import os from GateMIcateLib import EvaluationManager if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("trainReaderargs", help="args for train reader") parser.add_argument("--testReaderargs", help="args for test reader") parser.add_argument("--valReaderargs", help="args for val reader") parser.add_argument("--configFile", help="config files if needed") parser.add_argument("--cachePath", help="save models") parser.add_argument("--nFold", type=int, default=5, help="n fold") parser.add_argument("--randomSeed", type=int, help="randomSeed for reproduction") parser.add_argument("--preEmbd", default=False, action='store_true', help="calculate embedding before training") parser.add_argument("--dynamicSampling", default=False, action='store_true', help="sample based on class count") parser.add_argument("--model", default='clsTopic', help="model used for evaluation") parser.add_argument("--num_epoches", type=int, default=200, help="num epoches") parser.add_argument("--patient", type=int, default=4, help="early_stop_patient") parser.add_argument("--max_sent_len", type=int, default=300, help="maximum words in the sentence") parser.add_argument("--earlyStopping", default='cls_loss', help="early stopping") parser.add_argument("--corpusType", default='wvmisinfo', help="corpus type, for select reader") parser.add_argument("--splitValidation", type=float, help="split data from training for validation") parser.add_argument("--inspectTest", default=False, action='store_true', help="inspect testing data performance") parser.add_argument("--x_fields", help="x fileds", default='Claim,Explaination') parser.add_argument("--y_field", help="y filed", default='category') parser.add_argument("--trainOnly", help="only train the model, no split or test", default=False, action='store_true') parser.add_argument("--export_json", help="export json for scholar, need file path") parser.add_argument("--export_doc", help="export doc for npmi, need file path") parser.add_argument("--trainLDA", help="lda test", default=False, action='store_true') args = parser.parse_args() dictargs = vars(args) trainReaderargs = args.trainReaderargs.split(',') if args.testReaderargs: testReaderargs = args.testReaderargs.split(',') else: testReaderargs = None if args.valReaderargs: valReaderargs = args.valReaderargs.split(',') else: valReaderargs = None evaluaton = EvaluationManager(trainReaderargs, dictargs, testReaderargs=testReaderargs, valReaderargs=valReaderargs) if args.export_json: jsonData = evaluaton.get_covid_train_json_for_scholar() with open(args.export_json, 'w') as fo: json.dump(jsonData, fo) elif args.export_doc: all_doc = evaluaton.outputCorpus4NPMI() with open(args.export_doc, 'w') as fo: for item in all_doc: item = item.strip() fo.write(item+'\n') elif args.trainOnly: evaluaton.train_model_only() elif testReaderargs: evaluaton.train_test_evaluation() else: evaluaton.cross_fold_evaluation()	3,273	42.653333	121	py
CANTM	CANTM-main/updateTopics.py	import sys from GateMIcateLib import ModelUltiUpdateCAtopic as ModelUlti from GateMIcateLib import BatchIterBert, DictionaryProcess from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor from GateMIcateLib import ScholarPostProcessor as ReaderPostProcessor from GateMIcateLib.readers import ACLimdbReader as dataIter from configobj import ConfigObj from torch.nn import init import argparse import json if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("testReaderargs", help="args for test reader") parser.add_argument("--configFile", help="config files if needed") parser.add_argument("--cachePath", help="save models") parser.add_argument("--randomSeed", type=int, help="randomSeed for reproduction") parser.add_argument("--num_epoches", type=int, default=5, help="num epoches") parser.add_argument("--patient", type=int, default=40, help="early_stop_patient") parser.add_argument("--earlyStopping", default='cls_loss', help="early stopping") parser.add_argument("--corpusType", default='wvmisinfo', help="corpus type, for select reader") parser.add_argument("--x_fields", help="x fileds", default='text') parser.add_argument("--y_field", help="y filed", default='selected_label') args = parser.parse_args() testReaderargs = args.testReaderargs.split(',') x_fields = args.x_fields.split(',') config = ConfigObj(args.configFile) mUlti = ModelUlti(load_path=args.cachePath, gpu=True) trainable_weights = ['xy_topics.topic.weight', 'z_y_hidden.hidden1.weight', 'z2y_classifier.layer_output.weight', ] trainable_bias = [ 'xy_topics.topic.bias', 'z_y_hidden.hidden1.bias', 'z2y_classifier.layer_output.bias' ] for name, param in mUlti.net.named_parameters(): if name in trainable_weights: param.requires_grad = True param.data.uniform_(-1.0, 1.0) elif name in trainable_bias: param.requires_grad = True param.data.fill_(0) else: param.requires_grad = False postProcessor = ReaderPostProcessor(config=config, word2id=True, remove_single_list=False, add_spec_tokens=True, x_fields=x_fields, y_field=args.y_field, max_sent_len=510) postProcessor.dictProcess = mUlti.bowdict testDataIter = dataIter(*testReaderargs, postProcessor=postProcessor, config=config, shuffle=True) testBatchIter = BatchIterBert(testDataIter, filling_last_batch=True, postProcessor=batchPostProcessor, batch_size=32) mUlti.train(testBatchIter, num_epohs=args.num_epoches, cache_path=args.cachePath)	2,718	38.405797	175	py
CANTM	CANTM-main/wvCovidData/update_wv_unique_id.py	import json import re import nltk import argparse import copy import os import sys script_path = os.path.abspath(__file__) print(script_path) global parent parent = os.path.dirname(script_path) print(parent) gatePython = os.path.join(parent, 'GATEpythonInterface') sys.path.append(gatePython) from GateInterface import * import string from pathlib import Path parser = argparse.ArgumentParser() parser.add_argument("inputJson", help="args for test reader") parser.add_argument("outputJson", help="args for test reader") args = parser.parse_args() input_json = args.inputJson output_json = args.outputJson with open(input_json, 'r') as fj: raw_data = json.load(fj) doc_id = 0 new_data = [] for item in raw_data: doc_id += 1 item['unique_wv_id'] = str(doc_id) new_data.append(copy.deepcopy(item)) print(doc_id) with open(output_json, 'w') as fo: json.dump(new_data, fo)	902	19.066667	62	py
CANTM	CANTM-main/wvCovidData/downloadIFCNSource.py	import sys from bs4 import BeautifulSoup import json from selenium import webdriver from selenium.webdriver.firefox.options import Options def download_source(url): options = Options() options.headless = True driver = webdriver.Firefox(options=options) driver.get(url) html = driver.page_source driver.close() soup = BeautifulSoup(html, 'html.parser') factcheck_Org = soup.find('p', attrs={'class':'entry-content__text entry-content__text--org'}).get_text()[17:] date = soup.find('p', attrs={'class':'entry-content__text entry-content__text--topinfo'}).get_text()[0:10] country = soup.find('p', attrs={'class':'entry-content__text entry-content__text--topinfo'}).get_text()[13:] ct = soup.find('h1', attrs={'class':'entry-title'}) for tag in ct.find_all('span'): tag.replaceWith('') claim = ct.get_text() explain = soup.find('p', attrs={'class':'entry-content__text entry-content__text--explanation'}).get_text()[13:] print(claim) print(explain) print(country) print(date) print(factcheck_Org) return claim,explain,country,date,factcheck_Org input_json = sys.argv[1] output_json = sys.argv[2] with open(input_json, 'r') as fin: data = json.load(fin) for i, each_data in enumerate(data): claim,explain,country,date,factcheck_Org = download_source(each_data['Link']) each_data['Claim'] = claim each_data['Explaination'] = explain each_data['Country'] = country each_data['Date'] = date each_data['Factcheck_Org'] = factcheck_Org print(i, len(data)) #break with open(output_json, 'w') as fo: json.dump(data, fo)	1,648	29.537037	116	py
CANTM	CANTM-main/wvCovidData/update_label.py	import json import re import nltk import argparse import copy import os import sys script_path = os.path.abspath(__file__) print(script_path) global parent parent = os.path.dirname(script_path) print(parent) gatePython = os.path.join(parent, 'GATEpythonInterface') sys.path.append(gatePython) from GateInterface import * import string from pathlib import Path parser = argparse.ArgumentParser() parser.add_argument("inputJson", help="args for test reader") parser.add_argument("outputJson", help="args for test reader") args = parser.parse_args() input_json = args.inputJson output_json = args.outputJson label_dict = { 'no_evidence':['NO EVIDENCE', 'No evidence', 'No Evidence', 'no evidence', 'Unproven', 'Unverified'], 'misleading': ['misleading', 'Misleading', 'MISLEADING', 'mislEADING', 'MiSLEADING'], 'false': ['Pants on Fire!', 'False', 'FALSE', 'Not true', 'false and misleading', 'false', 'PANTS ON FIRE', 'Fake news', 'Misleading/False', 'Fake', 'Incorrect'], 'partial_false': ['Partially correct', 'mostly false', 'HALF TRUTH', 'HALF TRUE', 'partly false', 'Mostly true', 'Partly true', 'Mixed', 'half true', 'True but', 'MOSTLY FALSE', 'Partially false', 'PARTLY FALSE', 'Partially true', 'MOSTLY TRUE', 'Partly false', 'Mostly False', 'Partly False', 'PARTLY TRUE', 'Mostly True', 'Half True', 'Mostly false'], } with open(input_json, 'r') as fj: raw_data = json.load(fj) doc_id = 0 new_data = [] for item in raw_data: item_keys = item.keys() print(item_keys) label = item['Label'] label_group = 'other' for each_label_group in label_dict: if label in label_dict[each_label_group]: label_group = each_label_group break doc_id += 1 item['ori_label'] = label item['Label'] = label_group new_data.append(copy.deepcopy(item)) print(doc_id) with open(output_json, 'w') as fo: json.dump(new_data, fo)	1,941	30.322581	361	py
CANTM	CANTM-main/wvCovidData/update_type_media.py	import json import re import nltk import argparse import copy import os import sys script_path = os.path.abspath(__file__) print(script_path) global parent parent = os.path.dirname(script_path) print(parent) gatePython = os.path.join(parent, 'GATEpythonInterface') sys.path.append(gatePython) from GateInterface import * import string from pathlib import Path source_out_folder = os.path.join(parent, 'test_text') Path(source_out_folder).mkdir(parents=True, exist_ok=True) printable = set(string.printable) ignore_explist=[ ['please', 'click', 'the', 'link', 'to', 'read', 'the', 'full', 'article'], ['please', 'read', 'the', 'full', 'article'], ] def check_ori_claim_type(claim_web_ori): media_type = set() image = ['image', 'photo', 'picture', 'images', 'pictures', 'photos', 'infograph'] video = ['video', 'videos', 'television'] audio = ['audio', 'radio'] text = ['text', 'articles', 'article','message', 'messages'] #print(claim_web_ori) for each_tok in claim_web_ori: if each_tok in image: media_type.add('image') if each_tok in video: media_type.add('video') if each_tok in audio: media_type.add('audio') if each_tok in text: media_type.add('text') return list(media_type) class JapeCheck: def __init__(self): self.gate = GateInterFace() self.gate.init() self.checkPipeline = GatePipeline('checkPipeline') self.checkPipeline.loadPipelineFromFile(os.path.join(parent, 'japeCheck.xgapp')) def check_jape(self, doc_name, claim_website): print(doc_name) document = GateDocument() document.loadDocumentFromFile(doc_name) #print(document) testCorpus = GateCorpus('testCorpus') testCorpus.addDocument(document) self.checkPipeline.setCorpus(testCorpus) self.checkPipeline.runPipeline() ats = document.getAnnotations('') media_type = ats.getType('mediaType') #print(media_type.annotationSet) media_type_dict = [] for item in media_type: #print(item.features) media_type_dict.append(item.features) mediaTypeMatchedList, mediaTypeUnMatchedList = self.match_type(media_type_dict, claim_website) loose_media_dict = [] loose_media = ats.getType('looseMedia') for item in loose_media: #print(item.features) loose_media_dict.append(item.features) looseMatchedList, looseUnMatchedList = self.match_type(loose_media_dict, claim_website) document.clearDocument() testCorpus.clearCorpus() return self.select_type(mediaTypeMatchedList, mediaTypeUnMatchedList, looseMatchedList, looseUnMatchedList) def count_match(self, matchedList): current_max_type = None current_max_count = 0 all_possible_types = set(matchedList) for possible_type in all_possible_types: current_possible_count = matchedList.count(possible_type) if current_possible_count > current_max_count: current_max_type = possible_type current_max_count = current_possible_count elif current_possible_count == current_max_count: if possible_type == 'video': current_max_type == 'video' source_media = [current_max_type] return source_media def select_type(self, mediaTypeMatchedList, mediaTypeUnMatchedList, looseMatchedList, looseUnMatchedList): source_media = [] if len(mediaTypeMatchedList) > 0: source_media = mediaTypeMatchedList elif len(looseMatchedList) > 0: source_media = self.count_match(looseMatchedList) #source_media = [looseMatchedList[0]] elif len(mediaTypeUnMatchedList) > 0: source_media = mediaTypeUnMatchedList elif len(looseUnMatchedList) > 0: source_media = self.count_match(looseUnMatchedList) #current_max_type = None #current_max_count = 0 #all_possible_types = set(looseUnMatchedList) #for possible_type in all_possible_types: # current_possible_count = looseUnMatchedList.count(possible_type) # if current_possible_count > current_max_count: # current_max_type = possible_type # current_max_count = current_possible_count #source_media = [current_max_type] return source_media def match_type(self,media_type_dict, webSite_list): matched_list = [] unmatched_list = [] for item in media_type_dict: try: current_web = item['oriWeb'] except: current_web = 'unknown' try: current_media = item['mediaType'] except: current_media = 'unknown_web' #print(current_web, current_media) current_web_list = current_web.split(',') #print(current_web_list) current_media_list_raw = current_media.split(',') current_media_list = [] for media_item in current_media_list_raw: if len(media_item) > 0: current_media_list.append(media_item) web_match = False for current_web_item in current_web_list: if current_web_item in webSite_list: #matched_list.append(current_media) web_match = True break #unmatched_list.append(current_media) if web_match: matched_list += current_media_list else: unmatched_list += current_media_list return matched_list, unmatched_list parser = argparse.ArgumentParser() parser.add_argument("inputJson", help="args for test reader") parser.add_argument("outputJson", help="args for test reader") parser.add_argument("--updateOnly", help="reverse selection criteria", default=False, action='store_true') args = parser.parse_args() input_json = args.inputJson output_json = args.outputJson with open(input_json, 'r') as fj: raw_data = json.load(fj) japeCheck = JapeCheck() num_video_source = 0 doc_id = 0 new_data = [] punct_chars = list(set(string.punctuation)-set("_")) punctuation = ''.join(punct_chars) pun_replace = re.compile('[%s]' % re.escape(punctuation)) for item in raw_data: update = True type_of_media = item['Type_of_media'] claim = item['Claim'] explanation = item['Explaination'] claim = claim.lower() claim = pun_replace.sub(' ', claim) claim_tok = nltk.word_tokenize(claim) explanation = explanation.lower() explanation = pun_replace.sub(' ', explanation) explanation_tok = nltk.word_tokenize(explanation) #print(type_of_media) if args.updateOnly: if len(type_of_media) > 0: update = False else: print(doc_id) update = True #print(update) if update: item_keys = item.keys() #print(item_keys) claim_website = item['Claim_Website'] #print(claim_website) claim_website_ori = item['Claim_web_ori'] if 'p_tag' in item: text_source = item['p_tag'] elif 'Source_PageTextEnglish' in item: text_source = item['Source_PageTextEnglish'] else: #print(doc_id) text_source = item['Source_PageTextOriginal'] source_media = [] if ('youtube' in claim_website) or ('tv' in claim_website) or ('tiktok' in claim_website): num_video_source += 1 source_media = ['video'] else: media_type = check_ori_claim_type(claim_website_ori) claim_media_type = check_ori_claim_type(claim_tok) if explanation_tok not in ignore_explist: exp_media_type = check_ori_claim_type(explanation_tok) else: print('!!!!!!!!', explanation_tok) exp_media_type = [] if len(media_type) > 0: num_video_source += 1 source_media = media_type elif len(claim_media_type) > 0: num_video_source += 1 source_media = claim_media_type #print(source_media, claim_tok) elif len(exp_media_type) > 0: num_video_source += 1 source_media = exp_media_type print(source_media, explanation_tok) else: print(claim_website_ori) text_out = os.path.join(parent, 'test_text') text_out = os.path.join(text_out, str(doc_id)+'.txt') filterd_text_source = ''.join(filter(lambda x: x in printable, text_source)) #print(filterd_text_source) if len(filterd_text_source) > 10: with open(text_out, 'w') as fo: fo.write(filterd_text_source) source_media = japeCheck.check_jape(text_out, claim_website) num_video_source += 1 doc_id += 1 print(source_media) item['Type_of_media'] = list(set(source_media)) new_data.append(copy.deepcopy(item)) print(num_video_source) with open(output_json, 'w') as fo: json.dump(new_data, fo)	9,461	33.786765	115	py
CANTM	CANTM-main/wvCovidData/mergeAnnos.py	import sys from WvLibs import WVdataIter import re from itertools import combinations import argparse import json import logging global logger class Wv_loger: def __init__(self, logging_level): self.logger = logging.getLogger('root') if logging_level == 'info': self.logger.setLevel(logging.INFO) elif logging_level == 'debug': self.logger.setLevel(logging.DEBUG) elif logging_level == 'warning': self.logger.setLevel(logging.WARNING) #self.logger.setLevel(logging.INFO) handler = logging.StreamHandler(sys.stdout) formatter = logging.Formatter('ROOT: %(message)s') handler.setFormatter(formatter) self.logger.addHandler(handler) #self.logger.info('info message') def logging(self, args, logtype='debug', sep=' '): getattr(self.logger, logtype)(sep.join(str(a) for a in args)) def info(self, args, sep=' '): getattr(self.logger, 'info')(sep.join(str(a) for a in args)) def debug(self, args, sep=' '): getattr(self.logger, 'debug')(sep.join(str(a) for a in args)) def warning(self, args, sep=' '): getattr(self.logger, 'warning')(sep.join(str(a) for a in args)) def clean_string(text): text = text.strip() text = text.replace('\t','') text = text.replace('\n','') return text def getList(dict): list = [] for key in dict.keys(): list.append(key) return list def check_pare(ann1, ann2, agreeDict): combine1 = ann1+'\|'+ann2 combine2 = ann2+'\|'+ann1 combines = [combine1, combine2] combineInDict = False for combine in combines: if combine in agreeDict: combineInDict = True break return combine, combineInDict def update_disagreement_dict(disagreeLabel1, disagreeLabel2, class_disagree_check_dict): if disagreeLabel1 not in class_disagree_check_dict: class_disagree_check_dict[disagreeLabel1] = {} if disagreeLabel2 not in class_disagree_check_dict[disagreeLabel1]: class_disagree_check_dict[disagreeLabel1][disagreeLabel2] = 0 class_disagree_check_dict[disagreeLabel1][disagreeLabel2] += 1 return class_disagree_check_dict def calAgreement(dataIter, numlabels=11): compare_list = [] pe = 1/numlabels ## get all paires for item in dataIter: tmp_list = [] anno_list = [] for annotation in item['annotations']: label = annotation['label'] confident = annotation['confident'] annotator = annotation['annotator'] if annotator not in anno_list: anno_list.append(annotator) tmp_list.append([annotator, label]) combine_list = list(combinations(tmp_list, 2)) compare_list += combine_list t=0 a=0 agreeDict = {} for compare_pair in compare_list: ann1 = compare_pair[0][0] label1 = compare_pair[0][1] ann2 = compare_pair[1][0] label2 = compare_pair[1][1] t+=1 combine, combineInDict = check_pare(ann1, ann2, agreeDict) if combineInDict: agreeDict[combine]['t'] += 1 else: agreeDict[combine] = {} agreeDict[combine]['t'] = 1 agreeDict[combine]['a'] = 0 agreeDict[combine]['disagree'] = {} agreeDict[combine]['disagree'][ann1] = [] agreeDict[combine]['disagree'][ann2] = [] if label1 == label2: a+=1 agreeDict[combine]['a'] += 1 else: agreeDict[combine]['disagree'][ann1].append(label1) agreeDict[combine]['disagree'][ann2].append(label2) pa = a/t #pe = 1/numlabels #print(pe) overall_kappa = (pa-pe)/(1-pe) logger.warning('overall agreement: ', pa) logger.warning('overall kappa: ', overall_kappa) logger.warning('total pair compareed: ', t) logger.warning('annotator pair agreement kappa num_compared') class_disagree_check_dict = {} for annPair in agreeDict: logger.info('\n') logger.info('============') num_compared = agreeDict[annPair]['t'] cpa = agreeDict[annPair]['a']/agreeDict[annPair]['t'] kappa = (cpa-pe)/(1-pe) logger.info(annPair, cpa, kappa, num_compared) keys = getList(agreeDict[annPair]['disagree']) logger.info(keys) for i in range(len(agreeDict[annPair]['disagree'][keys[0]])): disagreeLabel1 = agreeDict[annPair]['disagree'][keys[0]][i] disagreeLabel2 = agreeDict[annPair]['disagree'][keys[1]][i] logger.info(disagreeLabel1, disagreeLabel2) class_disagree_check_dict = update_disagreement_dict(disagreeLabel1, disagreeLabel2, class_disagree_check_dict) class_disagree_check_dict = update_disagreement_dict(disagreeLabel2, disagreeLabel1, class_disagree_check_dict) logger.info('\n') logger.info('=========================') logger.info('class level disagreement') logger.info('=========================') for item_label in class_disagree_check_dict: logging.info(item_label) logging.info(class_disagree_check_dict[item_label]) logging.info('===================') logging.info('\n') return pa, overall_kappa if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("raw_json_dir", help="Unannotated Json file dir") parser.add_argument("annoed_json_dir", help="Annotated Json file dir") parser.add_argument("merged_json", help="merged json") parser.add_argument("--ignoreLabel", help="ignore label, splited by using ,") parser.add_argument("--ignoreUser", help="ignore user, splited by using ,") parser.add_argument("--min_anno_filter", type=int, default=1, help="min annotation frequence") parser.add_argument("--min_conf_filter", type=int, default=-1, help="min confident") parser.add_argument("--output2csv", help="output to csv") parser.add_argument("--transfer_label", default=None, help="trasfer label to another category, in format: orilabel1:tranlabel1,orilabel2:tranlabel2") parser.add_argument("--cal_agreement", action='store_true',help="calculate annotation agreement") parser.add_argument("--logging_level", default='warning', help="logging level, default warning, other option inlcude info and debug") parser.add_argument("--user_conf", default=None, help="User level confident cutoff threshold, in format: user1:thres1,user2:thres2") parser.add_argument("--set_reverse", default=False, action='store_true', help="reverse the selection condition, to check what discared") #parser.add_argument("--logging_file", default='default.log',help="logging file") args = parser.parse_args() #logger = logging.getLogger() output2csv = None trans_dict = {} raw_json_dir = args.raw_json_dir annoed_json_dir = args.annoed_json_dir merged_json = args.merged_json min_frequence = args.min_anno_filter min_conf = args.min_conf_filter list2ignor = [] user2ignor = [] if args.ignoreLabel: list2ignor = args.ignoreLabel.split(',') if args.ignoreUser: user2ignor = args.ignoreUser.split(',') logging_level = args.logging_level logger = Wv_loger(logging_level) if args.output2csv: output2csv = args.output2csv if args.transfer_label: all_trans_labels = args.transfer_label.split(',') for label_pair in all_trans_labels: tokened_pair = label_pair.split(':') trans_dict[tokened_pair[0]] = tokened_pair[1] user_conf_dict = {} if args.user_conf: all_user_conf = args.user_conf.split(',') for conf_pair in all_user_conf: tokened_pair = conf_pair.split(':') user_conf_dict[tokened_pair[0]] = int(tokened_pair[1]) logger.warning(trans_dict) dataIter = WVdataIter(annoed_json_dir, raw_json_dir, min_anno_filter=min_frequence, ignoreLabelList=list2ignor, ignoreUserList=user2ignor, label_trans_dict=trans_dict, check_validation=False, confThres=min_conf, reverse_selection_condition=args.set_reverse, logging_level=logging_level, annotator_level_confident=user_conf_dict) data2merge = dataIter.getMergedData() print('num selected data:', len(data2merge)) with open(merged_json, 'w') as fj: json.dump(data2merge, fj) num_labels = 11 if output2csv: t=0 num_anno_dict = {} num_label_dict = {} with open(output2csv, 'w') as fo: outline = 'id\tclaim\texplaination\tselected_label\tlables_from_annotator\n' fo.write(outline) for item in data2merge: t += 1 num_annotation = len(item['annotations']) if num_annotation not in num_anno_dict: num_anno_dict[num_annotation] = 0 num_anno_dict[num_annotation] += 1 claim = clean_string(item['Claim']) explaination = clean_string(item['Explaination']) unique_id = item['unique_wv_id'].strip() if 'selected_label' in item: selected_label = item['selected_label'] else: selected_label = '' outline = unique_id+'\t'+claim+'\t'+explaination+'\t'+selected_label for annotation in item['annotations']: label = annotation['label'] confident = annotation['confident'] annotator = annotation['annotator'] if label not in num_label_dict: num_label_dict[label] = 0 num_label_dict[label] += 1 outline += '\t'+label+'\t'+confident+'\t'+annotator outline += '\n' fo.write(outline) print(num_anno_dict) print(num_label_dict) num_labels = len(num_label_dict) if args.cal_agreement: pa, kappa = calAgreement(dataIter, num_labels) print('agreement: ', pa) print('kappa: ', kappa)	10,180	33.986254	332	py
CANTM	CANTM-main/wvCovidData/update_website.py	import json import re import nltk import sys import string import copy class MediaTypeOrg: def __init__(self): punct_chars = list(set(string.punctuation)-set("_")) print(punct_chars) punctuation = ''.join(punct_chars) self.replace = re.compile('[%s]' % re.escape(punctuation)) self.multi_word_dict = {'social media':'social_media'} self.facebook = ['facebook', 'fb', 'faceboos', 'facebok', 'faceboook', 'facebooks', 'facebbok','faacebook','facebookk'] self.twitter = ['twitter', 'tweets','tweet'] self.news = ['media', 'news', 'newspaper', 'newspapers', 'times', 'abcnews','cgtn'] self.whatsApp = ['whatsapp', 'wa'] self.email = ['email'] self.social_media = ['social_media', 'weibo', 'wechat'] ####### self.youtube = ['youtube', 'youtuber'] self.blog = ['blog', 'bloggers', 'blogs', 'blogger'] self.instagram = ['instagram', 'ig'] self.tv = ['tv', 'television'] self.line = ['line'] self.tiktok = ['tiktok'] self.chainMessage = ['chain', 'telegram'] self.type_dict={ 'facebook': self.facebook, 'twitter': self.twitter, 'news': self.news, 'whatsapp': self.whatsApp, 'email': self.email, 'youtube': self.youtube, 'blog': self.blog, 'instagram': self.instagram, 'tv': self.tv, 'line': self.line, 'chain_message': self.chainMessage, 'other_social_media': self.social_media, 'social_media': self.social_media+self.facebook+self.twitter+self.instagram, 'tiktok': self.tiktok } def type_org(self, data): addi_type_dict = {} ori_web_claim_dict = {} num_other = 0 new_data = [] for each_data in data: website_ori = each_data['Claim_web_ori'] included_type_list, tokened = self.get_type(website_ori) if len(included_type_list) == 0: num_other += 1 print(website_ori) included_type_list.append('other') each_data['Claim_Website'] = included_type_list new_data.append(copy.deepcopy(each_data)) return new_data def _check_item_in_list(self, tokened, check_list): for item in tokened: if item in check_list: return True return False def _get_multiword(self, lower_case_string): for item in self.multi_word_dict: lower_case_string = re.sub(item, self.multi_word_dict[item], lower_case_string) return lower_case_string def get_type(self, tokened): #print(tokened) #print(raw_media_type) #lc_raw_mt = raw_media_type.lower() #lc_raw_mt = self._get_multiword(lc_raw_mt) #lc_raw_mt_re = self.replace.sub(' ', lc_raw_mt) #tokened = nltk.word_tokenize(lc_raw_mt_re) included_type_list = [] for media_type in self.type_dict: check_list = self.type_dict[media_type] type_included = self._check_item_in_list(tokened, check_list) if type_included: included_type_list.append(media_type) ###check media #for search_string in self.social_media: # m = re.search(search_string, lc_raw_mt) # if m: # #print(raw_media_type) # included_type_list.append('social_media') #print(included_type_list) return included_type_list, tokened json_file = sys.argv[1] output_file = sys.argv[2] with open(json_file, 'r') as fj: data = json.load(fj) typeCheck = MediaTypeOrg() new_data = typeCheck.type_org(data) with open(output_file, 'w') as fo: json.dump(new_data, fo)	3,918	30.352	127	py
CANTM	CANTM-main/wvCovidData/getData.py	import wget import os import zipfile script_path = os.path.abspath(__file__) print(script_path) parent = os.path.dirname(script_path) print(parent) data_url = 'https://gate.ac.uk/g8/page/show/2/gatewiki/cow/covid19catedata/covidCateData.zip' wget.download(data_url, parent) zip_path = os.path.join(parent, 'covidCateData.zip') with zipfile.ZipFile(zip_path, 'r') as zip_ref: zip_ref.extractall(parent)	411	21.888889	93	py
CANTM	CANTM-main/wvCovidData/WvLibs/WVdataIter.py	from .DataReader import DataReader import random def disagreementSolver(annotations, local_logger=None): mergeMethodList = ['single_annotation', 'all_agreement', 'majority_agreement', 'majority_confidence', 'highest_confidence'] mergeMethod = None annotation_list = [] label_list = [] annotator_list = [] all_labels_include_duplicats = [] for annotation in annotations: label = annotation['label'] confident = annotation['confident'] annotator = annotation['annotator'] all_labels_include_duplicats.append([label, confident, annotator]) # for each annotator, we only consider once if annotator not in annotator_list: annotator_list.append(annotator) annotation_list.append([label, confident, annotator]) label_list.append(label) label_set = list(set(label_list)) set_count = [0]len(label_set) conf_sum = [0]len(label_set) for idx, current_label in enumerate(label_set): set_count[idx] = label_list.count(current_label) sorted_count = sorted(set_count, reverse=True) for each_annotation in annotation_list: label_set_idx = label_set.index(each_annotation[0]) try: conf_sum[label_set_idx] += int(each_annotation[1]) except: pass sorted_conf_sum = sorted(conf_sum, reverse=True) ## if only 1 label, or all annotator agree eachother if len(label_set) == 1: selected_label = label_set[0] if len(annotation_list) == 1: mergeMethod = 'single_annotation' else: mergeMethod = 'all_agreement' ## if have majority agreement elif sorted_count[0] > sorted_count[1]: selected_label_idx = set_count.index(sorted_count[0]) selected_label = label_set[selected_label_idx] mergeMethod = 'majority_agreement' ## if have higer summed confidence elif sorted_conf_sum[0] > sorted_conf_sum[1]: selected_label_idx = conf_sum.index(sorted_conf_sum[0]) selected_label = label_set[selected_label_idx] mergeMethod = 'majority_confidence' ## else pick the lable have highest confidence else: sorted_annotation_list = sorted(annotation_list, key=lambda s:s[1], reverse=True) selected_label = sorted_annotation_list[0][0] mergeMethod = 'highest_confidence' if local_logger: local_logger(selected_label, annotation_list, logtype='debug') return selected_label, mergeMethod class WVdataIter(DataReader): def __init__(self, annoed_json_dir, raw_json, min_anno_filter=-1, postProcessor=None, shuffle=False, check_validation=False, **kwargs): super().__init__(annoed_json_dir, raw_json, kwargs)#ignoreLabelList=ignoreLabelList, ignoreUserList=ignoreUserList, confThres=confThres, ignore_empty=ignore_empty) self.shuffle = shuffle self.check_validation = check_validation self.filterByMinAnno(min_anno_filter) self._reset_iter() self.postProcessor = postProcessor def filterByMinAnno(self, min_anno_filter): self.min_anno_filter = min_anno_filter all_links = [] for link in self.data_dict: num_annotations = len(self.data_dict[link]['annotations']) if num_annotations >= self.min_anno_filter: if self.check_validation: if self._check_annotation_valid(self.data_dict[link]['annotations']): all_links.append(link) else: all_links.append(link) self.all_links = all_links def _check_annotation_valid(self, annotation): at_least_one_ture = False for item in annotation: current_label = item['label'] current_confident = item['confident'] if len(current_label) > 0 and len(current_confident)>0: at_least_one_ture = True return at_least_one_ture def __iter__(self): if self.shuffle: random.shuffle(self.all_links) self._reset_iter() return self def __next__(self): if self.current_sample_idx < len(self.all_links): current_sample = self._readNextSample() self.current_sample_idx += 1 if self.postProcessor: return self.postProcessor(current_sample) else: return current_sample else: self._reset_iter() raise StopIteration def _readNextSample(self): current_link = self.all_links[self.current_sample_idx] current_sample = self.data_dict[current_link] return current_sample def __len__(self): return len(self.all_links) def _reset_iter(self): self.current_sample_idx = 0 def getMergedData(self, disagreementSolver=disagreementSolver): merge_method_dict = {} merged_data_list = [] merged_label_count = {} for item in self: if (len(item['annotations']) > 0) and disagreementSolver: annotations = item['annotations'] selected_label,merge_method = disagreementSolver(annotations, self.logging) if merge_method not in merge_method_dict: merge_method_dict[merge_method] = 0 if selected_label not in merged_label_count: merged_label_count[selected_label] = 0 item['selected_label'] = selected_label merge_method_dict[merge_method] += 1 merged_label_count[selected_label] += 1 merged_data_list.append(item) self.logging(merge_method_dict, logtype='info') self.logging(merged_label_count, logtype='info') return merged_data_list	5,834	36.403846	171	py
CANTM	CANTM-main/wvCovidData/WvLibs/__init__.py	from .DataReader import DataReader from .WVdataIter import WVdataIter	70	22.666667	34	py
CANTM	CANTM-main/wvCovidData/WvLibs/DataReader.py	import json import glob import os import re import hashlib import logging import sys class DataReader: def __init__(self, annoed_json_dir, raw_json_dir, kwargs): self._read_input_options(kwargs) print('label ignored: ', self.ignoreLabelList) print('user ignored: ', self.ignoreUserList) self._setReaderLogger() self._anno_user_regex() self._read_raw_json(raw_json_dir) self._read_annoed_data(annoed_json_dir) def _setReaderLogger(self): self.readerLogger = logging.getLogger('readerLogger') if self.logging_level == 'info': self.readerLogger.setLevel(logging.INFO) elif self.logging_level == 'debug': self.readerLogger.setLevel(logging.DEBUG) elif self.logging_level == 'warning': self.readerLogger.setLevel(logging.WARNING) handler = logging.StreamHandler(sys.stdout) formatter = logging.Formatter('DataReader: %(message)s') handler.setFormatter(formatter) self.readerLogger.addHandler(handler) def logging(self, args, logtype='info', sep=' '): getattr(self.readerLogger, logtype)(sep.join(str(a) for a in args)) def _read_input_options(self, kwargs): self.ignoreLabelList = kwargs.get('ignoreLabelList', []) self.ignoreUserList = kwargs.get('ignoreUserList', []) self.confThres = kwargs.get('confThres', -1) self.filter_no_conf = kwargs.get('filter_no_conf', False) self.ignore_empty = kwargs.get('ignore_empty', False) self.label_trans_dict = kwargs.get('label_trans_dict', None) self.logging_level = kwargs.get('logging_level', 'warning') self.reverse_selection_condition = kwargs.get('reverse_selection_condition', False) self.annotator_level_confident = kwargs.get('annotator_level_confident', {}) def _anno_user_regex(self): self.label_field_regex = re.compile('ann\d\_label') self.annotator_id_regex = re.compile('(?<=ann)\d(?=\_label)') self.confident_field_regex = re.compile('ann\d\_conf') self.remark_field_regex = re.compile('ann\d\_remarks') def _read_annoed_data(self, annoed_json_dir): all_jsons = glob.glob(annoed_json_dir+'/.json') for each_annoed_json_file in all_jsons: self._read_annoed_json(each_annoed_json_file) #print(all_jsons) def _read_annoed_json(self, annoed_json): with open(annoed_json, 'r') as f_json: all_json_data = json.load(f_json) for each_annoed_data in all_json_data: uniqueIdentifier = self._get_unique_identifier(each_annoed_data) annotation_info, include = self._get_annotation_info(each_annoed_data) if len(annotation_info['label']) < 0 and ignore_empty: include = False if self.reverse_selection_condition: if include == False: include = True else: include = False if include: self.data_dict[uniqueIdentifier]['annotations'].append(annotation_info) def _translabel(self, label): if label in self.label_trans_dict: transferd_label = self.label_trans_dict[label] else: transferd_label = label return transferd_label def _get_annotation_info(self, dict_data): annotation_info_dict = {} dict_keys = dict_data.keys() include = True annotator_id = None for current_key in dict_keys: m_label = self.label_field_regex.match(current_key) if m_label: raw_label_field = m_label.group() #print(raw_label_field) annotator_id = self.annotator_id_regex.search(raw_label_field).group() #print(annotator_id) annotation_info_dict['annotator'] = annotator_id label = dict_data[raw_label_field] if self.label_trans_dict: label = self._translabel(label) annotation_info_dict['label'] = label if label in self.ignoreLabelList: include = False if annotator_id in self.ignoreUserList: include = False m_conf = self.confident_field_regex.match(current_key) if m_conf: raw_conf_field = m_conf.group() confident = dict_data[raw_conf_field] annotation_info_dict['confident'] = confident if len(confident) < 1: if self.filter_no_conf: include = False elif annotator_id in self.annotator_level_confident: if int(confident) <= self.annotator_level_confident[annotator_id]: include = False elif (int(confident) <= self.confThres): include = False m_remark = self.remark_field_regex.match(current_key) if m_remark: raw_remark_field = m_remark.group() remark = dict_data[raw_remark_field] #print(remark) annotation_info_dict['remark'] = remark return annotation_info_dict, include def _get_unique_identifier(self, each_data): source_link = each_data['Source'].strip() claim = each_data['Claim'].strip() explaination = each_data['Explaination'].strip() sourceToken = source_link.split('/') top3Source = ' '.join(sourceToken[:3]) top200claim = claim[:200] top200expl = explaination[:200] uniqueString = top200claim+top200expl+top3Source #sourceQuesionToken = source_link.split('?') #uniqueString = sourceQuesionToken[0] #print(uniqueString) uniqueIdentifier = hashlib.sha224(uniqueString.encode('utf-8')).hexdigest() return uniqueIdentifier def _read_raw_json(self, raw_json_dir): self.data_dict = {} all_raw_jsons = glob.glob(raw_json_dir+'/*.json') duplicated = 0 total_data = 0 for each_raw_json in all_raw_jsons: ct=0 cd=0 with open(each_raw_json, 'r') as f_json: raw_data = json.load(f_json) for each_data in raw_data: #data_link = each_data['Link'] uniqueIdentifier = self._get_unique_identifier(each_data) if uniqueIdentifier not in self.data_dict: each_data['unique_wv_id'] = uniqueIdentifier each_data['annotations'] = [] self.data_dict[uniqueIdentifier] = each_data else: duplicated += 1 cd+=1 self.logging(uniqueIdentifier, logtype='debug') self.logging('id: ', self.data_dict[uniqueIdentifier]['unique_wv_id'], logtype='debug') self.logging(self.data_dict[uniqueIdentifier], logtype='debug') self.logging(each_data, logtype='debug') self.logging('\n', logtype='debug') total_data += 1 ct+=1 #print(ct, cd) #self.logging('Num selected data: ', len(self.data_dict), logtype='warning') self.logging('num duplicated: ', duplicated, logtype='info') self.logging('total num data: ', total_data, logtype='info')	7,530	39.058511	107	py
CANTM	CANTM-main/wvCovidData/GATEpythonInterface/example.py	from GateInterface import * #initialise gate gate= GateInterFace() # path to gate interface, for my case is /Users/xingyi/Gate/gateCodes/pythonInferface # this will open java tcp server (on port 7899) to execute GATE operation #gate.init('/Users/xingyi/Gate/gateCodes/pythonInferface') gate.init('./') # load document from url document = GateDocument() document.loadDocumentFromURL("https://gate.ac.uk") # load document from local file e.g. #document.loadDocumentFromFile("/Users/xingyi/Gate/gateCodes/pythonInferface/ft-airlines-27-jul-2001.xml") # get content from file content = document.getDocumentContent() print(content) # get annotation set name atsName = document.getAnnotationSetNames() print(atsName) #get Original markups annotation set ats = document.getAnnotations('Original markups') # load maven plugins gate.loadMvnPlugins("uk.ac.gate.plugins", "annie", "8.5") # load PRs gate.loadPRs('gate.creole.annotdelete.AnnotationDeletePR', 'anndeletePR') gate.loadPRs('gate.creole.tokeniser.DefaultTokeniser', 'defToken') # set initialise parameter for PR #prparameter['grammarURL'] = 'file:////Users/xingyi//Gate/ifpri/JAPE/main.jape' #gate.loadPRs('gate.creole.Transducer', prparameter) # create a pipeline testPipeLine = GatePipeline('testpipeline') testPipeLine.createPipeline() # add PRs to the pipeline testPipeLine.addPR('anndeletePR') testPipeLine.addPR('defToken') # get params print(testPipeLine.checkRunTimeParams('anndeletePR', 'setsToKeep')) # set run time params testPipeLine.setRunTimeParams('anndeletePR', 'keepOriginalMarkupsAS', 'false', 'Boolean') testPipeLine.setRunTimeParams('anndeletePR', 'setsToKeep', 'Key,Target', 'List') print(testPipeLine.checkRunTimeParams('anndeletePR', 'setsToKeep')) # create gate corpus testCorpus = GateCorpus('testCorpus') # add document to the corpus testCorpus.addDocument(document) # set corpus for pipeline testPipeLine.setCorpus(testCorpus) # run pipeline testPipeLine.runPipeline() # get default annotation set after run the pipeline, the annotation is stored in local python format defaultats = document.getAnnotations('') # load appilication from file #testPipeline2 = GatePipeline('testpipeline2') #testPipeLine.loadPipelineFromFile('/Users/xingyi/Gate/ifpri/ifpri.xgapp') # close the port gate.close()	2,298	28.101266	106	py
CANTM	CANTM-main/wvCovidData/GATEpythonInterface/GateInterface/GateInterface.py	import socket import json import re import os import subprocess import time import signal import pathlib class MyEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, np.integer): return int(obj) elif isinstance(obj, np.floating): return float(obj) elif isinstance(obj, np.ndarray): return obj.tolist() else: return super(MyEncoder, self).default(obj) class GateInterFace: def __init__(self): self.PORT = 7899 self.HOST = "localhost" self.maxSendChar = 512 self.maxRecvChar = 512 self.loadedPlugins = [] self.loadedPrs = [] self.pro = None self.logFile = "gpiterface"+str(self.PORT)+".log" self.interfaceJavaPath = None def init(self, interfaceJavaPath=None): if interfaceJavaPath: self.interfaceJavaPath = interfaceJavaPath else: interfaceJavaPath = pathlib.Path(__file__).parent.absolute() self.interfaceJavaPath = interfaceJavaPath.parent cwd = os.getcwd() runScript = os.path.join(self.interfaceJavaPath,'run.sh') os.chdir(self.interfaceJavaPath) if (os.path.isfile(self.logFile)): print("logfile existed, try to close previous session") try: with open(self.logFile,'r') as fin: line = fin.readline().strip() pid = int(line) os.killpg(os.getpgid(pid), signal.SIGTERM) except: print("can not kill process-"+str(pid)+"please close manully") print(runScript) #self.pro = subprocess.Popen(["bash", runScript]) hostportArg = "-Dexec.args=\""+str(self.PORT)+"\"" args=["mvn", "exec:java", "-Dexec.mainClass=uk.ac.gate.python.pythonInferface.GateServer",hostportArg] #self.pro = subprocess.Popen(["mvn", "exec:java -Dexec.mainClass=uk.ac.gate.python.pythonInferface.GateServer -Dexec.args="7899""]) self.pro = subprocess.Popen(args) with open(self.logFile,'w') as fo: fo.write(str(self.pro.pid)) os.chdir(cwd) time.sleep(5) def close(self): print(self.pro.pid) logFile = os.path.join(self.interfaceJavaPath, self.logFile) print(logFile) os.remove(logFile) os.killpg(os.getpgid(self.pro.pid), signal.SIGTERM) #try: # os.killpg(os.getpgid(self.pro.pid), signal.SIGTERM) # logFile = os.path.join(self.interfaceJavaPath, self.logFile) # print(logFile) # os.remove(logFile) #except: # print("logfile not correctly removed") def test(self): self._sendDoc2Java('test','this is test sent') def _sendDoc2Java(self, jsonKey, jsonValue): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.connect((self.HOST, self.PORT)) previ = 0 i = self.maxSendChar eov = False #if value length larger than self.maxChar then send in sepate packages to java valueLen = len(jsonValue) while(eov == False): subValue = jsonValue[previ:i] if i < valueLen: eov = False else: eov = True jsonSend = {'fromClient':{jsonKey:subValue, 'eov':eov}} previ = i i += self.maxSendChar sock.sendall((json.dumps(jsonSend, cls=MyEncoder)+"\n").encode('utf-8')) serverReturn = self._recvDocFromJava(sock) sock.close() #print(serverReturn) return serverReturn def _send2Java(self, jsonDict): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) #print(self.HOST,self.PORT) sock.connect((self.HOST, int(self.PORT))) #print(jsonDict) for jsonKey in jsonDict: #print(jsonKey) jsonValue = jsonDict[jsonKey] valueLen = len(jsonValue) previ = 0 i = self.maxSendChar eov = False #if value length larger than self.maxChar then send in sepate packages to java while(eov == False): subValue = jsonValue[previ:i] if i < valueLen: eov = False else: eov = True jsonSend = {'fromClient':{jsonKey:subValue, 'eov':False}} previ = i i += self.maxSendChar #print(jsonSend) sock.sendall((json.dumps(jsonSend, cls=MyEncoder)+"\n").encode('utf-8')) #print('finish') jsonSend = {'fromClient':{'eov':True}} sock.sendall((json.dumps(jsonSend, cls=MyEncoder)+"\n").encode('utf-8')) serverReturn = self._recvDocFromJava(sock) sock.close() #print(serverReturn) return serverReturn def _recvDocFromJava(self, sock): fullReturn = "" eov = False fullReturn = {} while(eov ==False): data_recv = sock.recv(self.maxRecvChar) data = json.loads(data_recv) eov = data['eov'] for item in data: partReturn = data[item] if item not in fullReturn: fullReturn[item] = partReturn else: fullReturn[item] += partReturn sock.sendall("success\n".encode('utf-8')) return fullReturn def loadMvnPlugins(self, group, artifact, version): jsonDict = {} jsonDict['plugin'] = 'maven' jsonDict['group'] = group jsonDict['artifact'] = artifact jsonDict['version'] = version response = self._send2Java(jsonDict) if response['message'] == 'success': self.loadedPlugins.append(response['pluginLoaded']) return response def loadPRs(self, resourcePath, name, features=None): jsonDict = {} if features == None: jsonDict['loadPR'] = 'withoutFeature' else: jsonDict['loadPR'] = 'withFeature' i = 0 for featureKey in features: keyName = 'prFeatureName'+str(i) valueName = 'prFeatureValue'+str(i) jsonDict[keyName] = featureKey jsonDict[valueName] = features[featureKey] i+=1 jsonDict['resourcePath'] = resourcePath jsonDict['name'] = name response = self._send2Java(jsonDict) if response['message'] == 'success': self.loadedPrs.append(response['PRLoaded']) return response def reinitPRs(self, name): jsonDict = {} jsonDict['reInitPR'] = 'none' jsonDict['name'] = name response = self._send2Java(jsonDict) if response['message'] != 'success': print('error unable to reinit pr', name) return response['message'] class AnnotationSet(GateInterFace): def __init__(self): GateInterFace.__init__(self) self.annotationSet = [] def __len__(self): return len(self.annotationSet) def __iter__(self): for node in self.annotationSet: yield node def getType(self, annotationType, startidx=None, endidx=None): newList = [] for annotation in self.annotationSet: if annotation.type == annotationType: if startidx and endidx: if annotation.startNode.offset >= startidx and annotation.endNode.offset <= endidx: newList.append(annotation) else: newList.append(annotation) subSet = AnnotationSet() subSet.annotationSet = newList return subSet def getbyRange(self, startidx=0, endidx=None): newList = [] for annotation in self.annotationSet: if annotation.startNode.offset >= startidx: if endidx: if annotation.endNode.offset <= endidx: newList.append(annotation) else: newList.append(annotation) subSet = AnnotationSet() subSet.annotationSet = newList return subSet def get(self,i): return self.annotationSet[i] def getbyId(self, annoId): returnAnno = None for annotation in self.annotationSet: if annotation.id == annoId: returnAnno = annotation break return returnAnno def append(self, annotation): self.annotationSet.append(annotation) def _getAnnotationFromResponse(self,response): annotationResponse = response['annotationSet'] annotationList = annotationResponse.split('\n, Anno') #print('getting annotation from response') #print(len(annotationList)) #print(annotationList[0]) idPattern = '(?<=tationImpl\: id\=)\d(?=\; type\=.\;)' typePattern = '(?<=; type\=).(?=\; features\=)' featurePattern = '(?<=; features\=\{)[\S\s](?=\}\; start\=.\;)' startNodePattern = '(?<=; start\=NodeImpl\: ).(?= end\=NodeImpl)' endNodePattern = '(?<=; end\=NodeImpl\: ).' fullPattern = 'AnnotationImpl\: id\=\d\; type\=.\; features\=\{.\}\; start\=NodeImpl\: id\=\d\; offset\=\d\; end\=NodeImpl\: id\=\d\; offset\=\d' for rawAnnotationLine in annotationList: #print(rawAnnotationLine) #m = re.search(fullPattern, rawAnnotationLine) #print(m) #if m: # print('match') try: #if 1: #print(rawAnnotationLine) currentAnnotation = Annotation() currentAnnotation.id = int(re.search(idPattern,rawAnnotationLine).group(0)) currentAnnotation.type = re.search(typePattern,rawAnnotationLine).group(0) #print(len(re.search(featurePattern,rawAnnotationLine).group(0))) currentAnnotation._setFeatureFromRawLine(re.search(featurePattern,rawAnnotationLine).group(0)) #print(currentAnnotation.id) #print(currentAnnotation.type) #print(currentAnnotation.features) currentAnnotation._setStartNode(re.search(startNodePattern,rawAnnotationLine).group(0)) currentAnnotation._setEndNode(re.search(endNodePattern,rawAnnotationLine).group(0)) #print(currentAnnotation.startNode.id, currentAnnotation.startNode.offset) #print(currentAnnotation.endNode.id, currentAnnotation.endNode.offset) self.annotationSet.append(currentAnnotation) except: #print('bad line, ignore') #print(rawAnnotationLine) pass #currentAnnotation = Annotation() #currentAnnotation.id = int(re.search(idPattern,rawAnnotationLine).group(0)) #currentAnnotation.type = re.search(typePattern,rawAnnotationLine).group(0) #currentAnnotation._setFeatureFromRawLine(re.search(featurePattern,rawAnnotationLine).group(0)) #print(currentAnnotation.id) #print(currentAnnotation.type) #print(currentAnnotation.features) #currentAnnotation._setStartNode(re.search(startNodePattern,rawAnnotationLine).group(0)) #currentAnnotation._setEndNode(re.search(endNodePattern,rawAnnotationLine).group(0)) #print(currentAnnotation.startNode.id, currentAnnotation.startNode.offset) #print(currentAnnotation.endNode.id, currentAnnotation.endNode.offset) #self.annotationSet.append(currentAnnotation) class Annotation: def __init__(self): self.id = None self.type = None self.features = {} self.startNode = None self.endNode = None def overlap_set(self, compareSet): overlap = False for compare_annotation in compareSet: overlap = self.overlaps(compare_annotation) if overlap: break return overlap def matches(self, compareAnno): startNodeOffsetMatch = self.startNode.offset == compareAnno.startNode.offset endNodeOffsetMatch = self.endNode.offset == compareAnno.endNode.offset if startNodeOffsetMatch and endNodeOffsetMatch: return True def overlaps(self, compareAnno): selfStart = self.startNode.offset selfEnd = self.endNode.offset-1 compareStart = compareAnno.startNode.offset compareEnd = compareAnno.endNode.offset-1 if selfStart <= compareStart: if selfEnd >= compareStart: return True else: return False elif selfStart >= compareStart: if compareEnd >= selfStart: return True else: return False def _setFeatureFromRawLine(self, rawFeatureLine): #print(rawFeatureLine) if len(rawFeatureLine) > 0: #replace comma in list to \|\|\| listPattern = '(?<=\=)\[[\w \,]+\]' listFeatures = re.findall(listPattern, rawFeatureLine) for listFeature in listFeatures: newPattern = re.sub(', ',' \|\|\| ', listFeature) rawFeatureLine = rawFeatureLine.replace(listFeature, newPattern) splittedFeatures = re.split(', ',rawFeatureLine) #print(splittedFeatures) for splittedFeature in splittedFeatures: featureTok = splittedFeature.split('=') featureKey = featureTok[0] featureValue = featureTok[1] if self._isListFeature(featureValue): #self.features[featureKey] = [] listValues = featureValue[1:-1].split(' \|\|\| ') self.features[featureKey] = listValues else: self.features[featureKey] = featureValue def _isListFeature(self, featureValue): pattern = '\[.\]' m = re.match(pattern, featureValue) if m: return True else: return False def _setStartNode(self, rawLine): idPattern = '(?<=id\=)\d(?=\;)' offSetPattern = '(?<=offset\=)\d(?=(\;)\|($))' nodeId = int(re.search(idPattern, rawLine).group(0)) offset = int(re.search(offSetPattern, rawLine).group(0)) #print(nodeId, offset) startNode = Node() startNode.id = nodeId startNode.offset = offset self.startNode = startNode def _setEndNode(self, rawLine): idPattern = '(?<=id\=)\d(?=\;)' offSetPattern = '(?<=offset\=)\d*(?=(\;)\|($))' nodeId = int(re.search(idPattern, rawLine).group(0)) offset = int(re.search(offSetPattern, rawLine).group(0)) #print(nodeId, offset) endNode = Node() endNode.id = nodeId endNode.offset = offset self.endNode = endNode class Node: def __init__(self): self.id = None self.offset = None class GateDocument(GateInterFace): def __init__(self): GateInterFace.__init__(self) self.documentName = None def loadDocumentFromURL(self, documentURL): documentName = documentURL serverReturn = self._sendDoc2Java('loadDocumentFromURL', documentURL) if serverReturn['message'] == 'success': self.documentName = documentName #print(serverReturn) def loadDocumentFromFile(self, documentPath): documentName = documentPath serverReturn = self._sendDoc2Java('loadDocumentFromFile', documentPath) #print(serverReturn) if serverReturn['message'] == 'success': self.documentName = documentName #print(serverReturn) def getDocumentContent(self): jsonDict = {} jsonDict['document'] = 'getDocumentContent' jsonDict['docName'] = self.documentName response = self._send2Java(jsonDict) docContent = response['docContent'] return docContent def getAnnotationSetNames(self): jsonDict = {} jsonDict['document'] = 'getAnnotationSetName' jsonDict['docName'] = self.documentName response = self._send2Java(jsonDict) astName = response['annotationSetName'] return astName def getAnnotations(self, annotationSetName): jsonDict = {} jsonDict['document'] = 'getAnnotations' jsonDict['docName'] = self.documentName jsonDict['annotationSetName'] = annotationSetName currentAnnotationSet = AnnotationSet() #print(jsonDict) response = self._send2Java(jsonDict) #print(response) currentAnnotationSet._getAnnotationFromResponse(response) return currentAnnotationSet def clearDocument(self): jsonDict = {} jsonDict['clearDocument'] = self.documentName response = self._send2Java(jsonDict) class GatePipeline(GateInterFace): def __init__(self, pipelineName): GateInterFace.__init__(self) self.pipelineName = pipelineName self.corpus = None self.prList = [] def loadPipelineFromFile(self, filePath): jsonDict = {} jsonDict['pipeline'] = 'loadPipelineFromFile' jsonDict['pipelineName'] = self.pipelineName jsonDict['filtPath'] = filePath response = self._send2Java(jsonDict) #print(response) def createPipeline(self): jsonDict = {} jsonDict['pipeline'] = 'createPipeline' jsonDict['pipelineName'] = self.pipelineName response = self._send2Java(jsonDict) #print(response) def addPR(self, prName): jsonDict = {} jsonDict['pipeline'] = 'addPR' jsonDict['pipelineName'] = self.pipelineName jsonDict['prName'] = prName response = self._send2Java(jsonDict) #print(response) self.prList.append(prName) def setCorpus(self, corpus): corpusName = corpus.corpusName jsonDict = {} jsonDict['pipeline'] = 'setCorpus' jsonDict['pipelineName'] = self.pipelineName jsonDict['corpusName'] = corpusName response = self._send2Java(jsonDict) #print(response) self.corpus = corpus def runPipeline(self): jsonDict = {} jsonDict['pipeline'] = 'runPipeline' jsonDict['pipelineName'] = self.pipelineName response = self._send2Java(jsonDict) #print(response) def checkRunTimeParams(self, prName, paramName): jsonDict = {} jsonDict['pipeline'] = 'checkParams' jsonDict['pipelineName'] = self.pipelineName jsonDict['resourceName'] = prName jsonDict['paramsName'] = paramName response = self._send2Java(jsonDict) #print(response) return response['message'] def setRunTimeParams(self, prName, paramName, paramValue, paramType): jsonDict = {} jsonDict['pipeline'] = 'setParams' jsonDict['pipelineName'] = self.pipelineName jsonDict['resourceName'] = prName jsonDict['paramsName'] = paramName jsonDict['paramsValue'] = paramValue jsonDict['paramsType'] = paramType response = self._send2Java(jsonDict) class GateCorpus(GateInterFace): def __init__(self, corpusName): GateInterFace.__init__(self) self.corpusName = corpusName self._createCorpus() self.documentList = [] def _createCorpus(self): jsonDict = {} jsonDict['corpus'] = 'createCorpus' jsonDict['corpusName'] = self.corpusName response = self._send2Java(jsonDict) #print(response) def clearCorpus(self): jsonDict = {} jsonDict['corpus'] = 'clearCorpus' jsonDict['corpusName'] = self.corpusName response = self._send2Java(jsonDict) def addDocument(self, document): documentName = document.documentName jsonDict = {} jsonDict['corpus'] = 'addDocument' jsonDict['corpusName'] = self.corpusName jsonDict['documentName'] = documentName response = self._send2Java(jsonDict) #print(response) self.documentList.append(document) #if __name__ == "__main__": # gate= GateInterFace() # gate.init('/Users/xingyi/Gate/gateCodes/pythonInferface') # #gate.test() # document = GateDocument() # document.loadDocumentFromURL("https://gate.ac.uk") # #document.loadDocumentFromFile("/Users/xingyi/Gate/gateCodes/pythonInferface/ft-airlines-27-jul-2001.xml") # #print(document.documentName) # #content = document.getDocumentContent() # #print(content) # #atsName = document.getAnnotationSetNames() # #print(atsName) # #ats = document.getAnnotations('') # #print(len(ats.annotationSet)) # #print(ats.annotationSet[0]) # response=gate.loadMvnPlugins("uk.ac.gate.plugins", "annie", "8.5") # print(response) # prparameter={} # response=gate.loadPRs('gate.creole.annotdelete.AnnotationDeletePR') # response=gate.loadPRs('gate.creole.tokeniser.DefaultTokeniser') # prparameter['grammarURL'] = 'file:////Users/xingyi//Gate/ifpri/JAPE/main.jape' # response=gate.loadPRs('gate.creole.Transducer', prparameter) # print(response) # print(gate.loadedPrs) # testPipeLine = GatePipeline('testpipeline') # testPipeLine.createPipeline() # testPipeLine.addPR('gate.creole.annotdelete.AnnotationDeletePR') # testPipeLine.addPR('gate.creole.tokeniser.DefaultTokeniser') # testCorpus = GateCorpus('testCorpus') # testCorpus.addDocument(document) # testPipeLine.setCorpus(testCorpus) # testPipeLine.runPipeline() # #ats = document.getAnnotations('Original markups') # ats = document.getAnnotations('') # print(len(ats.annotationSet)) # print(ats.annotationSet[0]) # testPipeline2 = GatePipeline('testpipeline2') # testPipeLine.loadPipelineFromFile('/Users/xingyi/Gate/ifpri/ifpri.xgapp') # gate.close() #	22,190	34.335987	160	py
CANTM	CANTM-main/wvCovidData/GATEpythonInterface/GateInterface/__init__.py	from .GateInterface import GateInterFace, AnnotationSet, Annotation, Node, GateDocument, GatePipeline, GateCorpus	115	37.666667	113	py
CANTM	CANTM-main/GateMIcateLib/modelUltiClassTopic.py	import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import numpy as np import copy import os from pathlib import Path import pickle import datetime from .modelUlti import modelUlti class ModelUltiClass(modelUlti): def __init__(self, net=None, gpu=False, load_path=None): super().__init__(net=net, gpu=gpu) if load_path: self.loadModel(load_path) if self.gpu: self.net.cuda() def train(self, trainBatchIter, num_epohs=100, valBatchIter=None, cache_path=None, earlyStopping='cls_loss', patience=5): pytorch_total_params = sum(p.numel() for p in self.net.parameters()) print('total_params: ',pytorch_total_params) pytorch_train_params = sum(p.numel() for p in self.net.parameters() if p.requires_grad) print('train_params: ',pytorch_train_params) self.bowdict = trainBatchIter.dataIter.postProcessor.dictProcess self.labels = trainBatchIter.dataIter.postProcessor.labelsFields if earlyStopping == 'None': earlyStopping = None self.cache_path = cache_path output_dict = {} output_dict['accuracy'] = 'no val iter' output_dict['perplexity'] = 'no val iter' output_dict['perplexity_x_only'] = 'no val iter' self.evaluation_history = [] self.optimizer = optim.Adam(self.net.parameters()) print(num_epohs) for epoch in range(num_epohs): begin_time = datetime.datetime.now() all_loss = [] all_elboz1 = [] all_elboz2 = [] all_bow = [] trainIter = self.pred(trainBatchIter, train=True) for current_prediction in trainIter: self.optimizer.zero_grad() pred = current_prediction['pred'] y = current_prediction['y'] atted = current_prediction['atted'] loss = pred['loss'] cls_loss = pred['cls_loss'].sum() elbo_z1 = pred['elbo_x'].to('cpu').detach().numpy() elbo_z2 = pred['elbo_xy'].to('cpu').detach().numpy() bow_x = current_prediction['x_bow'].to('cpu').detach().numpy() all_elboz1.append(elbo_z1) all_elboz2.append(elbo_z2) all_bow.append(bow_x) loss.backward() self.optimizer.step() loss_value = float(cls_loss.data.item()) all_loss.append(loss_value) all_elboz1 if epoch % 3 == 0: topics = self.getTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) #print('===========') x_only_topic = self.get_x_only_Topics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) #print('========================') self.getClassTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) if valBatchIter: output_dict = self.eval(valBatchIter, get_perp=True) avg_loss = sum(all_loss)/len(all_loss) output_dict['cls_loss'] = -avg_loss perplexity_z1, log_perp_z1 = self._get_prep(all_elboz1, all_bow) perplexity_z2, log_perp_z2 = self._get_prep(all_elboz2, all_bow) output_dict['train_ppl_loss'] = -perplexity_z1 if earlyStopping: stop_signal = self.earlyStop(output_dict, patience=patience, metric=earlyStopping, num_epoch=num_epohs) if stop_signal: print('stop signal received, stop training') cache_load_path = os.path.join(self.cache_path, 'best_net.model') print('finish training, load model from ', cache_load_path) self.loadWeights(cache_load_path) break end_time = datetime.datetime.now() timeused = end_time - begin_time print('epoch ', epoch, 'loss', avg_loss, ' val acc: ', output_dict['accuracy'], 'test_pplz2: ', output_dict['perplexity'], 'test_perpz1: ', output_dict['perplexity_x_only'], 'train_pplz2: ', perplexity_z2, 'train_perpz1: ', perplexity_z1, 'time: ', timeused) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) self.saveModel(self.cache_path) self.getTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) #print('===========') self.getClassTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) #print('===========') x_only_topic = self.get_x_only_Topics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) def getClassTopics(self, dictProcess, ntop=10, cache_path=None): termMatrix = self.net.get_class_topics() topicWordList = [] for each_topic in termMatrix: trans_list = list(enumerate(each_topic.cpu().numpy())) #print(trans_list) trans_list = sorted(trans_list, key=lambda k: k[1], reverse=True) #print(trans_list) topic_words = [dictProcess.get(item[0]) for item in trans_list[:ntop]] #print(topic_words) topicWordList.append(topic_words) if cache_path: save_path = os.path.join(cache_path, 'classtopics.txt') self.saveTopic(topicWordList, save_path) return topicWordList def saveModel(self, cache_path): model_path = os.path.join(cache_path, 'net.model') dict_path = os.path.join(cache_path, 'dict.pkl') label_path = os.path.join(cache_path, 'label.pkl') torch.save(self.net, model_path) with open(dict_path, 'wb') as fp: pickle.dump(self.bowdict, fp) with open(label_path, 'wb') as fp: pickle.dump(self.labels, fp) def loadModel(self, cache_path): model_path = os.path.join(cache_path, 'net.model') dict_path = os.path.join(cache_path, 'dict.pkl') label_path = os.path.join(cache_path, 'label.pkl') self.net = torch.load(model_path, map_location=torch.device("cpu")) self.net.eval() with open(dict_path, 'rb') as fp: self.bowdict = pickle.load(fp) with open(label_path, 'rb') as fp: self.labels = pickle.load(fp) def pred(self, batchGen, train=False, updateTopic=False): if train or updateTopic: self.net.train() #self.optimizer.zero_grad() else: self.net.eval() i=0 pre_embd = False for x, x_bow, y in batchGen: i+=1 print("processing batch", i, end='\r') if self.gpu: y = y.type(torch.cuda.LongTensor) x_bow = x_bow.type(torch.cuda.FloatTensor) x_bow.cuda() y.cuda() if batchGen.dataIter.postProcessor.embd_ready: pre_embd = True x = x.type(torch.cuda.FloatTensor).squeeze(1) x.cuda() else: x = x.type(torch.cuda.LongTensor) x.cuda() if train: one_hot_y = self.y2onehot(y) if batchGen.dataIter.label_weights_list: n_samples = self.get_num_samples(y, batchGen.dataIter.label_weights_list) else: n_samples = 10 #print(n_samples) pred, atted = self.net(x, bow=x_bow, train=True, true_y=one_hot_y, n_samples=n_samples, pre_embd=pre_embd, true_y_ids=y) elif updateTopic: pred, atted = self.net(x, bow=x_bow, pre_embd=pre_embd, update_catopic=True) else: pred, atted = self.net(x, bow=x_bow, pre_embd=pre_embd) #pred = pred['y_hat'] output_dict = {} output_dict['pred'] = pred output_dict['y'] = y output_dict['atted'] = atted output_dict['x_bow'] = x_bow yield output_dict def application_oneSent(self, x): if self.gpu: x = x.type(torch.cuda.LongTensor) x.cuda() pred, atted = self.net(x) output_dict = {} output_dict['pred'] = pred output_dict['atted'] = atted return output_dict def get_num_samples(self, y, weight_list): n_samples = 0 for y_item in y: n_samples += weight_list[y_item.item()] return n_samples def y2onehot(self, y): num_class = self.net.n_classes one_hot_y_list = [] for i in range(len(y)): current_one_hot = [0]*num_class current_one_hot[y[i].item()] = 1 one_hot_y_list.append(copy.deepcopy(current_one_hot)) tensor_one_hot_y = torch.tensor(one_hot_y_list) if self.gpu: tensor_one_hot_y = tensor_one_hot_y.type(torch.cuda.FloatTensor) tensor_one_hot_y = tensor_one_hot_y.cuda() return tensor_one_hot_y def getTopics(self, dictProcess, ntop=10, cache_path=None): termMatrix = self.net.get_topics() #print(termMatrix.shape) topicWordList = [] for each_topic in termMatrix: trans_list = list(enumerate(each_topic.cpu().numpy())) #print(trans_list) trans_list = sorted(trans_list, key=lambda k: k[1], reverse=True) #print(trans_list) topic_words = [dictProcess.get(item[0]) for item in trans_list[:ntop]] #print(topic_words) topicWordList.append(topic_words) if cache_path: save_path = os.path.join(cache_path, 'topics.txt') self.saveTopic(topicWordList, save_path) return topicWordList def get_x_only_Topics(self, dictProcess, ntop=10, cache_path=None): termMatrix = self.net.get_x_only_topics() #print(termMatrix.shape) topicWordList = [] for each_topic in termMatrix: trans_list = list(enumerate(each_topic.cpu().numpy())) #print(trans_list) trans_list = sorted(trans_list, key=lambda k: k[1], reverse=True) #print(trans_list) topic_words = [dictProcess.get(item[0]) for item in trans_list[:ntop]] #print(topic_words) topicWordList.append(topic_words) if cache_path: save_path = os.path.join(cache_path, 'x_only_topics.txt') self.saveTopic(topicWordList, save_path) return topicWordList def saveTopic(self, topics, save_path): with open(save_path, 'w') as fo: for each_topic in topics: topic_line = ' '.join(each_topic) fo.write(topic_line+'\n')	11,148	37.711806	270	py
CANTM	CANTM-main/GateMIcateLib/PostprocessorBase.py	import nltk from nltk.corpus import stopwords import os import re class ReaderPostProcessorBase: def __init__(self, keep_case=False, label2id=True, config=None, word2id=False, exteralWord2idFunction=None, return_mask=False, remove_single_list=True, max_sent_len = 300, add_spec_tokens = False, add_CLS_token = False, ): self._init_defaults() self.add_CLS_token = add_CLS_token self.add_spec_tokens = add_spec_tokens self.max_sent_len = max_sent_len self.keep_case = keep_case self.label2id = label2id self.word2id = word2id self.exteralWord2idFunction = exteralWord2idFunction self.config = config self.return_mask = return_mask self.remove_single_list = remove_single_list def _init_defaults(self): self.labelsFields = None self.stop_words = set(stopwords.words('english')) self.dictProcess = None self.embd_ready = False def _remove_stop_words(self, tokened): remain_list = [] for token in tokened: contain_symbol, contain_number, contain_char, all_asii = self._check_string(token) keep = True if token in self.stop_words: keep = False elif len(token) == 1: keep = False elif token.isdigit(): keep = False elif not contain_char: keep = False elif not all_asii: keep = False elif len(token) > 18: keep = False if keep == True: remain_list.append(token) else: pass #print(token) return remain_list def _check_string(self, inputString): contain_symbol = False contain_number = False contain_char = False all_asii = True have_symbol = re.compile('[@_!#$%^&*()<>?/\\|}{~:]') have_number = re.compile('\d') have_char = re.compile('[a-zA-Z]') ms = have_symbol.search(inputString) if ms: contain_symbol = True mn = have_number.search(inputString) if mn: contain_number = True mc = have_char.search(inputString) if mc: contain_char = True if contain_char and not contain_number and not contain_symbol: all_asii = all(ord(c) < 128 for c in inputString) return contain_symbol, contain_number, contain_char, all_asii def _removeSingleList(self, y): if len(y) == 1: return y[0] else: return y def _get_sample(self, sample, sample_field): current_rawx = sample[sample_field] if self.keep_case == False: current_rawx = current_rawx.lower() return current_rawx def label2ids(self, label): label_index = self.labelsFields.index(label) return label_index def x_pipeline(self, raw_x, add_special_tokens=True): raw_x = self.tokenizerProcessor(raw_x) if self.word2id: raw_x = self.word2idProcessor(raw_x, add_special_tokens=add_special_tokens) return raw_x def nltkTokenizer(self, text): return nltk.word_tokenize(text) def bertTokenizer(self, text): tokened = self.bert_tokenizer.tokenize(text) #print(tokened) #ided = self.bert_tokenizer.encode_plus(tokened, max_length=100, pad_to_max_length=True, is_pretokenized=True, add_special_tokens=True)['input_ids'] #print(ided) return tokened def bertWord2id(self,tokened, add_special_tokens=True): encoded = self.bert_tokenizer.encode_plus(tokened, max_length=self.max_sent_len, pad_to_max_length=True, is_pretokenized=True, add_special_tokens=add_special_tokens) #print(encoded) ided = encoded['input_ids'] if self.return_mask: mask = encoded['attention_mask'] return ided, mask else: return ided def get_label_desc_ids(self): label_desc_list = [] for label in self.labelsFields: label_desc = self.desctiptionDict[label] current_desc_ids = self.x_pipeline(label_desc, max_length=100) label_desc_list.append(current_desc_ids) label_ids = [s[0] for s in label_desc_list] label_mask_ids = [s[1] for s in label_desc_list] return label_ids, label_mask_ids	4,597	29.25	173	py
CANTM	CANTM-main/GateMIcateLib/modelUltiUpdateCATopic.py	import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import numpy as np import copy import os from pathlib import Path import pickle from .modelUltiClassTopic import ModelUltiClass class ModelUltiUpdateCAtopic(ModelUltiClass): def __init__(self, net=None, gpu=False, load_path=None): super().__init__(net=net, gpu=gpu, load_path=load_path) def train(self, trainBatchIter, num_epohs=100, valBatchIter=None, cache_path=None, earlyStopping='cls_loss', patience=5): self.bowdict = trainBatchIter.dataIter.postProcessor.dictProcess self.labels = trainBatchIter.dataIter.postProcessor.labelsFields if earlyStopping == 'None': earlyStopping = None self.cache_path = cache_path output_dict = {} output_dict['accuracy'] = 'no val iter' output_dict['perplexity'] = 'no val iter' output_dict['perplexity_x_only'] = 'no val iter' self.evaluation_history = [] self.optimizer = optim.Adam(self.net.parameters()) print(num_epohs) for epoch in range(num_epohs): all_loss = [] all_elboz1 = [] all_elboz2 = [] all_bow = [] trainIter = self.pred(trainBatchIter, train=False, updateTopic=True) for current_prediction in trainIter: self.optimizer.zero_grad() pred = current_prediction['pred'] atted = current_prediction['atted'] loss = pred['loss'] bow_x = current_prediction['x_bow'].to('cpu').detach().numpy() all_bow.append(bow_x) loss.backward() self.optimizer.step() topics = self.getTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) topics = self.getTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) print('finish epoch ', epoch) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) self.saveModel(self.cache_path) self.getTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path)	2,399	31	125	py
CANTM	CANTM-main/GateMIcateLib/batchPostProcessors.py	import torch def xonlyBatchProcessor(x, y): ss = [s[1] for s in x] return ss[0] def bowBertBatchProcessor(raw_x, y): x = [s[0] for s in raw_x] idded_words = [s[1] for s in raw_x] y_class = y return torch.tensor(x), torch.tensor(idded_words), torch.tensor(y_class) def xyOnlyBertBatchProcessor(raw_x, y): x = [s[0] for s in raw_x] y_class = y return torch.tensor(x), torch.tensor(y_class) def singleProcessor_noy(raw_x): x = [raw_x[0]] return torch.tensor(x)	508	21.130435	76	py
CANTM	CANTM-main/GateMIcateLib/modelUltiVAEtm_noatt.py	import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import numpy as np import copy import os from pathlib import Path from .modelUlti import modelUlti class ModelUltiVAEtmNOatt(modelUlti): def __init__(self, net=None, gpu=False): super().__init__(net=net, gpu=gpu) def train(self, trainBatchIter, num_epohs=100, valBatchIter=None, cache_path=None, earlyStopping='cls_loss', patience=5): self.cache_path = cache_path output_dict = {} output_dict['accuracy'] = 'no val iter' self.evaluation_history = [] classifier_paramters = list(self.net.wv_hidden.parameters()) + list(self.net.wv_classifier.parameters()) topic_model_paramters = list(self.net.mu.parameters())+ list(self.net.log_sigma.parameters()) + list(self.net.topics.parameters()) self.optimizer_classifier = optim.Adam(classifier_paramters) self.optimizer_topic_modelling = optim.Adam(topic_model_paramters) #self.optimizer = optim.Adam(self.net.parameters()) self.criterion = nn.CrossEntropyLoss() if self.gpu: self.criterion.cuda() for epoch in range(num_epohs): all_loss = [] trainIter = self.pred(trainBatchIter, train=True) for current_prediction in trainIter: #self.optimizer.zero_grad() self.optimizer_classifier.zero_grad() self.optimizer_topic_modelling.zero_grad() pred = current_prediction['pred'] y = current_prediction['y'] atted = current_prediction['atted'] #y_desc_representation = current_prediction['y_desc_representation'] #class_loss = self.criterion(pred['pred'], y) #topic_loss = pred['loss'] #print(class_loss) #desc_loss = self.desc_criterion(input=atted, target=y_desc_representation) loss = pred['loss'] cls_loss = pred['cls_loss'].sum() loss.backward() #self.optimizer.step() self.optimizer_classifier.step() self.optimizer_topic_modelling.step() #loss_value = float(loss.data.item()) loss_value = float(cls_loss.data.item()) all_loss.append(loss_value) if epoch % 20 == 0: self.getTopics(trainBatchIter.dataIter.postProcessor.dictProcess) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) print("Finish Epoch ", epoch) if valBatchIter: output_dict = self.eval(valBatchIter) avg_loss = sum(all_loss)/len(all_loss) output_dict['cls_loss'] = -avg_loss if earlyStopping: stop_signal = self.earlyStop(output_dict, patience=patience, metric=earlyStopping, num_epoch=num_epohs) if stop_signal: print('stop signal received, stop training') cache_load_path = os.path.join(self.cache_path, 'best_net.model') print('finish training, load model from ', cache_load_path) self.loadWeights(cache_load_path) break print('epoch ', epoch, 'loss', avg_loss, ' val acc: ', output_dict['accuracy']) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) #cache_load_path = os.path.join(self.cache_path, 'best_net.model') #print('finish training, load model from ', cache_load_path) #self.loadWeights(cache_load_path) def pred(self, batchGen, train=False): if train: self.net.train() #self.optimizer.zero_grad() else: self.net.eval() i=0 pre_embd = False for x, x_bow, y in batchGen: i+=1 print("processing batch", i, end='\r') if self.gpu: y = y.type(torch.cuda.LongTensor) x_bow = x_bow.type(torch.cuda.FloatTensor) x_bow.cuda() y.cuda() if batchGen.dataIter.postProcessor.embd_ready: pre_embd = True x = x.type(torch.cuda.FloatTensor).squeeze(1) x.cuda() else: x = x.type(torch.cuda.LongTensor) x.cuda() if train: one_hot_y = self.y2onehot(y) if batchGen.dataIter.label_weights_list: n_samples = self.get_num_samples(y, batchGen.dataIter.label_weights_list) else: n_samples = 10 #print(n_samples) pred, atted = self.net(x, bow=x_bow, train=True, true_y=one_hot_y, n_samples=n_samples, pre_embd=pre_embd, true_y_ids=y) else: pred, atted = self.net(x, bow=x_bow, pre_embd=pre_embd) output_dict = {} output_dict['pred'] = pred output_dict['y'] = y output_dict['atted'] = atted yield output_dict def application_oneSent(self, x): if self.gpu: x = x.type(torch.cuda.LongTensor) x.cuda() pred, atted = self.net(x) output_dict = {} output_dict['pred'] = pred output_dict['atted'] = atted return output_dict def get_num_samples(self, y, weight_list): n_samples = 0 for y_item in y: n_samples += weight_list[y_item.item()] return n_samples def y2onehot(self, y): num_class = self.net.n_classes one_hot_y_list = [] for i in range(len(y)): current_one_hot = [0]*num_class current_one_hot[y[i].item()] = 1 one_hot_y_list.append(copy.deepcopy(current_one_hot)) tensor_one_hot_y = torch.tensor(one_hot_y_list) if self.gpu: tensor_one_hot_y = tensor_one_hot_y.type(torch.cuda.FloatTensor) tensor_one_hot_y = tensor_one_hot_y.cuda() return tensor_one_hot_y def getTopics(self, dictProcess, ntop=10): termMatrix = self.net.get_topics() #print(termMatrix.shape) topicWordList = [] for each_topic in termMatrix: trans_list = list(enumerate(each_topic.cpu().numpy())) #print(trans_list) trans_list = sorted(trans_list, key=lambda k: k[1], reverse=True) #print(trans_list) topic_words = [dictProcess.get(item[0]) for item in trans_list[:ntop]] #print(topic_words) topicWordList.append(topic_words) return topicWordList	6,838	38.304598	138	py
CANTM	CANTM-main/GateMIcateLib/ScholarPostProcessor.py	import nltk from nltk.corpus import stopwords import os import re from .WVPostProcessor import WVPostProcessor from transformers import BertTokenizer import glob import string from collections import Counter from nltk.tokenize import sent_tokenize punct_chars = list(set(string.punctuation) - set("'")) punct_chars.sort() punctuation = ''.join(punct_chars) replace = re.compile('[%s]' % re.escape(punctuation)) punct_chars_more = list(set(string.punctuation) - set(["'",","])) punct_chars_more.sort() punctuation_more = ''.join(punct_chars_more) replace_more = re.compile('[%s]' % re.escape(punctuation_more)) alpha = re.compile('^[a-zA-Z_]+$') alpha_or_num = re.compile('^[a-zA-Z_]+\|[0-9_]+$') alphanum = re.compile('^[a-zA-Z0-9_]+$') def tokenize(text, strip_html=False, lower=True, keep_emails=False, keep_at_mentions=False, keep_numbers=False, keep_alphanum=False, min_length=3, stopwords=None, vocab=None, keep_pun=False): text = clean_text(text, strip_html, lower, keep_emails, keep_at_mentions, keep_pun=keep_pun) tokens = text.split() if stopwords is not None: tokens = ['_' if t in stopwords else t for t in tokens] # remove tokens that contain numbers if not keep_alphanum and not keep_numbers: tokens = [t if alpha.match(t) else '_' for t in tokens] # or just remove tokens that contain a combination of letters and numbers elif not keep_alphanum: tokens = [t if alpha_or_num.match(t) else '_' for t in tokens] # drop short tokens if min_length > 0: tokens = [t if len(t) >= min_length else '_' for t in tokens] counts = Counter() unigrams = [t for t in tokens if t != '_'] counts.update(unigrams) if vocab is not None: tokens = [token for token in unigrams if token in vocab] else: tokens = unigrams return tokens, counts def clean_text(text, strip_html=False, lower=True, keep_emails=False, keep_at_mentions=False, keep_pun=False): # remove html tags if strip_html: text = re.sub(r'<[^>]+>', '', text) else: # replace angle brackets text = re.sub(r'<', '(', text) text = re.sub(r'>', ')', text) # lower case if lower: text = text.lower() # eliminate email addresses if not keep_emails: text = re.sub(r'\S+@\S+', ' ', text) # eliminate @mentions if not keep_at_mentions: text = re.sub(r'\s@\S+', ' ', text) # replace underscores with spaces text = re.sub(r'_', ' ', text) # break off single quotes at the ends of words text = re.sub(r'\s\'', ' ', text) text = re.sub(r'\'\s', ' ', text) if not keep_pun: # remove periods text = re.sub(r'\.', '', text) # replace all other punctuation (except single quotes) with spaces text = replace.sub(' ', text) else: # remove periods text = re.sub(r'\.', '', text) # replace all other punctuation (except single quotes) with spaces text = replace_more.sub(' ', text) # remove single quotes text = re.sub(r'\'', '', text) # replace all whitespace with a single space text = re.sub(r'\s', ' ', text) # strip off spaces on either end text = text.strip() return text class ScholarPostProcessor(WVPostProcessor): def __init__(self, stopwords_source=['snowball'], min_token_length=3, kwargs): super().__init__(kwargs) script_path = os.path.abspath(__file__) parent = os.path.dirname(script_path) self.stopwords_source = stopwords_source self.min_token_length = min_token_length stop_list_dir = os.path.join(parent, 'stopwords') self._get_stop_words(stop_list_dir) print(self.stop_words) def _get_stop_words(self, stop_list_dir): self.stop_words = set() snowball_stopwords_list_file = os.path.join(stop_list_dir, 'snowball_stopwords.txt') mallet_stopwords_list_file = os.path.join(stop_list_dir, 'mallet_stopwords.txt') scholar_stopwords_list_file = os.path.join(stop_list_dir, 'custom_stopwords.txt') if 'snowball' in self.stopwords_source: with open(snowball_stopwords_list_file, 'r') as fin: for line in fin: stop_word = line.strip() self.stop_words.add(stop_word) def clean_source(self, source_text): #split_lines = source_text.split('\n \| _') split_lines = re.split('\n\|_\|=\|\*\|\\|\|\/', source_text) added_sent = [] for splited_line in split_lines: all_sents_split = sent_tokenize(splited_line) for each_sent in all_sents_split: keep = True line_tok, _= tokenize(splited_line, stopwords=self.stop_words) if len(line_tok) < 3: keep=False if keep: added_sent.append(splited_line) return ' '.join(added_sent) def postProcess(self, sample): split_x = [] for x_field in self.x_fields: current_rawx = self._get_sample(sample, x_field) split_x.append(current_rawx) current_rawx = ' '.join(split_x) current_rawx_tokened, _ = tokenize(current_rawx, keep_numbers=True, keep_alphanum=True, min_length=1, keep_pun=True) current_rawx = ' '.join(current_rawx_tokened) #print(current_rawx) ## Bert toknise for hidden layers. add_special_tokens not added, additional attention will be applied on token level (CLS not used) if self.embd_ready: current_x = sample['embd'] else: #cleaned_raw_x = self.clean_source(current_rawx) #print(cleaned_raw_x) cleaned_raw_x = current_rawx if len(cleaned_raw_x) > 10: current_x = self.x_pipeline(cleaned_raw_x, add_special_tokens=self.add_spec_tokens) else: current_x = self.x_pipeline(current_rawx, add_special_tokens=self.add_spec_tokens) ## NLTK tokenise and remove stopwords for topic modelling #current_x_nltk_tokened = self.nltkTokenizer(current_rawx) #current_x_nltk_tokened = self._remove_stop_words(current_x_nltk_tokened) current_x_nltk_tokened,_ = tokenize(current_rawx, stopwords=self.stop_words) if self.dictProcess: current_x_nltk_tokened = self.dictProcess.doc2countHot(current_x_nltk_tokened) x=[current_x, current_x_nltk_tokened] y = sample[self.y_field] if self.label2id: y = self.label2ids(y) if self.remove_single_list: x = self._removeSingleList(x) y = self._removeSingleList(y) return x, y def _remove_stop_words(self, tokened): remain_list = [] for token in tokened: contain_symbol, contain_number, contain_char, all_asii = self._check_string(token) keep = True if token in self.stop_words: keep = False elif len(token) < self.min_token_length: keep = False elif token.isdigit(): keep = False elif not contain_char: keep = False elif not all_asii: keep = False elif contain_number: keep = False if keep == True: remain_list.append(token) else: pass #print(token) return remain_list	7,529	34.023256	191	py
CANTM	CANTM-main/GateMIcateLib/BatchIter.py	import math import random class BatchIterBert: def __init__(self, dataIter, batch_size=32, filling_last_batch=False, postProcessor=None): self.dataIter = dataIter self.batch_size = batch_size self.num_batches = self._get_num_batches() self.filling_last_batch = filling_last_batch self.postProcessor = postProcessor self.fillter = [] self._reset_iter() def _get_num_batches(self): num_batches = math.ceil(len(self.dataIter)/self.batch_size) return num_batches def _reset_iter(self): self.current_batch_idx = 0 def __iter__(self): self._reset_iter() return self def __next__(self): if self.current_batch_idx < self.num_batches: current_batch_x, current_batch_y = self._readNextBatch() self.current_batch_idx += 1 if self.postProcessor: return self.postProcessor(current_batch_x, current_batch_y) else: return current_batch_x, current_batch_y else: self._reset_iter() raise StopIteration def __len__(self): return self.num_batches def _readNextBatch(self): i = 0 batch_list_x = [] batch_list_y = [] while i < self.batch_size: try: x, y = next(self.dataIter) if self.filling_last_batch: self._update_fillter(x, y) batch_list_x.append(x) batch_list_y.append(y) i+=1 except StopIteration: if self.filling_last_batch: batch_list_x, batch_list_y = self._filling_last_batch(batch_list_x, batch_list_y) i = self.batch_size return batch_list_x, batch_list_y def _filling_last_batch(self, batch_list_x, batch_list_y): num_current_batch = len(batch_list_x) num_filling = self.batch_size - num_current_batch random.shuffle(self.fillter) filler_x = [s[0] for s in self.fillter[:num_filling]] filler_y = [s[1] for s in self.fillter[:num_filling]] batch_list_x += filler_x batch_list_y += filler_y return batch_list_x, batch_list_y def _update_fillter(self, x, y): r = random.random() if len(self.fillter) < self.batch_size: self.fillter.append([x, y]) elif r>0.9: self.fillter.pop(0) self.fillter.append([x, y])	2,508	31.166667	101	py
CANTM	CANTM-main/GateMIcateLib/DictionaryProcess.py	class DictionaryProcess: def __init__(self, common_dictionary): self.common_dictionary = common_dictionary self.num_vocab = len(self.common_dictionary) def doc2bow(self, input_doc): gensim_bow_doc = self.common_dictionary.doc2bow(input_doc) return gensim_bow_doc def doc2countHot(self, input_doc): gensim_bow_doc = self.doc2bow(input_doc) doc_vec = [0] * self.num_vocab for item in gensim_bow_doc: vocab_idx = item[0] vovab_counts = item[1] doc_vec[vocab_idx] = vovab_counts return doc_vec def get(self, wordidx): return self.common_dictionary[wordidx] def __len__(self): return self.num_vocab	734	29.625	66	py
CANTM	CANTM-main/GateMIcateLib/ScholarProcessor.py	import nltk from nltk.corpus import stopwords import os import re from .PostprocessorBase import ReaderPostProcessorBase from transformers import BertTokenizer def tokenize(text, strip_html=False, lower=True, keep_emails=False, keep_at_mentions=False, keep_numbers=False, keep_alphanum=False, min_length=3, stopwords=None, vocab=None): text = clean_text(text, strip_html, lower, keep_emails, keep_at_mentions) tokens = text.split() if stopwords is not None: tokens = ['_' if t in stopwords else t for t in tokens] # remove tokens that contain numbers if not keep_alphanum and not keep_numbers: tokens = [t if alpha.match(t) else '_' for t in tokens] # or just remove tokens that contain a combination of letters and numbers elif not keep_alphanum: tokens = [t if alpha_or_num.match(t) else '_' for t in tokens] # drop short tokens if min_length > 0: tokens = [t if len(t) >= min_length else '_' for t in tokens] counts = Counter() unigrams = [t for t in tokens if t != '_'] counts.update(unigrams) if vocab is not None: tokens = [token for token in unigrams if token in vocab] else: tokens = unigrams return tokens, counts def clean_text(text, strip_html=False, lower=True, keep_emails=False, keep_at_mentions=False): # remove html tags if strip_html: text = re.sub(r'<[^>]+>', '', text) else: # replace angle brackets text = re.sub(r'<', '(', text) text = re.sub(r'>', ')', text) # lower case if lower: text = text.lower() # eliminate email addresses if not keep_emails: text = re.sub(r'\S+@\S+', ' ', text) # eliminate @mentions if not keep_at_mentions: text = re.sub(r'\s@\S+', ' ', text) # replace underscores with spaces text = re.sub(r'_', ' ', text) # break off single quotes at the ends of words text = re.sub(r'\s\'', ' ', text) text = re.sub(r'\'\s', ' ', text) # remove periods text = re.sub(r'\.', '', text) # replace all other punctuation (except single quotes) with spaces text = replace.sub(' ', text) # remove single quotes text = re.sub(r'\'', '', text) # replace all whitespace with a single space text = re.sub(r'\s', ' ', text) # strip off spaces on either end text = text.strip() return text class ScholarProcessor(ReaderPostProcessorBase): def __init__(self, x_fields=['Claim', 'Explaination'], y_field='selected_label', kwargs): super().__init__(kwargs) self.x_fields = x_fields self.y_field = y_field self.initProcessor() def initProcessor(self): bert_tokenizer_path = os.path.join(self.config['BERT'].get('bert_path'), 'tokenizer') self.bert_tokenizer = BertTokenizer.from_pretrained(bert_tokenizer_path) self.tokenizerProcessor = self.bertTokenizer self.word2idProcessor = self.bertWord2id if 'TARGET' in self.config: self.labelsFields = self.config['TARGET'].get('labels') else: self.labelsFields = ['PubAuthAction', 'CommSpread', 'GenMedAdv', 'PromActs', 'Consp', 'VirTrans', 'VirOrgn', 'PubPrep', 'Vacc', 'Prot', 'None'] #print(self.labelsFields) def postProcess(self, sample): split_x = [] for x_field in self.x_fields: current_rawx = self._get_sample(sample, x_field) split_x.append(current_rawx) current_rawx = ' '.join(split_x) ## Bert toknise for hidden layers. add_special_tokens not added, additional attention will be applied on token level (CLS not used) if self.embd_ready: current_x = sample['embd'] else: current_x = self.x_pipeline(current_rawx, add_special_tokens=self.add_spec_tokens) ## NLTK tokenise and remove stopwords for topic modelling #current_x_nltk_tokened = self.nltkTokenizer(current_rawx) #current_x_nltk_tokened = self._remove_stop_words(current_x_nltk_tokened) current_x_nltk_tokened = tokenize(current_rawx) if self.dictProcess: current_x_nltk_tokened = self.dictProcess.doc2countHot(current_x_nltk_tokened) x=[current_x, current_x_nltk_tokened] y = sample[self.y_field] if self.label2id: y = self.label2ids(y) if self.remove_single_list: x = self._removeSingleList(x) y = self._removeSingleList(y) return x, y	4,509	35.370968	175	py
CANTM	CANTM-main/GateMIcateLib/__init__.py	from .BatchIter import BatchIterBert from .DictionaryProcess import DictionaryProcess from .WVPostProcessor import WVPostProcessor from .ScholarPostProcessor import ScholarPostProcessor from .modelUltiClassTopic import ModelUltiClass from .modelUlti import modelUlti as ModelUlti from .EvaluationManager import EvaluationManager from .modelUltiUpdateCATopic import ModelUltiUpdateCAtopic	388	42.222222	58	py
CANTM	CANTM-main/GateMIcateLib/modelUlti.py	import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import numpy as np import os from pathlib import Path class modelUlti: def __init__(self, net=None, gpu=False): if net: self.net = net self.gpu = gpu if self.gpu and net: self.net.cuda() def train(self, trainBatchIter, num_epohs=100, valBatchIter=None, cache_path=None, patience=15, earlyStopping='cls_loss'): self.cache_path = cache_path output_dict = {} output_dict['accuracy'] = 'no val iter' self.evaluation_history = [] self.optimizer = optim.Adam(self.net.parameters()) self.criterion = nn.CrossEntropyLoss() if self.gpu: self.criterion.cuda() for epoch in range(num_epohs): all_loss = [] trainIter = self.pred(trainBatchIter, train=True) for current_prediction in trainIter: pred = current_prediction['pred']['y_hat'] y = current_prediction['y'] self.optimizer.zero_grad() loss = self.criterion(pred, y) loss.backward() self.optimizer.step() loss_value = float(loss.data.item()) all_loss.append(loss_value) print("Finish batch") if valBatchIter: output_dict = self.eval(valBatchIter) avg_loss = sum(all_loss)/len(all_loss) output_dict['cls_loss'] = -avg_loss if earlyStopping: stop_signal = self.earlyStop(output_dict, num_epoch=num_epohs, patience=patience, metric=earlyStopping) if stop_signal: print('stop signal received, stop training') cache_load_path = os.path.join(self.cache_path, 'best_net.model') print('finish training, load model from ', cache_load_path) self.loadWeights(cache_load_path) break print('epoch ', epoch, 'loss', avg_loss, ' val acc: ', output_dict['accuracy']) if epoch % 20 == 0: cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) def earlyStop(self, output_dict, metric='accuracy', patience=40, num_epoch=None): result = output_dict[metric] stop_signal = False self.evaluation_history.append(result) num_epochs = len(self.evaluation_history) max_result = max(self.evaluation_history) max_epoch = self.evaluation_history.index(max_result) max_passed = num_epochs - max_epoch if max_passed >= patience: stop_signal = True if num_epoch: #print('num epoch passed: ', len(self.evaluation_history)) #print('max_epoches:', num_epoch) if len(self.evaluation_history) == num_epoch: stop_signal = True if max_passed == 1: print('caching best ') cache_path = os.path.join(self.cache_path, 'best_net.model') self.saveWeights(cache_path) return stop_signal def pred(self, batchGen, train=False): pre_embd = False if train: self.net.train() else: self.net.eval() i=0 for x, y in batchGen: i+=1 print("processing batch", i, end='\r') if self.gpu: y = y.type(torch.cuda.LongTensor) y.cuda() if batchGen.dataIter.postProcessor.embd_ready: pre_embd = True x = x.type(torch.cuda.FloatTensor).squeeze(1) x.cuda() else: x = x.type(torch.cuda.LongTensor) x.cuda() pred = self.net(x, pre_embd=pre_embd) output_dict = {} output_dict['pred'] = pred output_dict['y'] = y yield output_dict def eval(self, batchGen, get_perp=False): output_dict = {} all_prediction = [] all_true_label = [] all_elbo_x = [] all_elbo_xy = [] all_bow_x = [] #print(len(batchGen)) #print(len(batchGen.dataIter)) for current_prediction in self.pred(batchGen): pred = current_prediction['pred']['y_hat'] y = current_prediction['y'] current_batch_out = F.softmax(pred, dim=-1) label_prediction = torch.max(current_batch_out, -1)[1] current_batch_out_list = current_batch_out.to('cpu').detach().numpy() label_prediction_list = label_prediction.to('cpu').detach().numpy() y_list = y.to('cpu').detach().numpy() all_prediction.append(label_prediction_list) all_true_label.append(y_list) if get_perp: elbo_x = current_prediction['pred']['elbo_x'].to('cpu').detach().numpy() elbo_xy = current_prediction['pred']['elbo_xy'].to('cpu').detach().numpy() bow_x = current_prediction['x_bow'].to('cpu').detach().numpy() #print(elbo_xy) all_elbo_x.append(elbo_x) all_elbo_xy.append(elbo_xy) all_bow_x.append(bow_x) if get_perp: perplexity, log_perp = self._get_prep(all_elbo_xy, all_bow_x) output_dict['perplexity'] = perplexity output_dict['log_perplexity'] = log_perp perplexity_x_only, log_perp_x_only = self._get_prep(all_elbo_x, all_bow_x) output_dict['perplexity_x_only'] = perplexity_x_only output_dict['log_perplexity_x_only'] = log_perp_x_only all_prediction = np.concatenate(all_prediction) all_true_label = np.concatenate(all_true_label) #print(len(all_true_label)) num_correct = (all_prediction == all_true_label).sum() accuracy = num_correct / len(all_prediction) output_dict['accuracy'] = accuracy output_dict['f-measure'] = {} num_classes = len(batchGen.dataIter.postProcessor.labelsFields) for class_id in list(range(num_classes)): f_measure_score = self.fMeasure(all_prediction, all_true_label, class_id) output_dict['f-measure']['class '+str(class_id)] = f_measure_score return output_dict def _get_prep(self, all_elbo_list, all_bow_list): all_elbo = np.concatenate(all_elbo_list) all_bow = np.concatenate(all_bow_list) ############################################### ##num_token = all_bow.sum(axis=1) ##print(num_token) ##print(num_token.shape) #log_perp = np.mean(all_elbo / all_bow.sum(axis=1)) #print(log_perp) ############################################# num_token = all_bow.sum() log_perp = all_elbo.sum() / num_token ############################################# #print(log_perp) perplexity = np.exp(log_perp) #print(perplexity) return perplexity, log_perp def saveWeights(self, save_path): torch.save(self.net.state_dict(), save_path) def loadWeights(self, load_path, cpu=True): if cpu: self.net.load_state_dict(torch.load(load_path, map_location=torch.device('cpu')), strict=False) else: self.net.load_state_dict(torch.load(load_path), strict=False) self.net.eval() def fMeasure(self, all_prediction, true_label, class_id, ignoreid=None): #print(class_id) mask = [class_id] * len(all_prediction) mask_arrary = np.array(mask) pred_mask = np.argwhere(all_prediction==class_id) #print(pred_mask) true_mask = np.argwhere(true_label==class_id) #print(true_mask) #print(len(true_mask)) total_pred = 0 total_true = 0 pc = 0 for i in pred_mask: if all_prediction[i[0]] == true_label[i[0]]: pc+=1 if true_label[i[0]] != ignoreid: total_pred += 1 rc = 0 for i in true_mask: if all_prediction[i[0]] == true_label[i[0]]: rc+=1 if true_label[i[0]] != ignoreid: total_true += 1 if total_pred == 0: precision = 0 else: precision = float(pc)/total_pred if total_true == 0: recall = 0 else: recall = float(rc)/total_true if (precision+recall)==0: f_measure = 0 else: f_measure = 2((precisionrecall)/(precision+recall)) #print(total_true) return precision, recall, f_measure, total_pred, total_true, pc, rc	8,968	35.02008	126	py
CANTM	CANTM-main/GateMIcateLib/EvaluationManager.py	import sys import nltk import math from GateMIcateLib import BatchIterBert, DictionaryProcess #from GateMIcateLib import WVPostProcessor as ReaderPostProcessor from configobj import ConfigObj import torch import argparse import copy from sklearn.model_selection import KFold import random import os from pathlib import Path from gensim.corpora.dictionary import Dictionary from gensim.models import LdaModel import numpy as np def get_average_fmeasure_score(results_dict, field): t=0 score = 0 for class_field in results_dict['f-measure']: score += sum(results_dict['f-measure'][class_field][field]) t += len(results_dict['f-measure'][class_field][field]) return score/t def get_micro_fmeasure(results_dict, num_field, de_field): score = 0 for class_field in results_dict['f-measure']: numerator = sum(results_dict['f-measure'][class_field][num_field]) denominator = sum(results_dict['f-measure'][class_field][de_field]) if denominator != 0: score += numerator/denominator t = len(results_dict['f-measure']) return score/t class EvaluationManager: def __init__(self, trainReaderargs, envargs, testReaderargs=None, valReaderargs=None): self._initParams(envargs) self.trainReaderargs = trainReaderargs self.testReaderargs = testReaderargs self.valReaderargs = valReaderargs self.getLibs() self._get_train_DataIter() def get_covid_train_json_for_scholar(self): current_traindataIter=dataIter(self.trainReaderargs, config=self.config, shuffle=False) all_json = [] for item in current_traindataIter: claim = item['Claim'] explaination = item['Explaination'] label = item['selected_label'] sample_id = item['unique_wv_id'] text = claim+' '+explaination current_dict = {} current_dict['text'] = text current_dict['sentiment'] = label current_dict['id'] = sample_id all_json.append(current_dict) return all_json def outputCorpus4NPMI(self): all_doc = [] token_count = [] current_traindataIter=dataIter(self.trainReaderargs, config=self.config, shuffle=False) for item in current_traindataIter: alltext=[] for field in self.x_fields: current_text = nltk.word_tokenize(item[field]) token_count.append(len(current_text)) alltext.append(' '.join(current_text)) all_doc.append(' '.join(alltext)) if self.testReaderargs: self.testDataIter = dataIter(self.testReaderargs, config=self.config, shuffle=False) for item in current_traindataIter: alltext=[] for field in self.x_fields: current_text = nltk.word_tokenize(item[field]) token_count.append(len(current_text)) alltext.append(' '.join(current_text)) all_doc.append(' '.join(alltext)) print(sum(token_count)/len(token_count)) return all_doc def _get_train_DataIter(self): self.postProcessor = ReaderPostProcessor(config=self.config, word2id=True, remove_single_list=False, add_spec_tokens=True, x_fields=self.x_fields, y_field=self.y_field, max_sent_len=self.max_sent_len) print(self.trainReaderargs) self.trainDataIter = dataIter(self.trainReaderargs, postProcessor=self.postProcessor, config=self.config, shuffle=True) if self.testReaderargs: self.testDataIter = dataIter(self.testReaderargs, postProcessor=self.postProcessor, config=self.config, shuffle=False) print(self.get_dict) if self.get_dict: print('building dict') self.buildDict() if self.preEmbd: print('pre calculating embedding') net = Model(self.config, vocab_dim=self.vocab_dim) mUlti = modelUlti(net, gpu=self.gpu) self.trainDataIter.preCalculateEmbed(mUlti.net.bert_embedding, 0) if not self.testReaderargs: self.all_ids = copy.deepcopy(self.trainDataIter.all_ids) random.shuffle(self.all_ids) ## deep copy train reader to test reader self.testDataIter = copy.deepcopy(self.trainDataIter) self.valDataIter = None def _initParams(self,envargs): print(envargs) self.get_perp = False self.get_dict = False self.vocab_dim = None self.have_dict = False self.config_file = envargs.get('configFile',None) self.config = ConfigObj(self.config_file) self.cache_path = envargs.get('cachePath',None) self.n_fold = envargs.get('nFold',5) self.randomSeed = envargs.get('randomSeed',None) self.preEmbd = envargs.get('preEmbd',False) self.dynamicSampling = envargs.get('dynamicSampling',False) self.modelType = envargs.get('model', 'clsTopic') self.corpusType = envargs.get('corpusType', 'wvmisinfo') self.max_sent_len = envargs.get('max_sent_len', '300') self.num_epoches = envargs.get('num_epoches', 150) self.patient = envargs.get('patient', 40) self.batch_size = envargs.get('batch_size', 32) self.earlyStopping = envargs.get('earlyStopping', 'cls_loss') self.x_fields = envargs.get('x_fields', 'Claim,Explaination') self.x_fields = self.x_fields.split(',') print(self.x_fields) self.y_field = envargs.get('y_field', 'selected_label') self.dict_no_below = envargs.get('dict_no_below', 0) self.dict_no_above = envargs.get('dict_no_above', 1.0) self.dict_keep_n = envargs.get('dict_keep_n', 5000) self.splitValidation = envargs.get('splitValidation',None) self.inspectTest = envargs.get('inspectTest', True) self.trainLDA = envargs.get('trainLDA', False) self.gpu = envargs.get('gpu', True) self.envargs = envargs def train_lda(self, cache_path): print(cache_path) trainBatchIter = BatchIterBert(self.trainDataIter, filling_last_batch=False, postProcessor=batchPostProcessor, batch_size=1) bow_list = [] for item in trainBatchIter: bow = item[1].squeeze().detach().numpy().tolist() bow_list.append(self.bow_2_gensim(bow)) print(len(bow_list)) #print(self.dictProcess.common_dictionary.id2token) lda = LdaModel(np.array(bow_list), num_topics=50, passes=200, chunksize=len(bow_list), id2word=self.dictProcess.common_dictionary) #print(lda.show_topic(1, topn=10)) output_topic_line = '' for topic_id in range(50): current_topic_list = [] current_topic = lda.show_topic(topic_id, topn=10) for topic_tuple in current_topic: current_topic_list.append(topic_tuple[0]) output_topic_line += ' '.join(current_topic_list)+'\n' #print(current_topic_list) topic_file = os.path.join(cache_path, 'ldatopic.txt') with open(topic_file, 'w') as fo: fo.write(output_topic_line) testBatchIter = BatchIterBert(self.testDataIter, filling_last_batch=False, postProcessor=batchPostProcessor, batch_size=1) test_bow_list = [] word_count = 0 for item in testBatchIter: bow = item[1].squeeze().detach().numpy().tolist() word_count += sum(bow) test_bow_list.append(self.bow_2_gensim(bow)) print(word_count) ppl = lda.log_perplexity(test_bow_list, len(test_bow_list)) print(ppl) bound = lda.bound(test_bow_list) print(bound/word_count) print(np.exp2(-bound/word_count)) def bow_2_gensim(self, bow): gensim_format = [] for idx, count in enumerate(bow): if count > 0: gensim_format.append((idx,count)) return gensim_format def train(self, cache_path=None): if self.inspectTest and (not self.splitValidation): print('inspecting test, please dont use val acc as early stoping') self.valDataIter = self.testDataIter elif self.inspectTest and self.splitValidation: print('inspectTest and splitValidation can not use same time') print('deset inspectTest') self.inspectTest = False if self.splitValidation: print('splitting test for validation') self.valDataIter = copy.deepcopy(self.trainDataIter) train_val_ids = copy.deepcopy(self.trainDataIter.all_ids) random.shuffle(train_val_ids) split_4_train = 1-self.splitValidation top_n_4_train = math.floor(len(train_val_ids) * split_4_train) id_4_train = train_val_ids[:top_n_4_train] id_4_val = train_val_ids[top_n_4_train:] self.trainDataIter.all_ids = id_4_train self.valDataIter.all_ids = id_4_val assert self.inspectTest != self.splitValidation, 'splitValidation will overwrite inspectTest, dont use at the same time' if self.dynamicSampling: print('get training data sample weights') trainDataIter.cal_sample_weights() self.trainDataIter._reset_iter() trainBatchIter = BatchIterBert(self.trainDataIter, filling_last_batch=True, postProcessor=batchPostProcessor, batch_size=self.batch_size) if self.valDataIter: self.valDataIter._reset_iter() valBatchIter = BatchIterBert(self.valDataIter, filling_last_batch=False, postProcessor=batchPostProcessor, batch_size=self.batch_size) else: valBatchIter = None print(self.vocab_dim) net = Model(self.config, vocab_dim=self.vocab_dim) self.mUlti = modelUlti(net, gpu=self.gpu) #print(next(trainBatchIter)) self.mUlti.train(trainBatchIter, cache_path=cache_path, num_epohs=self.num_epoches, valBatchIter=valBatchIter, patience=self.patient, earlyStopping=self.earlyStopping) def train_test_evaluation(self): path = Path(self.cache_path) path.mkdir(parents=True, exist_ok=True) self.train(cache_path=self.cache_path) testBatchIter = BatchIterBert(self.testDataIter, filling_last_batch=False, postProcessor=batchPostProcessor, batch_size=self.batch_size) results = self.mUlti.eval(testBatchIter, get_perp=self.get_perp) print(results) def train_model_only(self): path = Path(self.cache_path) path.mkdir(parents=True, exist_ok=True) self.train(cache_path=self.cache_path) def cross_fold_evaluation(self): kf = KFold(n_splits=self.n_fold) fold_index = 1 results_dict = {} results_dict['accuracy'] = [] results_dict['perplexity'] = [] results_dict['log_perplexity'] = [] results_dict['perplexity_x_only'] = [] results_dict['f-measure'] = {} for each_fold in kf.split(self.all_ids): train_ids, test_ids = self.reconstruct_ids(each_fold) self.trainDataIter.all_ids = train_ids self.testDataIter.all_ids = test_ids self.testDataIter._reset_iter() fold_cache_path = os.path.join(self.cache_path, 'fold'+str(fold_index)) path = Path(fold_cache_path) path.mkdir(parents=True, exist_ok=True) if self.trainLDA: self.train_lda(cache_path=fold_cache_path) else: self.train(cache_path=fold_cache_path) testBatchIter = BatchIterBert(self.testDataIter, filling_last_batch=False, postProcessor=batchPostProcessor, batch_size=self.batch_size) results = self.mUlti.eval(testBatchIter, get_perp=self.get_perp) print(results) results_dict['accuracy'].append(results['accuracy']) if 'perplexity' in results: results_dict['perplexity'].append(results['perplexity']) results_dict['log_perplexity'].append(results['log_perplexity']) results_dict['perplexity_x_only'].append(results['perplexity_x_only']) for f_measure_class in results['f-measure']: if f_measure_class not in results_dict['f-measure']: results_dict['f-measure'][f_measure_class] = {'precision':[], 'recall':[], 'f-measure':[], 'total_pred':[], 'total_true':[], 'matches':[]} results_dict['f-measure'][f_measure_class]['precision'].append(results['f-measure'][f_measure_class][0]) results_dict['f-measure'][f_measure_class]['recall'].append(results['f-measure'][f_measure_class][1]) results_dict['f-measure'][f_measure_class]['f-measure'].append(results['f-measure'][f_measure_class][2]) results_dict['f-measure'][f_measure_class]['total_pred'].append(results['f-measure'][f_measure_class][3]) results_dict['f-measure'][f_measure_class]['total_true'].append(results['f-measure'][f_measure_class][4]) results_dict['f-measure'][f_measure_class]['matches'].append(results['f-measure'][f_measure_class][5]) fold_index += 1 print(results_dict) overall_accuracy = sum(results_dict['accuracy'])/len(results_dict['accuracy']) if len(results_dict['perplexity']) >0: overall_perplexity = sum(results_dict['perplexity'])/len(results_dict['perplexity']) print('perplexity: ', overall_perplexity) overall_log_perplexity = sum(results_dict['log_perplexity'])/len(results_dict['log_perplexity']) print('log perplexity: ', overall_log_perplexity) overall_perplexity_x = sum(results_dict['perplexity_x_only'])/len(results_dict['perplexity_x_only']) print('perplexity_x_only: ', overall_perplexity_x) macro_precision = get_average_fmeasure_score(results_dict, 'precision') macro_recall = get_average_fmeasure_score(results_dict, 'recall') macro_fmeasure = get_average_fmeasure_score(results_dict, 'f-measure') micro_precision = get_micro_fmeasure(results_dict, 'matches', 'total_pred') micro_recall = get_micro_fmeasure(results_dict, 'matches', 'total_true') micro_fmeasure = 2((micro_precisionmicro_recall)/(micro_precision+micro_recall)) print('accuracy: ', overall_accuracy) print('micro_precision: ', micro_precision) print('micro_recall: ', micro_recall) print('micro_f-measure: ', micro_fmeasure) print('macro_precision: ', macro_precision) print('macro_recall: ', macro_recall) print('macro_f-measure: ', macro_fmeasure) def reconstruct_ids(self, each_fold): output_ids = [[],[]] #[train_ids, test_ids] for sp_id in range(len(each_fold)): current_output_ids = output_ids[sp_id] current_fold_ids = each_fold[sp_id] for doc_id in current_fold_ids: current_output_ids.append(self.all_ids[doc_id]) return output_ids def buildDict(self): batchiter = BatchIterBert(self.trainDataIter, filling_last_batch=False, postProcessor=xonlyBatchProcessor, batch_size=1) common_dictionary = Dictionary(batchiter) print(len(common_dictionary)) if self.testReaderargs: print('update vocab from test set') batchiter = BatchIterBert(self.testDataIter, filling_last_batch=False, postProcessor=xonlyBatchProcessor, batch_size=1) common_dictionary.add_documents(batchiter) print(len(common_dictionary)) common_dictionary.filter_extremes(no_below=self.dict_no_below, no_above=self.dict_no_above, keep_n=self.dict_keep_n) self.dictProcess = DictionaryProcess(common_dictionary) self.postProcessor.dictProcess = self.dictProcess self.vocab_dim = len(self.dictProcess) self.have_dict = True if 1: count_list = [] self.trainDataIter._reset_iter() batchiter = BatchIterBert(self.trainDataIter, filling_last_batch=False, postProcessor=xonlyBatchProcessor, batch_size=1) for item in batchiter: current_count = sum(item) count_list.append(current_count) #print(current_count) print(sum(count_list)/len(count_list)) def getModel(self): self.net = Model(config, vocab_dim=vocab_dim) def getLibs(self): print('getting libs') print(self.modelType) global modelUlti global Model global xonlyBatchProcessor global batchPostProcessor global dataIter global ReaderPostProcessor if self.modelType == 'clsTopic': from GateMIcateLib import ModelUltiClass as modelUlti from GateMIcateLib.models import CLSAW_TopicModel as Model from GateMIcateLib.batchPostProcessors import xonlyBatchProcessor from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor self.get_dict = True self.get_perp = True elif self.modelType == 'clsTopicSL': from GateMIcateLib import ModelUltiClass as modelUlti from GateMIcateLib.models import CLSAW_TopicModelSL as Model from GateMIcateLib.batchPostProcessors import xonlyBatchProcessor from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor self.get_dict = True self.get_perp = True elif self.modelType == 'baselineBert': from GateMIcateLib import ModelUlti as modelUlti from GateMIcateLib.models import BERT_Simple as Model from GateMIcateLib.batchPostProcessors import xyOnlyBertBatchProcessor as batchPostProcessor elif self.modelType == 'nvdm': from GateMIcateLib import ModelUltiClass as modelUlti from GateMIcateLib.models import NVDM as Model from GateMIcateLib.batchPostProcessors import xonlyBatchProcessor from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor self.get_dict = True self.get_perp = True elif self.modelType == 'orinvdm': from GateMIcateLib import ModelUltiClass as modelUlti from GateMIcateLib.models import ORINVDM as Model from GateMIcateLib.batchPostProcessors import xonlyBatchProcessor from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor self.get_dict = True self.get_perp = True elif self.modelType == 'clsTopicBE': from GateMIcateLib import ModelUltiClass as modelUlti from GateMIcateLib.models import CLSAW_TopicModel_BERTEN as Model from GateMIcateLib.batchPostProcessors import xonlyBatchProcessor from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor self.get_dict = True self.get_perp = True print(self.corpusType) if self.corpusType == 'wvmisinfo': from GateMIcateLib.readers import WVmisInfoDataIter as dataIter from GateMIcateLib import WVPostProcessor as ReaderPostProcessor self.dict_no_below = 3 self.dict_no_above = 0.7 elif self.corpusType == 'wvmisinfoScholar': from GateMIcateLib.readers import WVmisInfoDataIter as dataIter from GateMIcateLib import ScholarPostProcessor as ReaderPostProcessor self.dict_keep_n = 2000 elif self.corpusType == 'aclIMDB': from GateMIcateLib.readers import ACLimdbReader as dataIter from GateMIcateLib import ScholarPostProcessor as ReaderPostProcessor elif self.corpusType == 'tsvBinary': from GateMIcateLib.readers import TsvBinaryFolderReader as dataIter from GateMIcateLib import WVPostProcessor as ReaderPostProcessor self.dict_no_below = 3 self.dict_no_above = 0.7	20,370	42.342553	208	py
CANTM	CANTM-main/GateMIcateLib/WVPostProcessor.py	import nltk from nltk.corpus import stopwords import os import re from .PostprocessorBase import ReaderPostProcessorBase from transformers import BertTokenizer class WVPostProcessor(ReaderPostProcessorBase): def __init__(self, x_fields=['Claim', 'Explaination'], y_field='category', kwargs): super().__init__(kwargs) self.x_fields = x_fields self.y_field = y_field self.initProcessor() def initProcessor(self): bert_tokenizer_path = os.path.join(self.config['BERT'].get('bert_path'), 'tokenizer') self.bert_tokenizer = BertTokenizer.from_pretrained(bert_tokenizer_path) self.tokenizerProcessor = self.bertTokenizer self.word2idProcessor = self.bertWord2id if 'TARGET' in self.config: self.labelsFields = self.config['TARGET'].get('labels') else: self.labelsFields = ['PubAuthAction', 'CommSpread', 'GenMedAdv', 'PromActs', 'Consp', 'VirTrans', 'VirOrgn', 'PubPrep', 'Vacc', 'Prot', 'None'] #print(self.labelsFields) def postProcess(self, sample): split_x = [] for x_field in self.x_fields: current_rawx = self._get_sample(sample, x_field) split_x.append(current_rawx) current_rawx = ' '.join(split_x) ## Bert toknise for hidden layers. add_special_tokens not added, additional attention will be applied on token level (CLS not used) if self.embd_ready: current_x = sample['embd'] else: current_x = self.x_pipeline(current_rawx, add_special_tokens=self.add_spec_tokens) ## NLTK tokenise and remove stopwords for topic modelling current_x_nltk_tokened = self.nltkTokenizer(current_rawx) current_x_nltk_tokened = self._remove_stop_words(current_x_nltk_tokened) if self.dictProcess: current_x_nltk_tokened = self.dictProcess.doc2countHot(current_x_nltk_tokened) x=[current_x, current_x_nltk_tokened] y = sample[self.y_field] if self.label2id: y = self.label2ids(y) if self.remove_single_list: x = self._removeSingleList(x) y = self._removeSingleList(y) return x, y	2,216	37.894737	155	py
CANTM	CANTM-main/GateMIcateLib/models/CLSAW_TopicModel_simple_loss.py	import torch from torch import nn from torch.nn import init from torch.nn import functional as F import math from .miscLayer import BERT_Embedding, WVHidden, WVClassifier, Identity, Topics, kld, CLSAW_TopicModel_Base class CLSAW_TopicModelSL(CLSAW_TopicModel_Base): def __init__(self, config, vocab_dim=None): super().__init__(config=config) default_config = {} self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim if self.banlance_loss: self.banlance_lambda = float(math.ceil(vocab_dim/self.n_classes)) else: self.banlance_lambda = 1 #self.wv_hidden = WVHidden(bert_dim, self.hidden_dim) self.hidden_dim = bert_dim ##############M1########################################### self.mu_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.x_only_topics = Topics(self.z_dim, vocab_dim) self.xy_classifier = WVClassifier(self.z_dim, self.n_classes) self.class_criterion = nn.CrossEntropyLoss() #############M2############################################ self.hidden_y_dim = self.hidden_dim + self.n_classes self.z_y_dim = self.z_dim + self.n_classes self.x_y_hidden = WVHidden(self.hidden_y_dim, self.hidden_dim) self.z_y_hidden = WVHidden(self.z_y_dim, self.ntopics) self.mu_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.xy_topics = Topics(self.ntopics, vocab_dim) self.z2y_classifier = WVClassifier(self.ntopics, self.n_classes) ############################################################ self.h_to_z = Identity() self.class_topics = Topics(self.n_classes, vocab_dim) self.reset_parameters() def forward(self,x, mask=None, n_samples=1, bow=None, train=False, true_y=None, pre_embd=False, true_y_ids=None): #print(true_y.shape) if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] atted = bert_rep[:,0] #hidden = self.wv_hidden(atted) hidden = atted mu_z1 = self.mu_z1(hidden) log_sigma_z1 = self.log_sigma_z1(hidden) kldz1 = kld(mu_z1, log_sigma_z1) rec_loss_z1 = 0 classifier_loss = 0 kldz2 = 0 rec_loss_z2 = 0 log_y_hat_rec_loss = 0 class_topic_rec_loss = 0 if not train: ### for discriminator, we only use mean z1 = mu_z1 y_hat_logis = self.xy_classifier(z1) log_probz_1 = self.x_only_topics(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) log_prob_class_topic = self.class_topics(y_hat) #y = y_hat_logis for i in range(n_samples): if train: z1 = torch.zeros_like(mu_z1).normal_() * torch.exp(log_sigma_z1) + mu_z1 z1 = self.h_to_z(z1) log_probz_1 = self.x_only_topics(z1) y_hat_logis = self.xy_classifier(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) log_prob_class_topic = self.class_topics(y_hat) classifier_loss += self.class_criterion(y_hat_logis, true_y_ids) y_hat_h = torch.cat((hidden, y_hat), dim=-1) x_y_hidden = self.x_y_hidden(y_hat_h) mu_z2 = self.mu_z2(x_y_hidden) log_sigma_z2 = self.log_sigma_z2(x_y_hidden) z2 = torch.zeros_like(mu_z2).normal_() * torch.exp(log_sigma_z2) + mu_z2 topic = z2 log_prob_z2 = self.xy_topics(topic) #y_hat_rec = self.z2y_classifier(topic) #log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) rec_loss_z1 = rec_loss_z1-(log_probz_1 * bow).sum(dim=-1) kldz2 += kld(mu_z2, log_sigma_z2) rec_loss_z2 = rec_loss_z2 - (log_prob_z2 * bow).sum(dim=-1) #log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_rectrue_y).sum(dim=-1) #log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_recy_hat).sum(dim=-1) class_topic_rec_loss = class_topic_rec_loss - (log_prob_class_topicbow).sum(dim=-1) rec_loss_z1 = rec_loss_z1/n_samples #print(rec_loss_z1.shape) classifier_loss = classifier_loss/n_samples kldz2 = kldz2/n_samples rec_loss_z2 = rec_loss_z2/n_samples log_y_hat_rec_loss = log_y_hat_rec_loss/n_samples class_topic_rec_loss = class_topic_rec_loss/n_samples elbo_z1 = kldz1 + rec_loss_z1 #print(elbo_z1.shape) #elbo_z1 = elbo_z1.sum() elbo_z2 = kldz2 + rec_loss_z2# + log_y_hat_rec_loss #print(elbo_z2) #elbo_z2 = elbo_z2.sum() #class_topic_rec_loss = class_topic_rec_loss.sum() classifier_loss = classifier_loss total_loss = elbo_z1.sum() + elbo_z2.sum() + class_topic_rec_loss.sum() + classifier_lossself.banlance_lambdaself.classification_loss_lambda y = { 'loss': total_loss, 'elbo_xy': elbo_z2, 'rec_loss': rec_loss_z2, 'kld': kldz2, 'cls_loss': classifier_loss, 'class_topic_loss': class_topic_rec_loss, 'y_hat': y_hat_logis, 'elbo_x': elbo_z1 } #################################################################################################################################################### # else: # z1 = mu_z1 # y_hat_logis = self.xy_classifier(z1) # y_hat = torch.softmax(y_hat_logis, dim=-1) # y = y_hat_logis # # # y_hat_h = torch.cat((hidden, y_hat), dim=-1) # x_y_hidden = self.x_y_hidden(y_hat_h) # mu_z2 = self.mu_z2(x_y_hidden) # log_sigma_z2 = self.log_sigma_z2(x_y_hidden) # z2 = torch.zeros_like(mu_z2).normal_() torch.exp(log_sigma_z2) + mu_z2 # # kldz2 = kld(mu_z2, log_sigma_z2) # log_prob_z2 = self.xy_topics(z2) # y_hat_rec = self.z2y_classifier(z2) # log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) # # return y, None def reset_parameters(self): init.zeros_(self.log_sigma_z1.weight) init.zeros_(self.log_sigma_z1.bias) init.zeros_(self.log_sigma_z2.weight) init.zeros_(self.log_sigma_z2.bias) def get_topics(self): return self.xy_topics.get_topics() def get_class_topics(self): return self.class_topics.get_topics() def get_x_only_topics(self): return self.x_only_topics.get_topics()	6,776	35.435484	150	py
CANTM	CANTM-main/GateMIcateLib/models/CLSAW_TopicModel.py	import torch from torch import nn from torch.nn import init from torch.nn import functional as F import math from .miscLayer import BERT_Embedding, WVHidden, WVClassifier, Identity, Topics, kld, CLSAW_TopicModel_Base class CLSAW_TopicModel(CLSAW_TopicModel_Base): def __init__(self, config, vocab_dim=None): super().__init__(config=config) default_config = {} self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim if self.banlance_loss: self.banlance_lambda = float(math.ceil(vocab_dim/self.n_classes)) else: self.banlance_lambda = 1 #self.wv_hidden = WVHidden(bert_dim, self.hidden_dim) self.hidden_dim = bert_dim ##############M1########################################### self.mu_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.x_only_topics = Topics(self.z_dim, vocab_dim) self.xy_classifier = WVClassifier(self.z_dim, self.n_classes) self.class_criterion = nn.CrossEntropyLoss() #############M2############################################ self.hidden_y_dim = self.hidden_dim + self.n_classes self.z_y_dim = self.z_dim + self.n_classes self.x_y_hidden = WVHidden(self.hidden_y_dim, self.hidden_dim) self.z_y_hidden = WVHidden(self.z_y_dim, self.ntopics) self.mu_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.xy_topics = Topics(self.ntopics, vocab_dim) self.z2y_classifier = WVClassifier(self.ntopics, self.n_classes) ############################################################ self.h_to_z = Identity() self.class_topics = Topics(self.n_classes, vocab_dim) self.reset_parameters() def forward(self,x, mask=None, n_samples=1, bow=None, train=False, true_y=None, pre_embd=False, true_y_ids=None, update_catopic=False): #print(true_y.shape) if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] atted = bert_rep[:,0] #hidden = self.wv_hidden(atted) hidden = atted mu_z1 = self.mu_z1(hidden) log_sigma_z1 = self.log_sigma_z1(hidden) kldz1 = kld(mu_z1, log_sigma_z1) rec_loss_z1 = 0 classifier_loss = 0 kldz2 = 0 rec_loss_z2 = 0 log_y_hat_rec_loss = 0 class_topic_rec_loss = 0 #if not train: # ### for discriminator, we only use mean # z1 = mu_z1 # y_hat_logis = self.xy_classifier(z1) # log_probz_1 = self.x_only_topics(z1) # y_hat = torch.softmax(y_hat_logis, dim=-1) # log_prob_class_topic = self.class_topics(y_hat) # #y = y_hat_logis for i in range(n_samples): z1 = torch.zeros_like(mu_z1).normal_() * torch.exp(log_sigma_z1) + mu_z1 z1 = self.h_to_z(z1) log_probz_1 = self.x_only_topics(z1) #if train or update_catopic: # z1 = torch.zeros_like(mu_z1).normal_() * torch.exp(log_sigma_z1) + mu_z1 # z1 = self.h_to_z(z1) # log_probz_1 = self.x_only_topics(z1) # y_hat_logis = self.xy_classifier(z1) # y_hat = torch.softmax(y_hat_logis, dim=-1) # log_prob_class_topic = self.class_topics(y_hat) if train or update_catopic: y_hat_logis = self.xy_classifier(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) #print(y_hat.shape) else: y_hat_logis = self.xy_classifier(mu_z1) y_hat = torch.softmax(y_hat_logis, dim=-1) if train: classifier_loss += self.class_criterion(y_hat_logis, true_y_ids) log_prob_class_topic = self.class_topics(y_hat) y_hat_h = torch.cat((hidden, y_hat), dim=-1) x_y_hidden = self.x_y_hidden(y_hat_h) mu_z2 = self.mu_z2(x_y_hidden) log_sigma_z2 = self.log_sigma_z2(x_y_hidden) z2 = torch.zeros_like(mu_z2).normal_() * torch.exp(log_sigma_z2) + mu_z2 y_hat_z = torch.cat((z2, y_hat), dim=-1) topic = self.z_y_hidden(y_hat_z) log_prob_z2 = self.xy_topics(topic) y_hat_rec = self.z2y_classifier(topic) log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) rec_loss_z1 = rec_loss_z1-(log_probz_1 * bow).sum(dim=-1) kldz2 += kld(mu_z2, log_sigma_z2) rec_loss_z2 = rec_loss_z2 - (log_prob_z2 * bow).sum(dim=-1) #log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_rectrue_y).sum(dim=-1) log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_recy_hat).sum(dim=-1) class_topic_rec_loss = class_topic_rec_loss - (log_prob_class_topicbow).sum(dim=-1) rec_loss_z1 = rec_loss_z1/n_samples #print(rec_loss_z1.shape) classifier_loss = classifier_loss/n_samples kldz2 = kldz2/n_samples rec_loss_z2 = rec_loss_z2/n_samples log_y_hat_rec_loss = log_y_hat_rec_loss/n_samples class_topic_rec_loss = class_topic_rec_loss/n_samples elbo_z1 = kldz1 + rec_loss_z1 #print(elbo_z1.shape) #elbo_z1 = elbo_z1.sum() elbo_z2 = kldz2 + rec_loss_z2 + log_y_hat_rec_loss #print(elbo_z2) #elbo_z2 = elbo_z2.sum() #class_topic_rec_loss = class_topic_rec_loss.sum() classifier_loss = classifier_loss total_loss = elbo_z1.sum() + elbo_z2.sum() + class_topic_rec_loss.sum() + classifier_lossself.banlance_lambdaself.classification_loss_lambda if update_catopic: total_loss = elbo_z2.sum() y = { 'loss': total_loss, 'elbo_xy': elbo_z2, 'rec_loss': rec_loss_z2, 'kld': kldz2, 'cls_loss': classifier_loss, 'class_topic_loss': class_topic_rec_loss, 'y_hat': y_hat_logis, 'elbo_x': elbo_z1 } #################################################################################################################################################### # else: # z1 = mu_z1 # y_hat_logis = self.xy_classifier(z1) # y_hat = torch.softmax(y_hat_logis, dim=-1) # y = y_hat_logis # # # y_hat_h = torch.cat((hidden, y_hat), dim=-1) # x_y_hidden = self.x_y_hidden(y_hat_h) # mu_z2 = self.mu_z2(x_y_hidden) # log_sigma_z2 = self.log_sigma_z2(x_y_hidden) # z2 = torch.zeros_like(mu_z2).normal_() torch.exp(log_sigma_z2) + mu_z2 # # kldz2 = kld(mu_z2, log_sigma_z2) # log_prob_z2 = self.xy_topics(z2) # y_hat_rec = self.z2y_classifier(z2) # log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) # # return y, None def reset_parameters(self): init.zeros_(self.log_sigma_z1.weight) init.zeros_(self.log_sigma_z1.bias) init.zeros_(self.log_sigma_z2.weight) init.zeros_(self.log_sigma_z2.bias) def get_topics(self): return self.xy_topics.get_topics() def get_class_topics(self): return self.class_topics.get_topics() def get_x_only_topics(self): return self.x_only_topics.get_topics()	7,541	35.434783	150	py
CANTM	CANTM-main/GateMIcateLib/models/CLSAW_TopicModelBertEnrich.py	import torch from torch import nn from torch.nn import init from torch.nn import functional as F import math from .miscLayer import BERT_Embedding, WVHidden, WVClassifier, Identity, Topics, kld, CLSAW_TopicModel_Base class CLSAW_TopicModel_BERTEN(CLSAW_TopicModel_Base): def __init__(self, config, vocab_dim=None): super().__init__(config=config) default_config = {} self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim if self.banlance_loss: self.banlance_lambda = float(math.ceil(vocab_dim/self.n_classes)) else: self.banlance_lambda = 1 self.hidden_dim = 500 self.bow_hidden = WVHidden(vocab_dim, 500) self.mix_bert = WVHidden(500+ bert_dim, self.hidden_dim) ##############M1########################################### self.mu_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.x_only_topics = Topics(self.z_dim, vocab_dim) self.xy_classifier = WVClassifier(self.z_dim, self.n_classes) self.class_criterion = nn.CrossEntropyLoss() #############M2############################################ self.hidden_y_dim = self.hidden_dim + self.n_classes self.z_y_dim = self.z_dim + self.n_classes self.x_y_hidden = WVHidden(self.hidden_y_dim, self.hidden_dim) self.z_y_hidden = WVHidden(self.z_y_dim, self.ntopics) self.mu_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.xy_topics = Topics(self.ntopics, vocab_dim) self.z2y_classifier = WVClassifier(self.ntopics, self.n_classes) ############################################################ self.h_to_z = Identity() self.class_topics = Topics(self.n_classes, vocab_dim) self.reset_parameters() def forward(self,x, mask=None, n_samples=1, bow=None, train=False, true_y=None, pre_embd=False, true_y_ids=None): #print(true_y.shape) if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] bow_hidden = self.bow_hidden(bow) atted = bert_rep[:,0] bert_bow = torch.cat((atted, bow_hidden), dim=-1) hidden = self.mix_bert(bert_bow) #hidden = atted mu_z1 = self.mu_z1(hidden) log_sigma_z1 = self.log_sigma_z1(hidden) kldz1 = kld(mu_z1, log_sigma_z1) rec_loss_z1 = 0 classifier_loss = 0 kldz2 = 0 rec_loss_z2 = 0 log_y_hat_rec_loss = 0 class_topic_rec_loss = 0 if not train: ### for discriminator, we only use mean z1 = mu_z1 y_hat_logis = self.xy_classifier(z1) log_probz_1 = self.x_only_topics(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) log_prob_class_topic = self.class_topics(y_hat) #y = y_hat_logis for i in range(n_samples): if train: z1 = torch.zeros_like(mu_z1).normal_() * torch.exp(log_sigma_z1) + mu_z1 z1 = self.h_to_z(z1) log_probz_1 = self.x_only_topics(z1) y_hat_logis = self.xy_classifier(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) log_prob_class_topic = self.class_topics(y_hat) classifier_loss += self.class_criterion(y_hat_logis, true_y_ids) y_hat_h = torch.cat((hidden, y_hat), dim=-1) x_y_hidden = self.x_y_hidden(y_hat_h) mu_z2 = self.mu_z2(x_y_hidden) log_sigma_z2 = self.log_sigma_z2(x_y_hidden) z2 = torch.zeros_like(mu_z2).normal_() * torch.exp(log_sigma_z2) + mu_z2 y_hat_z = torch.cat((z2, y_hat), dim=-1) topic = self.z_y_hidden(y_hat_z) log_prob_z2 = self.xy_topics(topic) y_hat_rec = self.z2y_classifier(topic) log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) rec_loss_z1 = rec_loss_z1-(log_probz_1 * bow).sum(dim=-1) kldz2 += kld(mu_z2, log_sigma_z2) rec_loss_z2 = rec_loss_z2 - (log_prob_z2 * bow).sum(dim=-1) #log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_rectrue_y).sum(dim=-1) log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_recy_hat).sum(dim=-1) class_topic_rec_loss = class_topic_rec_loss - (log_prob_class_topicbow).sum(dim=-1) rec_loss_z1 = rec_loss_z1/n_samples #print(rec_loss_z1.shape) classifier_loss = classifier_loss/n_samples kldz2 = kldz2/n_samples rec_loss_z2 = rec_loss_z2/n_samples log_y_hat_rec_loss = log_y_hat_rec_loss/n_samples class_topic_rec_loss = class_topic_rec_loss/n_samples elbo_z1 = kldz1 + rec_loss_z1 #print(elbo_z1.shape) #elbo_z1 = elbo_z1.sum() elbo_z2 = kldz2 + rec_loss_z2 + log_y_hat_rec_loss #print(elbo_z2) #elbo_z2 = elbo_z2.sum() #class_topic_rec_loss = class_topic_rec_loss.sum() classifier_loss = classifier_loss total_loss = elbo_z1.sum() + elbo_z2.sum() + class_topic_rec_loss.sum() + classifier_lossself.banlance_lambdaself.classification_loss_lambda y = { 'loss': total_loss, 'elbo_xy': elbo_z2, 'rec_loss': rec_loss_z2, 'kld': kldz2, 'cls_loss': classifier_loss, 'class_topic_loss': class_topic_rec_loss, 'y_hat': y_hat_logis, 'elbo_x': elbo_z1 } #################################################################################################################################################### # else: # z1 = mu_z1 # y_hat_logis = self.xy_classifier(z1) # y_hat = torch.softmax(y_hat_logis, dim=-1) # y = y_hat_logis # # # y_hat_h = torch.cat((hidden, y_hat), dim=-1) # x_y_hidden = self.x_y_hidden(y_hat_h) # mu_z2 = self.mu_z2(x_y_hidden) # log_sigma_z2 = self.log_sigma_z2(x_y_hidden) # z2 = torch.zeros_like(mu_z2).normal_() torch.exp(log_sigma_z2) + mu_z2 # # kldz2 = kld(mu_z2, log_sigma_z2) # log_prob_z2 = self.xy_topics(z2) # y_hat_rec = self.z2y_classifier(z2) # log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) # # return y, None def reset_parameters(self): init.zeros_(self.log_sigma_z1.weight) init.zeros_(self.log_sigma_z1.bias) init.zeros_(self.log_sigma_z2.weight) init.zeros_(self.log_sigma_z2.bias) def get_topics(self): return self.xy_topics.get_topics() def get_class_topics(self): return self.class_topics.get_topics() def get_x_only_topics(self): return self.x_only_topics.get_topics()	7,008	34.760204	150	py
CANTM	CANTM-main/GateMIcateLib/models/miscLayer.py	from transformers import BertModel import math import os import torch.nn.functional as F import torch import torch.nn as nn class SingleHeadAttention(nn.Module): def __init__(self, d_model, d_output, dropout = 0.1): super().__init__() self.q = nn.Parameter(torch.randn([d_output, 1]).float()) self.v_linear = nn.Linear(d_model, d_output) self.dropout_v = nn.Dropout(dropout) self.k_linear = nn.Linear(d_model, d_output) self.dropout_k = nn.Dropout(dropout) self.softmax_simi = nn.Softmax(dim=1) self.dropout = nn.Dropout(dropout) #self.out = nn.Linear(d_output, d_output) def forward(self, x, mask=None): k = self.k_linear(x) k = F.relu(k) k = self.dropout_k(k) v = self.v_linear(x) v = F.relu(v) v = self.dropout_v(v) dotProducSimi = k.matmul(self.q) normedSimi = self.softmax_simi(dotProducSimi) attVector = v.mul(normedSimi) weightedSum = torch.sum(attVector, dim=1) #output = self.out(weightedSum) return weightedSum class Norm(nn.Module): def __init__(self, d_model, eps = 1e-6): super().__init__() self.size = d_model # create two learnable parameters to calibrate normalisation self.alpha = nn.Parameter(torch.ones(self.size)) self.bias = nn.Parameter(torch.zeros(self.size)) self.eps = eps def forward(self, x): norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) \ / (x.std(dim=-1, keepdim=True) + self.eps) + self.bias return norm def attention(q, k, v, d_k, mask=None, dropout=None): scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k) if mask is not None: mask = mask.unsqueeze(1) scores = scores.masked_fill(mask == 0, -1e9) scores = F.softmax(scores, dim=-1) if dropout is not None: scores = dropout(scores) output = torch.matmul(scores, v) return output class EncoderLayer(nn.Module): def __init__(self, d_model, heads, dropout=0.1): super().__init__() self.norm_1 = Norm(d_model) self.norm_2 = Norm(d_model) self.attn = MultiHeadAttention(heads, d_model, dropout=dropout) self.ff = FeedForward(d_model, dropout=dropout) self.dropout_1 = nn.Dropout(dropout) self.dropout_2 = nn.Dropout(dropout) def forward(self, x, mask): x2 = self.norm_1(x) x = x + self.dropout_1(self.attn(x2,x2,x2,mask)) x2 = self.norm_2(x) x = x + self.dropout_2(self.ff(x2)) return x class MultiHeadAttention(nn.Module): def __init__(self, heads, d_model, dropout = 0.1): super().__init__() self.d_model = d_model self.d_k = d_model // heads self.h = heads self.q_linear = nn.Linear(d_model, d_model) self.v_linear = nn.Linear(d_model, d_model) self.k_linear = nn.Linear(d_model, d_model) self.dropout = nn.Dropout(dropout) self.out = nn.Linear(d_model, d_model) def forward(self, q, k, v, mask=None): bs = q.size(0) # perform linear operation and split into N heads # bs, sl, d_model --> bs, sl, heads, sub_d_model # d_model = heads * sub_d_model k = self.k_linear(k).view(bs, -1, self.h, self.d_k) q = self.q_linear(q).view(bs, -1, self.h, self.d_k) v = self.v_linear(v).view(bs, -1, self.h, self.d_k) # transpose to get dimensions bs * N * sl * d_model k = k.transpose(1,2) q = q.transpose(1,2) v = v.transpose(1,2) # calculate attention using function we will define next scores = attention(q, k, v, self.d_k, mask, self.dropout) # concatenate heads and put through final linear layer concat = scores.transpose(1,2).contiguous()\ .view(bs, -1, self.d_model) output = self.out(concat) return output class FeedForward(nn.Module): def __init__(self, d_model, d_ff=1024, dropout = 0.1): super().__init__() # We set d_ff as a default to 2048 self.linear_1 = nn.Linear(d_model, d_ff) self.dropout = nn.Dropout(dropout) self.linear_2 = nn.Linear(d_ff, d_model) def forward(self, x): x = self.dropout(F.relu(self.linear_1(x))) x = self.linear_2(x) return x class BERT_Embedding(nn.Module): def __init__(self, config): super().__init__() bert_model_path = os.path.join(config['BERT'].get('bert_path'), 'model') self.bert_dim = int(config['BERT'].get('bert_dim')) self.trainable_layers = config['BERT'].get('trainable_layers') self.bert = BertModel.from_pretrained(bert_model_path) if self.trainable_layers: #print(self.trainable_layers) #self.bert = BertModel.from_pretrained(bert_model_path) for name, param in self.bert.named_parameters(): if name in self.trainable_layers: param.requires_grad = True #print(name, param) else: param.requires_grad = False else: for p in self.bert.parameters(): p.requires_grad = False def forward(self, x, mask=None): if mask == None: mask = x != 0 mask.type(x.type()) bert_rep = self.bert(x, attention_mask=mask) return bert_rep class Dense(nn.Module): def __init__(self, input_dim, out_dim, non_linear=None): super().__init__() self.dense = nn.Linear(input_dim, out_dim) self.non_linear = non_linear def forward(self, x): output = self.dense(x) if self.non_linear: output = self.non_linear(output) return output class Topics(nn.Module): def __init__(self, k, vocab_size, bias=True): super(Topics, self).__init__() self.k = k self.vocab_size = vocab_size self.topic = nn.Linear(k, vocab_size, bias=bias) def forward(self, logit): # return the log_prob of vocab distribution return torch.log_softmax(self.topic(logit), dim=-1) def get_topics(self): #print('hey') #print(self.topic.weight) return torch.softmax(self.topic.weight.data.transpose(0, 1), dim=-1) def get_topic_word_logit(self): """topic x V. Return the logits instead of probability distribution """ return self.topic.weight.transpose(0, 1) class Identity(nn.Module): def __init__(self): super(Identity, self).__init__() def forward(self, input): if len(input) == 1: return input[0] return input def kld(mu, log_sigma): """log q(z) \|\| log p(z). mu: batch_size x dim log_sigma: batch_size x dim """ return -0.5 (1 - mu ** 2 + 2 * log_sigma - torch.exp(2 * log_sigma)).sum(dim=-1) class BERT_Mapping_mapping(nn.Module): def __init__(self, bert_dim): super().__init__() self.att = SingleHeadAttention(bert_dim, bert_dim) def forward(self,x): atted = self.att(x) return atted class WVHidden(nn.Module): def __init__(self, input_dim, hidden_dim): super().__init__() self.hidden1 = nn.Linear(input_dim, hidden_dim) def forward(self, x): hidden = F.leaky_relu(self.hidden1(x)) return hidden class WVClassifier(nn.Module): def __init__(self, n_hidden, n_classes): super().__init__() self.layer_output = torch.nn.Linear(n_hidden, n_classes) def forward(self, x): out = self.layer_output(x) return out class CLSAW_TopicModel_Base(nn.Module): def __init__(self, config=None): super().__init__() self._init_params() if config: self._read_config(config) def _init_params(self): self.hidden_dim = 300 self.z_dim = 100 self.ntopics = 50 self.class_topic_loss_lambda = 1 self.classification_loss_lambda = 1 self.banlance_loss = False def _read_config(self, config): self.n_classes = len(config['TARGET'].get('labels')) if 'MODEL' in config: if 'hidden_dim' in config['MODEL']: self.hidden_dim = int(config['MODEL'].get('hidden_dim')) if 'z_dim' in config['MODEL']: self.z_dim = int(config['MODEL'].get('z_dim')) if 'ntopics' in config['MODEL']: self.ntopics = int(config['MODEL'].get('ntopics')) if 'class_topic_loss_lambda' in config['MODEL']: self.class_topic_loss_lambda = float(config['MODEL'].get('class_topic_loss_lambda')) if 'classification_loss_lambda' in config['MODEL']: self.class_topic_loss_lambda = float(config['MODEL'].get('classification_loss_lambda')) if 'banlance_loss' in config['MODEL']: self.banlance_loss = config['MODEL'].as_bool('banlance_loss') self.n_class_topics = self.z_dim+self.n_classes	9,182	29.407285	103	py
CANTM	CANTM-main/GateMIcateLib/models/CLSAW_TopicModel_linear.py	import torch from torch import nn from torch.nn import init from torch.nn import functional as F import math from .miscLayer import BERT_Embedding, WVHidden, WVClassifier, Identity, Topics, kld, CLSAW_TopicModel_Base class CLSAW_TopicModel(CLSAW_TopicModel_Base): def __init__(self, config, vocab_dim=None): super().__init__(config=config) default_config = {} self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim if self.banlance_loss: self.banlance_lambda = float(math.ceil(vocab_dim/self.n_classes)) else: self.banlance_lambda = 1 #self.wv_hidden = WVHidden(bert_dim, self.hidden_dim) self.hidden_dim = bert_dim ##############M1########################################### self.mu_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.x_only_topics = Topics(self.z_dim, vocab_dim) self.xy_classifier = WVClassifier(self.z_dim, self.n_classes) self.class_criterion = nn.CrossEntropyLoss() #############M2############################################ self.hidden_y_dim = self.hidden_dim + self.n_classes self.z_y_dim = self.z_dim + self.n_classes self.x_y_hidden = WVHidden(self.hidden_y_dim, self.hidden_dim) self.z_y_hidden = WVHidden(self.z_y_dim, self.ntopics) self.mu_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.xy_topics = Topics(self.ntopics, vocab_dim) self.z2y_classifier = WVClassifier(self.ntopics, self.n_classes) ############################################################ self.h_to_z = Identity() self.class_topics = Topics(self.n_classes, vocab_dim) self.reset_parameters() def forward(self,x, mask=None, n_samples=1, bow=None, train=False, true_y=None, pre_embd=False, true_y_ids=None): #print(true_y.shape) if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] atted = bert_rep[:,0] #hidden = self.wv_hidden(atted) hidden = atted mu_z1 = self.mu_z1(hidden) log_sigma_z1 = self.log_sigma_z1(hidden) kldz1 = kld(mu_z1, log_sigma_z1) rec_loss_z1 = 0 classifier_loss = 0 kldz2 = 0 rec_loss_z2 = 0 log_y_hat_rec_loss = 0 class_topic_rec_loss = 0 if not train: ### for discriminator, we only use mean z1 = mu_z1 y_hat_logis = self.xy_classifier(z1) log_probz_1 = self.x_only_topics(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) log_prob_class_topic = self.class_topics(y_hat) #y = y_hat_logis for i in range(n_samples): if train: z1 = torch.zeros_like(mu_z1).normal_() * torch.exp(log_sigma_z1) + mu_z1 z1 = self.h_to_z(z1) log_probz_1 = self.x_only_topics(z1) y_hat_logis = self.xy_classifier(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) log_prob_class_topic = self.class_topics(y_hat) classifier_loss += self.class_criterion(y_hat_logis, true_y_ids) y_hat_h = torch.cat((hidden, y_hat), dim=-1) x_y_hidden = self.x_y_hidden(y_hat_h) mu_z2 = self.mu_z2(x_y_hidden) log_sigma_z2 = self.log_sigma_z2(x_y_hidden) z2 = torch.zeros_like(mu_z2).normal_() * torch.exp(log_sigma_z2) + mu_z2 y_hat_z = torch.cat((z2, y_hat), dim=-1) topic = self.z_y_hidden(y_hat_z) log_prob_z2 = self.xy_topics(topic) y_hat_rec = self.z2y_classifier(topic) log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) rec_loss_z1 = rec_loss_z1-(log_probz_1 * bow).sum(dim=-1) kldz2 += kld(mu_z2, log_sigma_z2) rec_loss_z2 = rec_loss_z2 - (log_prob_z2 * bow).sum(dim=-1) #log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_rectrue_y).sum(dim=-1) log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_recy_hat).sum(dim=-1) class_topic_rec_loss = class_topic_rec_loss - (log_prob_class_topicbow).sum(dim=-1) rec_loss_z1 = rec_loss_z1/n_samples #print(rec_loss_z1.shape) classifier_loss = classifier_loss/n_samples kldz2 = kldz2/n_samples rec_loss_z2 = rec_loss_z2/n_samples log_y_hat_rec_loss = log_y_hat_rec_loss/n_samples class_topic_rec_loss = class_topic_rec_loss/n_samples elbo_z1 = kldz1 + rec_loss_z1 #print(elbo_z1.shape) #elbo_z1 = elbo_z1.sum() elbo_z2 = kldz2 + rec_loss_z2 + log_y_hat_rec_loss #print(elbo_z2) #elbo_z2 = elbo_z2.sum() #class_topic_rec_loss = class_topic_rec_loss.sum() classifier_loss = classifier_loss total_loss = elbo_z1.sum() + elbo_z2.sum() + class_topic_rec_loss.sum() + classifier_lossself.banlance_lambdaself.classification_loss_lambda y = { 'loss': total_loss, 'elbo_xy': elbo_z2, 'rec_loss': rec_loss_z2, 'kld': kldz2, 'cls_loss': classifier_loss, 'class_topic_loss': class_topic_rec_loss, 'y_hat': y_hat_logis, 'elbo_x': elbo_z1 } #################################################################################################################################################### # else: # z1 = mu_z1 # y_hat_logis = self.xy_classifier(z1) # y_hat = torch.softmax(y_hat_logis, dim=-1) # y = y_hat_logis # # # y_hat_h = torch.cat((hidden, y_hat), dim=-1) # x_y_hidden = self.x_y_hidden(y_hat_h) # mu_z2 = self.mu_z2(x_y_hidden) # log_sigma_z2 = self.log_sigma_z2(x_y_hidden) # z2 = torch.zeros_like(mu_z2).normal_() torch.exp(log_sigma_z2) + mu_z2 # # kldz2 = kld(mu_z2, log_sigma_z2) # log_prob_z2 = self.xy_topics(z2) # y_hat_rec = self.z2y_classifier(z2) # log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) # # return y, None def reset_parameters(self): init.zeros_(self.log_sigma_z1.weight) init.zeros_(self.log_sigma_z1.bias) init.zeros_(self.log_sigma_z2.weight) init.zeros_(self.log_sigma_z2.bias) def get_topics(self): return self.xy_topics.get_topics() def get_class_topics(self): return self.class_topics.get_topics() def get_x_only_topics(self): return self.x_only_topics.get_topics()	6,843	35.795699	150	py
CANTM	CANTM-main/GateMIcateLib/models/bertSimple.py	from transformers import BertModel from .miscLayer import BERT_Embedding, CLSAW_TopicModel_Base, WVHidden import os import torch.nn.functional as F import torch import torch.nn as nn class BERT_Simple(CLSAW_TopicModel_Base): def __init__(self, config, **kwargs): super().__init__(config=config) self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim self.hidden_dim = bert_dim self.hidden2 = WVHidden(self.hidden_dim, self.z_dim) self.layer_output = torch.nn.Linear(self.z_dim, self.n_classes) #self.layer_output = torch.nn.Linear(bert_dim, self.n_classes) def forward(self, x, mask=None, pre_embd=False): if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] bert_rep = bert_rep[:,0] #hidden = self.hidden1(bert_rep) hidden = bert_rep hidden = self.hidden2(hidden) out = self.layer_output(hidden) y = { 'y_hat':out } return y	1,100	25.853659	71	py
CANTM	CANTM-main/GateMIcateLib/models/bertPure.py	from transformers import BertModel from .miscLayer import BERT_Embedding, CLSAW_TopicModel_Base, WVHidden import os import torch.nn.functional as F import torch import torch.nn as nn class BERT_Simple(CLSAW_TopicModel_Base): def __init__(self, config, **kwargs): super().__init__(config=config) self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim self.hidden_dim = bert_dim #self.hidden2 = WVHidden(self.hidden_dim, self.z_dim) #self.layer_output = torch.nn.Linear(self.z_dim, self.n_classes) self.layer_output = torch.nn.Linear(bert_dim, self.n_classes) def forward(self, x, mask=None, pre_embd=False): if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] bert_rep = bert_rep[:,0] #hidden = self.hidden1(bert_rep) hidden = bert_rep #hidden = self.hidden2(hidden) out = self.layer_output(hidden) y = { 'y_hat':out } return y	1,102	25.902439	72	py
CANTM	CANTM-main/GateMIcateLib/models/NVDM.py	import torch from torch import nn from torch.nn import init from torch.nn import functional as F import math from .miscLayer import BERT_Embedding, WVHidden, WVClassifier, Identity, Topics, kld, CLSAW_TopicModel_Base class NVDM(CLSAW_TopicModel_Base): def __init__(self, config, vocab_dim=None): super().__init__(config=config) default_config = {} self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim if self.banlance_loss: self.banlance_lambda = float(math.ceil(vocab_dim/self.n_classes)) else: self.banlance_lambda = 1 #self.wv_hidden = WVHidden(bert_dim, self.hidden_dim) self.hidden_dim = bert_dim ##############M1########################################### self.mu_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.x_only_topics = Topics(self.z_dim, vocab_dim) #self.xy_classifier = WVClassifier(self.z_dim, self.n_classes) #self.class_criterion = nn.CrossEntropyLoss() #############M2############################################ #self.hidden_y_dim = self.hidden_dim + self.n_classes #self.z_y_dim = self.z_dim + self.n_classes #self.x_y_hidden = WVHidden(self.hidden_y_dim, self.hidden_dim) #self.z_y_hidden = WVHidden(self.z_y_dim, self.ntopics) #self.mu_z2 = nn.Linear(self.hidden_dim, self.z_dim) #self.log_sigma_z2 = nn.Linear(self.hidden_dim, self.z_dim) #self.xy_topics = Topics(self.ntopics, vocab_dim) #self.z2y_classifier = WVClassifier(self.ntopics, self.n_classes) ############################################################ self.h_to_z = Identity() #self.class_topics = Topics(self.n_classes, vocab_dim) self.reset_parameters() def forward(self,x, mask=None, n_samples=1, bow=None, train=False, true_y=None, pre_embd=False, true_y_ids=None): #print(true_y.shape) if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] atted = bert_rep[:,0] #hidden = self.wv_hidden(atted) hidden = atted mu_z1 = self.mu_z1(hidden) log_sigma_z1 = self.log_sigma_z1(hidden) kldz1 = kld(mu_z1, log_sigma_z1) rec_loss_z1 = 0 classifier_loss = 0 kldz2 = 0 rec_loss_z2 = 0 log_y_hat_rec_loss = 0 class_topic_rec_loss = 0 for i in range(n_samples): z1 = torch.zeros_like(mu_z1).normal_() * torch.exp(log_sigma_z1) + mu_z1 z1 = self.h_to_z(z1) log_probz_1 = self.x_only_topics(z1) rec_loss_z1 = rec_loss_z1-(log_probz_1 * bow).sum(dim=-1) rec_loss_z1 = rec_loss_z1/n_samples elbo_z1 = kldz1 + rec_loss_z1 total_loss = elbo_z1.sum() y_hat_logis = torch.zeros(x.shape[0], self.n_classes) elbo_z2 = torch.zeros_like(elbo_z1) classifier_loss = torch.tensor(0) y = { 'loss': total_loss, 'elbo_xy': elbo_z2, 'rec_loss': rec_loss_z2, 'kld': kldz2, 'cls_loss': classifier_loss, 'class_topic_loss': class_topic_rec_loss, 'y_hat': y_hat_logis, 'elbo_x': elbo_z1 } return y, None def reset_parameters(self): init.zeros_(self.log_sigma_z1.weight) init.zeros_(self.log_sigma_z1.bias) def get_topics(self): return self.x_only_topics.get_topics() def get_class_topics(self): return self.x_only_topics.get_topics() def get_x_only_topics(self): return self.x_only_topics.get_topics()	3,804	31.801724	117	py

gecco-2022

gecco-2022-main/AND-XOR/DynamicalMatrix.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Oct 10 22:09:09 2017 @author: Hightoutou """ def DM_mass(N, x0, y0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] M = np.zeros((2*N, 2*N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly rijsq = dx**2+dy**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij m_sqrt = np.zeros((2*N, 2*N)) m_inv = np.zeros((2*N, 2*N)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_3D(N, x0, y0, z0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] Lz = L[2] M = np.zeros((3*N, 3*N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly dz = z0[i]-z0[j] dz = dz-round(dz/Lz)*Lz rijsq = dx**2+dy**2+dz**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy, dx*dz], [dy*dx, dy*dy, dy*dz], [dz*dx, dz*dy, dz*dz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3*i:3*i+3,3*j:3*j+3] = Mij M[3*j:3*j+3,3*i:3*i+3] = Mij M[3*i:3*i+3,3*i:3*i+3] = M[3*i:3*i+3,3*i:3*i+3] - Mij M[3*j:3*j+3,3*j:3*j+3] = M[3*j:3*j+3,3*j:3*j+3] - Mij m_sqrt = np.zeros((3*N, 3*N)) m_inv = np.zeros((3*N, 3*N)) for i in range(N): m_sqrt[3*i, 3*i] = 1/np.sqrt(m0[i]) m_sqrt[3*i+1, 3*i+1] = 1/np.sqrt(m0[i]) m_sqrt[3*i+2, 3*i+2] = 1/np.sqrt(m0[i]) m_inv[3*i, 3*i] = 1/m0[i] m_inv[3*i+1, 3*i+1] = 1/m0[i] m_inv[3*i+2, 3*i+2] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_Yfixed(N, x0, y0, D0, m0, Lx, y_bot, y_top, k): import numpy as np M = np.zeros((2*N, 2*N)) contactNum = 0 for i in range(N): r_now = 0.5*D0[i] if y0[i]-y_bot<r_now or y_top-y0[i]<r_now: M[2*i+1, 2*i+1] = M[2*i+1, 2*i+1]+1/r_now/r_now for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] rijsq = dx**2+dy**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij M = k*M m_sqrt = np.zeros((2*N, 2*N)) m_inv = np.zeros((2*N, 2*N)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_Xfixed(N, x0, y0, D0, m0, Ly): import numpy as np M = np.zeros((2*N, 2*N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly rijsq = dx**2+dy**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij m_sqrt = np.zeros((2*N, 2*N)) m_inv = np.zeros((2*N, 2*N)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_DiffK_Yfixed(N, x0, y0, D0, m0, Lx, y_bot, y_top, k_list, k_type): import numpy as np M = np.zeros((2*N, 2*N)) contactNum = 0 for i in range(N): r_now = 0.5*D0[i] if y0[i]-y_bot<r_now or y_top-y0[i]<r_now: M[2*i+1, 2*i+1] = M[2*i+1, 2*i+1] + k_list[k_type[i]] / r_now / r_now for j in range(i): dij = 0.5 * (D0[i] + D0[j]) dijsq = dij**2 dx = x0[i] - x0[j] dx = dx - round(dx / Lx) * Lx dy = y0[i] - y0[j] rijsq = dx**2 + dy**2 if rijsq < dijsq: contactNum += 1 k = k_list[(k_type[i] ^ k_type[j]) + np.maximum(k_type[i], k_type[j])] rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -k * rijmat / rijsq / dijsq Mij2 = -k * (1.0 - rij / dij) * (rijmat / rijsq - [[1,0],[0,1]]) / rij / dij Mij = Mij1 + Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij m_sqrt = np.zeros((2*N, 2*N)) m_inv = np.zeros((2*N, 2*N)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) w,v = np.linalg.eig(M) return w,v def DM_mass_Zfixed_3D(N, x0, y0, z0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] Lz = L[2] M = np.zeros((3*N, 3*N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly dz = z0[i]-z0[j] rijsq = dx**2+dy**2+dz**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy, dx*dz], [dy*dx, dy*dy, dy*dz], [dz*dz, dz*dy, dz*dz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3*i:3*i+3,3*j:3*j+3] = Mij M[3*j:3*j+3,3*i:3*i+3] = Mij M[3*i:3*i+3,3*i:3*i+3] = M[3*i:3*i+3,3*i:3*i+3] - Mij M[3*j:3*j+3,3*j:3*j+3] = M[3*j:3*j+3,3*j:3*j+3] - Mij m_sqrt = np.zeros((3*N, 3*N)) m_inv = np.zeros((3*N, 3*N)) for i in range(N): m_sqrt[3*i, 3*i] = 1/np.sqrt(m0[i]) m_sqrt[3*i+1, 3*i+1] = 1/np.sqrt(m0[i]) m_sqrt[3*i+2, 3*i+2] = 1/np.sqrt(m0[i]) m_inv[3*i, 3*i] = 1/m0[i] m_inv[3*i+1, 3*i+1] = 1/m0[i] m_inv[3*i+2, 3*i+2] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_UpPlate(N, x0, y0, D0, m0, Lx, y_up, m_up): import numpy as np M = np.zeros((2*N+1, 2*N+1)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] rijsq = dx**2+dy**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij for i in range(N): r_now = 0.5*D0[i] if y0[i]<r_now: M[2*i+1, 2*i+1] = M[2*i+1, 2*i+1]+1/r_now**2 if y_up-y0[i]<r_now: M[2*i+1, 2*i+1] = M[2*i+1, 2*i+1]+1/r_now**2 M[2*N, 2*N] = M[2*N, 2*N]+1/r_now**2 M[2*i+1, 2*N] = M[2*i+1, 2*N]-1/r_now**2 M[2*N, 2*i+1] = M[2*N, 2*i+1]-1/r_now**2 m_sqrt = np.zeros((2*N+1, 2*N+1)) m_inv = np.zeros((2*N+1, 2*N+1)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] m_sqrt[2*N, 2*N] = 1/np.sqrt(m_up) m_inv[2*N, 2*N] = 1/m_up #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_UpPlate_3D(N, x0, y0, z0, D0, m0, Lx, Ly, z_up, m_up): import numpy as np M = np.zeros((3*N+1, 3*N+1)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly dz = z0[i]-z0[j] rijsq = dx**2+dy**2+dz**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy, dx*dz], [dy*dx, dy*dy, dy*dz], [dz*dz, dz*dy, dz*dz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3*i:3*i+3,3*j:3*j+3] = Mij M[3*j:3*j+3,3*i:3*i+3] = Mij M[3*i:3*i+3,3*i:3*i+3] = M[3*i:3*i+3,3*i:3*i+3] - Mij M[3*j:3*j+3,3*j:3*j+3] = M[3*j:3*j+3,3*j:3*j+3] - Mij for i in range(N): r_now = 0.5*D0[i] if z0[i]<r_now: M[3*i+2, 3*i+2] = M[3*i+2, 3*i+2]+1/r_now**2 if z_up-z0[i]<r_now: M[3*i+2, 3*i+2] = M[3*i+2, 3*i+2]+1/r_now**2 M[3*N, 3*N] = M[3*N, 3*N]+1/r_now**2 M[3*i+2, 3*N] = M[3*i+2, 3*N]-1/r_now**2 M[3*N, 3*i+2] = M[3*N, 3*i+2]-1/r_now**2 m_sqrt = np.zeros((3*N+1, 3*N+1)) m_inv = np.zeros((3*N+1, 3*N+1)) for i in range(N): m_sqrt[3*i, 3*i] = 1/np.sqrt(m0[i]) m_sqrt[3*i+1, 3*i+1] = 1/np.sqrt(m0[i]) m_sqrt[3*i+2, 3*i+2] = 1/np.sqrt(m0[i]) m_inv[3*i, 3*i] = 1/m0[i] m_inv[3*i+1, 3*i+1] = 1/m0[i] m_inv[3*i+2, 3*i+2] = 1/m0[i] m_sqrt[3*N, 3*N] = 1/np.sqrt(m_up) m_inv[3*N, 3*N] = 1/m_up #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v

12,537

30.423559

104

py

gecco-2022

gecco-2022-main/AND-XOR/ConfigPlot.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Oct 10 21:01:26 2017 @author: Hightoutou """ import numpy as np def ConfigPlot_DiffSize(N, x, y, D, L, mark_print): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse Dmin = min(D) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] D_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) D_all.append(D[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_alpha(0.3) if D_all[i] > Dmin: e.set_facecolor('C1') else: e.set_facecolor('C0') i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass(N, x, y, D, L, m, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffMass2(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness(N, x, y, D, L, m, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C2') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness2(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C2') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness3(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='^', s=80, color=(0, 1, 0, 1)) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='s', s=80, color=(0, 0, 1, 1)) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='*', s=100, color=(1, 0, 0, 1)) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('k') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) import matplotlib.lines as mlines red_star = mlines.Line2D([], [], color=(1, 0, 0), marker='*', linestyle='None', markersize=10, label='Output') blue_square = mlines.Line2D([], [], color=(0, 0, 1), marker='s', linestyle='None', markersize=10, label='Input 2') green_triangle = mlines.Line2D([], [], color=(0, 1, 0), marker='^', linestyle='None', markersize=10, label='Input 1') plt.legend(handles=[red_star, green_triangle, blue_square], bbox_to_anchor=(1.215, 1)) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffMass_3D(N, x, y, z, D, L, m, mark_print): import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D m_min = min(m) m_max = max(m) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.set_aspect('equal') sphes = [] m_all = [] for i in range(int(N/2)): x_now = x[i]%L[0] y_now = y[i]%L[1] z_now = z[i]%L[2] r_now = 0.5*D[i] #alpha_now = 0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3 alpha_now = 0.3 pos1 = 0 pos2 = 1 for j in range(pos1, pos2): for k in range(pos1, pos2): for l in range(pos1, pos2): u = np.linspace(0, 2 * np.pi, 100) v = np.linspace(0, np.pi, 100) x_plot = x_now+j*L[0]+r_now * np.outer(np.cos(u), np.sin(v)) y_plot = y_now+k*L[1]+r_now * np.outer(np.sin(u), np.sin(v)) z_plot = z_now+l*L[2]+r_now * np.outer(np.ones(np.size(u)), np.cos(v)) ymin = y_plot[y_plot>0].min() ymax = y_plot[y_plot>0].max() print (i, ymin, ymax) ax.plot_surface(x_plot,y_plot,z_plot,rstride=4,cstride=4, color='C0',linewidth=0,alpha=alpha_now) #sphes.append(e) #m_all.append(m[i]) # i = 0 # for e in sphes: # ax.add_artist(e) # e.set_clip_box(ax.bbox) # e.set_facecolor('C0') # e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) # i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) ax.set_zlim(0, L[2]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_YFixed_rec(N, x, y, D, Lx, y_top, y_bot, m, mark_order): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_print = 0 m_min = min(m) m_max = max(m) if m_min == m_max: m_max *= 1.001 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) for k in range(-1, 2): e = Ellipse((x_now+k*Lx, y_now), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.3+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 #rect = Rectangle([0, y_top], Lx, 0.2*D[0], color='C0') #ax.add_patch(rect) for nn in np.arange(N): x1 = x[nn]%Lx d_up = y_top-y[nn] d_bot = y[nn]-y_bot r_now = 0.5*D[nn] if d_up<r_now: ax.plot([x1, x1], [y[nn], y[nn]+r_now], '-', color='w') if d_bot<r_now: ax.plot([x1, x1], [y[nn], y[nn]-r_now], '-', color='w') for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < Dmn: x2 = x[mm]%Lx if x2>x1: xl = x1 xr = x2 yl = y[nn] yr = y[mm] else: xl = x2 xr = x1 yl = y[mm] yr = y[nn] dx0 = xr-xl dx = dx0-round(dx0/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: if dx0<Dmn: ax.plot([xl, xr], [yl, yr], '-', color='w') else: ax.plot([xl, xr-Lx], [yl, yr], '-', color='w') ax.plot([xl+Lx, xr], [yl, yr], '-', color='w') ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/plot_test/fig'+str(int(ind_nt+1e4))+'.png', dpi = 150) def ConfigPlot_DiffMass_SP(N, x, y, D, L, m, mark_print, ind_in, ind_out, ind_fix): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)*0.3) if i == ind_in: e.set_edgecolor('r') e.set_linewidth(width) if i == ind_out: e.set_edgecolor('b') e.set_linewidth(width) if i == ind_fix: e.set_edgecolor('k') e.set_linewidth(width) ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass_FixLx(N, x, y, D, L, m, mark_print, ind_wall): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i] y_now = y[i]%L[1] for l in range(-1, 2): e = Ellipse((x_now, y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) if ind_wall[i] > 0: e.set_edgecolor('k') e.set_linewidth(width) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass_SP_rec(N, x, y, D, L, m, mark_print, ind_in, ind_out, ind_fix): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)*0.3) if i == ind_in: e.set_edgecolor('r') e.set_linewidth(width) if i == ind_out: e.set_edgecolor('b') e.set_linewidth(width) if i == ind_fix: e.set_edgecolor('k') e.set_linewidth(width) Lx = L[0] Ly = L[1] for nn in np.arange(N): x1 = x[nn]%Lx y1 = y[nn]%Ly for mm in np.arange(nn+1, N): x2 = x[mm]%Lx y2 = y[mm]%Ly if x2>x1: xl = x1 xr = x2 yl = y1 yr = y2 else: xl = x2 xr = x1 yl = y2 yr = y1 dx0 = xr-xl dx = dx0-round(dx0/Lx)*Lx if y2>y1: xd = x1 xu = x2 yd = y1 yu = y2 else: xd = x2 xu = x1 yd = y2 yu = y1 dy0 = yu-yd dy = dy0-round(dy0/Ly)*Ly Dmn = 0.5*(D[mm]+D[nn]) dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: if dx0<Dmn and dy0<Dmn: ax.plot([xl, xr], [yl, yr], '-', color='w') else: if dx0>Dmn and dy0>Dmn: if yr>yl: ax.plot([xl, xr-Lx], [yl, yr-Ly], '-', color='w') ax.plot([xl+Lx, xr], [yl+Ly, yr], '-', color='w') else: ax.plot([xl, xr-Lx], [yl, yr+Ly], '-', color='w') ax.plot([xl+Lx, xr], [yl-Ly, yr], '-', color='w') else: if dx0>Dmn: ax.plot([xl, xr-Lx], [yl, yr], '-', color='w') ax.plot([xl+Lx, xr], [yl, yr], '-', color='w') if dy0>Dmn: ax.plot([xd, xu], [yd, yu-Ly], '-', color='w') ax.plot([xd, xu], [yd+Ly, yu], '-', color='w') ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) return fig def ConfigPlot_EigenMode_DiffMass(N, x, y, D, L, m, em, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) if m_min == m_max: m_max *= 1.001 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 r_now = D[0]*0.5 dr = np.zeros(N) for i in range(N): dr[i] = np.sqrt(em[2*i]**2+em[2*i+1]**2) dr_max = max(dr) for i in range(N): ratio = dr[i]/dr_max*r_now/dr_max plt.arrow(x[i], y[i],em[2*i]*ratio, em[2*i+1]*ratio, head_width=0.005) ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_YFixed_SelfAssembly(N, Nl, x, y, theta, n, d1, d2, Lx, y_top, y_bot): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_order = 0 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] alpha_all = [] alpha1 = 0.6 alpha2 = 0.3 for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) alpha = alpha1 if i < Nl else alpha2 for k in range(-1, 2): e = Ellipse((x_now+k*Lx, y_now), d1,d1,0) ells.append(e) alpha_all.append(alpha) if i >= Nl: for ind in range(n): x_i = x_now+k*Lx+0.5*(d1+d2)*np.cos(theta[i]+ind*2*np.pi/n) y_i = y_now+0.5*(d1+d2)*np.sin(theta[i]+ind*2*np.pi/n) e = Ellipse((x_i, y_i), d2,d2,0) ells.append(e) alpha_all.append(alpha) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(alpha_all[i]) i += 1 ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show() def ConfigPlot_YFixed_SelfAssembly_BumpyBd(N, n_col, Nl, x, y, theta, n, d0, d1, d2, Lx, y_top, y_bot): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_order = 0 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] alpha_all = [] alpha1 = 0.6 alpha2 = 0.3 for i in range(n_col+1): x_now = i*d0 e1 = Ellipse((x_now, y_bot), d0,d0,0) e2 = Ellipse((x_now, y_top), d0,d0,0) ells.append(e1) alpha_all.append(alpha1) ells.append(e2) alpha_all.append(alpha1) for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) alpha = alpha1 if i < Nl else alpha2 for k in range(-1, 2): e = Ellipse((x_now+k*Lx, y_now), d1,d1,0) ells.append(e) alpha_all.append(alpha) if i >= Nl: for ind in range(n): x_i = x_now+k*Lx+0.5*(d1+d2)*np.cos(theta[i]+ind*2*np.pi/n) y_i = y_now+0.5*(d1+d2)*np.sin(theta[i]+ind*2*np.pi/n) e = Ellipse((x_i, y_i), d2,d2,0) ells.append(e) alpha_all.append(alpha) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(alpha_all[i]) i += 1 ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show()

22,835

29.488652

117

py

gecco-2022

gecco-2022-main/AND-XOR/MOO2.py

# multi-objective optimization to evolve AND and XOR gates in the same material # imports for DEAP import time, array, random, copy, math import numpy as np from deap import algorithms, base, benchmarks, tools, creator import matplotlib.pyplot as plt import seaborn #seaborn.set(style='whitegrid') import pandas as pd # imports for the simulator from ConfigPlot import ConfigPlot_EigenMode_DiffMass, ConfigPlot_YFixed_rec, ConfigPlot_DiffMass_SP from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK from MD_functions import FIRE_YFixed_ConstV_DiffK from DynamicalMatrix import DM_mass_DiffK_Yfixed from plot_functions import Line_single, Line_multi from ConfigPlot import ConfigPlot_DiffStiffness import random import matplotlib.pyplot as plt import pickle from os.path import exists from scoop import futures import multiprocessing import os def evaluate(indices): #%% Initial Configuration m1 = 1 m2 = 10 k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem #w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) #w = np.real(w) #v = np.real(v) #freq = np.sqrt(np.absolute(w)) #ind_sort = np.argsort(freq) #freq = freq[ind_sort] #v = v[:, ind_sort] #ind = freq > 1e-4 #eigen_freq = freq[ind] #eigen_mode = v[:, ind] #w_delta = eigen_freq[1:] - eigen_freq[0:-1] #index = np.argmax(w_delta) #F_low_exp = eigen_freq[index] #F_high_exp = eigen_freq[index+1] #print("specs:") #print(F_low_exp) #print(F_high_exp) #print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) andness = 2*gain1/(gain2+gain3) # we are designing an and gait at this frequency Freq_Vibr = 10 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) XOR = (gain2+gain3)/(2*gain1) print("done eval", flush=True) return andness, XOR #cleaning up the data files try: os.remove("results.pickle") except OSError: pass try: os.remove("logs.pickle") except OSError: pass try: os.remove("hofs.pickle") except OSError: pass try: os.remove("hostfile") except OSError: pass try: os.remove("scoop-python.sh") except OSError: pass # start of the optimization: random.seed(a=42) creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 30) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evaluate) toolbox.register("mate", tools.cxOnePoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selNSGA2) # parallelization? toolbox.register("map", futures.map) stats = tools.Statistics() #stats.register("avg", np.mean, axis=0) #stats.register("std", np.std, axis=0) #stats.register("min", np.min, axis=0) #stats.register("max", np.max, axis=0) # also save the population of each generation stats.register("pop", copy.deepcopy) def main(): toolbox.pop_size = 50 toolbox.max_gen = 250 toolbox.mut_prob = 0.8 logbook = tools.Logbook() logbook.header = ["gen", "evals"] + stats.fields hof = tools.HallOfFame(1, similar=np.array_equal) #can change the size def run_ea(toolbox, stats=stats, verbose=True, hof=hof): pop = toolbox.population(n=toolbox.pop_size) pop = toolbox.select(pop, len(pop)) return algorithms.eaMuPlusLambda(pop, toolbox, mu=toolbox.pop_size, lambda_=toolbox.pop_size, cxpb=1-toolbox.mut_prob, #: no cross-over? mutpb=toolbox.mut_prob, stats=stats, ngen=toolbox.max_gen, verbose=verbose, halloffame=hof) res,log = run_ea(toolbox, stats=stats, verbose=True, hof=hof) return res, log, hof if __name__ == '__main__': print("starting") res, log, hof = main() print("done") pickle.dump(res, open('results.pickle', 'wb')) pickle.dump(log, open('logs.pickle', 'wb')) pickle.dump(hof, open('hofs.pickle', 'wb'))

12,452

35.626471

248

py

gecco-2022

gecco-2022-main/AND-XOR/MOO.py

# multi-objective optimization to evolve AND and XOR gates in the same material # imports for DEAP import time, array, random, copy, math import numpy as np from deap import algorithms, base, benchmarks, tools, creator import matplotlib.pyplot as plt import seaborn #seaborn.set(style='whitegrid') import pandas as pd # imports for the simulator from ConfigPlot import ConfigPlot_EigenMode_DiffMass, ConfigPlot_YFixed_rec, ConfigPlot_DiffMass_SP from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK from MD_functions import FIRE_YFixed_ConstV_DiffK from DynamicalMatrix import DM_mass_DiffK_Yfixed from plot_functions import Line_single, Line_multi from ConfigPlot import ConfigPlot_DiffStiffness import random import matplotlib.pyplot as plt import pickle from os.path import exists from scoop import futures import multiprocessing import os def evaluate(indices): #%% Initial Configuration m1 = 1 m2 = 10 k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem #w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) #w = np.real(w) #v = np.real(v) #freq = np.sqrt(np.absolute(w)) #ind_sort = np.argsort(freq) #freq = freq[ind_sort] #v = v[:, ind_sort] #ind = freq > 1e-4 #eigen_freq = freq[ind] #eigen_mode = v[:, ind] #w_delta = eigen_freq[1:] - eigen_freq[0:-1] #index = np.argmax(w_delta) #F_low_exp = eigen_freq[index] #F_high_exp = eigen_freq[index+1] #print("specs:") #print(F_low_exp) #print(F_high_exp) #print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) andness = 2*gain1/(gain2+gain3) # we are designing an and gait at this frequency Freq_Vibr = 10 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) XOR = (gain2+gain3)/(2*gain1) print("done eval", flush=True) return andness, XOR #cleaning up the data files try: os.remove("results.pickle") except OSError: pass try: os.remove("logs.pickle") except OSError: pass try: os.remove("hofs.pickle") except OSError: pass try: os.remove("hostfile") except OSError: pass try: os.remove("scoop-python.sh") except OSError: pass # start of the optimization: random.seed(a=42) creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 30) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evaluate) toolbox.register("mate", tools.cxOnePoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selNSGA2) # parallelization? toolbox.register("map", futures.map) stats = tools.Statistics(key=lambda ind: ind.fitness.values) stats.register("avg", np.mean, axis=0) stats.register("std", np.std, axis=0) stats.register("min", np.min, axis=0) stats.register("max", np.max, axis=0) # also save the population of each generation stats.register("pop", copy.deepcopy) def main(): toolbox.pop_size = 50 toolbox.max_gen = 250 toolbox.mut_prob = 0.8 logbook = tools.Logbook() logbook.header = ["gen", "evals"] + stats.fields hof = tools.HallOfFame(1, similar=np.array_equal) #can change the size def run_ea(toolbox, stats=stats, verbose=True, hof=hof): pop = toolbox.population(n=toolbox.pop_size) pop = toolbox.select(pop, len(pop)) return algorithms.eaMuPlusLambda(pop, toolbox, mu=toolbox.pop_size, lambda_=toolbox.pop_size, cxpb=1-toolbox.mut_prob, #: no cross-over? mutpb=toolbox.mut_prob, stats=stats, ngen=toolbox.max_gen, verbose=verbose, halloffame=hof) res,log = run_ea(toolbox, stats=stats, verbose=True, hof=hof) return res, log, hof if __name__ == '__main__': print("starting") res, log, hof = main() print("done") pickle.dump(res, open('results.pickle', 'wb')) pickle.dump(log, open('logs.pickle', 'wb')) pickle.dump(hof, open('hofs.pickle', 'wb')) # print info for best solution found: #print("-----") #print(len(hof)) #best = hof.items[0] #print("-- Best Individual = ", best) #print("-- Best Fitness = ", best.fitness.values) # print hall of fame members info: #print("- Best solutions are:") # for i in range(HALL_OF_FAME_SIZE): # print(i, ": ", hof.items[i].fitness.values[0], " -> ", hof.items[i]) #print("Hall of Fame Individuals = ", *hof.items, sep="\n") #fronts = tools.emo.sortLogNondominated(res, len(res)) #for i,inds in enumerate(fronts): # counter = 0 # for ind in inds: # counter += 1 # if counter == 1 or counter == 15 or counter==30: # print("####") # evaluateAndPlot(ind) #logbook.record(gen=0, evals=30, **record) avg = log.select("avg") std = log.select("std") avg_stack = np.stack(avg, axis=0) avg_f1 = avg_stack[:, 0] avg_f2 = avg_stack[:, 1] std_stack = np.stack(std, axis=0) std_f1 = std_stack[:, 0] std_f2 = std_stack[:, 1] plt.figure(figsize=(6.4,4.8)) plt.plot(avg_f1, color='blue') plt.fill_between(list(range(0, toolbox.max_gen+1)), avg_f1-std_f1, avg_f1+std_f1, color='cornflowerblue', alpha=0.2) plt.xlabel("Generations") plt.ylabel("Average Fitness") plt.title("Average Fitness of Individuals in the Population - F1", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() plt.show() plt.savefig("avg_F1.jpg", dpi = 300) plt.figure(figsize=(6.4,4.8)) plt.plot(avg_f2, color='blue') plt.fill_between(list(range(0, toolbox.max_gen+1)), avg_f2-std_f2, avg_f2+std_f2, color='cornflowerblue', alpha=0.2) plt.xlabel("Generations") plt.ylabel("Average Fitness") plt.title("Average Fitness of Individuals in the Population - F2", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() plt.show() plt.savefig("avg_F2.jpg", dpi = 300) seaborn.set(style='whitegrid') plot_colors = seaborn.color_palette("Set1", n_colors=10) fig, ax = plt.subplots(1, figsize=(4,4)) for i,inds in enumerate(fronts): par = [toolbox.evaluate(ind) for ind in inds] print("fronts:") print(par) df = pd.DataFrame(par) df.plot(ax=ax, kind='scatter', x=df.columns[0], y=df.columns[1], color=plot_colors[i]) plt.xlabel('$f_1(\mathbf{x})$');plt.ylabel('$f_2(\mathbf{x})$'); plt.title("Pareto Front", fontsize='small') plt.show() plt.savefig("paretoFront.jpg", dpi = 300)

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gecco-2022-main/AND-XOR/plotMOO3.py

## plot stuff after loading everything from the pickled files for MOO import time, array, random, copy, math import numpy as np from deap import algorithms, base, benchmarks, tools, creator import matplotlib.pyplot as plt import seaborn import pandas as pd import random import pickle from os.path import exists import os from ConfigPlot import ConfigPlot_DiffStiffness2, ConfigPlot_DiffStiffness3 from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK, FIRE_YFixed_ConstV_DiffK, MD_VibrSP_ConstV_Yfixed_DiffK2 from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK_Freqs, MD_VibrSP_ConstV_Yfixed_DiffK2_Freqs from DynamicalMatrix import DM_mass_DiffK_Yfixed from joblib import Parallel, delayed import multiprocessing plt.rcParams['pdf.fonttype'] = 42 plt.rcParams['ps.fonttype'] = 42 def evaluate(indices): #%% Initial Configuration m1 = 1 m2 = 10 k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem #w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) #w = np.real(w) #v = np.real(v) #freq = np.sqrt(np.absolute(w)) #ind_sort = np.argsort(freq) #freq = freq[ind_sort] #v = v[:, ind_sort] #ind = freq > 1e-4 #eigen_freq = freq[ind] #eigen_mode = v[:, ind] #w_delta = eigen_freq[1:] - eigen_freq[0:-1] #index = np.argmax(w_delta) #F_low_exp = eigen_freq[index] #F_high_exp = eigen_freq[index+1] #print("specs:") #print(F_low_exp) #print(F_high_exp) #print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) andness = 2*gain1/(gain2+gain3) # we are designing an and gait at this frequency Freq_Vibr = 10 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) XOR = (gain2+gain3)/(2*gain1) print("done eval", flush=True) return andness, XOR random.seed(a=42) creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 30) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evaluate) toolbox.register("mate", tools.cxOnePoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selNSGA2) # parallelization? #toolbox.register("map", futures.map) stats = tools.Statistics(key=lambda ind: ind.fitness.values) stats.register("avg", np.mean, axis=0) stats.register("std", np.std, axis=0) stats.register("min", np.min, axis=0) stats.register("max", np.max, axis=0) toolbox.pop_size = 50 toolbox.max_gen = 250 toolbox.mut_prob = 0.8 logbook = tools.Logbook() logbook.header = ["gen", "evals"] + stats.fields hof = tools.HallOfFame(1, similar=np.array_equal) #can change the size inds = pickle.load(open('test.pickle', 'rb')) indices = [] for i in inds: indices.append(np.array(i)) # plot the pareto front plt.figure(figsize=(4,4)) num_cores = multiprocessing.cpu_count() outputs = Parallel(n_jobs=num_cores)(delayed(evaluate)(ind) for ind in indices) print(type(outputs)) print(outputs) print(inds) for point in outputs: plt.scatter(x=point[0], y=point[1], color='blue', marker='o', alpha=0.4) plt.scatter(x=point[0], y=point[1], color='blue', marker='o', alpha=0.4, label='front') plt.xlabel('$f_1(\mathbf{x})$'+' = AND-ness', fontsize=16) plt.ylabel('$f_2(\mathbf{x})$'+' = XOR-ness', fontsize=16) plt.title("Pareto Front", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.legend(bbox_to_anchor=(1.215, 1)) plt.tight_layout() plt.show()

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gecco-2022-main/AND-XOR/plot_functions.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Oct 22 14:48:04 2017 @author: Hightoutou """ import matplotlib.pyplot as plt #import matplotlib #matplotlib.use('TkAgg') def Line_single(xdata, ydata, line_spec, xlabel, ylabel, mark_print, fn = '', xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.5*3/4) pos1 = ax1.get_position() pos2 = [pos1.x0 + 0.12, pos1.y0 + 0.05, pos1.width-0.1, pos1.height] ax1.set_position(pos2) #ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) plt.ylabel(ylabel, fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') ax1.plot(xdata, ydata, line_spec) if mark_print == 1: fig.savefig(fn, dpi = 300) fig.show() def Line_multi(xdata, ydata, line_spec, xlabel, ylabel, xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.5*3/4) ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) ax1.set_ylabel(ylabel, fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') for ii in range(len(xdata)): ax1.plot(xdata[ii], ydata[ii], line_spec[ii]) plt.show() def Line_yy(xdata, ydata, line_spec, xlabel, ylabel, xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.5*3/4) ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) ax1.set_ylabel(ylabel[0], fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') ax1.plot(xdata[0], ydata[0], line_spec[0]) ax2 = ax1.twinx() ax2.set_ylabel(ylabel[1], fontsize=12) ax2.plot(xdata[1], ydata[1], line_spec[1]) plt.show()

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gecco-2022

gecco-2022-main/AND-XOR/FFT_functions.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Oct 17 15:10:21 2017 @author: Hightoutou """ import numpy as np import matplotlib.pyplot as plt from plot_functions import Line_multi, Line_single #from numba import jit def FFT_Fup(Nt, F, dt, Freq_Vibr): sampling_rate = 1/dt t = np.arange(Nt)*dt fft_size = Nt xs = F[:fft_size] xf = np.absolute(np.fft.rfft(xs)/fft_size) freqs = (2*np.pi)*np.linspace(0, sampling_rate/2, fft_size//2+1) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 0: Line_multi([freqs[1:], [Freq_Vibr, Freq_Vibr]], [xf[1:], [min(xf[1:]), max(xf[1:])]], ['o', 'r--'], 'Frequency', 'FFT', 'linear', 'log') return freqs[1:], xf[1:] def FFT_Fup_RealImag(Nt, F, dt, Freq_Vibr): sampling_rate = 1/dt t = np.arange(Nt)*dt fft_size = Nt xs = F[:fft_size] xf = np.fft.rfft(xs)/fft_size freqs = (2*np.pi)*np.linspace(0, sampling_rate/2, fft_size/2+1) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] xf_real = xf.real xf_imag = xf.imag if 1 == 0: Line_multi([freqs[1:], [Freq_Vibr, Freq_Vibr]], [xf[1:], [min(xf[1:]), max(xf[1:])]], ['o', 'r--'], 'Frequency', 'FFT') return freqs[1:], xf_real[1:], xf_imag[1:] #@jit def vCorr_Cal(fft_size, Nt, y_raw): y_fft = np.zeros(fft_size) for jj in np.arange(fft_size): sum_vcf = 0 sum_tt = 0 count = 0 for kk in np.arange(Nt-jj): count = count+1 sum_vcf += y_raw[kk]*y_raw[kk+jj]; sum_tt = sum_tt+y_raw[kk]*y_raw[kk]; y_fft[jj] = sum_vcf/sum_tt; return y_fft def FFT_vCorr(Nt, N, vx_rec, vy_rec, dt): sampling_rate = 1/dt fft_size = Nt-1 freqs = (2*np.pi)*np.linspace(0, sampling_rate/2, fft_size/2+1) for ii in np.arange(2*N): #for ii in [0,4]: if np.mod(ii, 10) == 0: print('ii=%d\n' % (ii)) if ii >= N: y_raw = vy_rec[:, ii-N] else: y_raw = vx_rec[:, ii] y_fft = vCorr_Cal(fft_size, Nt, y_raw) if ii == 0: xf = np.absolute(np.fft.rfft(y_fft)/fft_size) else: xf += np.absolute(np.fft.rfft(y_fft)/fft_size) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 1: Line_single(freqs[1:], xf[1:], 'o', 'Frequency', 'FFT') return freqs[1:], xf[1:] def FFT_vCorr_3D(Nt, N, vx_rec, vy_rec, vz_rec, dt): sampling_rate = 1/dt fft_size = Nt-1 freqs = (2*np.pi)*np.linspace(0, sampling_rate/2, fft_size/2+1) for ii in np.arange(3*N): #for ii in [0,4]: if np.mod(ii, 10) == 0: print('ii=%d\n' % (ii)) if ii >= 2*N: y_raw = vz_rec[:, ii-2*N] elif ii < N: y_raw = vx_rec[:, ii] else: y_raw = vy_rec[:, ii-N] y_fft = vCorr_Cal(fft_size, Nt, y_raw) if ii == 0: xf = np.absolute(np.fft.rfft(y_fft)/fft_size) else: xf += np.absolute(np.fft.rfft(y_fft)/fft_size) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 1: Line_single(freqs[1:], xf[1:], 'o', 'Frequency', 'FFT') return freqs[1:], xf[1:]

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25.225564

152

py

gecco-2022

gecco-2022-main/AND-XOR/plotMOO.py

## plot stuff after loading everything from the pickled files for MOO import time, array, random, copy, math import numpy as np from deap import algorithms, base, benchmarks, tools, creator import matplotlib.pyplot as plt import seaborn import pandas as pd import random import pickle from os.path import exists import os from ConfigPlot import ConfigPlot_DiffStiffness2, ConfigPlot_DiffStiffness3 from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK, FIRE_YFixed_ConstV_DiffK, MD_VibrSP_ConstV_Yfixed_DiffK2 from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK_Freqs, MD_VibrSP_ConstV_Yfixed_DiffK2_Freqs from DynamicalMatrix import DM_mass_DiffK_Yfixed plt.rcParams['pdf.fonttype'] = 42 plt.rcParams['ps.fonttype'] = 42 def evaluate(indices): #%% Initial Configuration m1 = 1 m2 = 10 k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem #w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) #w = np.real(w) #v = np.real(v) #freq = np.sqrt(np.absolute(w)) #ind_sort = np.argsort(freq) #freq = freq[ind_sort] #v = v[:, ind_sort] #ind = freq > 1e-4 #eigen_freq = freq[ind] #eigen_mode = v[:, ind] #w_delta = eigen_freq[1:] - eigen_freq[0:-1] #index = np.argmax(w_delta) #F_low_exp = eigen_freq[index] #F_high_exp = eigen_freq[index+1] #print("specs:") #print(F_low_exp) #print(F_high_exp) #print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) andness = 2*gain1/(gain2+gain3) # we are designing an and gait at this frequency Freq_Vibr = 10 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) XOR = (gain2+gain3)/(2*gain1) print("done eval", flush=True) return andness, XOR def showPacking(indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row m1=1 m2=10 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) # show packing ConfigPlot_DiffStiffness3(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf', ind_in1, ind_in2, ind_out) def plotInOut_and(indices): #%% Initial Configuration k1 = 1. k2 = 10. m1 = 1 m2 = 10 n_col = 6 n_row = 5 N = n_col*n_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) w = np.real(w) v = np.real(v) freq = np.sqrt(np.absolute(w)) ind_sort = np.argsort(freq) freq = freq[ind_sort] v = v[:, ind_sort] ind = freq > 1e-4 eigen_freq = freq[ind] eigen_mode = v[:, ind] w_delta = eigen_freq[1:] - eigen_freq[0:-1] index = np.argmax(w_delta) F_low_exp = eigen_freq[index] F_high_exp = eigen_freq[index+1] plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.scatter(np.arange(0, len(eigen_freq)), eigen_freq, marker='x', color='blue') plt.xlabel(r"Index $(k)$", fontsize=16) plt.ylabel(r"Frequency $(\omega)$", fontsize=16) plt.title("Frequency Spectrum", fontsize=16, fontweight="bold") plt.grid(color='skyblue', linestyle=':', linewidth=0.5) props = dict(facecolor='green', alpha=0.1) myText = r'$\omega_{low}=$'+"{:.2f}".format(F_low_exp)+"\n"+r'$\omega_{high}=$'+"{:.2f}".format(F_high_exp)+"\n"+r'$\Delta \omega=$'+"{:.2f}".format(max(w_delta)) #plt.text(0.78, 0.15, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16, bbox=props) plt.text(0.2, 0.8, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16, bbox=props) plt.hlines(y=7, xmin=0, xmax=50, linewidth=1, linestyle='dashdot', color='limegreen', alpha=0.9) plt.hlines(y=10, xmin=0, xmax=50, linewidth=1, linestyle='dotted', color='brown', alpha=0.9) plt.text(51, 5, '$\omega=7$', fontsize=12, color='limegreen', alpha=0.9) plt.text(51, 12, '$\omega=10$', fontsize=12, color='brown', alpha=0.9) plt.xticks(fontsize=16) plt.yticks(fontsize=16) plt.tight_layout() plt.show() print("specs:") print(F_low_exp) print(F_high_exp) print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 0, input [0, 0] fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.hlines(y=0, xmin=0, xmax=30, color='green', label='Input1', linestyle='dotted') plt.hlines(y=0, xmin=0, xmax=30, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.hlines(y=0, xmin=0, xmax=30, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 00", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.ylim(0, 0.005) plt.tight_layout() plt.show() fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.hlines(y=0, xmin=0, xmax=10000, color='green', label='Input1', linestyle='solid') plt.hlines(y=0, xmin=0, xmax=10000, color='blue', label='Input2', linestyle='dotted') plt.hlines(y=0, xmin=0, xmax=10000, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 00", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.ylim(-0.0100, 0.0100) plt.tight_layout() plt.show() # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 11", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain1) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 11", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 10", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain2) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 10", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 01", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain3) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 01", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() print("gain1:") print(gain1) print("gain2:") print(gain2) print("gain3:") print(gain3) andness = 2*gain1/(gain2+gain3) return andness def plotInOut_xor(indices): #%% Initial Configuration k1 = 1. k2 = 10. m1 = 1 m2 = 10 n_col = 6 n_row = 5 N = n_col*n_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) w = np.real(w) v = np.real(v) freq = np.sqrt(np.absolute(w)) ind_sort = np.argsort(freq) freq = freq[ind_sort] v = v[:, ind_sort] ind = freq > 1e-4 eigen_freq = freq[ind] eigen_mode = v[:, ind] w_delta = eigen_freq[1:] - eigen_freq[0:-1] index = np.argmax(w_delta) F_low_exp = eigen_freq[index] F_high_exp = eigen_freq[index+1] plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.scatter(np.arange(0, len(eigen_freq)), eigen_freq, marker='x', color='blue') plt.xlabel(r"Index $(k)$", fontsize=16) plt.ylabel(r"Frequency $(\omega)$", fontsize=16) plt.title("Frequency Spectrum", fontsize=16, fontweight="bold") plt.grid(color='skyblue', linestyle=':', linewidth=0.5) props = dict(facecolor='green', alpha=0.1) myText = r'$\omega_{low}=$'+"{:.2f}".format(F_low_exp)+"\n"+r'$\omega_{high}=$'+"{:.2f}".format(F_high_exp)+"\n"+r'$\Delta \omega=$'+"{:.2f}".format(max(w_delta)) plt.text(0.78, 0.15, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16, bbox=props) plt.xticks(fontsize=16) plt.yticks(fontsize=16) plt.tight_layout() plt.show() print("specs:") print(F_low_exp) print(F_high_exp) print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 10 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 11", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain1) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 11", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 10", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain2) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 10", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 01", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain3) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 01", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() print("gain1:") print(gain1) print("gain2:") print(gain2) print("gain3:") print(gain3) XOR = (gain2+gain3)/(2*gain1) return XOR def plotInOut_adder(indices): #%% Initial Configuration k1 = 1. k2 = 10. m1 = 1 m2 = 10 n_col = 6 n_row = 5 N = n_col*n_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr1 = 7 Freq_Vibr2 = 10 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK_Freqs(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr1, Freq_Vibr2, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='A', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='B', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='C/S', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 11", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) #myText = 'Gain='+"{:.3f}".format(gain1) #plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2_Freqs(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr1, Freq_Vibr2, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) print(np.mean(x_out, axis=0)) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='A', linestyle='solid') plt.plot(x_in2, color='blue', label='B', linestyle='dotted') plt.plot(x_out, color='red', label='C/S', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 11", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() B=1 # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK_Freqs(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr1, Freq_Vibr2, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='A', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='B', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='C/S', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 10", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) #myText = 'Gain='+"{:.3f}".format(gain2) #plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2_Freqs(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr1, Freq_Vibr2, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='A', linestyle='solid') plt.plot(x_in2, color='blue', label='B', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='C/S', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 10", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK_Freqs(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr1, Freq_Vibr2, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='A', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='B', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='C/S', linestyle='dashed') plt.xlabel("Frequency", fontsize=16) plt.ylabel("Amplitude of FFT", fontsize=16) plt.title("Logic Gate Response - input = 01", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) #myText = 'Gain='+"{:.3f}".format(gain3) #plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2_Freqs(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr1, Freq_Vibr2, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='A', linestyle='solid') plt.plot(x_in2, color='blue', label='B', linestyle='dotted') plt.plot(x_out-np.mean(x_out, axis=0), color='red', label='C/S', linestyle='solid') plt.xlabel("Time Steps", fontsize=16) plt.ylabel("Displacement", fontsize=16) plt.title("Logic Gate Response - input = 01", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() return 0 #cleaning up the data files #try: # os.remove("indices.pickle") #except OSError: # pass #try: # os.remove("outputs.pickle") #except OSError: # pass # deap setup: random.seed(a=42) creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 30) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evaluate) toolbox.register("mate", tools.cxOnePoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selNSGA2) # parallelization? #toolbox.register("map", futures.map) stats = tools.Statistics(key=lambda ind: ind.fitness.values) stats.register("avg", np.mean, axis=0) stats.register("std", np.std, axis=0) stats.register("min", np.min, axis=0) stats.register("max", np.max, axis=0) toolbox.pop_size = 50 toolbox.max_gen = 250 toolbox.mut_prob = 0.8 logbook = tools.Logbook() logbook.header = ["gen", "evals"] + stats.fields hof = tools.HallOfFame(1, similar=np.array_equal) #can change the size # load the results from the files res = pickle.load(open('results.pickle', 'rb')) hof = pickle.load(open('hofs.pickle', 'rb')) log = pickle.load(open('logs.pickle', 'rb')) # evaluate and plot an individual #[0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0] #showPacking([0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0]) #plotInOut_and([0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0]) #plotInOut_xor([0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0]) plotInOut_adder([0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0]) # plot average fitness vs generations avg = log.select("avg") std = log.select("std") max_ = log.select("max") min_ = log.select("min") avg_stack = np.stack(avg, axis=0) avg_f1 = avg_stack[:, 0] avg_f2 = avg_stack[:, 1] std_stack = np.stack(std, axis=0) std_f1 = std_stack[:, 0] std_f2 = std_stack[:, 1] max_stack = np.stack(max_, axis=0) max_f1 = max_stack[:, 0] max_f2 = max_stack[:, 1] min_stack = np.stack(min_, axis=0) min_f1 = min_stack[:, 0] min_f2 = min_stack[:, 1] plt.figure(figsize=(6.4,4.8)) plt.plot(avg_f1, color='blue', label='Average', linestyle='solid') plt.plot(max_f1, color='red', label='Maximum', linestyle='dashed') plt.plot(min_f1, color='green', label='Minimum', linestyle='dashed') plt.fill_between(list(range(0, toolbox.max_gen+1)), avg_f1-std_f1, avg_f1+std_f1, color='cornflowerblue', alpha=0.2, linestyle='dotted', label='STD') plt.xlabel("Generations", fontsize=16) plt.ylabel("Fitness", fontsize=16) plt.title("Multi-objective Optimization, Fitness 1 = AND-ness", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper left', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() plt.figure(figsize=(6.4,4.8)) plt.plot(avg_f2, color='blue', label='Average', linestyle='solid') plt.plot(max_f2, color='red', label='Maximum', linestyle='dashed') plt.plot(min_f2, color='green', label='Minimum', linestyle='dashed') plt.fill_between(list(range(0, toolbox.max_gen+1)), avg_f2-std_f2, avg_f2+std_f2, color='cornflowerblue', alpha=0.2, linestyle='dotted', label='STD') plt.xlabel("Generations", fontsize=16) plt.ylabel("Fitness", fontsize=16) plt.title("Multi-objective Optimization, Fitness 2 = XOR-ness", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper left', fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() # print info for best solution found: #print("-----") #print(len(hof)) #best = hof.items[0] #print("-- Best Individual = ", best) #print("-- Best Fitness = ", best.fitness.values) # print hall of fame members info: #print("- Best solutions are:") # for i in range(HALL_OF_FAME_SIZE): # print(i, ": ", hof.items[i].fitness.values[0], " -> ", hof.items[i]) #print("Hall of Fame Individuals = ", *hof.items, sep="\n") # get the pareto front from the results fronts = tools.emo.sortLogNondominated(res, len(res)) # print(fronts[0][0]) # 50 indvs in fronts[0] # plot the pareto front plt.figure(figsize=(4,4)) counter = 1 outputs = [] indices = [] for i,inds in enumerate(fronts): for ind in inds: indices.append(ind) print(str(counter)+': '+str(ind)) output = toolbox.evaluate(ind) outputs.append(output) print(output) plt.scatter(x=output[0], y=output[1], color='blue') plt.annotate(str(counter), (output[0]+0.1, output[1]+0.1)) counter = counter + 1 plt.xlabel('$f_1(\mathbf{x})$'+' = AND-ness', fontsize=16) plt.ylabel('$f_2(\mathbf{x})$'+' = XOR-ness', fontsize=16) plt.title("Pareto Front", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() pickle.dump(indices, open('indices.pickle', 'wb')) pickle.dump(outputs, open('outputs.pickle', 'wb')) # plot the pareto front again without annotation plt.figure(figsize=(4,4)) for output in outputs: plt.scatter(x=output[0], y=output[1], color='blue') plt.xlabel('$f_1(\mathbf{x})$'+' = AND-ness', fontsize=16) plt.ylabel('$f_2(\mathbf{x})$'+' = XOR-ness', fontsize=16) plt.title("Pareto Front", fontsize=16) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.tight_layout() plt.show() #seaborn.set(style='whitegrid') #plot_colors = seaborn.color_palette("Set1", n_colors=10) #fig, ax = plt.subplots(1, figsize=(4,4)) #for i,inds in enumerate(fronts): # par = [toolbox.evaluate(ind) for ind in inds] # print("fronts:") # print(par) # df = pd.DataFrame(par) # df.plot(ax=ax, kind='scatter', # x=df.columns[0], y=df.columns[1], # color=plot_colors[i]) #plt.xlabel('$f_1(\mathbf{x})$');plt.ylabel('$f_2(\mathbf{x})$'); #plt.title("Pareto Front", fontsize='small') #plt.show() # simulate and plot all of the fronts #for i,inds in enumerate(fronts): # counter = 0 # for ind in inds: # counter += 1 # if counter == 1 or counter == 15 or counter==30: # print("####") # plotInOut_and(ind) # plotInOut_xor(ind)

51,850

38.281061

267

py

gecco-2022

gecco-2022-main/RandomSearch/AND-NESS/switch_binary.py

import numpy as np from ConfigPlot import ConfigPlot_EigenMode_DiffMass, ConfigPlot_YFixed_rec, ConfigPlot_DiffMass_SP from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK from MD_functions import FIRE_YFixed_ConstV_DiffK from DynamicalMatrix import DM_mass_DiffK_Yfixed from plot_functions import Line_single, Line_multi from ConfigPlot import ConfigPlot_DiffStiffness import random import matplotlib.pyplot as plt import pickle from os.path import exists class switch(): def evaluate(m1, m2, N_light, indices): #%% Initial Configuration k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem #w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) #w = np.real(w) #v = np.real(v) #freq = np.sqrt(np.absolute(w)) #ind_sort = np.argsort(freq) #freq = freq[ind_sort] #v = v[:, ind_sort] #ind = freq > 1e-4 #eigen_freq = freq[ind] #eigen_mode = v[:, ind] #w_delta = eigen_freq[1:] - eigen_freq[0:-1] #index = np.argmax(w_delta) #F_low_exp = eigen_freq[index] #F_high_exp = eigen_freq[index+1] #print("specs:") #print(F_low_exp) #print(F_high_exp) #print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) andness = 2*gain1/(gain2+gain3) return andness def showPacking(m1, m2, N_light, indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # show packing ConfigPlot_DiffStiffness(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf') def evaluateAndPlot(m1, m2, N_light, indices): #%% Initial Configuration k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) w = np.real(w) v = np.real(v) freq = np.sqrt(np.absolute(w)) ind_sort = np.argsort(freq) freq = freq[ind_sort] v = v[:, ind_sort] ind = freq > 1e-4 eigen_freq = freq[ind] eigen_mode = v[:, ind] w_delta = eigen_freq[1:] - eigen_freq[0:-1] index = np.argmax(w_delta) F_low_exp = eigen_freq[index] F_high_exp = eigen_freq[index+1] plt.figure(figsize=(6.4,4.8)) plt.scatter(np.arange(0, len(eigen_freq)), eigen_freq, marker='x', color='blue') plt.xlabel("Number") plt.ylabel("Frequency") plt.title("Vibrational Reponse", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() plt.show() print("specs:") print(F_low_exp) print(F_high_exp) print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) print("gain1:") print(gain1) print("gain2:") print(gain2) print("gain3:") print(gain3) andness = 2*gain1/(gain2+gain3) return andness

14,736

36.594388

252

py

gecco-2022

gecco-2022-main/RandomSearch/AND-NESS/MD_functions.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Oct 13 13:09:27 2017 @author: Hightoutou """ import numpy as np import time #from numba import jit from FFT_functions import FFT_Fup, FFT_vCorr from plot_functions import Line_multi, Line_yy, Line_single from ConfigPlot import ConfigPlot_YFixed_rec import matplotlib.pyplot as plt #import IPython.core.debugger #dbg = IPython.core.debugger.Pdb() #@jit def force_YFixed(Fx, Fy, N, x, y, D, Lx, y_bot, y_up): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5*D[nn] if d_up<r_now: F = -(1-d_up/r_now)/(r_now) Fup -= F Fy[nn] += F Ep += (1/2)*(1-d_up/r_now)**2 cont_up += 1 cont += 1 #dbg.set_trace() if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fbot += F Fy[nn] -= F Ep += (1/2)*(1-d_bot/r_now)**2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up def force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D, Lx, y_bot, y_up, k_list, k_type, VL_list, VL_counter): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 for nn in np.arange(N): d_up = y_up - y[nn] d_bot = y[nn] - y_bot r_now = 0.5 * D[nn] if d_up < r_now: F = -k_list[k_type[nn]] * (1 - d_up / r_now) / (r_now) Fup -= F Fy[nn] += F Ep += 0.5 * k_list[k_type[nn]] * (1 - d_up / r_now)**2 cont_up += 1 cont += 1 #dbg.set_trace() if d_bot < r_now: F = -k_list[k_type[nn]] * (1 - d_bot / r_now) / (r_now) Fbot += F Fy[nn] -= F Ep += 0.5 * k_list[k_type[nn]] * (1 - d_bot / r_now)**2 cont += 1 for vl_idx in np.arange(VL_counter): nn = VL_list[vl_idx][0] mm = VL_list[vl_idx][1] dy = y[mm] - y[nn] Dmn = 0.5 * (D[mm] + D[nn]) if abs(dy) < Dmn: dx = x[mm] - x[nn] dx = dx - round(dx / Lx) * Lx if abs(dx) < Dmn: dmn = np.sqrt(dx**2 + dy**2) if dmn < Dmn: k = k_list[(k_type[nn] ^ k_type[mm]) + np.maximum(k_type[nn], k_type[mm])] F = -k * (1 - dmn / Dmn) / Dmn / dmn Fx[nn] += F * dx Fx[mm] -= F * dx Fy[nn] += F * dy Fy[mm] -= F * dy Ep += 0.5 * k * (1 - dmn / Dmn)**2 cont += 1 p_now += (-F) * (dx**2 + dy**2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up def force_YFixed_upDS(Fx, Fy, N, x, y, D, Lx, y_bot, y_up, ind_up): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5*D[nn] if ind_up[nn] == 1: F = -(1-d_up/r_now)/(r_now) Fup -= F Fy[nn] += F Ep += (1/2)*(1-d_up/r_now)**2 #dbg.set_trace() if d_up<r_now: cont_up = cont_up+1 cont += 1 if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fbot += F Fy[nn] -= F Ep += (1/2)*(1-d_bot/r_now)**2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up #@jit def force_Regular(Fx, Fy, N, x, y, D, Lx, Ly): Ep = 0 cont = 0 p_now = 0 for nn in np.arange(N): for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] dy = dy-round(dy/Ly)*Ly Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, Ep, cont, p_now def MD_UpDownFixed_SD(N, x0, y0, D0, m0, L): dt = min(D0)/40 Nt = int(1e4) Ep = np.zeros(Nt) F_up = np.zeros(Nt) F_bot = np.zeros(Nt) F_tot = np.zeros(Nt) Fup_now = 0 vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) #Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) F_up[nt] = Fup_now F_bot[nt] = Fbot_now Ep[nt] = Ep_now vx = np.divide(Fx, m0) vy = np.divide(Fy, m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) t_end = time.time() print ("time=%.3e" %(t_end-t_start)) if 1 == 0: # Plot the amplitide of F Line_single(range(Nt), F_tot[0:Nt], '-', 't', 'Ftot', 'log', yscale='log') return x, y def MD_VibrBot_ForceUp(N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr): dt = min(D0)/40 Nt = int(5e4) Ep = np.zeros(Nt) Ek = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr #y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)) vx = np.zeros(N) vy = np.zeros(N) if 1 == 0: y_bot = np.zeros(Nt) vx = np.random.rand(N) vy = np.random.rand(N) T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) T_set = 1e-6 vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now = force_YFixed(Fx, Fy, N, x, y, D0, L[0], y_bot[nt], L[1]) #Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) F_up[nt] = Fup_now Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) freq_now, fft_now = FFT_Fup(Nt, F_up[:Nt], dt, Freq_Vibr) # Plot the amplitide of F if 1 == 1: Line_yy([dt*range(Nt), dt*range(Nt)], [F_up[0:Nt],y_bot[0:Nt]], ['-', ':'], 't', ['$F_{up}$', '$y_{bottom}$']) Etot = Ep[1:Nt]+Ek[1:Nt] xdata = [dt*range(Nt), dt*range(Nt), dt*range(Nt-1)] ydata = [Ep[0:Nt], Ek[0:Nt], Etot] line_spec = ['--', ':', 'r-'] Line_multi(xdata, ydata, line_spec, 't', '$E$', 'log') print("std(Etot)=%e\n" %(np.std(Etot))) #dt2 = 1e-3 #xx = np.arange(0, 5, dt2) #yy = np.sin(50*xx)+np.sin(125*xx) #print("dt=%e, w=%f\n" % (dt, Freq_Vibr)) FFT_Fup(Nt, F_up[:Nt], dt, Freq_Vibr) #FFT_Fup(yy.size, yy, dt2, 50) return freq_now, fft_now, np.mean(cont) def MD_Periodic_equi(Nt, N, x0, y0, D0, m0, L, T_set, V_em, n_em): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) vx_rec = np.zeros([Nt, N]) vy_rec = np.zeros([Nt, N]) vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): #for ii in [60]: ind1 = 2*np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx_rec[nt] = vx vy_rec[nt] = vy t_end = time.time() print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) #Etot = Ep[1:Nt]+Ek[1:Nt] #xdata = [dt*range(Nt), dt*range(Nt), dt*range(Nt-1)] #ydata = [Ep[0:Nt], Ek[0:Nt], Etot] #line_spec = ['--', ':', 'r-'] #Line_multi(xdata, ydata, line_spec, 't', '$E$', 'log', 'log') freq_now, fft_now = FFT_vCorr(Nt, N, vx_rec, vy_rec, dt) return freq_now, fft_now, np.mean(cont) def MD_YFixed_ConstP_SD(Nt, N, x0, y0, D0, m0, L, F0_up): dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e6) #Nt = int(5e2) Ep = np.zeros(Nt) F_up = np.zeros(Nt) F_bot = np.zeros(Nt) F_tot = np.zeros(Nt) Fup_now = 0 y_up = y0[N] vx = np.zeros(N+1) vy = np.zeros(N+1) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], 0, y[N]) F_up[nt] = Fup_now+F0_up F_bot[nt] = Fbot_now Ep[nt] = Ep_now+(y_up-y[N])*F0_up vx = 0.1*np.divide(np.append(Fx,0), m0) vy = 0.1*np.divide(np.append(Fy, F_up[nt]), m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #print("nt=%d, Fup=%e, Fup_tot=%e\n" % (nt, Fup_now, F_up[nt])) #dbg.set_trace() t_end = time.time() print ("F_tot=%.3e\n" %(F_tot[nt])) print ("time=%.3e" %(t_end-t_start)) if 1 == 0: # Plot the amplitide of F Line_single(range(Nt), F_tot[0:Nt], '-', 't', 'Ftot', 'log', yscale='log') #Line_single(range(Nt), -F_up[0:Nt], '-', 't', 'Fup', 'log', yscale='log') #Line_single(range(Nt), Ep[0:Nt], '-', 't', 'Ep', 'log', yscale='linear') return x, y, p_now def MD_VibrBot_DispUp_ConstP(mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up): dt = D0[0]/40 B = 0.1 # damping coefficient Nt = int(5e7) #Nt = int(5e2) Ep = np.zeros(Nt) Ek = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr #y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)) vx = np.zeros(N+1) vy = np.zeros(N+1) # for test if 1 == 0: y_bot = np.zeros(Nt) vx = np.random.rand(N+1) vx[N] = 0 vy = np.random.rand(N+1) vy[N] = 0 T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) T_set = 1e-6 vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5*D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) if mark_upDS == 0: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) elif mark_upDS == 1: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_upDS(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N], ind_up) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])*F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0)-B*vx Fy_all = np.append(Fy, F_up[nt])-B*vy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-y_up0 freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) #freq_y, fft_y_real, fft_y_imag = FFT_Fup_RealImag(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) # plot the energy to see when the system reaches steady state if 1 == 0: Etot = Ep+Ek nt_start = int(1e3) xdata = [range(nt_start, Nt), range(nt_start, Nt), range(Nt)] ydata = [Ep[nt_start:Nt], Ek[nt_start:Nt], Etot] line_spec = [':', ':', 'r-'] Line_multi(xdata, ydata, line_spec, 't', '$E$', 'linear', 'log') # Plot the amplitide of F if 1 == 0: Line_yy([dt*range(Nt), dt*range(Nt)], [F_up[0:Nt],y_bot[0:Nt]], ['-', ':'], 't', ['$F_{up}$', '$y_{bottom}$']) Line_yy([dt*range(Nt), dt*range(Nt)], [y_up[0:Nt],y_bot[0:Nt]], ['-', ':'], 't', ['$y_{up}$', '$y_{bottom}$']) Line_single(range(Nt), p[0:Nt], '-', 't', 'p', 'log', 'linear') Etot = Ep[1:Nt]+Ek[1:Nt] xdata = [dt*range(Nt), dt*range(Nt), dt*range(Nt-1)] ydata = [Ep[0:Nt], Ek[0:Nt], Etot] line_spec = ['--', ':', 'r-'] #Line_multi(xdata, ydata, line_spec, 't', '$E$', 'log') print("std(Etot)=%e\n" %(np.std(Etot))) return freq_y, fft_y, freq_bot, fft_bot, np.mean(cont), np.mean(cont_up) #return freq_y, fft_y_real, fft_y_imag, freq_bot, fft_bot, np.mean(cont) def MD_VibrBot_DispUp_ConstP_ConfigRec(N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up, fn): dt = D0[0]/40 B = 0.1 # damping coefficient Nt = int(5e6) nt_rec = np.linspace(Nt-5e4, Nt, 500) #Nt = int(1e4) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) ind_nt = 0 Ep = np.zeros(Nt) Ek = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr #y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): if nt == nt_rec[ind_nt]: ConfigPlot_YFixed_rec(N, x[0:N], y[0:N], D0[0:N], L[0], y[N], y_bot[nt], m0[0:N], ind_nt, fn) ind_nt += 1 x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])*F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0)-B*vx Fy_all = np.append(Fy, F_up[nt])-B*vy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-y_up0 freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, freq_bot, fft_bot, np.mean(cont), np.mean(cont_up) def MD_VibrBot_DispUp_ConstP_EkCheck(Nt, mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up): dt = D0[0]/40 B = 0.1 # damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ek_up_now = np.array(0) Ep_now = np.array(0) Ep_up_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ep_up = np.zeros(Nt) Ek = np.zeros(Nt) Ek_up = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5*D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) if mark_upDS == 0: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) elif mark_upDS == 1: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_upDS(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N], ind_up) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])*F0_up Ep_up[nt] = (y_up0-y[N])*F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0)-B*vx Fy_all = np.append(Fy, F_up[nt])-B*vy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_up[nt] = 0.5*m0[N]*(vx[N]**2+vy[N]**2) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ek_up_now = np.append(Ek_up_now, np.mean(Ek_up[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) Ep_up_now = np.append(Ep_up_now, np.mean(Ep_up[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-np.mean(y_up) y_up = y_up/np.mean(np.absolute(y_up)) freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, freq_bot, fft_bot, np.mean(cont), np.mean(cont_up) #@jit def force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D, Lx, y_bot, v_bot, y_up): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 #betta = 1 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5*D[nn] if d_up<r_now: F = -(1-d_up/r_now)/(r_now) Fup -= F Fy[nn] += F dvy = vy[N]-vy[nn] FD = beta*dvy #FD = np.absolute(FD) Fy[nn] += FD Fup -= FD Ep += (1/2)*(1-d_up/r_now)**2 cont_up += 1 cont += 1 #dbg.set_trace() if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fbot += F Fy[nn] -= F dvy = v_bot-vy[nn] FD = beta*dvy Fy[nn] += FD Ep += (1/2)*(1-d_bot/r_now)**2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy dvx = vx[mm]-vx[nn] dvy = vy[mm]-vy[nn] FD = beta*(dvx*dx+dvy*dy)/dmn #FD = np.absolute(FD) Fx[nn] += FD*dx/dmn Fx[mm] -= FD*dx/dmn Fy[nn] += FD*dy/dmn Fy[mm] -= FD*dy/dmn Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up def MD_VibrBot_DispUp_ConstP_EkCheck_Collision(beta, Nt, mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up, mark_norm): dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ek_up_now = np.array(0) Ep_now = np.array(0) Ep_up_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ep_up = np.zeros(Nt) Ek = np.zeros(Nt) Ek_up = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vy_bot = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5*D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D0[0:N], L[0], y_bot[nt], vy_bot[nt], y[N]) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])*F0_up Ep_up[nt] = (y_up0-y[N])*F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0) Fy_all = np.append(Fy, F_up[nt]) ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_up[nt] = 0.5*m0[N]*(vx[N]**2+vy[N]**2) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ek_up_now = np.append(Ek_up_now, np.mean(Ek_up[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) Ep_up_now = np.append(Ep_up_now, np.mean(Ep_up[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-np.mean(y_up) if mark_norm == 1: y_up = y_up/np.mean(np.absolute(y_up)) freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, fft_bot, np.mean(cont), np.mean(cont_up), nt_rec[1:], Ek_now[1:],Ek_up_now[1:],Ep_now[1:],Ep_up_now[1:] def MD_YFixed_ConstP_Gravity_SD(N, x0, y0, D0, m0, L, F0_up): g = 1e-5 dt = D0[0]/40 Nt = int(5e6) #Nt = int(1e4) Ep = np.zeros(Nt) F_up = np.zeros(Nt) F_bot = np.zeros(Nt) F_tot = np.zeros(Nt) Fup_now = 0 y_up = y0[N] vx = np.zeros(N+1) vy = np.zeros(N+1) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], 0, y[N]) Fy -= g*m0[0:N] F_up[nt] = Fup_now+F0_up-g*m0[N] F_bot[nt] = Fbot_now Ep[nt] = Ep_now+(y_up-y[N])*F0_up+sum(g*np.multiply(m0, y-y0)) vx = 0.1*np.divide(np.append(Fx,0), m0) vy = 0.1*np.divide(np.append(Fy, F_up[nt]), m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #print("nt=%d, Fup=%e, Fup_tot=%e\n" % (nt, Fup_now, F_up[nt])) #dbg.set_trace() t_end = time.time() print ("F_tot=%.3e\n" %(F_tot[nt])) print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def MD_VibrBot_DispUp_ConstP_EkCheck_Gravity(Nt, mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up): dt = D0[0]/40 #B = 0.1 # damping coefficient g = 1e-5 Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ek_up_now = np.array(0) Ep_now = np.array(0) Ep_up_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ep_up = np.zeros(Nt) Ek = np.zeros(Nt) Ek_up = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vy_bot = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5*D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) #Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) beta = 1 Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D0[0:N], L[0], y_bot[nt], vy_bot[nt], y[N]) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])*F0_up+sum(g*np.multiply(m0, y-y_ini)) Ep_up[nt] = (y_up0-y[N])*F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now #Fx_all = np.append(Fx,0)-B*vx #Fy_all = np.append(Fy, F_up[nt])-B*vy-g*m0 Fx_all = np.append(Fx,0) Fy_all = np.append(Fy, F_up[nt])-g*m0 ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_up[nt] = 0.5*m0[N]*(vx[N]**2+vy[N]**2) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ek_up_now = np.append(Ek_up_now, np.mean(Ek_up[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) Ep_up_now = np.append(Ep_up_now, np.mean(Ep_up[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-np.mean(y_up) #y_up = y_up/np.mean(np.absolute(y_up)) freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, fft_bot, np.mean(cont), np.mean(cont_up), nt_rec[1:], Ek_now[1:],Ek_up_now[1:],Ep_now[1:],Ep_up_now[1:] def MD_YFixed_ConstV_SP_SD(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now vx = 0.1*np.divide(Fx, m0) vy = 0.1*np.divide(Fy, m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) t_end = time.time() print ("F_tot=%.3e" %(F_tot[nt])) print ("time=%.3e" %(t_end-t_start)) plt.figure(figsize=(6.4,4.8)) plt.plot(range(Nt), F_tot[0:Nt], color='blue') ax = plt.gca() ax.set_yscale('log') plt.xlabel("t") plt.ylabel("F_total") plt.title("Finding the Equilibrium", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() plt.show() return x, y, p_now def MD_YFixed_ConstV_SP_SD_2(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now vx = 0.1*np.divide(Fx, m0) vy = 0.1*np.divide(Fy, m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) # putting a threshold on total force if (F_tot[nt]<1e-11): break t_end = time.time() #print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now #@jit def force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D, Lx, y_bot, y_up): Ep = 0 cont = 0 p_now = 0 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5*D[nn] if d_up<r_now: F = -(1-d_up/r_now)/(r_now) Fy[nn] += F dvy = -vy[nn] FD = beta*dvy Fy[nn] += FD Ep += (1/2)*(1-d_up/r_now)**2 cont += 1 #dbg.set_trace() if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fy[nn] -= F dvy = -vy[nn] FD = beta*dvy Fy[nn] += FD Ep += (1/2)*(1-d_bot/r_now)**2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy dvx = vx[mm]-vx[nn] dvy = vy[mm]-vy[nn] FD = beta*(dvx*dx+dvy*dy)/dmn #FD = np.absolute(FD) Fx[nn] += FD*dx/dmn Fx[mm] -= FD*dx/dmn Fy[nn] += FD*dy/dmn Fy[mm] -= FD*dy/dmn Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, Ep, cont, p_now def MD_VibrSP_ConstV_Collision(beta, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, mark_vibrY): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in] vx_in = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)) elif mark_vibrY == 1: y_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in] vy_in = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] vx[ind_in] = vx_in[nt] vy[ind_in] = 0 elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] vx[ind_in] = 0 vy[ind_in] = vy_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx Fy_all = Fy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, mark_vibrY, mark_resonator): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in] elif mark_vibrY == 1: y_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-B*vx Fy_all = Fy-B*vy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) if Nt == 5e5: print(x[ind_out], y[ind_out]) print(fft_x_out[100], fft_y_out[100]) print(fft_in[100]) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_Periodic_ConstV_SP_SD(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, Lx, Ly) Ep[nt] = Ep_now vx = 0.1*np.divide(Fx, m0) vy = 0.1*np.divide(Fy, m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) t_end = time.time() print ("F_tot=%.3e\n" %(F_tot[nt])) print ("nt=%e, time=%.3e" %(nt, t_end-t_start)) return x, y, p_now def MD_Periodic_equi_Ekcheck(Nt, N, x0, y0, D0, m0, L, T_set, V_em, n_em): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) vx_rec = np.zeros([Nt, N]) vy_rec = np.zeros([Nt, N]) nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) #nt_rec = np.linspace(0, Nt, int(Nt/1e2)+1) nt_rec = nt_rec.astype(int) Ek_now = np.array(0) Ep_now = np.array(0) vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2*np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx_rec[nt] = vx vy_rec[nt] = vy for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) freq_now, fft_now = FFT_vCorr(int(Nt/2), N, vx_rec[int(Nt/2):Nt], vy_rec[int(Nt/2):Nt], dt) return freq_now, fft_now, np.mean(cont), nt_rec, Ek_now, Ep_now #@jit def force_Xfixed(Fx, Fy, N, x, y, D, x_l, x_r, Ly, ind_wall): F_l = 0 F_r = 0 Ep = 0 cont = 0 p_now = 0 for nn in np.arange(N): d_l = x[nn]-x_l d_r = x_r-x[nn] r_now = 0.5*D[nn] if (ind_wall[nn]==0) and (d_r<r_now): F = -(1-d_r/r_now)/(r_now) F_r -= F Fx[nn] += F Ep += (1/2)*(1-d_r/r_now)**2 cont += 1 #dbg.set_trace() if (ind_wall[nn]==0) and (d_l<r_now): F = -(1-d_l/r_now)/(r_now) F_l += F Fx[nn] -= F Ep += (1/2)*(1-d_l/r_now)**2 cont += 1 for mm in np.arange(nn+1, N): dx = x[mm]-x[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dx) < Dmn: dy = y[mm]-y[nn] dy = dy-round(dy/Ly)*Ly dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, F_l, F_r, Ep, cont, p_now def MD_Xfixed_SD(Nt, N, x0, y0, D0, m0, Lx, Ly, ind_wall): wall = np.where(ind_wall>0) dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e6) #Nt = int(5e2) Ep = np.zeros(Nt) F_l = np.zeros(Nt) F_r = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_Xfixed(Fx, Fy, N, x, y, D0, 0, Lx, Ly, ind_wall) F_l[nt] = Fl_now F_r[nt] = Fr_now Ep[nt] = Ep_now Fx[wall] = 0 Fy[wall] = 0 vx = 0.1*np.divide(Fx, m0) vy = 0.1*np.divide(Fy, m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #print("nt=%d, Fup=%e, Fup_tot=%e\n" % (nt, Fup_now, F_up[nt])) #dbg.set_trace() t_end = time.time() print ("F_tot=%.3e" %(F_tot[nt])) #print ("Ep_tot=%.3e\n" %(Ep[nt])) print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def MD_VibrWall_DiffP_Xfixed(Nt, N, x_ini, y_ini,D0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_wall, B): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_Xfixed(Fx, Fy, N, x, y, D0, x_l[nt], Lx, Ly, ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-B*vx Fy_all = Fy-B*vy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #for ii in np.arange(len(nt_rec)-1): # Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) # Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) #CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) freq_fft, fft_receive = FFT_Fup(int(Nt/2), F_r[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_drive = FFT_Fup(int(Nt/2), x_l[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_receive, fft_drive, cont_now, nt_rec, Ek_now, Ep_now #@jit def force_XFixed_collision_VibrLx(beta, Fx, Fy, N, x, y, vx, vy, D, x_l, Lx, Ly, vx_l, ind_wall): Fr = 0 Fl = 0 Ep = 0 cont = 0 p_now = 0 #betta = 1 for nn in np.arange(N): if ind_wall[nn] == 0: d_r = Lx-x[nn] d_l = x[nn]-x_l r_now = 0.5*D[nn] if d_r<r_now: F = -(1-d_r/r_now)/(r_now) Fr -= F Fx[nn] += F dvx = -vx[nn] FD = beta*dvx Fx[nn] += FD Fr -= FD Ep += (1/2)*(1-d_r/r_now)**2 cont += 1 #dbg.set_trace() if d_l<r_now: F = -(1-d_l/r_now)/(r_now) Fl += F Fx[nn] -= F dvx = vx_l-vx[nn] FD = beta*dvx Fx[nn] += FD Ep += (1/2)*(1-d_l/r_now)**2 cont += 1 for mm in np.arange(nn+1, N): dx = x[mm]-x[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dx) < Dmn: dy = y[mm]-y[nn] dy = dy-round(dy/Ly)*Ly dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy dvx = vx[mm]-vx[nn] dvy = vy[mm]-vy[nn] FD = beta*(dvx*dx+dvy*dy)/dmn #FD = np.absolute(FD) Fx[nn] += FD*dx/dmn Fx[mm] -= FD*dx/dmn Fy[nn] += FD*dy/dmn Fy[mm] -= FD*dy/dmn Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, Fl, Fr, Ep, cont, p_now def MD_VibrWall_DiffP_Xfixed_Collision(Nt, N, x_ini, y_ini,D0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_wall, beta): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vx_l = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi) wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] vx[wall_l] = vx_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_XFixed_collision_VibrLx(beta, Fx, Fy, N, x, y, vx, vy, D0, x_l[nt], Lx, Ly, vx_l[nt], ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx Fy_all = Fy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) freq_fft, fft_receive = FFT_Fup(int(Nt/2), F_r[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_drive = FFT_Fup(int(Nt/2), x_l[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_receive, fft_drive, cont_now, nt_rec, Ek_now, Ep_now def MD_VibrWall_LySignal_Collision(Nt, N, x_ini, y_ini,D0, m0, Lx0, Ly0, Freq_Vibr, Amp_Vibr, ind_wall, beta, dLy_scheme, num_gap): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) dLy_max = 0.1 nt_transition = int(Nt/num_gap/20) dLy_inc = np.linspace(0, dLy_max, nt_transition) dLy_dec = np.linspace(dLy_max, 0, nt_transition) if dLy_scheme == 0: dLy_all = np.zeros(Nt) elif dLy_scheme == 1: dLy_all = np.ones(Nt)*dLy_max dLy_all[0:nt_transition] = dLy_inc elif dLy_scheme == 2: dLy_all = np.zeros(Nt) nt_Ly = np.linspace(0, Nt, num_gap+1) nt_Ly = nt_Ly.astype(int) for ii in np.arange(1, num_gap): nt1 = nt_Ly[ii]-int(nt_transition/2) nt2 = nt_Ly[ii]+int(nt_transition/2) if ii%2 == 1: dLy_all[nt_Ly[ii]:nt_Ly[ii+1]] = dLy_max dLy_all[nt1:nt2] = dLy_inc else: dLy_all[nt1:nt2] = dLy_dec nt_rec = np.linspace(0, Nt, int(Nt/5e4*num_gap/5)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vx_l = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi) wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): Ly = Ly0+dLy_all[nt] x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] vx[wall_l] = vx_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_XFixed_collision_VibrLx(beta, Fx, Fy, N, x, y, vx, vy, D0, x_l[nt], Lx0, Ly, vx_l[nt], ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx Fy_all = Fy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) nt_dLy = np.arange(0, Nt, 100) return nt_dLy, dLy_all[nt_dLy], F_r, nt_rec, Ek_now, Ep_now def MD_VibrWall_LySignal(Nt, N, x_ini, y_ini,D0, m0, Lx0, Ly0, Freq_Vibr, Amp_Vibr, ind_wall, B, dLy_scheme, num_gap): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) dLy_max = 0.1 nt_transition = int(Nt/num_gap/20) dLy_inc = np.linspace(0, dLy_max, nt_transition) dLy_dec = np.linspace(dLy_max, 0, nt_transition) if dLy_scheme == 0: dLy_all = np.zeros(Nt) elif dLy_scheme == 1: dLy_all = np.ones(Nt)*dLy_max dLy_all[0:nt_transition] = dLy_inc elif dLy_scheme == 2: dLy_all = np.zeros(Nt) nt_Ly = np.linspace(0, Nt, num_gap+1) nt_Ly = nt_Ly.astype(int) for ii in np.arange(1, num_gap): nt1 = nt_Ly[ii]-int(nt_transition/2) nt2 = nt_Ly[ii]+int(nt_transition/2) if ii%2 == 1: dLy_all[nt_Ly[ii]:nt_Ly[ii+1]] = dLy_max dLy_all[nt1:nt2] = dLy_inc else: dLy_all[nt1:nt2] = dLy_dec nt_rec = np.linspace(0, Nt, int(Nt/5e4*num_gap/5)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vx_l = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi) wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): Ly = Ly0+dLy_all[nt] x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] vx[wall_l] = vx_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_Xfixed(Fx, Fy, N, x, y, D0, x_l[nt], Lx0, Ly, ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-B*vx Fy_all = Fy-B*vy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) nt_dLy = np.arange(0, Nt, 100) return nt_dLy, dLy_all[nt_dLy], F_r, nt_rec, Ek_now, Ep_now def MD_VibrBot_FSignal_Collision(beta, Nt, N, x_ini, y_ini, D0, m0, Lx, Freq_Vibr, Amp_Vibr, F_scheme, num_gap): dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) F_max = 0.01 F_min = 1e-8 nt_transition = int(Nt/num_gap/20) F_inc = np.linspace(F_min, F_max, nt_transition) F_dec = np.linspace(F_max, F_min, nt_transition) if F_scheme == 1: F_all = np.ones(Nt)*F_max elif F_scheme == 0: F_all = np.ones(Nt)*F_min F_all[0:nt_transition] = F_dec elif F_scheme == 2: F_all = np.ones(Nt)*F_max nt_F = np.linspace(0, Nt, num_gap+1) nt_F = nt_F.astype(int) for ii in np.arange(1, num_gap): nt1 = nt_F[ii]-int(nt_transition/2) nt2 = nt_F[ii]+int(nt_transition/2) if ii%2 == 1: F_all[nt_F[ii]:nt_F[ii+1]] = F_min F_all[nt1:nt2] = F_dec else: F_all[nt1:nt2] = F_inc nt_rec = np.linspace(0, Nt, int(Nt/5e4*num_gap/5)+1) Ek_now = np.array(0) Ep_now = np.array(0) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vy_bot = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D0[0:N], Lx, y_bot[nt], vy_bot[nt], y[N]) F_up[nt] = Fup_now-F_all[nt] Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = np.append(Fx,0) Fy_all = np.append(Fy, F_up[nt]) ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek_up = 0.5*m0[N]*(vx[N]**2+vy[N]**2) Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy)))-Ek_up for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) print ("freq=%f, cont_min=%d, cont_max=%d, cont_ave=%f\n" %(Freq_Vibr, min(cont), max(cont), np.mean(cont))) nt_F = np.arange(0, Nt, 100) return nt_F, F_all[nt_F], y_up, nt_rec, Ek_now, Ep_now def MD_SPSignal(mark_collision, beta, Nt, N, x_ini, y_ini,D0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_in, ind_out, ind_fix, dr_scheme, num_gap, mark_vibrY, dr_one, dr_two): dt = D0[0]/40 Nt = int(Nt) d_ini = D0[0] d0 = 0.1 dr_all = np.zeros(Nt)+dr_one if abs(dr_scheme) <= 2: nt_dr = np.linspace(0, Nt, 3) nt_dr = nt_dr.astype(int) dr_all[nt_dr[1]:nt_dr[2]] = dr_two num_gap = 5 elif dr_scheme == 3 or dr_scheme == 4: nt_dr = np.linspace(0, Nt, num_gap+1) nt_dr = nt_dr.astype(int) for ii in np.arange(1, num_gap, 2): dr_all[nt_dr[ii]:nt_dr[ii+1]] = dr_two D_fix = d_ini+dr_all*d_ini nt_rec = np.linspace(0, Nt, int(Nt/5e4*num_gap/5)+1) Ek_rec = np.array(0) Ep_rec = np.array(0) cont_rec = np.array(0) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 1: y_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in] vy_in = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)) else: x_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in] vx_in = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): D0[ind_fix] = D_fix[nt] x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 1: y[ind_in] = y_in[nt] x[ind_in] = x_ini[ind_in] vy[ind_in] = vy_in[nt] vx[ind_in] = 0 else: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] vx[ind_in] = vx_in[nt] vy[ind_in] = 0 Fx = np.zeros(N) Fy = np.zeros(N) if mark_collision == 1: Fx, Fy, Ep_now, cont_now, p_now = force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D0, Lx, 0, Ly) Fx_all = Fx Fy_all = Fy else: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Fx_all = Fx-beta*vx Fy_all = Fy-beta*vy Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if nt % 2000 == 0: print ("nt = %d, Ek = %.2e, cont = %.2e" %(nt, Ek[nt], cont[nt])) for ii in np.arange(len(nt_rec)-1): Ek_rec = np.append(Ek_rec, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec = np.append(Ep_rec, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec = np.append(cont_rec, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) print ("freq=%f, cont_min=%d, cont_max=%d, cont_ave=%f\n" %(Freq_Vibr, min(cont), max(cont), np.mean(cont))) nt_dr = np.arange(0, Nt, 100) if mark_vibrY == 1: xy_out = y_out else: xy_out = x_out return nt_dr, dr_all[nt_dr], xy_out, nt_rec, Ek_rec, Ep_rec, cont_rec def MD_YFixed_equi_SP_modecheck(Nt, N, x0, y0, D0, m0, Lx, Ly, T_set, V_em, n_em, ind_out): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2*np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) Freq_Vibr = 0 freq_x, fft_x = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_y, fft_y = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) ind1 = freq_x<30 ind2 = freq_y<30 return freq_x[ind1], freq_y[ind2], fft_x[ind1], fft_y[ind2], np.mean(cont), nt_rec, Ek_rec, Ep_rec, cont_rec def MD_YFixed_SPVibr_SP_modecheck(Nt, N, x0, y0, D0, m0, Lx, Ly, T_set, ind_in, ind_out, mark_vibrY): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ek_rec = np.array(0) Ep_rec = np.array(0) cont_rec = np.array(0) vx = np.zeros(N) vy = np.zeros(N) if mark_vibrY == 1: vy[ind_in] = 1 vy_mc = sum(np.multiply(vy,m0))/sum(m0) vy = vy-vy_mc else: vx[ind_in] = 1 vx_mc = sum(np.multiply(vx,m0))/sum(m0) vx = vx-vx_mc T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_vibrY == 1: vy = vy*np.sqrt(N*T_set/T_rd) print("|vy|_Max=%.3e, |vy|_Min=%.3e" %(max(abs(vy)), min(abs(vy)))) else: vx = vx*np.sqrt(N*T_set/T_rd) print("|vx|_Max=%.3e, |vx|_Min=%.3e" %(max(abs(vx)), min(abs(vx)))) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() mark_CB = 0 for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now cont[nt] = cont_now if mark_CB == 0 and cont_now<cont[0]: print("nt_CB=%d" % nt) mark_CB = 1 ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec = np.append(Ek_rec, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec = np.append(Ep_rec, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec = np.append(cont_rec, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) Freq_Vibr = 0 freq_x, fft_x = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_y, fft_y = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) ind1 = freq_x<30 ind2 = freq_y<30 return freq_x[ind1], freq_y[ind2], fft_x[ind1], fft_y[ind2], cont_rec, nt_rec, Ek_rec, Ep_rec #181105 def MD_YFixed_SPVibr_vCorr_modecheck(Nt_MD, Nt_FFT, N, x0, y0, D0, m0, Lx, Ly, T_set, ind_in, ind_out, mark_vibrY): N = int(N) Nt_FFT = int(Nt_FFT) Nt_MD = int(Nt_MD) dt = min(D0)/40 Nt = Nt_MD+Nt_FFT Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) mark_FFT = np.zeros(Nt) mark_FFT[Nt_MD:Nt] = 1 nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) if mark_vibrY == 1: vy[ind_in] = 1 vy_mc = sum(np.multiply(vy,m0))/sum(m0) vy = vy-vy_mc else: vx[ind_in] = 1 vx_mc = sum(np.multiply(vx,m0))/sum(m0) vx = vx-vx_mc T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_vibrY == 1: vy = vy*np.sqrt(N*T_set/T_rd) print("|vy|_Max=%.3e, |vy|_Min=%.3e" %(max(abs(vy)), min(abs(vy)))) else: vx = vx*np.sqrt(N*T_set/T_rd) print("|vx|_Max=%.3e, |vx|_Min=%.3e" %(max(abs(vx)), min(abs(vx)))) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_FFT[nt] == 1: if mark_FFT[nt-1] == 0: nt_ref = nt vx_rec = np.zeros([Nt_FFT, N]) vy_rec = np.zeros([Nt_FFT, N]) nt_delta = nt-nt_ref vx_rec[nt_delta] = vx vy_rec[nt_delta] = vy if nt_delta == Nt_FFT-1: freq_now, fft_now = FFT_vCorr(Nt_FFT, N, vx_rec, vy_rec, dt) print ("Nt_End="+str(nt)) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return freq_now, fft_now, (nt_rec[:-1]+nt_rec[1:])/2, Ek_rec, Ep_rec, cont_rec def MD_YFixed_ConstV(B, Nt, N, x0, y0, D0, m0, Lx, Ly): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() mark_CB = 0 for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Fx = Fx-B*vx Fy = Fy-B*vy Ep[nt] = Ep_now cont[nt] = cont_now if mark_CB == 0 and cont_now<cont[0]: print("nt_CB=%d" % nt) mark_CB = 1 ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[0:-1]+nt_rec[1:])/2 CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) print ("Ek_last=%.3e" % Ek[-1]) return x, y, nt_rec, Ek_rec, Ep_rec, cont_rec def MD_Vibr3Part_ConstV(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in_all, ind_out, mark_vibrY, eigen_mode_now): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) num_in = ind_in_all.size Phase_Vibr = np.sin(Freq_Vibr*dt*np.arange(Nt)) Amp_Vibr_all = np.zeros(num_in) for i_in in np.arange(num_in): ind_in = ind_in_all[i_in] if mark_vibrY == 0: Amp_Vibr_all[i_in] = eigen_mode_now[2*ind_in] elif mark_vibrY == 1: Amp_Vibr_all[i_in] = eigen_mode_now[2*ind_in+1] print(ind_in_all) print(Amp_Vibr_all) Amp_Vibr_all = Amp_Vibr_all*Amp_Vibr/max(np.abs(Amp_Vibr_all)) print(Amp_Vibr_all) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; for i_in in np.arange(num_in): ind_in = ind_in_all[i_in] if mark_vibrY == 0: x[ind_in] = Phase_Vibr[nt]*Amp_Vibr_all[i_in]+x_ini[ind_in] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = Phase_Vibr[nt]*Amp_Vibr_all[i_in]+y_ini[ind_in] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-B*vx Fy_all = Fy-B*vy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: x_in = Phase_Vibr*Amp_Vibr freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: y_in = Phase_Vibr*Amp_Vibr freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_dPhiSignal(mark_collision, beta, Nt, N, x_ini, y_ini, d0, phi0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_in, ind_out, dphi_scheme, dphi_on, dphi_off, num_gap, mark_vibrY): dt = d0/40 Nt = int(Nt) if dphi_scheme == 1: nt_dphi = np.linspace(0, Nt, 3) nt_dphi = nt_dphi.astype(int) dphi_all = np.zeros(Nt)+dphi_on dphi_all[nt_dphi[1]:nt_dphi[2]] = dphi_off elif dphi_scheme == -1: nt_dphi = np.linspace(0, Nt, 3) nt_dphi = nt_dphi.astype(int) dphi_all = np.zeros(Nt)+dphi_off dphi_all[nt_dphi[1]:nt_dphi[2]] = dphi_on else: dphi_all = np.zeros(Nt)+dphi_on nt_dphi = np.linspace(0, Nt, num_gap+1) nt_dphi = nt_dphi.astype(int) for ii in np.arange(1, num_gap, 2): dphi_all[nt_dphi[ii]:nt_dphi[ii+1]] = dphi_off D_ini = np.zeros(N)+d0 nt_rec = np.linspace(0, Nt, int(Nt/5e4*num_gap/5)+1) Ek_rec = np.array(0) Ep_rec = np.array(0) cont_rec = np.array(0) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 1: y_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in] vy_in = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)) else: x_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in] vx_in = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): D0 = D_ini*np.sqrt(1+dphi_all[nt]/phi0) #if np.mod(nt,100000) == 0: #print(D0[3]) x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 1: y[ind_in] = y_in[nt] x[ind_in] = x_ini[ind_in] vy[ind_in] = vy_in[nt] vx[ind_in] = 0 else: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] vx[ind_in] = vx_in[nt] vy[ind_in] = 0 Fx = np.zeros(N) Fy = np.zeros(N) if mark_collision == 1: Fx, Fy, Ep_now, cont_now, p_now = force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D0, Lx, 0, Ly) Fx_all = Fx Fy_all = Fy else: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Fx_all = Fx-beta*vx Fy_all = Fy-beta*vy Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec = np.append(Ek_rec, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec = np.append(Ep_rec, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec = np.append(cont_rec, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) print ("freq=%f, cont_min=%d, cont_max=%d, cont_ave=%f\n" %(Freq_Vibr, min(cont), max(cont), np.mean(cont))) nt_dphi = np.arange(0, Nt, 100) if mark_vibrY == 1: xy_out = y_out else: xy_out = x_out return nt_dphi, dphi_all[nt_dphi], xy_out, nt_rec[1:], Ek_rec[1:], Ep_rec[1:], cont_rec[1:] def Damping_calc(Damp_scheme, B, N, x, y, vx, vy, Lx, Ly): Fx_damp = np.zeros(N) Fy_damp = np.zeros(N) if Damp_scheme == 1: Fx_damp = -B*vx Fy_damp = -B*vy if Damp_scheme == 2: Fx_damp = -B*vx*np.abs(vx)*5e5 Fy_damp = -B*vy*np.abs(vy)*5e5 if Damp_scheme == 3: Fx_damp = -B*vx/np.sqrt(np.abs(vx))*np.sqrt(2e-6) Fy_damp = -B*vy/np.sqrt(np.abs(vy))*np.sqrt(2e-6) if Damp_scheme == 4: Fx_damp = -B*vx*np.exp(-5e4*np.abs(vx)+1)*0.1 Fy_damp = -B*vy*np.exp(-5e4*np.abs(vy)+1)*0.1 if Damp_scheme == 5: Fx_damp = -B*vx*np.exp(-5e5*np.abs(vx)+1) Fy_damp = -B*vy*np.exp(-5e5*np.abs(vy)+1) if Damp_scheme == 6: Fx_damp = -B*vx*np.exp(-5e6*np.abs(vx)+1)*10 Fy_damp = -B*vy*np.exp(-5e6*np.abs(vy)+1)*10 if Damp_scheme == 7: Fx_damp = -B*vx*np.exp(-5e7*np.abs(vx)+1)*100 Fy_damp = -B*vy*np.exp(-5e7*np.abs(vy)+1)*100 return Fx_damp, Fy_damp def Force_FixedPos_calc(k, N, x, y, x0, y0, D0, vx, vy, Lx, Ly): Fx_damp = np.zeros(N) Fy_damp = np.zeros(N) Ep = 0 for nn in np.arange(N): dy = y[nn]-y0[nn] dy = dy-round(dy/Ly)*Ly Dmn = 0.5*D0[nn] dx = x[nn]-x0[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if (dmn > 0): F = -k*(dmn/Dmn/Dmn)/dmn Fx_damp[nn] += F*dx Fy_damp[nn] += F*dy Ep += (1/2)*k*(dmn/Dmn)**2 return Fx_damp, Fy_damp, Ep def MD_FilterCheck_Periodic_Equi_vCorr(Nt_damp, Nt_FFT, num_period, Damp_scheme, B, N, x0, y0, D0, m0, L, T_set, V_em, n_em): if Damp_scheme < 0: return N = int(N) Nt_FFT = int(Nt_FFT) Nt_damp = int(Nt_damp) dt = min(D0)/40 Nt_period = int(2*Nt_damp+Nt_FFT) Nt = Nt_period*num_period Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) mark_damp = np.zeros(Nt) mark_FFT = np.zeros(Nt) for ii in np.arange(num_period): if ii > 0: t1 = ii*Nt_period t2 = t1+Nt_damp mark_damp[t1:t2] = 1 t3 = ii*Nt_period+2*Nt_damp t4 = t3+Nt_FFT mark_FFT[t3:t4] = 1 nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] num_FFT = 0 vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2*np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) if mark_damp[nt] == 1: Fx_damp, Fy_damp = Damping_calc(Damp_scheme, B, N, x, y, vx, vy, L[0], L[1]) Fx = Fx + Fx_damp Fy = Fy + Fy_damp Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_FFT[nt] == 1: if mark_FFT[nt-1] == 0: nt_ref = nt vx_rec = np.zeros([Nt_FFT, N]) vy_rec = np.zeros([Nt_FFT, N]) nt_delta = nt-nt_ref vx_rec[nt_delta] = vx vy_rec[nt_delta] = vy if nt_delta == Nt_FFT-1: num_FFT += 1 freq_now, fft_now = FFT_vCorr(Nt_FFT, N, vx_rec, vy_rec, dt) if num_FFT == 1: fft_all = np.array([fft_now]) freq_all = np.array([freq_now]) len_fft_ref = len(fft_now) len_freq_ref = len(freq_now) else: fft_add = np.zeros(len_fft_ref) freq_add = np.zeros(len_freq_ref) len_fft_now = len(fft_now) len_freq_now = len(freq_now) if len_fft_now >= len_fft_ref: fft_add[0:len_fft_ref] = fft_now[0:len_fft_ref] else: fft_add[0:len_fft_now] = fft_now[0:len_fft_now] fft_add[len_fft_now:] = fft_now[len_fft_now] if len_freq_now >= len_freq_ref: freq_add[0:len_freq_ref] = freq_now[0:len_freq_ref] else: freq_add[0:len_freq_now] = freq_now[0:len_freq_now] freq_add[len_freq_now:] = freq_now[len_freq_now] fft_all = np.append(fft_all, [fft_add], axis=0) freq_all = np.append(freq_all, [freq_add], axis=0) print("FFT_iteration: %d" % num_FFT) print("Ek_ave: %e" %(np.mean(Ek[nt_ref:nt]))) ind1 = m0>5 ind2 = m0<5 print("|vx|_ave(heavy):%e" % np.mean(np.abs(vx[ind1]))) print("|vx|_ave(light):%e" % np.mean(np.abs(vx[ind2]))) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return freq_all, fft_all, (nt_rec[:-1]+nt_rec[1:])/2, Ek_rec, Ep_rec, cont_rec def MD_FilterCheck_Periodic_Equi_vCorr_Seperate(Nt_damp, Nt_FFT, num_period, Damp_scheme, k, B, N, x0, y0, D0, m0, L, T_set, V_em, n_em): # for damping scheme = -1 (fixed spring at initial position) if Damp_scheme != -1: return N = int(N) Nt_FFT = int(Nt_FFT) Nt_damp = int(Nt_damp) dt = min(D0)/40 Nt = Nt_damp*num_period+Nt_FFT if num_period == 0: Nt = Nt_damp+Nt_FFT Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) mark_FFT = np.zeros(Nt) t1 = Nt_damp * num_period if num_period == 0: t1 = Nt_damp t2 = t1 + Nt_FFT mark_FFT[t1:t2] = 1 nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2*np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) # always have damping exceot num_period = 0 if num_period > 0: Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_calc(k, N, x, y, x0, y0, D0, vx, vy, L[0], L[1]) if (B > 0): Fx_damp += -B*vx Fy_damp += -B*vy elif num_period == 0: Fx_damp = 0 Fy_damp = 0 Ep_fix = 0 Ep_now += Ep_fix Fx = Fx + Fx_damp Fy = Fy + Fy_damp Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_FFT[nt] == 1: if mark_FFT[nt-1] == 0: nt_ref = nt vx_rec = np.zeros([Nt_FFT, N]) vy_rec = np.zeros([Nt_FFT, N]) nt_delta = nt-nt_ref vx_rec[nt_delta] = vx vy_rec[nt_delta] = vy if nt_delta == Nt_FFT-1: freq_now, fft_now = FFT_vCorr(Nt_FFT, N, vx_rec, vy_rec, dt) print ("Nt_End="+str(nt)) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return freq_now, fft_now, (nt_rec[:-1]+nt_rec[1:])/2, Ek_rec, Ep_rec, cont_rec def MD_Periodic_Equi_vDistr(Nt_MD, Nt_rec, N, x0, y0, D0, m0, L, T_set, V_em, n_em): N = int(N) Nt_MD = int(Nt_MD) Nt_rec = int(Nt_rec) dt = min(D0)/40 Nt = Nt_MD+Nt_rec Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] ind1 = m0>5 ind2 = m0<5 vx_light = [] vx_heavy = [] vy_light = [] vy_heavy = [] vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2*np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if nt >= Nt_MD: vx_light.extend(vx[ind2]) vy_light.extend(vy[ind2]) vx_heavy.extend(vx[ind1]) vy_heavy.extend(vy[ind1]) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return nt_rec, Ek_rec, Ep_rec, cont_rec, vx_light, vx_heavy, vy_light, vy_heavy def Output_resonator_1D(Nt, x_drive, x0, m0, w0, dt): dx = x_drive - x0 k = w0**2*m0 x = 0 vx = 0 ax_old = 0 Nt = int(Nt) x_rec = np.zeros(Nt) for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration x_rec[nt] = x Fx = k*(dx[nt]-x) ax = Fx/m0; vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration ax_old = ax; freq_fft, fft_x_rec = FFT_Fup(int(Nt/2), x_rec[int(Nt/2):Nt], dt, w0) return freq_fft, fft_x_rec def MD_Periodic_vCorr(Nt, N, x0, y0, D0, m0, vx0, vy0, L, T_set): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) vx_rec = np.zeros([int(Nt/2), N]) vy_rec = np.zeros([int(Nt/2), N]) vx = vx0 vy = vy0 T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if (nt >= Nt/2): vx_rec[int(nt-Nt/2)] = vx vy_rec[int(nt-Nt/2)] = vy CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) freq_now, fft_now = FFT_vCorr(int(Nt/2), N, vx_rec, vy_rec, dt) return freq_now, fft_now, np.mean(cont) def MD_Period_ConstV_SD(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, Lx, Ly) Ep[nt] = Ep_now vx = 0.1*np.divide(Fx, m0) vy = 0.1*np.divide(Fy, m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #t_end = time.time() print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def Force_FixedPos_YFixed_calc(k, N, x, y, x0, y0, D0, vx, vy, Lx, Ly): Fx_damp = np.zeros(N) Fy_damp = np.zeros(N) Ep = 0 for nn in np.arange(N): dy = y[nn]-y0[nn] Dmn = 0.5*D0[nn] dx = x[nn]-x0[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if (dmn > 0): F = -k*(dmn/Dmn/Dmn)/dmn Fx_damp[nn] += F*dx Fy_damp[nn] += F*dy Ep += (1/2)*k*(dmn/Dmn)**2 return Fx_damp, Fy_damp, Ep def MD_VibrSP_ConstV_Yfixed_FixSpr(k, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out): mark_vibrY = 0 mark_resonator = 1 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in] elif mark_vibrY == 1: y_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_YFixed_calc(k, N, x, y, x_ini, y_ini, D0, vx, vy, L[0], L[1]) #Fx_damp = 0; Fy_damp = 0; Ep_fix = 0 Fx_damp += -B*vx Fy_damp += -B*vy Ep[nt] = Ep_now + Ep_fix cont[nt] = cont_now p[nt] = p_now Fx_all = Fx+Fx_damp Fy_all = Fy+Fy_damp x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_Yfixed_FixSpr2(k, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out): mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in2] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_YFixed_calc(k, N, x, y, x_ini, y_ini, D0, vx, vy, L[0], L[1]) #Fx_damp = 0; Fy_damp = 0; Ep_fix = 0 Fx_damp += -B*vx Fy_damp += -B*vy Ep[nt] = Ep_now + Ep_fix cont[nt] = cont_now p[nt] = p_now Fx_all = Fx+Fx_damp Fy_all = Fy+Fy_damp x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_Yfixed_FixSpr3(k, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr1, Amp_Vibr1, ind_in1, Freq_Vibr2, Amp_Vibr2, ind_in2, ind_out): mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1*np.sin(Freq_Vibr1*dt*np.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2*np.sin(Freq_Vibr2*dt*np.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1*np.sin(Freq_Vibr1*dt*np.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2*np.sin(Freq_Vibr2*dt*np.arange(Nt))+y_ini[ind_in2] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_YFixed_calc(k, N, x, y, x_ini, y_ini, D0, vx, vy, L[0], L[1]) #Fx_damp = 0; Fy_damp = 0; Ep_fix = 0 Fx_damp += -B*vx Fy_damp += -B*vy Ep[nt] = Ep_now + Ep_fix cont[nt] = cont_now p[nt] = p_now Fx_all = Fx+Fx_damp Fy_all = Fy+Fy_damp x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr1, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr1) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr2) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr1) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr2) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr1) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr1) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr1, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr1, dt) return freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_Force_ConstV(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, mark_vibrY, mark_resonator): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) x_in = np.zeros(Nt) y_in = np.zeros(Nt) if mark_vibrY == 0: Fx_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)) elif mark_vibrY == 1: Fy_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; x_in[nt] = x[ind_in] y_in[nt] = y[ind_in] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-B*vx Fy_all = Fy-B*vy if mark_vibrY == 0: Fx_all[ind_in] += Fx_in[nt] elif mark_vibrY == 1: Fy_all[ind_in] += Fy_in[nt] x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) if Nt == 5e5: print(x[ind_out], y[ind_out]) print(fft_x_out[100], fft_y_out[100]) print(fft_in[100]) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_ConfigCB(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, Nt_rec): mark_vibrY = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in] elif mark_vibrY == 1: y_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): if nt == Nt_rec: x_rec = x[:] y_rec = y[:] x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-B*vx Fy_all = Fy-B*vy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) return x_rec, y_rec def VL_YFixed_ConstV(N, x, y, D, Lx, VL_list, VL_counter_old, x_save, y_save, first_call): r_factor = 1.2 r_cut = np.amax(D) r_list = r_factor * r_cut r_list_sq = r_list**2 r_skin_sq = ((r_factor - 1.0) * r_cut)**2 if first_call == 0: dr_sq_max = 0.0 for nn in np.arange(N): dy = y[nn] - y_save[nn] dx = x[nn] - x_save[nn] dx = dx - round(dx / Lx) * Lx dr_sq = dx**2 + dy**2 if dr_sq > dr_sq_max: dr_sq_max = dr_sq if dr_sq_max < r_skin_sq: return VL_list, VL_counter_old, x_save, y_save VL_counter = 0 for nn in np.arange(N): r_now = 0.5*D[nn] for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < r_list: dx = x[mm]-x[nn] dx = dx - round(dx / Lx) * Lx if abs(dx) < r_list: dmn_sq = dx**2 + dy**2 if dmn_sq < r_list_sq: VL_list[VL_counter][0] = nn VL_list[VL_counter][1] = mm VL_counter += 1 return VL_list, VL_counter, x, y def MD_YFixed_ConstV_SP_SD_DiffK(Nt, N, x0, y0, D0, m0, Lx, Ly, k_list, k_type): dt = D0[0] * np.sqrt(k_list[2]) / 20.0 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) x_save = np.array(x0) y_save = np.array(y0) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now vx = 0.1 * Fx vy = 0.1 * Fy x += vx * dt y += vy * dt F_tot[nt] = sum(np.absolute(Fx) + np.absolute(Fy)) # putting a threshold on total force if (F_tot[nt] < 1e-11): break print(nt) print(F_tot[nt]) t_end = time.time() #print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def FIRE_YFixed_ConstV_DiffK(Nt, N, x0, y0, D0, m0, Lx, Ly, k_list, k_type): dt_md = 0.01 * D0[0] * np.sqrt(k_list[2]) N_delay = 20 N_pn_max = 2000 f_inc = 1.1 f_dec = 0.5 a_start = 0.15 f_a = 0.99 dt_max = 10.0 * dt_md dt_min = 0.05 * dt_md initialdelay = 1 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) x_save = np.array(x0) y_save = np.array(y0) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) a_fire = a_start delta_a_fire = 1.0 - a_fire dt = dt_md dt_half = dt / 2.0 N_pp = 0 # number of P being positive N_pn = 0 # number of P being negative ## FIRE for nt in np.arange(Nt): # FIRE update P = np.dot(vx, Fx) + np.dot(vy, Fy) if P > 0.0: N_pp += 1 N_pn = 0 if N_pp > N_delay: dt = min(f_inc * dt, dt_max) dt_half = dt / 2.0 a_fire = f_a * a_fire delta_a_fire = 1.0 - a_fire else: N_pp = 0 N_pn += 1 if N_pn > N_pn_max: break if (initialdelay < 0.5) or (nt >= N_delay): if f_dec * dt > dt_min: dt = f_dec * dt dt_half = dt / 2.0 a_fire = a_start delta_a_fire = 1.0 - a_fire x -= vx * dt_half y -= vy * dt_half vx = np.zeros(N) vy = np.zeros(N) # MD using Verlet method vx += Fx * dt_half vy += Fy * dt_half rsc_fire = np.sqrt(np.sum(vx**2 + vy**2)) / np.sqrt(np.sum(Fx**2 + Fy**2)) vx = delta_a_fire * vx + a_fire * rsc_fire * Fx vy = delta_a_fire * vy + a_fire * rsc_fire * Fy x += vx * dt y += vy * dt Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now F_tot[nt] = sum(np.absolute(Fx) + np.absolute(Fy)) # putting a threshold on total force if (F_tot[nt] < 1e-11): break vx += Fx * dt_half vy += Fy * dt_half #print(nt) #print(F_tot[nt]) t_end = time.time() #print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out): Lx = L[0] Ly = L[1] mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) F_tot = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in2] vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) x_save = np.array(x_ini) y_save = np.array(y_ini) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2 # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2 if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx - B*vx Fy_all = Fy - B*vy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2 # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2 ax_old = ax ay_old = ay Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out): Lx = L[0] Ly = L[1] mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) F_tot = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in2] vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) x_save = np.array(x_ini) y_save = np.array(y_ini) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2 # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2 if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx - B*vx Fy_all = Fy - B*vy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2 # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2 ax_old = ax ay_old = ay Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return x_in1-x_ini[ind_in1], x_in2-x_ini[ind_in2], x_out-x_ini[ind_out]

133,858

30.675106

178

py

gecco-2022

gecco-2022-main/RandomSearch/AND-NESS/constants.py

# number of runs RUNS = 3 worstFitness = +1000000 # population size popSize = 50 # number of generations numGenerations = 200

129

10.818182

23

py

gecco-2022

gecco-2022-main/RandomSearch/AND-NESS/afpo_seed_binary.py

import constants as c import copy import numpy as np import operator import statistics import pickle from joblib import Parallel, delayed import multiprocessing from genome_seed_binary import GENOME class AFPO: def __init__(self,randomSeed): self.randomSeed = randomSeed self.currentGeneration = 0 self.nextAvailableID = 0 self.genomes = {} for populationPosition in range(c.popSize): self.genomes[populationPosition] = GENOME(self.nextAvailableID) self.nextAvailableID = self.nextAvailableID + 1 def Evolve(self): self.Perform_First_Generation() for self.currentGeneration in range(1,c.numGenerations): self.Perform_One_Generation() self.SaveLastGen() # -------------------------- Private methods ---------------------- def Age(self): for genome in self.genomes: self.genomes[genome].Age() def Aggressor_Dominates_Defender(self,aggressor,defender): return self.genomes[aggressor].Dominates(self.genomes[defender]) def Choose_Aggressor(self): return np.random.randint(c.popSize) def Choose_Defender(self,aggressor): defender = np.random.randint(c.popSize) while defender == aggressor: defender = np.random.randint(c.popSize) return defender def Contract(self): while len(self.genomes) > c.popSize: aggressorDominatesDefender = False while not aggressorDominatesDefender: aggressor = self.Choose_Aggressor() defender = self.Choose_Defender(aggressor) aggressorDominatesDefender = self.Aggressor_Dominates_Defender(aggressor,defender) for genomeToMove in range(defender,len(self.genomes)-1): self.genomes[genomeToMove] = self.genomes.pop(genomeToMove+1) def process(self, i): return i*i def Evaluate_Genomes(self): num_cores = multiprocessing.cpu_count() print("we have") print(num_cores) print("cores") outs = Parallel(n_jobs=num_cores)(delayed(self.genomes[genome].Evaluate)() for genome in self.genomes) #print("done") #print(type(outs)) #self.genomes = copy.deepcopy(np.array(outs)) #self.genomes = dict(zip(list(range(0, c.popSize)), outs)) #print(type(self.genomes)) for genome in self.genomes: # print(genome) #print(self.genomes[genome].indv.fitness) # print(outs[genome]) self.genomes[genome].fitness = outs[genome] self.genomes[genome].indv.fitness = -outs[genome] def Expand(self): popSize = len(self.genomes) for newGenome in range( popSize , 2 * popSize - 1 ): spawner = self.Choose_Aggressor() self.genomes[newGenome] = copy.deepcopy(self.genomes[spawner]) self.genomes[newGenome].Set_ID(self.nextAvailableID) self.nextAvailableID = self.nextAvailableID + 1 self.genomes[newGenome].Mutate() def Find_Best_Genome(self): genomesSortedByFitness = sorted(self.genomes.values(), key=operator.attrgetter('fitness'),reverse=False) return genomesSortedByFitness[0] def Find_Avg_Fitness(self): add = 0 for g in self.genomes: add += self.genomes[g].fitness return add/c.popSize def Inject(self): popSize = len(self.genomes) self.genomes[popSize-1] = GENOME(self.nextAvailableID) self.nextAvailableID = self.nextAvailableID + 1 def Perform_First_Generation(self): self.Evaluate_Genomes() self.Print() self.Save_Best() self.Save_Avg() def Perform_One_Generation(self): self.Expand() self.Age() self.Inject() self.Evaluate_Genomes() self.Contract() self.Print() self.Save_Best() self.Save_Avg() def Print(self): print('Generation ', end='', flush=True) print(self.currentGeneration, end='', flush=True) print(' of ', end='', flush=True) print(str(c.numGenerations), end='', flush=True) print(': ', end='', flush=True) bestGenome = self.Find_Best_Genome() bestGenome.Print() def Save_Best(self): bestGenome = self.Find_Best_Genome() bestGenome.Save(self.randomSeed) def SaveLastGen(self): genomesSortedByFitness = sorted(self.genomes.values(), key=operator.attrgetter('fitness'),reverse=False) f = open('savedRobotsLastGenAfpoSeed.dat', 'ab') pickle.dump(genomesSortedByFitness, f) f.close() def Save_Avg(self): f = open('avgFitnessAfpoSeed.dat', 'ab') avg = self.Find_Avg_Fitness() print('Average ' + str(avg)) print() #f.write("%.3f\n" % avg) pickle.dump(avg, f) f.close() def Show_Best_Genome(self): bestGenome = self.Find_Best_Genome() bestGenome.Show()

5,048

30.955696

112

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gecco-2022

gecco-2022-main/RandomSearch/AND-NESS/DynamicalMatrix.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Oct 10 22:09:09 2017 @author: Hightoutou """ def DM_mass(N, x0, y0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] M = np.zeros((2*N, 2*N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly rijsq = dx**2+dy**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij m_sqrt = np.zeros((2*N, 2*N)) m_inv = np.zeros((2*N, 2*N)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_3D(N, x0, y0, z0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] Lz = L[2] M = np.zeros((3*N, 3*N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly dz = z0[i]-z0[j] dz = dz-round(dz/Lz)*Lz rijsq = dx**2+dy**2+dz**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy, dx*dz], [dy*dx, dy*dy, dy*dz], [dz*dx, dz*dy, dz*dz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3*i:3*i+3,3*j:3*j+3] = Mij M[3*j:3*j+3,3*i:3*i+3] = Mij M[3*i:3*i+3,3*i:3*i+3] = M[3*i:3*i+3,3*i:3*i+3] - Mij M[3*j:3*j+3,3*j:3*j+3] = M[3*j:3*j+3,3*j:3*j+3] - Mij m_sqrt = np.zeros((3*N, 3*N)) m_inv = np.zeros((3*N, 3*N)) for i in range(N): m_sqrt[3*i, 3*i] = 1/np.sqrt(m0[i]) m_sqrt[3*i+1, 3*i+1] = 1/np.sqrt(m0[i]) m_sqrt[3*i+2, 3*i+2] = 1/np.sqrt(m0[i]) m_inv[3*i, 3*i] = 1/m0[i] m_inv[3*i+1, 3*i+1] = 1/m0[i] m_inv[3*i+2, 3*i+2] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_Yfixed(N, x0, y0, D0, m0, Lx, y_bot, y_top, k): import numpy as np M = np.zeros((2*N, 2*N)) contactNum = 0 for i in range(N): r_now = 0.5*D0[i] if y0[i]-y_bot<r_now or y_top-y0[i]<r_now: M[2*i+1, 2*i+1] = M[2*i+1, 2*i+1]+1/r_now/r_now for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] rijsq = dx**2+dy**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij M = k*M m_sqrt = np.zeros((2*N, 2*N)) m_inv = np.zeros((2*N, 2*N)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_Xfixed(N, x0, y0, D0, m0, Ly): import numpy as np M = np.zeros((2*N, 2*N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly rijsq = dx**2+dy**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij m_sqrt = np.zeros((2*N, 2*N)) m_inv = np.zeros((2*N, 2*N)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_DiffK_Yfixed(N, x0, y0, D0, m0, Lx, y_bot, y_top, k_list, k_type): import numpy as np M = np.zeros((2*N, 2*N)) contactNum = 0 for i in range(N): r_now = 0.5*D0[i] if y0[i]-y_bot<r_now or y_top-y0[i]<r_now: M[2*i+1, 2*i+1] = M[2*i+1, 2*i+1] + k_list[k_type[i]] / r_now / r_now for j in range(i): dij = 0.5 * (D0[i] + D0[j]) dijsq = dij**2 dx = x0[i] - x0[j] dx = dx - round(dx / Lx) * Lx dy = y0[i] - y0[j] rijsq = dx**2 + dy**2 if rijsq < dijsq: contactNum += 1 k = k_list[(k_type[i] ^ k_type[j]) + np.maximum(k_type[i], k_type[j])] rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -k * rijmat / rijsq / dijsq Mij2 = -k * (1.0 - rij / dij) * (rijmat / rijsq - [[1,0],[0,1]]) / rij / dij Mij = Mij1 + Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij m_sqrt = np.zeros((2*N, 2*N)) m_inv = np.zeros((2*N, 2*N)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) w,v = np.linalg.eig(M) return w,v def DM_mass_Zfixed_3D(N, x0, y0, z0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] Lz = L[2] M = np.zeros((3*N, 3*N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly dz = z0[i]-z0[j] rijsq = dx**2+dy**2+dz**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy, dx*dz], [dy*dx, dy*dy, dy*dz], [dz*dz, dz*dy, dz*dz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3*i:3*i+3,3*j:3*j+3] = Mij M[3*j:3*j+3,3*i:3*i+3] = Mij M[3*i:3*i+3,3*i:3*i+3] = M[3*i:3*i+3,3*i:3*i+3] - Mij M[3*j:3*j+3,3*j:3*j+3] = M[3*j:3*j+3,3*j:3*j+3] - Mij m_sqrt = np.zeros((3*N, 3*N)) m_inv = np.zeros((3*N, 3*N)) for i in range(N): m_sqrt[3*i, 3*i] = 1/np.sqrt(m0[i]) m_sqrt[3*i+1, 3*i+1] = 1/np.sqrt(m0[i]) m_sqrt[3*i+2, 3*i+2] = 1/np.sqrt(m0[i]) m_inv[3*i, 3*i] = 1/m0[i] m_inv[3*i+1, 3*i+1] = 1/m0[i] m_inv[3*i+2, 3*i+2] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_UpPlate(N, x0, y0, D0, m0, Lx, y_up, m_up): import numpy as np M = np.zeros((2*N+1, 2*N+1)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] rijsq = dx**2+dy**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij for i in range(N): r_now = 0.5*D0[i] if y0[i]<r_now: M[2*i+1, 2*i+1] = M[2*i+1, 2*i+1]+1/r_now**2 if y_up-y0[i]<r_now: M[2*i+1, 2*i+1] = M[2*i+1, 2*i+1]+1/r_now**2 M[2*N, 2*N] = M[2*N, 2*N]+1/r_now**2 M[2*i+1, 2*N] = M[2*i+1, 2*N]-1/r_now**2 M[2*N, 2*i+1] = M[2*N, 2*i+1]-1/r_now**2 m_sqrt = np.zeros((2*N+1, 2*N+1)) m_inv = np.zeros((2*N+1, 2*N+1)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] m_sqrt[2*N, 2*N] = 1/np.sqrt(m_up) m_inv[2*N, 2*N] = 1/m_up #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_UpPlate_3D(N, x0, y0, z0, D0, m0, Lx, Ly, z_up, m_up): import numpy as np M = np.zeros((3*N+1, 3*N+1)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly dz = z0[i]-z0[j] rijsq = dx**2+dy**2+dz**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy, dx*dz], [dy*dx, dy*dy, dy*dz], [dz*dz, dz*dy, dz*dz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3*i:3*i+3,3*j:3*j+3] = Mij M[3*j:3*j+3,3*i:3*i+3] = Mij M[3*i:3*i+3,3*i:3*i+3] = M[3*i:3*i+3,3*i:3*i+3] - Mij M[3*j:3*j+3,3*j:3*j+3] = M[3*j:3*j+3,3*j:3*j+3] - Mij for i in range(N): r_now = 0.5*D0[i] if z0[i]<r_now: M[3*i+2, 3*i+2] = M[3*i+2, 3*i+2]+1/r_now**2 if z_up-z0[i]<r_now: M[3*i+2, 3*i+2] = M[3*i+2, 3*i+2]+1/r_now**2 M[3*N, 3*N] = M[3*N, 3*N]+1/r_now**2 M[3*i+2, 3*N] = M[3*i+2, 3*N]-1/r_now**2 M[3*N, 3*i+2] = M[3*N, 3*i+2]-1/r_now**2 m_sqrt = np.zeros((3*N+1, 3*N+1)) m_inv = np.zeros((3*N+1, 3*N+1)) for i in range(N): m_sqrt[3*i, 3*i] = 1/np.sqrt(m0[i]) m_sqrt[3*i+1, 3*i+1] = 1/np.sqrt(m0[i]) m_sqrt[3*i+2, 3*i+2] = 1/np.sqrt(m0[i]) m_inv[3*i, 3*i] = 1/m0[i] m_inv[3*i+1, 3*i+1] = 1/m0[i] m_inv[3*i+2, 3*i+2] = 1/m0[i] m_sqrt[3*N, 3*N] = 1/np.sqrt(m_up) m_inv[3*N, 3*N] = 1/m_up #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v

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gecco-2022

gecco-2022-main/RandomSearch/AND-NESS/ConfigPlot.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Oct 10 21:01:26 2017 @author: Hightoutou """ import numpy as np def ConfigPlot_DiffSize(N, x, y, D, L, mark_print): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse Dmin = min(D) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] D_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) D_all.append(D[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_alpha(0.3) if D_all[i] > Dmin: e.set_facecolor('C1') else: e.set_facecolor('C0') i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass(N, x, y, D, L, m, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffMass2(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness(N, x, y, D, L, m, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C2') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness2(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C2') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness3(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='^', s=80, color=(0, 1, 0, 1)) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='s', s=80, color=(0, 0, 1, 1)) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='*', s=100, color=(1, 0, 0, 1)) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('k') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) import matplotlib.lines as mlines red_star = mlines.Line2D([], [], color=(1, 0, 0), marker='*', linestyle='None', markersize=10, label='Output') blue_square = mlines.Line2D([], [], color=(0, 0, 1), marker='s', linestyle='None', markersize=10, label='Input 2') green_triangle = mlines.Line2D([], [], color=(0, 1, 0), marker='^', linestyle='None', markersize=10, label='Input 1') plt.legend(handles=[red_star, green_triangle, blue_square], bbox_to_anchor=(1.215, 1)) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffMass_3D(N, x, y, z, D, L, m, mark_print): import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D m_min = min(m) m_max = max(m) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.set_aspect('equal') sphes = [] m_all = [] for i in range(int(N/2)): x_now = x[i]%L[0] y_now = y[i]%L[1] z_now = z[i]%L[2] r_now = 0.5*D[i] #alpha_now = 0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3 alpha_now = 0.3 pos1 = 0 pos2 = 1 for j in range(pos1, pos2): for k in range(pos1, pos2): for l in range(pos1, pos2): u = np.linspace(0, 2 * np.pi, 100) v = np.linspace(0, np.pi, 100) x_plot = x_now+j*L[0]+r_now * np.outer(np.cos(u), np.sin(v)) y_plot = y_now+k*L[1]+r_now * np.outer(np.sin(u), np.sin(v)) z_plot = z_now+l*L[2]+r_now * np.outer(np.ones(np.size(u)), np.cos(v)) ymin = y_plot[y_plot>0].min() ymax = y_plot[y_plot>0].max() print (i, ymin, ymax) ax.plot_surface(x_plot,y_plot,z_plot,rstride=4,cstride=4, color='C0',linewidth=0,alpha=alpha_now) #sphes.append(e) #m_all.append(m[i]) # i = 0 # for e in sphes: # ax.add_artist(e) # e.set_clip_box(ax.bbox) # e.set_facecolor('C0') # e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) # i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) ax.set_zlim(0, L[2]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_YFixed_rec(N, x, y, D, Lx, y_top, y_bot, m, mark_order): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_print = 0 m_min = min(m) m_max = max(m) if m_min == m_max: m_max *= 1.001 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) for k in range(-1, 2): e = Ellipse((x_now+k*Lx, y_now), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.3+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 #rect = Rectangle([0, y_top], Lx, 0.2*D[0], color='C0') #ax.add_patch(rect) for nn in np.arange(N): x1 = x[nn]%Lx d_up = y_top-y[nn] d_bot = y[nn]-y_bot r_now = 0.5*D[nn] if d_up<r_now: ax.plot([x1, x1], [y[nn], y[nn]+r_now], '-', color='w') if d_bot<r_now: ax.plot([x1, x1], [y[nn], y[nn]-r_now], '-', color='w') for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < Dmn: x2 = x[mm]%Lx if x2>x1: xl = x1 xr = x2 yl = y[nn] yr = y[mm] else: xl = x2 xr = x1 yl = y[mm] yr = y[nn] dx0 = xr-xl dx = dx0-round(dx0/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: if dx0<Dmn: ax.plot([xl, xr], [yl, yr], '-', color='w') else: ax.plot([xl, xr-Lx], [yl, yr], '-', color='w') ax.plot([xl+Lx, xr], [yl, yr], '-', color='w') ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/plot_test/fig'+str(int(ind_nt+1e4))+'.png', dpi = 150) def ConfigPlot_DiffMass_SP(N, x, y, D, L, m, mark_print, ind_in, ind_out, ind_fix): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)*0.3) if i == ind_in: e.set_edgecolor('r') e.set_linewidth(width) if i == ind_out: e.set_edgecolor('b') e.set_linewidth(width) if i == ind_fix: e.set_edgecolor('k') e.set_linewidth(width) ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass_FixLx(N, x, y, D, L, m, mark_print, ind_wall): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i] y_now = y[i]%L[1] for l in range(-1, 2): e = Ellipse((x_now, y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) if ind_wall[i] > 0: e.set_edgecolor('k') e.set_linewidth(width) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass_SP_rec(N, x, y, D, L, m, mark_print, ind_in, ind_out, ind_fix): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)*0.3) if i == ind_in: e.set_edgecolor('r') e.set_linewidth(width) if i == ind_out: e.set_edgecolor('b') e.set_linewidth(width) if i == ind_fix: e.set_edgecolor('k') e.set_linewidth(width) Lx = L[0] Ly = L[1] for nn in np.arange(N): x1 = x[nn]%Lx y1 = y[nn]%Ly for mm in np.arange(nn+1, N): x2 = x[mm]%Lx y2 = y[mm]%Ly if x2>x1: xl = x1 xr = x2 yl = y1 yr = y2 else: xl = x2 xr = x1 yl = y2 yr = y1 dx0 = xr-xl dx = dx0-round(dx0/Lx)*Lx if y2>y1: xd = x1 xu = x2 yd = y1 yu = y2 else: xd = x2 xu = x1 yd = y2 yu = y1 dy0 = yu-yd dy = dy0-round(dy0/Ly)*Ly Dmn = 0.5*(D[mm]+D[nn]) dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: if dx0<Dmn and dy0<Dmn: ax.plot([xl, xr], [yl, yr], '-', color='w') else: if dx0>Dmn and dy0>Dmn: if yr>yl: ax.plot([xl, xr-Lx], [yl, yr-Ly], '-', color='w') ax.plot([xl+Lx, xr], [yl+Ly, yr], '-', color='w') else: ax.plot([xl, xr-Lx], [yl, yr+Ly], '-', color='w') ax.plot([xl+Lx, xr], [yl-Ly, yr], '-', color='w') else: if dx0>Dmn: ax.plot([xl, xr-Lx], [yl, yr], '-', color='w') ax.plot([xl+Lx, xr], [yl, yr], '-', color='w') if dy0>Dmn: ax.plot([xd, xu], [yd, yu-Ly], '-', color='w') ax.plot([xd, xu], [yd+Ly, yu], '-', color='w') ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) return fig def ConfigPlot_EigenMode_DiffMass(N, x, y, D, L, m, em, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) if m_min == m_max: m_max *= 1.001 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 r_now = D[0]*0.5 dr = np.zeros(N) for i in range(N): dr[i] = np.sqrt(em[2*i]**2+em[2*i+1]**2) dr_max = max(dr) for i in range(N): ratio = dr[i]/dr_max*r_now/dr_max plt.arrow(x[i], y[i],em[2*i]*ratio, em[2*i+1]*ratio, head_width=0.005) ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_YFixed_SelfAssembly(N, Nl, x, y, theta, n, d1, d2, Lx, y_top, y_bot): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_order = 0 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] alpha_all = [] alpha1 = 0.6 alpha2 = 0.3 for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) alpha = alpha1 if i < Nl else alpha2 for k in range(-1, 2): e = Ellipse((x_now+k*Lx, y_now), d1,d1,0) ells.append(e) alpha_all.append(alpha) if i >= Nl: for ind in range(n): x_i = x_now+k*Lx+0.5*(d1+d2)*np.cos(theta[i]+ind*2*np.pi/n) y_i = y_now+0.5*(d1+d2)*np.sin(theta[i]+ind*2*np.pi/n) e = Ellipse((x_i, y_i), d2,d2,0) ells.append(e) alpha_all.append(alpha) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(alpha_all[i]) i += 1 ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show() def ConfigPlot_YFixed_SelfAssembly_BumpyBd(N, n_col, Nl, x, y, theta, n, d0, d1, d2, Lx, y_top, y_bot): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_order = 0 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] alpha_all = [] alpha1 = 0.6 alpha2 = 0.3 for i in range(n_col+1): x_now = i*d0 e1 = Ellipse((x_now, y_bot), d0,d0,0) e2 = Ellipse((x_now, y_top), d0,d0,0) ells.append(e1) alpha_all.append(alpha1) ells.append(e2) alpha_all.append(alpha1) for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) alpha = alpha1 if i < Nl else alpha2 for k in range(-1, 2): e = Ellipse((x_now+k*Lx, y_now), d1,d1,0) ells.append(e) alpha_all.append(alpha) if i >= Nl: for ind in range(n): x_i = x_now+k*Lx+0.5*(d1+d2)*np.cos(theta[i]+ind*2*np.pi/n) y_i = y_now+0.5*(d1+d2)*np.sin(theta[i]+ind*2*np.pi/n) e = Ellipse((x_i, y_i), d2,d2,0) ells.append(e) alpha_all.append(alpha) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(alpha_all[i]) i += 1 ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show()

22,835

29.488652

117

py

gecco-2022

gecco-2022-main/RandomSearch/AND-NESS/genome_seed_binary.py

import constants as c from individual_seed_binary import INDIVIDUAL import pickle class GENOME: def __init__(self,ID,fitness=c.worstFitness): self.Set_ID(ID) self.indv = INDIVIDUAL(ID) self.age = 0 self.fitness = fitness def Age(self): self.age = self.age + 1 def Dominates(self,other): if self.Get_Fitness() <= other.Get_Fitness(): if self.Get_Age() <= other.Get_Age(): equalFitnesses = self.Get_Fitness() == other.Get_Fitness() equalAges = self.Get_Age() == other.Get_Age() if not equalFitnesses and equalAges: return True else: return self.Is_Newer_Than(other) else: return False else: return False def Evaluate(self): #self.indv.Start_Evaluation(True) f = self.indv.Compute_Fitness() # if f < 0: # self.fitness = c.worstFitness # else: # self.fitness = 1/(1+f) self.fitness = -f #print(f) #print(self.indv.genome) return self.fitness def Get_Age(self): return self.age def Get_Fitness(self): return self.fitness def Mutate(self): self.indv.Mutate() def Print(self): print(' fitness: ' , end = '' ) print(self.fitness , end = '' ) print(' age: ' , end = '' ) print(self.age , end = '' ) print() def Save(self,randomSeed): f = open('savedRobotsAfpoSeed.dat', 'ab') pickle.dump(self.indv , f) f.close() pass def Set_ID(self,ID): self.ID = ID def Show(self): #self.indv.Start_Evaluation(False, 40) self.indv.Compute_Fitness(True) # def __add__(self, other): # total_fitness = self.fitness + other.fitness # print("I've been called") # return GENOME(1, total_fitness) # def __radd__(self, other): # if other == 0: # return self # else: # return self.__add__(other) # -------------------- Private methods ---------------------- def Get_ID(self): return self.ID def Is_Newer_Than(self,other): return self.Get_ID() > other.Get_ID()

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gecco-2022-main/RandomSearch/AND-NESS/afpoPlots_seed_binary.py

import pickle import matplotlib.pyplot as plt from switch_binary import switch import constants as c import numpy runs = c.RUNS gens = c.numGenerations fitnesses = numpy.zeros([runs, gens]) temp = [] individuals = [] with open('savedRobotsAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): for g in range(1, gens+1): try: temp.append(pickle.load(f).fitness) except EOFError: break fitnesses[r-1] = temp temp = [] f.close() mean_f = numpy.mean(fitnesses, axis=0) std_f = numpy.std(fitnesses, axis=0) plt.figure(figsize=(6.4,4.8)) plt.plot(list(range(1, gens+1)), mean_f, color='blue') plt.fill_between(list(range(1, gens+1)), mean_f-std_f, mean_f+std_f, color='cornflowerblue', alpha=0.2) plt.xlabel("Generations") plt.ylabel("Best Fitness") plt.title("Fitness of the Best Individual in the Population - AFPO", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() #plt.legend(['two robot', 'three robots'], loc='upper left') #plt.savefig("compare.pdf") plt.show() # running the best individuals m1 = 1 m2 = 10 N_light = 9 N = 30 bests = numpy.zeros([runs, gens]) temp = [] rubish = [] with open('savedRobotsLastGenAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): # population of the last generation temp = pickle.load(f) # best individual of last generation best = temp[0] switch.showPacking(m1, m2, N_light, best.indv.genome) print(switch.evaluate(m1, m2, N_light, best.indv.genome)) print(switch.evaluateAndPlot(m1, m2, N_light, best.indv.genome)) temp = [] f.close() # running all of the individuals of the last generation of each of the runs bests = numpy.zeros([runs, gens]) temp = [] rubish = [] with open('savedRobotsLastGenAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): print("run:") print(r) # population of the last generation temp = pickle.load(f) for g in range(0, gens): switch.showPacking(m1, m2, N_light, temp[g].indv.genome) print(switch.evaluateAndPlot(m1, m2, N_light, temp[g].indv.genome)) temp = [] f.close()

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gecco-2022-main/RandomSearch/AND-NESS/evolveAfpo_seed_binary.py

from afpo_seed_binary import AFPO import os import constants as c import random #cleaning up the data files try: os.remove("savedRobotsLastGenAfpoSeed.dat") except OSError: pass try: os.remove("avgFitnessAfpoSeed.dat") except OSError: pass try: os.remove("savedRobotsAfpoSeed.dat") except OSError: pass runs = c.RUNS for r in range(1, runs+1): print("*********************************************************", flush=True) print("run: "+str(r), flush=True) randomSeed = r random.seed(r) afpo = AFPO(randomSeed) afpo.Evolve() #afpo.Show_Best_Genome()

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gecco-2022-main/RandomSearch/AND-NESS/individual_seed_binary.py

from switch_binary import switch import constants as c import random import math import numpy as np import sys import pickle class INDIVIDUAL: def __init__(self, i): # assuming curves have one control point, [Sx, Ex, Cx, Cy] for each fiber # assuming we have two planes, each with c.FIBERS of fibers on them self.m1 = 1 self.m2 = 10 #[2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000] #10 is what is in fig2 of the paper self.N_light = 9 #[25, 50, 75] #[9, 15, 21] self.N = 30 #self.low = 7 #self.high = 12 #indices = random.sample(range(0, N), N_light) self.genome = np.random.randint(low=0, high=2, size=self.N)#random.sample(range(0, self.N), self.N_light) #np.random.randint(0, high=c.GRID_SIZE-1, size=(c.FIBERS*2, 4), dtype='int') self.fitness = 0 self.ID = i def Compute_Fitness(self, show=False): # wait for the simulation to end and get the fitness self.fitness = switch.evaluate(self.m1, self.m2, self.N_light, self.genome)#, self.low, self.high) if show: switch.showPacking(self.m1, self.m2, self.N_light, self.genome)#, self.low, self.high) print("fitness is:") print(self.fitness) return self.fitness def Mutate(self): mutationRate = 0.05 probToMutate = np.random.choice([False, True], size=self.genome.shape, p=[1-mutationRate, mutationRate]) candidate = np.where(probToMutate, 1-self.genome, self.genome) self.genome = candidate def Print(self): print('[', self.ID, self.fitness, ']', end=' ') def Save(self): f = open('savedFitnessSeed.dat', 'ab') pickle.dump(self.fitness , f) f.close() def SaveBest(self): f = open('savedBestsSeed.dat', 'ab') pickle.dump(self.genome , f) f.close()

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gecco-2022-main/RandomSearch/AND-NESS/randomSearch2.py

from switch_binary import switch import matplotlib.pyplot as plt import random import numpy as np from joblib import Parallel, delayed import multiprocessing import pickle from scipy.stats import norm import operator from ConfigPlot import ConfigPlot_DiffStiffness3 with open('outs.dat', "rb") as f: outs = pickle.load(f) f.close() with open('samples.dat', "rb") as f: samples = pickle.load(f) f.close() # compute the cumulative sum #Nk_cum = np.cumsum(Nk) # go to log scale #log_Nk_cum = np.log10(Nk_cum) #log_k = np.log10(k) # plot the original data #fig = plt.figure() #ax = plt.gca() #ax.scatter(log_k, log_Nk_cum, s=5, alpha=0.3) #ax.set_title('CCDF in log-log scale') #ax.set_xlabel('$Log_{10}(k)$') #ax.set_ylabel('$Log_{10}(Nk_{>k})$') def showPacking(indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row m1=1 m2=10 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) # show packing ConfigPlot_DiffStiffness3(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf', ind_in1, ind_in2, ind_out) print("done", flush=True) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() n, bins, patches = plt.hist(x=outs, bins='auto', color='#0504aa', alpha=0.7, cumulative=False)#, grid=True) # fitting a normal distribution mu, std = norm.fit(outs) xmin, xmax = plt.xlim() x = np.linspace(xmin, xmax, 1000) p = norm.pdf(x, mu, std) #plt.plot(x, p, linewidth=2) myText = "Mean={:.3f}, STD={:.3f}".format(mu, std) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium', color='g') plt.xlabel('AND-Ness') plt.ylabel('Counts') plt.title('Random Search', fontsize='medium') #plt.xlim([0, 8]) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) #plt.show() plt.savefig("histogram2.jpg", dpi=300) sortedList = list(zip(*sorted(zip(samples,outs), key=operator.itemgetter(1)))) showPacking(sortedList[0][0]) print(sortedList[1][0]) showPacking(sortedList[0][-1]) print(sortedList[1][-1])

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gecco-2022-main/RandomSearch/AND-NESS/plot_functions.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Oct 22 14:48:04 2017 @author: Hightoutou """ import matplotlib.pyplot as plt #import matplotlib #matplotlib.use('TkAgg') def Line_single(xdata, ydata, line_spec, xlabel, ylabel, mark_print, fn = '', xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.5*3/4) pos1 = ax1.get_position() pos2 = [pos1.x0 + 0.12, pos1.y0 + 0.05, pos1.width-0.1, pos1.height] ax1.set_position(pos2) #ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) plt.ylabel(ylabel, fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') ax1.plot(xdata, ydata, line_spec) if mark_print == 1: fig.savefig(fn, dpi = 300) fig.show() def Line_multi(xdata, ydata, line_spec, xlabel, ylabel, xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.5*3/4) ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) ax1.set_ylabel(ylabel, fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') for ii in range(len(xdata)): ax1.plot(xdata[ii], ydata[ii], line_spec[ii]) plt.show() def Line_yy(xdata, ydata, line_spec, xlabel, ylabel, xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.5*3/4) ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) ax1.set_ylabel(ylabel[0], fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') ax1.plot(xdata[0], ydata[0], line_spec[0]) ax2 = ax1.twinx() ax2.set_ylabel(ylabel[1], fontsize=12) ax2.plot(xdata[1], ydata[1], line_spec[1]) plt.show()

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gecco-2022-main/RandomSearch/AND-NESS/FFT_functions.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Oct 17 15:10:21 2017 @author: Hightoutou """ import numpy as np import matplotlib.pyplot as plt from plot_functions import Line_multi, Line_single #from numba import jit def FFT_Fup(Nt, F, dt, Freq_Vibr): sampling_rate = 1/dt t = np.arange(Nt)*dt fft_size = Nt xs = F[:fft_size] xf = np.absolute(np.fft.rfft(xs)/fft_size) freqs = (2*np.pi)*np.linspace(0, sampling_rate/2, fft_size//2+1) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 0: Line_multi([freqs[1:], [Freq_Vibr, Freq_Vibr]], [xf[1:], [min(xf[1:]), max(xf[1:])]], ['o', 'r--'], 'Frequency', 'FFT', 'linear', 'log') return freqs[1:], xf[1:] def FFT_Fup_RealImag(Nt, F, dt, Freq_Vibr): sampling_rate = 1/dt t = np.arange(Nt)*dt fft_size = Nt xs = F[:fft_size] xf = np.fft.rfft(xs)/fft_size freqs = (2*np.pi)*np.linspace(0, sampling_rate/2, fft_size/2+1) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] xf_real = xf.real xf_imag = xf.imag if 1 == 0: Line_multi([freqs[1:], [Freq_Vibr, Freq_Vibr]], [xf[1:], [min(xf[1:]), max(xf[1:])]], ['o', 'r--'], 'Frequency', 'FFT') return freqs[1:], xf_real[1:], xf_imag[1:] #@jit def vCorr_Cal(fft_size, Nt, y_raw): y_fft = np.zeros(fft_size) for jj in np.arange(fft_size): sum_vcf = 0 sum_tt = 0 count = 0 for kk in np.arange(Nt-jj): count = count+1 sum_vcf += y_raw[kk]*y_raw[kk+jj]; sum_tt = sum_tt+y_raw[kk]*y_raw[kk]; y_fft[jj] = sum_vcf/sum_tt; return y_fft def FFT_vCorr(Nt, N, vx_rec, vy_rec, dt): sampling_rate = 1/dt fft_size = Nt-1 freqs = (2*np.pi)*np.linspace(0, sampling_rate/2, fft_size/2+1) for ii in np.arange(2*N): #for ii in [0,4]: if np.mod(ii, 10) == 0: print('ii=%d\n' % (ii)) if ii >= N: y_raw = vy_rec[:, ii-N] else: y_raw = vx_rec[:, ii] y_fft = vCorr_Cal(fft_size, Nt, y_raw) if ii == 0: xf = np.absolute(np.fft.rfft(y_fft)/fft_size) else: xf += np.absolute(np.fft.rfft(y_fft)/fft_size) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 1: Line_single(freqs[1:], xf[1:], 'o', 'Frequency', 'FFT') return freqs[1:], xf[1:] def FFT_vCorr_3D(Nt, N, vx_rec, vy_rec, vz_rec, dt): sampling_rate = 1/dt fft_size = Nt-1 freqs = (2*np.pi)*np.linspace(0, sampling_rate/2, fft_size/2+1) for ii in np.arange(3*N): #for ii in [0,4]: if np.mod(ii, 10) == 0: print('ii=%d\n' % (ii)) if ii >= 2*N: y_raw = vz_rec[:, ii-2*N] elif ii < N: y_raw = vx_rec[:, ii] else: y_raw = vy_rec[:, ii-N] y_fft = vCorr_Cal(fft_size, Nt, y_raw) if ii == 0: xf = np.absolute(np.fft.rfft(y_fft)/fft_size) else: xf += np.absolute(np.fft.rfft(y_fft)/fft_size) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 1: Line_single(freqs[1:], xf[1:], 'o', 'Frequency', 'FFT') return freqs[1:], xf[1:]

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gecco-2022

gecco-2022-main/RandomSearch/AND-NESS/randomSearch.py

from switch_binary import switch import matplotlib.pyplot as plt import random import numpy as np from joblib import Parallel, delayed import multiprocessing import pickle m1 = 1 m2 = 10 N = 30 N_light = 9 samples = [] print("sampling", flush=True) for i in range(0, 5001): samples.append(np.random.randint(low=0, high=2, size=N)) print("sampling done", flush=True) num_cores = multiprocessing.cpu_count() outs = Parallel(n_jobs=num_cores)(delayed(switch.evaluate)(m1, m2, N_light, samples[i]) for i in range(0, 5001)) print("done", flush=True) f = open('outs.dat', 'ab') pickle.dump(outs , f) f.close() f = open('samples.dat', 'ab') pickle.dump(samples , f) f.close() n, bins, patches = plt.hist(x=outs, bins='auto', color='#0504aa', alpha=0.7, rwidth=0.85)#, grid=True) plt.xlabel('Andness') plt.ylabel('Counts') plt.title('Random Search') #plt.xlim([0, 8]) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.show() plt.savefig("histogram.jpg", dpi=300)

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gecco-2022-main/RandomSearch/AND-NESS/plotInOut.py

import constants as c import numpy as np from ConfigPlot import ConfigPlot_DiffStiffness2 from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK, FIRE_YFixed_ConstV_DiffK, MD_VibrSP_ConstV_Yfixed_DiffK2 from DynamicalMatrix import DM_mass_DiffK_Yfixed import random import matplotlib.pyplot as plt import pickle from os.path import exists from switch_binary import switch def showPacking(indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row m1=1 m2=10 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) # show packing ConfigPlot_DiffStiffness2(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf', ind_in1, ind_in2, ind_out) def plotInOut(indices): #%% Initial Configuration k1 = 1. k2 = 10. m1 = 1 m2 = 10 n_col = 6 n_row = 5 N = n_col*n_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) w = np.real(w) v = np.real(v) freq = np.sqrt(np.absolute(w)) ind_sort = np.argsort(freq) freq = freq[ind_sort] v = v[:, ind_sort] ind = freq > 1e-4 eigen_freq = freq[ind] eigen_mode = v[:, ind] w_delta = eigen_freq[1:] - eigen_freq[0:-1] index = np.argmax(w_delta) F_low_exp = eigen_freq[index] F_high_exp = eigen_freq[index+1] plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.scatter(np.arange(0, len(eigen_freq)), eigen_freq, marker='x', color='blue') plt.xlabel("Number") plt.ylabel("Frequency") plt.title("Vibrational Response", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) props = dict(facecolor='green', alpha=0.1) myText = 'f_low='+"{:.2f}".format(F_low_exp)+"\n"+'f_high='+"{:.2f}".format(F_high_exp)+"\n"+'band gap='+"{:.2f}".format(max(w_delta)) plt.text(0.85, 0.1, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='large', bbox=props) plt.tight_layout() plt.show() print("specs:") print(F_low_exp) print(F_high_exp) print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency") plt.ylabel("Amplitude of FFT") plt.title("Logic Gate Response - input = 11", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain1) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium') plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='solid') plt.plot(x_out, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps") plt.ylabel("Amplitude of Displacement") plt.title("Logic Gate Response - input = 11", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() plt.show() # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency") plt.ylabel("Amplitude of FFT") plt.title("Logic Gate Response - input = 10", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain2) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium') plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='solid') plt.plot(x_out, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps") plt.ylabel("Amplitude of Displacement") plt.title("Logic Gate Response - input = 10", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() plt.show() # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency") plt.ylabel("Amplitude of FFT") plt.title("Logic Gate Response - input = 01", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain3) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium') plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='solid') plt.plot(x_out, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps") plt.ylabel("Amplitude of Displacement") plt.title("Logic Gate Response - input = 01", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() plt.show() print("gain1:") print(gain1) print("gain2:") print(gain2) print("gain3:") print(gain3) andness = 2*gain1/(gain2+gain3) return andness runs = c.RUNS gens = c.numGenerations # running the best individuals temp = [] rubish = [] with open('savedRobotsLastGenAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): # population of the last generation temp = pickle.load(f) # best individual of last generation best = temp[0] showPacking(best.indv.genome) print(plotInOut(best.indv.genome)) temp = [] f.close()

13,392

35.997238

248

py

gecco-2022

gecco-2022-main/RandomSearch/XOR-NESS/switch_binary.py

import numpy as np from ConfigPlot import ConfigPlot_EigenMode_DiffMass, ConfigPlot_YFixed_rec, ConfigPlot_DiffMass_SP from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK from MD_functions import FIRE_YFixed_ConstV_DiffK from DynamicalMatrix import DM_mass_DiffK_Yfixed from plot_functions import Line_single, Line_multi from ConfigPlot import ConfigPlot_DiffStiffness import random import matplotlib.pyplot as plt import pickle from os.path import exists class switch(): def evaluate(m1, m2, N_light, indices): #%% Initial Configuration k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem #w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) #w = np.real(w) #v = np.real(v) #freq = np.sqrt(np.absolute(w)) #ind_sort = np.argsort(freq) #freq = freq[ind_sort] #v = v[:, ind_sort] #ind = freq > 1e-4 #eigen_freq = freq[ind] #eigen_mode = v[:, ind] #w_delta = eigen_freq[1:] - eigen_freq[0:-1] #index = np.argmax(w_delta) #F_low_exp = eigen_freq[index] #F_high_exp = eigen_freq[index+1] #print("specs:") #print(F_low_exp) #print(F_high_exp) #print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) XOR = (gain2+gain3)/(2*gain1) return XOR def showPacking(m1, m2, N_light, indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # show packing ConfigPlot_DiffStiffness(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf') def evaluateAndPlot(m1, m2, N_light, indices): #%% Initial Configuration k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) w = np.real(w) v = np.real(v) freq = np.sqrt(np.absolute(w)) ind_sort = np.argsort(freq) freq = freq[ind_sort] v = v[:, ind_sort] ind = freq > 1e-4 eigen_freq = freq[ind] eigen_mode = v[:, ind] w_delta = eigen_freq[1:] - eigen_freq[0:-1] index = np.argmax(w_delta) F_low_exp = eigen_freq[index] F_high_exp = eigen_freq[index+1] plt.figure(figsize=(6.4,4.8)) plt.scatter(np.arange(0, len(eigen_freq)), eigen_freq, marker='x', color='blue') plt.xlabel("Number") plt.ylabel("Frequency") plt.title("Vibrational Reponse", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() plt.show() print("specs:") print(F_low_exp) print(F_high_exp) print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) print("gain1:") print(gain1) print("gain2:") print(gain2) print("gain3:") print(gain3) XOR = (gain2+gain3)/(2*gain1) return XOR

14,724

36.563776

252

py

gecco-2022

gecco-2022-main/RandomSearch/XOR-NESS/MD_functions.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Oct 13 13:09:27 2017 @author: Hightoutou """ import numpy as np import time #from numba import jit from FFT_functions import FFT_Fup, FFT_vCorr from plot_functions import Line_multi, Line_yy, Line_single from ConfigPlot import ConfigPlot_YFixed_rec import matplotlib.pyplot as plt #import IPython.core.debugger #dbg = IPython.core.debugger.Pdb() #@jit def force_YFixed(Fx, Fy, N, x, y, D, Lx, y_bot, y_up): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5*D[nn] if d_up<r_now: F = -(1-d_up/r_now)/(r_now) Fup -= F Fy[nn] += F Ep += (1/2)*(1-d_up/r_now)**2 cont_up += 1 cont += 1 #dbg.set_trace() if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fbot += F Fy[nn] -= F Ep += (1/2)*(1-d_bot/r_now)**2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up def force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D, Lx, y_bot, y_up, k_list, k_type, VL_list, VL_counter): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 for nn in np.arange(N): d_up = y_up - y[nn] d_bot = y[nn] - y_bot r_now = 0.5 * D[nn] if d_up < r_now: F = -k_list[k_type[nn]] * (1 - d_up / r_now) / (r_now) Fup -= F Fy[nn] += F Ep += 0.5 * k_list[k_type[nn]] * (1 - d_up / r_now)**2 cont_up += 1 cont += 1 #dbg.set_trace() if d_bot < r_now: F = -k_list[k_type[nn]] * (1 - d_bot / r_now) / (r_now) Fbot += F Fy[nn] -= F Ep += 0.5 * k_list[k_type[nn]] * (1 - d_bot / r_now)**2 cont += 1 for vl_idx in np.arange(VL_counter): nn = VL_list[vl_idx][0] mm = VL_list[vl_idx][1] dy = y[mm] - y[nn] Dmn = 0.5 * (D[mm] + D[nn]) if abs(dy) < Dmn: dx = x[mm] - x[nn] dx = dx - round(dx / Lx) * Lx if abs(dx) < Dmn: dmn = np.sqrt(dx**2 + dy**2) if dmn < Dmn: k = k_list[(k_type[nn] ^ k_type[mm]) + np.maximum(k_type[nn], k_type[mm])] F = -k * (1 - dmn / Dmn) / Dmn / dmn Fx[nn] += F * dx Fx[mm] -= F * dx Fy[nn] += F * dy Fy[mm] -= F * dy Ep += 0.5 * k * (1 - dmn / Dmn)**2 cont += 1 p_now += (-F) * (dx**2 + dy**2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up def force_YFixed_upDS(Fx, Fy, N, x, y, D, Lx, y_bot, y_up, ind_up): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5*D[nn] if ind_up[nn] == 1: F = -(1-d_up/r_now)/(r_now) Fup -= F Fy[nn] += F Ep += (1/2)*(1-d_up/r_now)**2 #dbg.set_trace() if d_up<r_now: cont_up = cont_up+1 cont += 1 if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fbot += F Fy[nn] -= F Ep += (1/2)*(1-d_bot/r_now)**2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up #@jit def force_Regular(Fx, Fy, N, x, y, D, Lx, Ly): Ep = 0 cont = 0 p_now = 0 for nn in np.arange(N): for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] dy = dy-round(dy/Ly)*Ly Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, Ep, cont, p_now def MD_UpDownFixed_SD(N, x0, y0, D0, m0, L): dt = min(D0)/40 Nt = int(1e4) Ep = np.zeros(Nt) F_up = np.zeros(Nt) F_bot = np.zeros(Nt) F_tot = np.zeros(Nt) Fup_now = 0 vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) #Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) F_up[nt] = Fup_now F_bot[nt] = Fbot_now Ep[nt] = Ep_now vx = np.divide(Fx, m0) vy = np.divide(Fy, m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) t_end = time.time() print ("time=%.3e" %(t_end-t_start)) if 1 == 0: # Plot the amplitide of F Line_single(range(Nt), F_tot[0:Nt], '-', 't', 'Ftot', 'log', yscale='log') return x, y def MD_VibrBot_ForceUp(N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr): dt = min(D0)/40 Nt = int(5e4) Ep = np.zeros(Nt) Ek = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr #y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)) vx = np.zeros(N) vy = np.zeros(N) if 1 == 0: y_bot = np.zeros(Nt) vx = np.random.rand(N) vy = np.random.rand(N) T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) T_set = 1e-6 vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now = force_YFixed(Fx, Fy, N, x, y, D0, L[0], y_bot[nt], L[1]) #Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) F_up[nt] = Fup_now Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) freq_now, fft_now = FFT_Fup(Nt, F_up[:Nt], dt, Freq_Vibr) # Plot the amplitide of F if 1 == 1: Line_yy([dt*range(Nt), dt*range(Nt)], [F_up[0:Nt],y_bot[0:Nt]], ['-', ':'], 't', ['$F_{up}$', '$y_{bottom}$']) Etot = Ep[1:Nt]+Ek[1:Nt] xdata = [dt*range(Nt), dt*range(Nt), dt*range(Nt-1)] ydata = [Ep[0:Nt], Ek[0:Nt], Etot] line_spec = ['--', ':', 'r-'] Line_multi(xdata, ydata, line_spec, 't', '$E$', 'log') print("std(Etot)=%e\n" %(np.std(Etot))) #dt2 = 1e-3 #xx = np.arange(0, 5, dt2) #yy = np.sin(50*xx)+np.sin(125*xx) #print("dt=%e, w=%f\n" % (dt, Freq_Vibr)) FFT_Fup(Nt, F_up[:Nt], dt, Freq_Vibr) #FFT_Fup(yy.size, yy, dt2, 50) return freq_now, fft_now, np.mean(cont) def MD_Periodic_equi(Nt, N, x0, y0, D0, m0, L, T_set, V_em, n_em): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) vx_rec = np.zeros([Nt, N]) vy_rec = np.zeros([Nt, N]) vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): #for ii in [60]: ind1 = 2*np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx_rec[nt] = vx vy_rec[nt] = vy t_end = time.time() print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) #Etot = Ep[1:Nt]+Ek[1:Nt] #xdata = [dt*range(Nt), dt*range(Nt), dt*range(Nt-1)] #ydata = [Ep[0:Nt], Ek[0:Nt], Etot] #line_spec = ['--', ':', 'r-'] #Line_multi(xdata, ydata, line_spec, 't', '$E$', 'log', 'log') freq_now, fft_now = FFT_vCorr(Nt, N, vx_rec, vy_rec, dt) return freq_now, fft_now, np.mean(cont) def MD_YFixed_ConstP_SD(Nt, N, x0, y0, D0, m0, L, F0_up): dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e6) #Nt = int(5e2) Ep = np.zeros(Nt) F_up = np.zeros(Nt) F_bot = np.zeros(Nt) F_tot = np.zeros(Nt) Fup_now = 0 y_up = y0[N] vx = np.zeros(N+1) vy = np.zeros(N+1) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], 0, y[N]) F_up[nt] = Fup_now+F0_up F_bot[nt] = Fbot_now Ep[nt] = Ep_now+(y_up-y[N])*F0_up vx = 0.1*np.divide(np.append(Fx,0), m0) vy = 0.1*np.divide(np.append(Fy, F_up[nt]), m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #print("nt=%d, Fup=%e, Fup_tot=%e\n" % (nt, Fup_now, F_up[nt])) #dbg.set_trace() t_end = time.time() print ("F_tot=%.3e\n" %(F_tot[nt])) print ("time=%.3e" %(t_end-t_start)) if 1 == 0: # Plot the amplitide of F Line_single(range(Nt), F_tot[0:Nt], '-', 't', 'Ftot', 'log', yscale='log') #Line_single(range(Nt), -F_up[0:Nt], '-', 't', 'Fup', 'log', yscale='log') #Line_single(range(Nt), Ep[0:Nt], '-', 't', 'Ep', 'log', yscale='linear') return x, y, p_now def MD_VibrBot_DispUp_ConstP(mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up): dt = D0[0]/40 B = 0.1 # damping coefficient Nt = int(5e7) #Nt = int(5e2) Ep = np.zeros(Nt) Ek = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr #y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)) vx = np.zeros(N+1) vy = np.zeros(N+1) # for test if 1 == 0: y_bot = np.zeros(Nt) vx = np.random.rand(N+1) vx[N] = 0 vy = np.random.rand(N+1) vy[N] = 0 T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) T_set = 1e-6 vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5*D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) if mark_upDS == 0: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) elif mark_upDS == 1: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_upDS(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N], ind_up) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])*F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0)-B*vx Fy_all = np.append(Fy, F_up[nt])-B*vy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-y_up0 freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) #freq_y, fft_y_real, fft_y_imag = FFT_Fup_RealImag(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) # plot the energy to see when the system reaches steady state if 1 == 0: Etot = Ep+Ek nt_start = int(1e3) xdata = [range(nt_start, Nt), range(nt_start, Nt), range(Nt)] ydata = [Ep[nt_start:Nt], Ek[nt_start:Nt], Etot] line_spec = [':', ':', 'r-'] Line_multi(xdata, ydata, line_spec, 't', '$E$', 'linear', 'log') # Plot the amplitide of F if 1 == 0: Line_yy([dt*range(Nt), dt*range(Nt)], [F_up[0:Nt],y_bot[0:Nt]], ['-', ':'], 't', ['$F_{up}$', '$y_{bottom}$']) Line_yy([dt*range(Nt), dt*range(Nt)], [y_up[0:Nt],y_bot[0:Nt]], ['-', ':'], 't', ['$y_{up}$', '$y_{bottom}$']) Line_single(range(Nt), p[0:Nt], '-', 't', 'p', 'log', 'linear') Etot = Ep[1:Nt]+Ek[1:Nt] xdata = [dt*range(Nt), dt*range(Nt), dt*range(Nt-1)] ydata = [Ep[0:Nt], Ek[0:Nt], Etot] line_spec = ['--', ':', 'r-'] #Line_multi(xdata, ydata, line_spec, 't', '$E$', 'log') print("std(Etot)=%e\n" %(np.std(Etot))) return freq_y, fft_y, freq_bot, fft_bot, np.mean(cont), np.mean(cont_up) #return freq_y, fft_y_real, fft_y_imag, freq_bot, fft_bot, np.mean(cont) def MD_VibrBot_DispUp_ConstP_ConfigRec(N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up, fn): dt = D0[0]/40 B = 0.1 # damping coefficient Nt = int(5e6) nt_rec = np.linspace(Nt-5e4, Nt, 500) #Nt = int(1e4) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) ind_nt = 0 Ep = np.zeros(Nt) Ek = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr #y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): if nt == nt_rec[ind_nt]: ConfigPlot_YFixed_rec(N, x[0:N], y[0:N], D0[0:N], L[0], y[N], y_bot[nt], m0[0:N], ind_nt, fn) ind_nt += 1 x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])*F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0)-B*vx Fy_all = np.append(Fy, F_up[nt])-B*vy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-y_up0 freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, freq_bot, fft_bot, np.mean(cont), np.mean(cont_up) def MD_VibrBot_DispUp_ConstP_EkCheck(Nt, mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up): dt = D0[0]/40 B = 0.1 # damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ek_up_now = np.array(0) Ep_now = np.array(0) Ep_up_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ep_up = np.zeros(Nt) Ek = np.zeros(Nt) Ek_up = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5*D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) if mark_upDS == 0: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) elif mark_upDS == 1: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_upDS(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N], ind_up) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])*F0_up Ep_up[nt] = (y_up0-y[N])*F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0)-B*vx Fy_all = np.append(Fy, F_up[nt])-B*vy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_up[nt] = 0.5*m0[N]*(vx[N]**2+vy[N]**2) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ek_up_now = np.append(Ek_up_now, np.mean(Ek_up[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) Ep_up_now = np.append(Ep_up_now, np.mean(Ep_up[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-np.mean(y_up) y_up = y_up/np.mean(np.absolute(y_up)) freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, freq_bot, fft_bot, np.mean(cont), np.mean(cont_up) #@jit def force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D, Lx, y_bot, v_bot, y_up): Fup = 0 Fbot = 0 Ep = 0 cont = 0 cont_up = 0 p_now = 0 #betta = 1 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5*D[nn] if d_up<r_now: F = -(1-d_up/r_now)/(r_now) Fup -= F Fy[nn] += F dvy = vy[N]-vy[nn] FD = beta*dvy #FD = np.absolute(FD) Fy[nn] += FD Fup -= FD Ep += (1/2)*(1-d_up/r_now)**2 cont_up += 1 cont += 1 #dbg.set_trace() if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fbot += F Fy[nn] -= F dvy = v_bot-vy[nn] FD = beta*dvy Fy[nn] += FD Ep += (1/2)*(1-d_bot/r_now)**2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy dvx = vx[mm]-vx[nn] dvy = vy[mm]-vy[nn] FD = beta*(dvx*dx+dvy*dy)/dmn #FD = np.absolute(FD) Fx[nn] += FD*dx/dmn Fx[mm] -= FD*dx/dmn Fy[nn] += FD*dy/dmn Fy[mm] -= FD*dy/dmn Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, Fup, Fbot, Ep, cont, p_now, cont_up def MD_VibrBot_DispUp_ConstP_EkCheck_Collision(beta, Nt, mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up, mark_norm): dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ek_up_now = np.array(0) Ep_now = np.array(0) Ep_up_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ep_up = np.zeros(Nt) Ek = np.zeros(Nt) Ek_up = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vy_bot = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5*D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D0[0:N], L[0], y_bot[nt], vy_bot[nt], y[N]) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])*F0_up Ep_up[nt] = (y_up0-y[N])*F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now Fx_all = np.append(Fx,0) Fy_all = np.append(Fy, F_up[nt]) ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_up[nt] = 0.5*m0[N]*(vx[N]**2+vy[N]**2) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ek_up_now = np.append(Ek_up_now, np.mean(Ek_up[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) Ep_up_now = np.append(Ep_up_now, np.mean(Ep_up[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-np.mean(y_up) if mark_norm == 1: y_up = y_up/np.mean(np.absolute(y_up)) freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, fft_bot, np.mean(cont), np.mean(cont_up), nt_rec[1:], Ek_now[1:],Ek_up_now[1:],Ep_now[1:],Ep_up_now[1:] def MD_YFixed_ConstP_Gravity_SD(N, x0, y0, D0, m0, L, F0_up): g = 1e-5 dt = D0[0]/40 Nt = int(5e6) #Nt = int(1e4) Ep = np.zeros(Nt) F_up = np.zeros(Nt) F_bot = np.zeros(Nt) F_tot = np.zeros(Nt) Fup_now = 0 y_up = y0[N] vx = np.zeros(N+1) vy = np.zeros(N+1) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], 0, y[N]) Fy -= g*m0[0:N] F_up[nt] = Fup_now+F0_up-g*m0[N] F_bot[nt] = Fbot_now Ep[nt] = Ep_now+(y_up-y[N])*F0_up+sum(g*np.multiply(m0, y-y0)) vx = 0.1*np.divide(np.append(Fx,0), m0) vy = 0.1*np.divide(np.append(Fy, F_up[nt]), m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #print("nt=%d, Fup=%e, Fup_tot=%e\n" % (nt, Fup_now, F_up[nt])) #dbg.set_trace() t_end = time.time() print ("F_tot=%.3e\n" %(F_tot[nt])) print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def MD_VibrBot_DispUp_ConstP_EkCheck_Gravity(Nt, mark_upDS, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, F0_up): dt = D0[0]/40 #B = 0.1 # damping coefficient g = 1e-5 Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ek_up_now = np.array(0) Ep_now = np.array(0) Ep_up_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ep_up = np.zeros(Nt) Ek = np.zeros(Nt) Ek_up = np.zeros(Nt) y_up0 = y_ini[N] y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) cont_up = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vy_bot = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() if mark_upDS == 1: ind_up = np.zeros(N) for ii in np.arange(N): d_up = y[N]-y[ii] r_now = 0.5*D0[ii] if d_up<r_now: ind_up[ii] = 1 for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) #Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed(Fx, Fy, N, x[0:N], y[0:N], D0[0:N], L[0], y_bot[nt], y[N]) beta = 1 Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D0[0:N], L[0], y_bot[nt], vy_bot[nt], y[N]) F_up[nt] = Fup_now+F0_up Ep[nt] = Ep_now+(y_up0-y[N])*F0_up+sum(g*np.multiply(m0, y-y_ini)) Ep_up[nt] = (y_up0-y[N])*F0_up cont[nt] = cont_now cont_up[nt] = cont_up_now p[nt] = p_now #Fx_all = np.append(Fx,0)-B*vx #Fy_all = np.append(Fy, F_up[nt])-B*vy-g*m0 Fx_all = np.append(Fx,0) Fy_all = np.append(Fy, F_up[nt])-g*m0 ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_up[nt] = 0.5*m0[N]*(vx[N]**2+vy[N]**2) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ek_up_now = np.append(Ek_up_now, np.mean(Ek_up[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) Ep_up_now = np.append(Ep_up_now, np.mean(Ep_up[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) y_up = y_up-np.mean(y_up) #y_up = y_up/np.mean(np.absolute(y_up)) freq_y, fft_y = FFT_Fup(int(Nt/2), y_up[int(Nt/2):Nt], dt, Freq_Vibr) freq_bot, fft_bot = FFT_Fup(int(Nt/2), y_bot[int(Nt/2):Nt], dt, Freq_Vibr) return freq_y, fft_y, fft_bot, np.mean(cont), np.mean(cont_up), nt_rec[1:], Ek_now[1:],Ek_up_now[1:],Ep_now[1:],Ep_up_now[1:] def MD_YFixed_ConstV_SP_SD(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now vx = 0.1*np.divide(Fx, m0) vy = 0.1*np.divide(Fy, m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) t_end = time.time() print ("F_tot=%.3e" %(F_tot[nt])) print ("time=%.3e" %(t_end-t_start)) plt.figure(figsize=(6.4,4.8)) plt.plot(range(Nt), F_tot[0:Nt], color='blue') ax = plt.gca() ax.set_yscale('log') plt.xlabel("t") plt.ylabel("F_total") plt.title("Finding the Equilibrium", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() plt.show() return x, y, p_now def MD_YFixed_ConstV_SP_SD_2(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now vx = 0.1*np.divide(Fx, m0) vy = 0.1*np.divide(Fy, m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) # putting a threshold on total force if (F_tot[nt]<1e-11): break t_end = time.time() #print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now #@jit def force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D, Lx, y_bot, y_up): Ep = 0 cont = 0 p_now = 0 for nn in np.arange(N): d_up = y_up-y[nn] d_bot = y[nn]-y_bot r_now = 0.5*D[nn] if d_up<r_now: F = -(1-d_up/r_now)/(r_now) Fy[nn] += F dvy = -vy[nn] FD = beta*dvy Fy[nn] += FD Ep += (1/2)*(1-d_up/r_now)**2 cont += 1 #dbg.set_trace() if d_bot<r_now: F = -(1-d_bot/r_now)/(r_now) Fy[nn] -= F dvy = -vy[nn] FD = beta*dvy Fy[nn] += FD Ep += (1/2)*(1-d_bot/r_now)**2 cont += 1 for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < Dmn: dx = x[mm]-x[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy dvx = vx[mm]-vx[nn] dvy = vy[mm]-vy[nn] FD = beta*(dvx*dx+dvy*dy)/dmn #FD = np.absolute(FD) Fx[nn] += FD*dx/dmn Fx[mm] -= FD*dx/dmn Fy[nn] += FD*dy/dmn Fy[mm] -= FD*dy/dmn Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, Ep, cont, p_now def MD_VibrSP_ConstV_Collision(beta, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, mark_vibrY): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in] vx_in = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)) elif mark_vibrY == 1: y_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in] vy_in = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] vx[ind_in] = vx_in[nt] vy[ind_in] = 0 elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] vx[ind_in] = 0 vy[ind_in] = vy_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx Fy_all = Fy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, mark_vibrY, mark_resonator): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in] elif mark_vibrY == 1: y_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-B*vx Fy_all = Fy-B*vy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) if Nt == 5e5: print(x[ind_out], y[ind_out]) print(fft_x_out[100], fft_y_out[100]) print(fft_in[100]) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_Periodic_ConstV_SP_SD(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, Lx, Ly) Ep[nt] = Ep_now vx = 0.1*np.divide(Fx, m0) vy = 0.1*np.divide(Fy, m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) t_end = time.time() print ("F_tot=%.3e\n" %(F_tot[nt])) print ("nt=%e, time=%.3e" %(nt, t_end-t_start)) return x, y, p_now def MD_Periodic_equi_Ekcheck(Nt, N, x0, y0, D0, m0, L, T_set, V_em, n_em): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) vx_rec = np.zeros([Nt, N]) vy_rec = np.zeros([Nt, N]) nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) #nt_rec = np.linspace(0, Nt, int(Nt/1e2)+1) nt_rec = nt_rec.astype(int) Ek_now = np.array(0) Ep_now = np.array(0) vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2*np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx_rec[nt] = vx vy_rec[nt] = vy for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) freq_now, fft_now = FFT_vCorr(int(Nt/2), N, vx_rec[int(Nt/2):Nt], vy_rec[int(Nt/2):Nt], dt) return freq_now, fft_now, np.mean(cont), nt_rec, Ek_now, Ep_now #@jit def force_Xfixed(Fx, Fy, N, x, y, D, x_l, x_r, Ly, ind_wall): F_l = 0 F_r = 0 Ep = 0 cont = 0 p_now = 0 for nn in np.arange(N): d_l = x[nn]-x_l d_r = x_r-x[nn] r_now = 0.5*D[nn] if (ind_wall[nn]==0) and (d_r<r_now): F = -(1-d_r/r_now)/(r_now) F_r -= F Fx[nn] += F Ep += (1/2)*(1-d_r/r_now)**2 cont += 1 #dbg.set_trace() if (ind_wall[nn]==0) and (d_l<r_now): F = -(1-d_l/r_now)/(r_now) F_l += F Fx[nn] -= F Ep += (1/2)*(1-d_l/r_now)**2 cont += 1 for mm in np.arange(nn+1, N): dx = x[mm]-x[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dx) < Dmn: dy = y[mm]-y[nn] dy = dy-round(dy/Ly)*Ly dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, F_l, F_r, Ep, cont, p_now def MD_Xfixed_SD(Nt, N, x0, y0, D0, m0, Lx, Ly, ind_wall): wall = np.where(ind_wall>0) dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e6) #Nt = int(5e2) Ep = np.zeros(Nt) F_l = np.zeros(Nt) F_r = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_Xfixed(Fx, Fy, N, x, y, D0, 0, Lx, Ly, ind_wall) F_l[nt] = Fl_now F_r[nt] = Fr_now Ep[nt] = Ep_now Fx[wall] = 0 Fy[wall] = 0 vx = 0.1*np.divide(Fx, m0) vy = 0.1*np.divide(Fy, m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #print("nt=%d, Fup=%e, Fup_tot=%e\n" % (nt, Fup_now, F_up[nt])) #dbg.set_trace() t_end = time.time() print ("F_tot=%.3e" %(F_tot[nt])) #print ("Ep_tot=%.3e\n" %(Ep[nt])) print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def MD_VibrWall_DiffP_Xfixed(Nt, N, x_ini, y_ini,D0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_wall, B): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_Xfixed(Fx, Fy, N, x, y, D0, x_l[nt], Lx, Ly, ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-B*vx Fy_all = Fy-B*vy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) #for ii in np.arange(len(nt_rec)-1): # Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) # Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) #CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) freq_fft, fft_receive = FFT_Fup(int(Nt/2), F_r[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_drive = FFT_Fup(int(Nt/2), x_l[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_receive, fft_drive, cont_now, nt_rec, Ek_now, Ep_now #@jit def force_XFixed_collision_VibrLx(beta, Fx, Fy, N, x, y, vx, vy, D, x_l, Lx, Ly, vx_l, ind_wall): Fr = 0 Fl = 0 Ep = 0 cont = 0 p_now = 0 #betta = 1 for nn in np.arange(N): if ind_wall[nn] == 0: d_r = Lx-x[nn] d_l = x[nn]-x_l r_now = 0.5*D[nn] if d_r<r_now: F = -(1-d_r/r_now)/(r_now) Fr -= F Fx[nn] += F dvx = -vx[nn] FD = beta*dvx Fx[nn] += FD Fr -= FD Ep += (1/2)*(1-d_r/r_now)**2 cont += 1 #dbg.set_trace() if d_l<r_now: F = -(1-d_l/r_now)/(r_now) Fl += F Fx[nn] -= F dvx = vx_l-vx[nn] FD = beta*dvx Fx[nn] += FD Ep += (1/2)*(1-d_l/r_now)**2 cont += 1 for mm in np.arange(nn+1, N): dx = x[mm]-x[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dx) < Dmn: dy = y[mm]-y[nn] dy = dy-round(dy/Ly)*Ly dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: F = -(1-dmn/Dmn)/Dmn/dmn Fx[nn] += F*dx Fx[mm] -= F*dx Fy[nn] += F*dy Fy[mm] -= F*dy dvx = vx[mm]-vx[nn] dvy = vy[mm]-vy[nn] FD = beta*(dvx*dx+dvy*dy)/dmn #FD = np.absolute(FD) Fx[nn] += FD*dx/dmn Fx[mm] -= FD*dx/dmn Fy[nn] += FD*dy/dmn Fy[mm] -= FD*dy/dmn Ep += (1/2)*(1-dmn/Dmn)**2 cont += 1 p_now += (-F)*(dx**2+dy**2) return Fx, Fy, Fl, Fr, Ep, cont, p_now def MD_VibrWall_DiffP_Xfixed_Collision(Nt, N, x_ini, y_ini,D0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_wall, beta): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vx_l = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi) wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] vx[wall_l] = vx_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_XFixed_collision_VibrLx(beta, Fx, Fy, N, x, y, vx, vy, D0, x_l[nt], Lx, Ly, vx_l[nt], ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx Fy_all = Fy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) freq_fft, fft_receive = FFT_Fup(int(Nt/2), F_r[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_drive = FFT_Fup(int(Nt/2), x_l[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_receive, fft_drive, cont_now, nt_rec, Ek_now, Ep_now def MD_VibrWall_LySignal_Collision(Nt, N, x_ini, y_ini,D0, m0, Lx0, Ly0, Freq_Vibr, Amp_Vibr, ind_wall, beta, dLy_scheme, num_gap): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) dLy_max = 0.1 nt_transition = int(Nt/num_gap/20) dLy_inc = np.linspace(0, dLy_max, nt_transition) dLy_dec = np.linspace(dLy_max, 0, nt_transition) if dLy_scheme == 0: dLy_all = np.zeros(Nt) elif dLy_scheme == 1: dLy_all = np.ones(Nt)*dLy_max dLy_all[0:nt_transition] = dLy_inc elif dLy_scheme == 2: dLy_all = np.zeros(Nt) nt_Ly = np.linspace(0, Nt, num_gap+1) nt_Ly = nt_Ly.astype(int) for ii in np.arange(1, num_gap): nt1 = nt_Ly[ii]-int(nt_transition/2) nt2 = nt_Ly[ii]+int(nt_transition/2) if ii%2 == 1: dLy_all[nt_Ly[ii]:nt_Ly[ii+1]] = dLy_max dLy_all[nt1:nt2] = dLy_inc else: dLy_all[nt1:nt2] = dLy_dec nt_rec = np.linspace(0, Nt, int(Nt/5e4*num_gap/5)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vx_l = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi) wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): Ly = Ly0+dLy_all[nt] x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] vx[wall_l] = vx_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_XFixed_collision_VibrLx(beta, Fx, Fy, N, x, y, vx, vy, D0, x_l[nt], Lx0, Ly, vx_l[nt], ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx Fy_all = Fy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) nt_dLy = np.arange(0, Nt, 100) return nt_dLy, dLy_all[nt_dLy], F_r, nt_rec, Ek_now, Ep_now def MD_VibrWall_LySignal(Nt, N, x_ini, y_ini,D0, m0, Lx0, Ly0, Freq_Vibr, Amp_Vibr, ind_wall, B, dLy_scheme, num_gap): dt = D0[0]/40 # B damping coefficient Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) dLy_max = 0.1 nt_transition = int(Nt/num_gap/20) dLy_inc = np.linspace(0, dLy_max, nt_transition) dLy_dec = np.linspace(dLy_max, 0, nt_transition) if dLy_scheme == 0: dLy_all = np.zeros(Nt) elif dLy_scheme == 1: dLy_all = np.ones(Nt)*dLy_max dLy_all[0:nt_transition] = dLy_inc elif dLy_scheme == 2: dLy_all = np.zeros(Nt) nt_Ly = np.linspace(0, Nt, num_gap+1) nt_Ly = nt_Ly.astype(int) for ii in np.arange(1, num_gap): nt1 = nt_Ly[ii]-int(nt_transition/2) nt2 = nt_Ly[ii]+int(nt_transition/2) if ii%2 == 1: dLy_all[nt_Ly[ii]:nt_Ly[ii+1]] = dLy_max dLy_all[nt1:nt2] = dLy_inc else: dLy_all[nt1:nt2] = dLy_dec nt_rec = np.linspace(0, Nt, int(Nt/5e4*num_gap/5)+1) Ek_now = np.array(0) Ep_now = np.array(0) #nt_rec = np.linspace(0.5*Nt, Nt, 50) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) x_l = np.zeros(Nt) F_r = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_l = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vx_l = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi) wall_l = np.where(ind_wall==1) wall_r = np.where(ind_wall==2) wall = np.where(ind_wall>0) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) for nt in np.arange(Nt): Ly = Ly0+dLy_all[nt] x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) x[wall_l] = x_l[nt] vx[wall_l] = vx_l[nt] Fx, Fy, Fl_now, Fr_now, Ep_now, cont_now, p_now = force_Xfixed(Fx, Fy, N, x, y, D0, x_l[nt], Lx0, Ly, ind_wall) F_r[nt] = Fr_now+sum(Fx[wall_r]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-B*vx Fy_all = Fy-B*vy ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax[wall] = 0 ay[wall] = 0 vx[wall] = 0 vy[wall] = 0 ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) nt_dLy = np.arange(0, Nt, 100) return nt_dLy, dLy_all[nt_dLy], F_r, nt_rec, Ek_now, Ep_now def MD_VibrBot_FSignal_Collision(beta, Nt, N, x_ini, y_ini, D0, m0, Lx, Freq_Vibr, Amp_Vibr, F_scheme, num_gap): dt = D0[0]/40 Nt = int(Nt) #Nt = int(5e7) #Nt = int(1e4) F_max = 0.01 F_min = 1e-8 nt_transition = int(Nt/num_gap/20) F_inc = np.linspace(F_min, F_max, nt_transition) F_dec = np.linspace(F_max, F_min, nt_transition) if F_scheme == 1: F_all = np.ones(Nt)*F_max elif F_scheme == 0: F_all = np.ones(Nt)*F_min F_all[0:nt_transition] = F_dec elif F_scheme == 2: F_all = np.ones(Nt)*F_max nt_F = np.linspace(0, Nt, num_gap+1) nt_F = nt_F.astype(int) for ii in np.arange(1, num_gap): nt1 = nt_F[ii]-int(nt_transition/2) nt2 = nt_F[ii]+int(nt_transition/2) if ii%2 == 1: F_all[nt_F[ii]:nt_F[ii+1]] = F_min F_all[nt1:nt2] = F_dec else: F_all[nt1:nt2] = F_inc nt_rec = np.linspace(0, Nt, int(Nt/5e4*num_gap/5)+1) Ek_now = np.array(0) Ep_now = np.array(0) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) y_up = np.zeros(Nt) F_up = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) y_bot = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi)+Amp_Vibr vy_bot = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)+1.5*np.pi) vx = np.zeros(N+1) vy = np.zeros(N+1) ax_old = np.zeros(N+1) ay_old = np.zeros(N+1) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; y_up[nt] = y[N] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up_now = force_YFixed_collision_ConstP(beta, Fx, Fy, N, x, y, vx, vy, D0[0:N], Lx, y_bot[nt], vy_bot[nt], y[N]) F_up[nt] = Fup_now-F_all[nt] Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = np.append(Fx,0) Fy_all = np.append(Fy, F_up[nt]) ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek_up = 0.5*m0[N]*(vx[N]**2+vy[N]**2) Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy)))-Ek_up for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) print ("freq=%f, cont_min=%d, cont_max=%d, cont_ave=%f\n" %(Freq_Vibr, min(cont), max(cont), np.mean(cont))) nt_F = np.arange(0, Nt, 100) return nt_F, F_all[nt_F], y_up, nt_rec, Ek_now, Ep_now def MD_SPSignal(mark_collision, beta, Nt, N, x_ini, y_ini,D0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_in, ind_out, ind_fix, dr_scheme, num_gap, mark_vibrY, dr_one, dr_two): dt = D0[0]/40 Nt = int(Nt) d_ini = D0[0] d0 = 0.1 dr_all = np.zeros(Nt)+dr_one if abs(dr_scheme) <= 2: nt_dr = np.linspace(0, Nt, 3) nt_dr = nt_dr.astype(int) dr_all[nt_dr[1]:nt_dr[2]] = dr_two num_gap = 5 elif dr_scheme == 3 or dr_scheme == 4: nt_dr = np.linspace(0, Nt, num_gap+1) nt_dr = nt_dr.astype(int) for ii in np.arange(1, num_gap, 2): dr_all[nt_dr[ii]:nt_dr[ii+1]] = dr_two D_fix = d_ini+dr_all*d_ini nt_rec = np.linspace(0, Nt, int(Nt/5e4*num_gap/5)+1) Ek_rec = np.array(0) Ep_rec = np.array(0) cont_rec = np.array(0) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 1: y_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in] vy_in = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)) else: x_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in] vx_in = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): D0[ind_fix] = D_fix[nt] x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 1: y[ind_in] = y_in[nt] x[ind_in] = x_ini[ind_in] vy[ind_in] = vy_in[nt] vx[ind_in] = 0 else: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] vx[ind_in] = vx_in[nt] vy[ind_in] = 0 Fx = np.zeros(N) Fy = np.zeros(N) if mark_collision == 1: Fx, Fy, Ep_now, cont_now, p_now = force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D0, Lx, 0, Ly) Fx_all = Fx Fy_all = Fy else: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Fx_all = Fx-beta*vx Fy_all = Fy-beta*vy Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if nt % 2000 == 0: print ("nt = %d, Ek = %.2e, cont = %.2e" %(nt, Ek[nt], cont[nt])) for ii in np.arange(len(nt_rec)-1): Ek_rec = np.append(Ek_rec, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec = np.append(Ep_rec, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec = np.append(cont_rec, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) print ("freq=%f, cont_min=%d, cont_max=%d, cont_ave=%f\n" %(Freq_Vibr, min(cont), max(cont), np.mean(cont))) nt_dr = np.arange(0, Nt, 100) if mark_vibrY == 1: xy_out = y_out else: xy_out = x_out return nt_dr, dr_all[nt_dr], xy_out, nt_rec, Ek_rec, Ep_rec, cont_rec def MD_YFixed_equi_SP_modecheck(Nt, N, x0, y0, D0, m0, Lx, Ly, T_set, V_em, n_em, ind_out): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2*np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) Freq_Vibr = 0 freq_x, fft_x = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_y, fft_y = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) ind1 = freq_x<30 ind2 = freq_y<30 return freq_x[ind1], freq_y[ind2], fft_x[ind1], fft_y[ind2], np.mean(cont), nt_rec, Ek_rec, Ep_rec, cont_rec def MD_YFixed_SPVibr_SP_modecheck(Nt, N, x0, y0, D0, m0, Lx, Ly, T_set, ind_in, ind_out, mark_vibrY): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ek_rec = np.array(0) Ep_rec = np.array(0) cont_rec = np.array(0) vx = np.zeros(N) vy = np.zeros(N) if mark_vibrY == 1: vy[ind_in] = 1 vy_mc = sum(np.multiply(vy,m0))/sum(m0) vy = vy-vy_mc else: vx[ind_in] = 1 vx_mc = sum(np.multiply(vx,m0))/sum(m0) vx = vx-vx_mc T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_vibrY == 1: vy = vy*np.sqrt(N*T_set/T_rd) print("|vy|_Max=%.3e, |vy|_Min=%.3e" %(max(abs(vy)), min(abs(vy)))) else: vx = vx*np.sqrt(N*T_set/T_rd) print("|vx|_Max=%.3e, |vx|_Min=%.3e" %(max(abs(vx)), min(abs(vx)))) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() mark_CB = 0 for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now cont[nt] = cont_now if mark_CB == 0 and cont_now<cont[0]: print("nt_CB=%d" % nt) mark_CB = 1 ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec = np.append(Ek_rec, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec = np.append(Ep_rec, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec = np.append(cont_rec, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) Freq_Vibr = 0 freq_x, fft_x = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_y, fft_y = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) ind1 = freq_x<30 ind2 = freq_y<30 return freq_x[ind1], freq_y[ind2], fft_x[ind1], fft_y[ind2], cont_rec, nt_rec, Ek_rec, Ep_rec #181105 def MD_YFixed_SPVibr_vCorr_modecheck(Nt_MD, Nt_FFT, N, x0, y0, D0, m0, Lx, Ly, T_set, ind_in, ind_out, mark_vibrY): N = int(N) Nt_FFT = int(Nt_FFT) Nt_MD = int(Nt_MD) dt = min(D0)/40 Nt = Nt_MD+Nt_FFT Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) mark_FFT = np.zeros(Nt) mark_FFT[Nt_MD:Nt] = 1 nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) if mark_vibrY == 1: vy[ind_in] = 1 vy_mc = sum(np.multiply(vy,m0))/sum(m0) vy = vy-vy_mc else: vx[ind_in] = 1 vx_mc = sum(np.multiply(vx,m0))/sum(m0) vx = vx-vx_mc T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_vibrY == 1: vy = vy*np.sqrt(N*T_set/T_rd) print("|vy|_Max=%.3e, |vy|_Min=%.3e" %(max(abs(vy)), min(abs(vy)))) else: vx = vx*np.sqrt(N*T_set/T_rd) print("|vx|_Max=%.3e, |vx|_Min=%.3e" %(max(abs(vx)), min(abs(vx)))) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_FFT[nt] == 1: if mark_FFT[nt-1] == 0: nt_ref = nt vx_rec = np.zeros([Nt_FFT, N]) vy_rec = np.zeros([Nt_FFT, N]) nt_delta = nt-nt_ref vx_rec[nt_delta] = vx vy_rec[nt_delta] = vy if nt_delta == Nt_FFT-1: freq_now, fft_now = FFT_vCorr(Nt_FFT, N, vx_rec, vy_rec, dt) print ("Nt_End="+str(nt)) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return freq_now, fft_now, (nt_rec[:-1]+nt_rec[1:])/2, Ek_rec, Ep_rec, cont_rec def MD_YFixed_ConstV(B, Nt, N, x0, y0, D0, m0, Lx, Ly): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() mark_CB = 0 for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Fx = Fx-B*vx Fy = Fy-B*vy Ep[nt] = Ep_now cont[nt] = cont_now if mark_CB == 0 and cont_now<cont[0]: print("nt_CB=%d" % nt) mark_CB = 1 ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[0:-1]+nt_rec[1:])/2 CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) print ("Ek_last=%.3e" % Ek[-1]) return x, y, nt_rec, Ek_rec, Ep_rec, cont_rec def MD_Vibr3Part_ConstV(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in_all, ind_out, mark_vibrY, eigen_mode_now): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) num_in = ind_in_all.size Phase_Vibr = np.sin(Freq_Vibr*dt*np.arange(Nt)) Amp_Vibr_all = np.zeros(num_in) for i_in in np.arange(num_in): ind_in = ind_in_all[i_in] if mark_vibrY == 0: Amp_Vibr_all[i_in] = eigen_mode_now[2*ind_in] elif mark_vibrY == 1: Amp_Vibr_all[i_in] = eigen_mode_now[2*ind_in+1] print(ind_in_all) print(Amp_Vibr_all) Amp_Vibr_all = Amp_Vibr_all*Amp_Vibr/max(np.abs(Amp_Vibr_all)) print(Amp_Vibr_all) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; for i_in in np.arange(num_in): ind_in = ind_in_all[i_in] if mark_vibrY == 0: x[ind_in] = Phase_Vibr[nt]*Amp_Vibr_all[i_in]+x_ini[ind_in] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = Phase_Vibr[nt]*Amp_Vibr_all[i_in]+y_ini[ind_in] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-B*vx Fy_all = Fy-B*vy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: x_in = Phase_Vibr*Amp_Vibr freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: y_in = Phase_Vibr*Amp_Vibr freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_dPhiSignal(mark_collision, beta, Nt, N, x_ini, y_ini, d0, phi0, m0, Lx, Ly, Freq_Vibr, Amp_Vibr, ind_in, ind_out, dphi_scheme, dphi_on, dphi_off, num_gap, mark_vibrY): dt = d0/40 Nt = int(Nt) if dphi_scheme == 1: nt_dphi = np.linspace(0, Nt, 3) nt_dphi = nt_dphi.astype(int) dphi_all = np.zeros(Nt)+dphi_on dphi_all[nt_dphi[1]:nt_dphi[2]] = dphi_off elif dphi_scheme == -1: nt_dphi = np.linspace(0, Nt, 3) nt_dphi = nt_dphi.astype(int) dphi_all = np.zeros(Nt)+dphi_off dphi_all[nt_dphi[1]:nt_dphi[2]] = dphi_on else: dphi_all = np.zeros(Nt)+dphi_on nt_dphi = np.linspace(0, Nt, num_gap+1) nt_dphi = nt_dphi.astype(int) for ii in np.arange(1, num_gap, 2): dphi_all[nt_dphi[ii]:nt_dphi[ii+1]] = dphi_off D_ini = np.zeros(N)+d0 nt_rec = np.linspace(0, Nt, int(Nt/5e4*num_gap/5)+1) Ek_rec = np.array(0) Ep_rec = np.array(0) cont_rec = np.array(0) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 1: y_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in] vy_in = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)) else: x_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in] vx_in = Amp_Vibr*Freq_Vibr*np.cos(Freq_Vibr*dt*np.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): D0 = D_ini*np.sqrt(1+dphi_all[nt]/phi0) #if np.mod(nt,100000) == 0: #print(D0[3]) x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 1: y[ind_in] = y_in[nt] x[ind_in] = x_ini[ind_in] vy[ind_in] = vy_in[nt] vx[ind_in] = 0 else: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] vx[ind_in] = vx_in[nt] vy[ind_in] = 0 Fx = np.zeros(N) Fy = np.zeros(N) if mark_collision == 1: Fx, Fy, Ep_now, cont_now, p_now = force_YFixed_collision_ConstV(beta, Fx, Fy, N, x, y, vx, vy, D0, Lx, 0, Ly) Fx_all = Fx Fy_all = Fy else: Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, Lx, 0, Ly) Fx_all = Fx-beta*vx Fy_all = Fy-beta*vy Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) for ii in np.arange(len(nt_rec)-1): Ek_rec = np.append(Ek_rec, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec = np.append(Ep_rec, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec = np.append(cont_rec, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) print ("freq=%f, cont_min=%d, cont_max=%d, cont_ave=%f\n" %(Freq_Vibr, min(cont), max(cont), np.mean(cont))) nt_dphi = np.arange(0, Nt, 100) if mark_vibrY == 1: xy_out = y_out else: xy_out = x_out return nt_dphi, dphi_all[nt_dphi], xy_out, nt_rec[1:], Ek_rec[1:], Ep_rec[1:], cont_rec[1:] def Damping_calc(Damp_scheme, B, N, x, y, vx, vy, Lx, Ly): Fx_damp = np.zeros(N) Fy_damp = np.zeros(N) if Damp_scheme == 1: Fx_damp = -B*vx Fy_damp = -B*vy if Damp_scheme == 2: Fx_damp = -B*vx*np.abs(vx)*5e5 Fy_damp = -B*vy*np.abs(vy)*5e5 if Damp_scheme == 3: Fx_damp = -B*vx/np.sqrt(np.abs(vx))*np.sqrt(2e-6) Fy_damp = -B*vy/np.sqrt(np.abs(vy))*np.sqrt(2e-6) if Damp_scheme == 4: Fx_damp = -B*vx*np.exp(-5e4*np.abs(vx)+1)*0.1 Fy_damp = -B*vy*np.exp(-5e4*np.abs(vy)+1)*0.1 if Damp_scheme == 5: Fx_damp = -B*vx*np.exp(-5e5*np.abs(vx)+1) Fy_damp = -B*vy*np.exp(-5e5*np.abs(vy)+1) if Damp_scheme == 6: Fx_damp = -B*vx*np.exp(-5e6*np.abs(vx)+1)*10 Fy_damp = -B*vy*np.exp(-5e6*np.abs(vy)+1)*10 if Damp_scheme == 7: Fx_damp = -B*vx*np.exp(-5e7*np.abs(vx)+1)*100 Fy_damp = -B*vy*np.exp(-5e7*np.abs(vy)+1)*100 return Fx_damp, Fy_damp def Force_FixedPos_calc(k, N, x, y, x0, y0, D0, vx, vy, Lx, Ly): Fx_damp = np.zeros(N) Fy_damp = np.zeros(N) Ep = 0 for nn in np.arange(N): dy = y[nn]-y0[nn] dy = dy-round(dy/Ly)*Ly Dmn = 0.5*D0[nn] dx = x[nn]-x0[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if (dmn > 0): F = -k*(dmn/Dmn/Dmn)/dmn Fx_damp[nn] += F*dx Fy_damp[nn] += F*dy Ep += (1/2)*k*(dmn/Dmn)**2 return Fx_damp, Fy_damp, Ep def MD_FilterCheck_Periodic_Equi_vCorr(Nt_damp, Nt_FFT, num_period, Damp_scheme, B, N, x0, y0, D0, m0, L, T_set, V_em, n_em): if Damp_scheme < 0: return N = int(N) Nt_FFT = int(Nt_FFT) Nt_damp = int(Nt_damp) dt = min(D0)/40 Nt_period = int(2*Nt_damp+Nt_FFT) Nt = Nt_period*num_period Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) mark_damp = np.zeros(Nt) mark_FFT = np.zeros(Nt) for ii in np.arange(num_period): if ii > 0: t1 = ii*Nt_period t2 = t1+Nt_damp mark_damp[t1:t2] = 1 t3 = ii*Nt_period+2*Nt_damp t4 = t3+Nt_FFT mark_FFT[t3:t4] = 1 nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] num_FFT = 0 vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2*np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) if mark_damp[nt] == 1: Fx_damp, Fy_damp = Damping_calc(Damp_scheme, B, N, x, y, vx, vy, L[0], L[1]) Fx = Fx + Fx_damp Fy = Fy + Fy_damp Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_FFT[nt] == 1: if mark_FFT[nt-1] == 0: nt_ref = nt vx_rec = np.zeros([Nt_FFT, N]) vy_rec = np.zeros([Nt_FFT, N]) nt_delta = nt-nt_ref vx_rec[nt_delta] = vx vy_rec[nt_delta] = vy if nt_delta == Nt_FFT-1: num_FFT += 1 freq_now, fft_now = FFT_vCorr(Nt_FFT, N, vx_rec, vy_rec, dt) if num_FFT == 1: fft_all = np.array([fft_now]) freq_all = np.array([freq_now]) len_fft_ref = len(fft_now) len_freq_ref = len(freq_now) else: fft_add = np.zeros(len_fft_ref) freq_add = np.zeros(len_freq_ref) len_fft_now = len(fft_now) len_freq_now = len(freq_now) if len_fft_now >= len_fft_ref: fft_add[0:len_fft_ref] = fft_now[0:len_fft_ref] else: fft_add[0:len_fft_now] = fft_now[0:len_fft_now] fft_add[len_fft_now:] = fft_now[len_fft_now] if len_freq_now >= len_freq_ref: freq_add[0:len_freq_ref] = freq_now[0:len_freq_ref] else: freq_add[0:len_freq_now] = freq_now[0:len_freq_now] freq_add[len_freq_now:] = freq_now[len_freq_now] fft_all = np.append(fft_all, [fft_add], axis=0) freq_all = np.append(freq_all, [freq_add], axis=0) print("FFT_iteration: %d" % num_FFT) print("Ek_ave: %e" %(np.mean(Ek[nt_ref:nt]))) ind1 = m0>5 ind2 = m0<5 print("|vx|_ave(heavy):%e" % np.mean(np.abs(vx[ind1]))) print("|vx|_ave(light):%e" % np.mean(np.abs(vx[ind2]))) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return freq_all, fft_all, (nt_rec[:-1]+nt_rec[1:])/2, Ek_rec, Ep_rec, cont_rec def MD_FilterCheck_Periodic_Equi_vCorr_Seperate(Nt_damp, Nt_FFT, num_period, Damp_scheme, k, B, N, x0, y0, D0, m0, L, T_set, V_em, n_em): # for damping scheme = -1 (fixed spring at initial position) if Damp_scheme != -1: return N = int(N) Nt_FFT = int(Nt_FFT) Nt_damp = int(Nt_damp) dt = min(D0)/40 Nt = Nt_damp*num_period+Nt_FFT if num_period == 0: Nt = Nt_damp+Nt_FFT Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) mark_FFT = np.zeros(Nt) t1 = Nt_damp * num_period if num_period == 0: t1 = Nt_damp t2 = t1 + Nt_FFT mark_FFT[t1:t2] = 1 nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2*np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) # always have damping exceot num_period = 0 if num_period > 0: Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_calc(k, N, x, y, x0, y0, D0, vx, vy, L[0], L[1]) if (B > 0): Fx_damp += -B*vx Fy_damp += -B*vy elif num_period == 0: Fx_damp = 0 Fy_damp = 0 Ep_fix = 0 Ep_now += Ep_fix Fx = Fx + Fx_damp Fy = Fy + Fy_damp Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if mark_FFT[nt] == 1: if mark_FFT[nt-1] == 0: nt_ref = nt vx_rec = np.zeros([Nt_FFT, N]) vy_rec = np.zeros([Nt_FFT, N]) nt_delta = nt-nt_ref vx_rec[nt_delta] = vx vy_rec[nt_delta] = vy if nt_delta == Nt_FFT-1: freq_now, fft_now = FFT_vCorr(Nt_FFT, N, vx_rec, vy_rec, dt) print ("Nt_End="+str(nt)) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return freq_now, fft_now, (nt_rec[:-1]+nt_rec[1:])/2, Ek_rec, Ep_rec, cont_rec def MD_Periodic_Equi_vDistr(Nt_MD, Nt_rec, N, x0, y0, D0, m0, L, T_set, V_em, n_em): N = int(N) Nt_MD = int(Nt_MD) Nt_rec = int(Nt_rec) dt = min(D0)/40 Nt = Nt_MD+Nt_rec Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) nt_rec = np.linspace(0, Nt, int(Nt/1e3)+1) nt_rec = nt_rec.astype(int) Ek_rec = [] Ep_rec = [] cont_rec = [] ind1 = m0>5 ind2 = m0<5 vx_light = [] vx_heavy = [] vy_light = [] vy_heavy = [] vx = np.zeros(N) vy = np.zeros(N) for ii in np.arange(n_em): ind1 = 2*np.arange(N) ind2 = ind1+1 vx += V_em[ind1, ii] vy += V_em[ind2, ii] T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if nt >= Nt_MD: vx_light.extend(vx[ind2]) vy_light.extend(vy[ind2]) vx_heavy.extend(vx[ind1]) vy_heavy.extend(vy[ind1]) for ii in np.arange(len(nt_rec)-1): Ek_rec.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_rec.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_rec.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f" %(CB_ratio)) return nt_rec, Ek_rec, Ep_rec, cont_rec, vx_light, vx_heavy, vy_light, vy_heavy def Output_resonator_1D(Nt, x_drive, x0, m0, w0, dt): dx = x_drive - x0 k = w0**2*m0 x = 0 vx = 0 ax_old = 0 Nt = int(Nt) x_rec = np.zeros(Nt) for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration x_rec[nt] = x Fx = k*(dx[nt]-x) ax = Fx/m0; vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration ax_old = ax; freq_fft, fft_x_rec = FFT_Fup(int(Nt/2), x_rec[int(Nt/2):Nt], dt, w0) return freq_fft, fft_x_rec def MD_Periodic_vCorr(Nt, N, x0, y0, D0, m0, vx0, vy0, L, T_set): dt = min(D0)/40 Nt = int(Nt) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) vx_rec = np.zeros([int(Nt/2), N]) vy_rec = np.zeros([int(Nt/2), N]) vx = vx0 vy = vy0 T_rd = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) vx = vx*np.sqrt(N*T_set/T_rd) vy = vy*np.sqrt(N*T_set/T_rd) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x0) y = np.array(y0) for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, L[0], L[1]) Ep[nt] = Ep_now cont[nt] = cont_now ax = np.divide(Fx, m0); ay = np.divide(Fy, m0); vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) if (nt >= Nt/2): vx_rec[int(nt-Nt/2)] = vx vy_rec[int(nt-Nt/2)] = vy CB_ratio = min(cont)/max(cont) print ("cont_min/cont_max=%f\n" %(CB_ratio)) freq_now, fft_now = FFT_vCorr(int(Nt/2), N, vx_rec, vy_rec, dt) return freq_now, fft_now, np.mean(cont) def MD_Period_ConstV_SD(Nt, N, x0, y0, D0, m0, Lx, Ly): dt = D0[0]/40 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) #t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Ep_now, cont_now, p_now = force_Regular(Fx, Fy, N, x, y, D0, Lx, Ly) Ep[nt] = Ep_now vx = 0.1*np.divide(Fx, m0) vy = 0.1*np.divide(Fy, m0) x += vx*dt y += vy*dt F_tot[nt] = sum(np.absolute(Fx)+np.absolute(Fy)) #t_end = time.time() print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def Force_FixedPos_YFixed_calc(k, N, x, y, x0, y0, D0, vx, vy, Lx, Ly): Fx_damp = np.zeros(N) Fy_damp = np.zeros(N) Ep = 0 for nn in np.arange(N): dy = y[nn]-y0[nn] Dmn = 0.5*D0[nn] dx = x[nn]-x0[nn] dx = dx-round(dx/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if (dmn > 0): F = -k*(dmn/Dmn/Dmn)/dmn Fx_damp[nn] += F*dx Fy_damp[nn] += F*dy Ep += (1/2)*k*(dmn/Dmn)**2 return Fx_damp, Fy_damp, Ep def MD_VibrSP_ConstV_Yfixed_FixSpr(k, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out): mark_vibrY = 0 mark_resonator = 1 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in] elif mark_vibrY == 1: y_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_YFixed_calc(k, N, x, y, x_ini, y_ini, D0, vx, vy, L[0], L[1]) #Fx_damp = 0; Fy_damp = 0; Ep_fix = 0 Fx_damp += -B*vx Fy_damp += -B*vy Ep[nt] = Ep_now + Ep_fix cont[nt] = cont_now p[nt] = p_now Fx_all = Fx+Fx_damp Fy_all = Fy+Fy_damp x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_Yfixed_FixSpr2(k, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out): mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in2] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_YFixed_calc(k, N, x, y, x_ini, y_ini, D0, vx, vy, L[0], L[1]) #Fx_damp = 0; Fy_damp = 0; Ep_fix = 0 Fx_damp += -B*vx Fy_damp += -B*vy Ep[nt] = Ep_now + Ep_fix cont[nt] = cont_now p[nt] = p_now Fx_all = Fx+Fx_damp Fy_all = Fy+Fy_damp x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_Yfixed_FixSpr3(k, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr1, Amp_Vibr1, ind_in1, Freq_Vibr2, Amp_Vibr2, ind_in2, ind_out): mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1*np.sin(Freq_Vibr1*dt*np.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2*np.sin(Freq_Vibr2*dt*np.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1*np.sin(Freq_Vibr1*dt*np.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2*np.sin(Freq_Vibr2*dt*np.arange(Nt))+y_ini[ind_in2] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Fx_damp, Fy_damp, Ep_fix = Force_FixedPos_YFixed_calc(k, N, x, y, x_ini, y_ini, D0, vx, vy, L[0], L[1]) #Fx_damp = 0; Fy_damp = 0; Ep_fix = 0 Fx_damp += -B*vx Fy_damp += -B*vy Ep[nt] = Ep_now + Ep_fix cont[nt] = cont_now p[nt] = p_now Fx_all = Fx+Fx_damp Fy_all = Fy+Fy_damp x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr1, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr1) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr2) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr1) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr2) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr1) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr1) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr1, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr1, dt) return freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_Force_ConstV(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, mark_vibrY, mark_resonator): dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) x_in = np.zeros(Nt) y_in = np.zeros(Nt) if mark_vibrY == 0: Fx_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)) elif mark_vibrY == 1: Fy_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt)) #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; x_in[nt] = x[ind_in] y_in[nt] = y[ind_in] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-B*vx Fy_all = Fy-B*vy if mark_vibrY == 0: Fx_all[ind_in] += Fx_in[nt] elif mark_vibrY == 1: Fy_all[ind_in] += Fy_in[nt] x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) if mark_vibrY == 0: freq_fft, fft_in = FFT_Fup(int(Nt/2), x_in[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in = FFT_Fup(int(Nt/2), y_in[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) if Nt == 5e5: print(x[ind_out], y[ind_out]) print(fft_x_out[100], fft_y_out[100]) print(fft_in[100]) return freq_fft, fft_in, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_ConfigCB(B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr, ind_in, ind_out, Nt_rec): mark_vibrY = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in] elif mark_vibrY == 1: y_in = Amp_Vibr*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in] #y_bot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) #t_start = time.time() for nt in np.arange(Nt): if nt == Nt_rec: x_rec = x[:] y_rec = y[:] x = x+vx*dt+ax_old*dt**2/2; # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2; if mark_vibrY == 0: x[ind_in] = x_in[nt] y[ind_in] = y_ini[ind_in] elif mark_vibrY == 1: x[ind_in] = x_ini[ind_in] y[ind_in] = y_in[nt] Fx = np.zeros(N) Fy = np.zeros(N) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed(Fx, Fy, N, x, y, D0, L[0], 0, L[1]) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx-B*vx Fy_all = Fy-B*vy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2; # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2; ax_old = ax; ay_old = ay; Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = np.array(0) Ep_now = np.array(0) cont_now = np.array(0) for ii in np.arange(len(nt_rec)-1): Ek_now = np.append(Ek_now, np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now = np.append(Ep_now, np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now = np.append(cont_now, np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) cont_now[0] = cont_now[1] CB_ratio = min(cont)/max(cont) print ("freq=%f, cont_min/cont_max=%f\n" %(Freq_Vibr, CB_ratio)) return x_rec, y_rec def VL_YFixed_ConstV(N, x, y, D, Lx, VL_list, VL_counter_old, x_save, y_save, first_call): r_factor = 1.2 r_cut = np.amax(D) r_list = r_factor * r_cut r_list_sq = r_list**2 r_skin_sq = ((r_factor - 1.0) * r_cut)**2 if first_call == 0: dr_sq_max = 0.0 for nn in np.arange(N): dy = y[nn] - y_save[nn] dx = x[nn] - x_save[nn] dx = dx - round(dx / Lx) * Lx dr_sq = dx**2 + dy**2 if dr_sq > dr_sq_max: dr_sq_max = dr_sq if dr_sq_max < r_skin_sq: return VL_list, VL_counter_old, x_save, y_save VL_counter = 0 for nn in np.arange(N): r_now = 0.5*D[nn] for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < r_list: dx = x[mm]-x[nn] dx = dx - round(dx / Lx) * Lx if abs(dx) < r_list: dmn_sq = dx**2 + dy**2 if dmn_sq < r_list_sq: VL_list[VL_counter][0] = nn VL_list[VL_counter][1] = mm VL_counter += 1 return VL_list, VL_counter, x, y def MD_YFixed_ConstV_SP_SD_DiffK(Nt, N, x0, y0, D0, m0, Lx, Ly, k_list, k_type): dt = D0[0] * np.sqrt(k_list[2]) / 20.0 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) x_save = np.array(x0) y_save = np.array(y0) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) t_start = time.time() for nt in np.arange(Nt): Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now vx = 0.1 * Fx vy = 0.1 * Fy x += vx * dt y += vy * dt F_tot[nt] = sum(np.absolute(Fx) + np.absolute(Fy)) # putting a threshold on total force if (F_tot[nt] < 1e-11): break print(nt) print(F_tot[nt]) t_end = time.time() #print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def FIRE_YFixed_ConstV_DiffK(Nt, N, x0, y0, D0, m0, Lx, Ly, k_list, k_type): dt_md = 0.01 * D0[0] * np.sqrt(k_list[2]) N_delay = 20 N_pn_max = 2000 f_inc = 1.1 f_dec = 0.5 a_start = 0.15 f_a = 0.99 dt_max = 10.0 * dt_md dt_min = 0.05 * dt_md initialdelay = 1 Nt = int(Nt) Ep = np.zeros(Nt) F_tot = np.zeros(Nt) vx = np.zeros(N) vy = np.zeros(N) x = np.array(x0) y = np.array(y0) x_save = np.array(x0) y_save = np.array(y0) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) a_fire = a_start delta_a_fire = 1.0 - a_fire dt = dt_md dt_half = dt / 2.0 N_pp = 0 # number of P being positive N_pn = 0 # number of P being negative ## FIRE for nt in np.arange(Nt): # FIRE update P = np.dot(vx, Fx) + np.dot(vy, Fy) if P > 0.0: N_pp += 1 N_pn = 0 if N_pp > N_delay: dt = min(f_inc * dt, dt_max) dt_half = dt / 2.0 a_fire = f_a * a_fire delta_a_fire = 1.0 - a_fire else: N_pp = 0 N_pn += 1 if N_pn > N_pn_max: break if (initialdelay < 0.5) or (nt >= N_delay): if f_dec * dt > dt_min: dt = f_dec * dt dt_half = dt / 2.0 a_fire = a_start delta_a_fire = 1.0 - a_fire x -= vx * dt_half y -= vy * dt_half vx = np.zeros(N) vy = np.zeros(N) # MD using Verlet method vx += Fx * dt_half vy += Fy * dt_half rsc_fire = np.sqrt(np.sum(vx**2 + vy**2)) / np.sqrt(np.sum(Fx**2 + Fy**2)) vx = delta_a_fire * vx + a_fire * rsc_fire * Fx vy = delta_a_fire * vy + a_fire * rsc_fire * Fy x += vx * dt y += vy * dt Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now F_tot[nt] = sum(np.absolute(Fx) + np.absolute(Fy)) # putting a threshold on total force if (F_tot[nt] < 1e-11): break vx += Fx * dt_half vy += Fy * dt_half #print(nt) #print(F_tot[nt]) t_end = time.time() #print ("F_tot=%.3e" %(F_tot[nt])) #print ("time=%.3e" %(t_end-t_start)) return x, y, p_now def MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out): Lx = L[0] Ly = L[1] mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) F_tot = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in2] vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) x_save = np.array(x_ini) y_save = np.array(y_ini) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2 # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2 if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx - B*vx Fy_all = Fy - B*vy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2 # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2 ax_old = ax ay_old = ay Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, np.mean(cont), nt_rec, Ek_now, Ep_now, cont_now def MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D0, m0, L, Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out): Lx = L[0] Ly = L[1] mark_vibrY = 0 mark_resonator = 0 dt = D0[0]/40 Nt = int(Nt) nt_rec = np.linspace(0, Nt, int(Nt/5e4)+1) nt_rec = nt_rec.astype(int) Ep = np.zeros(Nt) Ek = np.zeros(Nt) cont = np.zeros(Nt) p = np.zeros(Nt) F_tot = np.zeros(Nt) x_out = np.zeros(Nt) y_out = np.zeros(Nt) if mark_vibrY == 0: x_in1 = Amp_Vibr1*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in1] x_in2 = Amp_Vibr2*np.sin(Freq_Vibr*dt*np.arange(Nt))+x_ini[ind_in2] elif mark_vibrY == 1: y_in1 = Amp_Vibr1*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in1] y_in2 = Amp_Vibr2*np.sin(Freq_Vibr*dt*np.arange(Nt))+y_ini[ind_in2] vx = np.zeros(N) vy = np.zeros(N) ax_old = np.zeros(N) ay_old = np.zeros(N) x = np.array(x_ini) y = np.array(y_ini) x_save = np.array(x_ini) y_save = np.array(y_ini) Fx = np.zeros(N) Fy = np.zeros(N) VL_list = np.zeros((N * 10, 2), dtype=int) VL_counter = 0 VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 1) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) for nt in np.arange(Nt): x = x+vx*dt+ax_old*dt**2/2 # first step in Verlet integration y = y+vy*dt+ay_old*dt**2/2 if mark_vibrY == 0: x[ind_in1] = x_in1[nt] y[ind_in1] = y_ini[ind_in1] x[ind_in2] = x_in2[nt] y[ind_in2] = y_ini[ind_in2] elif mark_vibrY == 1: x[ind_in1] = x_ini[ind_in1] y[ind_in1] = y_in1[nt] x[ind_in2] = x_ini[ind_in2] y[ind_in2] = y_in2[nt] Fx = np.zeros(N) Fy = np.zeros(N) VL_list, VL_counter, x_save, y_save = VL_YFixed_ConstV(N, x, y, D0, Lx, VL_list, VL_counter, x_save, y_save, 0) Fx, Fy, Fup_now, Fbot_now, Ep_now, cont_now, p_now, cont_up = force_YFixed_DiffK_VL(Fx, Fy, N, x, y, D0, Lx, 0, Ly, k_list, k_type, VL_list, VL_counter) Ep[nt] = Ep_now cont[nt] = cont_now p[nt] = p_now Fx_all = Fx - B*vx Fy_all = Fy - B*vy x_out[nt] = x[ind_out] y_out[nt] = y[ind_out] ax = np.divide(Fx_all, m0) ay = np.divide(Fy_all, m0) vx = vx+(ax_old+ax)*dt/2 # second step in Verlet integration vy = vy+(ay_old+ay)*dt/2 ax_old = ax ay_old = ay Ek[nt] = sum(0.5*np.multiply(m0,np.multiply(vx, vx)+np.multiply(vy, vy))) Ek_now = [] Ep_now = [] cont_now = [] for ii in np.arange(len(nt_rec)-1): Ek_now.append(np.mean(Ek[nt_rec[ii]:nt_rec[ii+1]])) Ep_now.append(np.mean(Ep[nt_rec[ii]:nt_rec[ii+1]])) cont_now.append(np.mean(cont[nt_rec[ii]:nt_rec[ii+1]])) nt_rec = (nt_rec[1:] + nt_rec[:-1]) / 2 #t_end = time.time() #print ("time=%.3e" %(t_end-t_start)) CB_ratio = min(cont)/max(cont) #print ("freq=%f, cont_min/cont_max=%f, Ek_mean=%.3e, Ep_mean=%.3e\n" %(Freq_Vibr, CB_ratio, np.mean(Ek), np.mean(Ep))) if mark_vibrY == 0: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), x_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), x_in2[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_vibrY == 1: freq_fft, fft_in1 = FFT_Fup(int(Nt/2), y_in1[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_in2 = FFT_Fup(int(Nt/2), y_in2[int(Nt/2):Nt], dt, Freq_Vibr) if mark_resonator == 0: freq_fft, fft_x_out = FFT_Fup(int(Nt/2), x_out[int(Nt/2):Nt], dt, Freq_Vibr) freq_fft, fft_y_out = FFT_Fup(int(Nt/2), y_out[int(Nt/2):Nt], dt, Freq_Vibr) elif mark_resonator == 1: freq_fft, fft_x_out = Output_resonator_1D(Nt, x_out[0:Nt], x_ini[ind_out], m0[ind_out], Freq_Vibr, dt) freq_fft, fft_y_out = Output_resonator_1D(Nt, y_out[0:Nt], y_ini[ind_out], m0[ind_out], Freq_Vibr, dt) return x_in1-x_ini[ind_in1], x_in2-x_ini[ind_in2], x_out-x_ini[ind_out]

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gecco-2022-main/RandomSearch/XOR-NESS/constants.py

# number of runs RUNS = 3 worstFitness = +1000000 # population size popSize = 50 # number of generations numGenerations = 200

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gecco-2022-main/RandomSearch/XOR-NESS/afpo_seed_binary.py

import constants as c import copy import numpy as np import operator import statistics import pickle from joblib import Parallel, delayed import multiprocessing from genome_seed_binary import GENOME class AFPO: def __init__(self,randomSeed): self.randomSeed = randomSeed self.currentGeneration = 0 self.nextAvailableID = 0 self.genomes = {} for populationPosition in range(c.popSize): self.genomes[populationPosition] = GENOME(self.nextAvailableID) self.nextAvailableID = self.nextAvailableID + 1 def Evolve(self): self.Perform_First_Generation() for self.currentGeneration in range(1,c.numGenerations): self.Perform_One_Generation() self.SaveLastGen() # -------------------------- Private methods ---------------------- def Age(self): for genome in self.genomes: self.genomes[genome].Age() def Aggressor_Dominates_Defender(self,aggressor,defender): return self.genomes[aggressor].Dominates(self.genomes[defender]) def Choose_Aggressor(self): return np.random.randint(c.popSize) def Choose_Defender(self,aggressor): defender = np.random.randint(c.popSize) while defender == aggressor: defender = np.random.randint(c.popSize) return defender def Contract(self): while len(self.genomes) > c.popSize: aggressorDominatesDefender = False while not aggressorDominatesDefender: aggressor = self.Choose_Aggressor() defender = self.Choose_Defender(aggressor) aggressorDominatesDefender = self.Aggressor_Dominates_Defender(aggressor,defender) for genomeToMove in range(defender,len(self.genomes)-1): self.genomes[genomeToMove] = self.genomes.pop(genomeToMove+1) def process(self, i): return i*i def Evaluate_Genomes(self): num_cores = multiprocessing.cpu_count() print("we have") print(num_cores) print("cores") outs = Parallel(n_jobs=num_cores)(delayed(self.genomes[genome].Evaluate)() for genome in self.genomes) #print("done") #print(type(outs)) #self.genomes = copy.deepcopy(np.array(outs)) #self.genomes = dict(zip(list(range(0, c.popSize)), outs)) #print(type(self.genomes)) for genome in self.genomes: # print(genome) #print(self.genomes[genome].indv.fitness) # print(outs[genome]) self.genomes[genome].fitness = outs[genome] self.genomes[genome].indv.fitness = -outs[genome] def Expand(self): popSize = len(self.genomes) for newGenome in range( popSize , 2 * popSize - 1 ): spawner = self.Choose_Aggressor() self.genomes[newGenome] = copy.deepcopy(self.genomes[spawner]) self.genomes[newGenome].Set_ID(self.nextAvailableID) self.nextAvailableID = self.nextAvailableID + 1 self.genomes[newGenome].Mutate() def Find_Best_Genome(self): genomesSortedByFitness = sorted(self.genomes.values(), key=operator.attrgetter('fitness'),reverse=False) return genomesSortedByFitness[0] def Find_Avg_Fitness(self): add = 0 for g in self.genomes: add += self.genomes[g].fitness return add/c.popSize def Inject(self): popSize = len(self.genomes) self.genomes[popSize-1] = GENOME(self.nextAvailableID) self.nextAvailableID = self.nextAvailableID + 1 def Perform_First_Generation(self): self.Evaluate_Genomes() self.Print() self.Save_Best() self.Save_Avg() def Perform_One_Generation(self): self.Expand() self.Age() self.Inject() self.Evaluate_Genomes() self.Contract() self.Print() self.Save_Best() self.Save_Avg() def Print(self): print('Generation ', end='', flush=True) print(self.currentGeneration, end='', flush=True) print(' of ', end='', flush=True) print(str(c.numGenerations), end='', flush=True) print(': ', end='', flush=True) bestGenome = self.Find_Best_Genome() bestGenome.Print() def Save_Best(self): bestGenome = self.Find_Best_Genome() bestGenome.Save(self.randomSeed) def SaveLastGen(self): genomesSortedByFitness = sorted(self.genomes.values(), key=operator.attrgetter('fitness'),reverse=False) f = open('savedRobotsLastGenAfpoSeed.dat', 'ab') pickle.dump(genomesSortedByFitness, f) f.close() def Save_Avg(self): f = open('avgFitnessAfpoSeed.dat', 'ab') avg = self.Find_Avg_Fitness() print('Average ' + str(avg)) print() #f.write("%.3f\n" % avg) pickle.dump(avg, f) f.close() def Show_Best_Genome(self): bestGenome = self.Find_Best_Genome() bestGenome.Show()

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gecco-2022-main/RandomSearch/XOR-NESS/DynamicalMatrix.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Oct 10 22:09:09 2017 @author: Hightoutou """ def DM_mass(N, x0, y0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] M = np.zeros((2*N, 2*N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly rijsq = dx**2+dy**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij m_sqrt = np.zeros((2*N, 2*N)) m_inv = np.zeros((2*N, 2*N)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_3D(N, x0, y0, z0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] Lz = L[2] M = np.zeros((3*N, 3*N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly dz = z0[i]-z0[j] dz = dz-round(dz/Lz)*Lz rijsq = dx**2+dy**2+dz**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy, dx*dz], [dy*dx, dy*dy, dy*dz], [dz*dx, dz*dy, dz*dz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3*i:3*i+3,3*j:3*j+3] = Mij M[3*j:3*j+3,3*i:3*i+3] = Mij M[3*i:3*i+3,3*i:3*i+3] = M[3*i:3*i+3,3*i:3*i+3] - Mij M[3*j:3*j+3,3*j:3*j+3] = M[3*j:3*j+3,3*j:3*j+3] - Mij m_sqrt = np.zeros((3*N, 3*N)) m_inv = np.zeros((3*N, 3*N)) for i in range(N): m_sqrt[3*i, 3*i] = 1/np.sqrt(m0[i]) m_sqrt[3*i+1, 3*i+1] = 1/np.sqrt(m0[i]) m_sqrt[3*i+2, 3*i+2] = 1/np.sqrt(m0[i]) m_inv[3*i, 3*i] = 1/m0[i] m_inv[3*i+1, 3*i+1] = 1/m0[i] m_inv[3*i+2, 3*i+2] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_Yfixed(N, x0, y0, D0, m0, Lx, y_bot, y_top, k): import numpy as np M = np.zeros((2*N, 2*N)) contactNum = 0 for i in range(N): r_now = 0.5*D0[i] if y0[i]-y_bot<r_now or y_top-y0[i]<r_now: M[2*i+1, 2*i+1] = M[2*i+1, 2*i+1]+1/r_now/r_now for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] rijsq = dx**2+dy**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij M = k*M m_sqrt = np.zeros((2*N, 2*N)) m_inv = np.zeros((2*N, 2*N)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_Xfixed(N, x0, y0, D0, m0, Ly): import numpy as np M = np.zeros((2*N, 2*N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly rijsq = dx**2+dy**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij m_sqrt = np.zeros((2*N, 2*N)) m_inv = np.zeros((2*N, 2*N)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_DiffK_Yfixed(N, x0, y0, D0, m0, Lx, y_bot, y_top, k_list, k_type): import numpy as np M = np.zeros((2*N, 2*N)) contactNum = 0 for i in range(N): r_now = 0.5*D0[i] if y0[i]-y_bot<r_now or y_top-y0[i]<r_now: M[2*i+1, 2*i+1] = M[2*i+1, 2*i+1] + k_list[k_type[i]] / r_now / r_now for j in range(i): dij = 0.5 * (D0[i] + D0[j]) dijsq = dij**2 dx = x0[i] - x0[j] dx = dx - round(dx / Lx) * Lx dy = y0[i] - y0[j] rijsq = dx**2 + dy**2 if rijsq < dijsq: contactNum += 1 k = k_list[(k_type[i] ^ k_type[j]) + np.maximum(k_type[i], k_type[j])] rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -k * rijmat / rijsq / dijsq Mij2 = -k * (1.0 - rij / dij) * (rijmat / rijsq - [[1,0],[0,1]]) / rij / dij Mij = Mij1 + Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij m_sqrt = np.zeros((2*N, 2*N)) m_inv = np.zeros((2*N, 2*N)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) w,v = np.linalg.eig(M) return w,v def DM_mass_Zfixed_3D(N, x0, y0, z0, D0, m0, L): import numpy as np Lx = L[0] Ly = L[1] Lz = L[2] M = np.zeros((3*N, 3*N)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly dz = z0[i]-z0[j] rijsq = dx**2+dy**2+dz**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy, dx*dz], [dy*dx, dy*dy, dy*dz], [dz*dz, dz*dy, dz*dz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3*i:3*i+3,3*j:3*j+3] = Mij M[3*j:3*j+3,3*i:3*i+3] = Mij M[3*i:3*i+3,3*i:3*i+3] = M[3*i:3*i+3,3*i:3*i+3] - Mij M[3*j:3*j+3,3*j:3*j+3] = M[3*j:3*j+3,3*j:3*j+3] - Mij m_sqrt = np.zeros((3*N, 3*N)) m_inv = np.zeros((3*N, 3*N)) for i in range(N): m_sqrt[3*i, 3*i] = 1/np.sqrt(m0[i]) m_sqrt[3*i+1, 3*i+1] = 1/np.sqrt(m0[i]) m_sqrt[3*i+2, 3*i+2] = 1/np.sqrt(m0[i]) m_inv[3*i, 3*i] = 1/m0[i] m_inv[3*i+1, 3*i+1] = 1/m0[i] m_inv[3*i+2, 3*i+2] = 1/m0[i] #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_UpPlate(N, x0, y0, D0, m0, Lx, y_up, m_up): import numpy as np M = np.zeros((2*N+1, 2*N+1)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] rijsq = dx**2+dy**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy], [dx*dy, dy*dy]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0],[0,1]])/rij/dij Mij = Mij1+Mij2 M[2*i:2*i+2,2*j:2*j+2] = Mij M[2*j:2*j+2,2*i:2*i+2] = Mij M[2*i:2*i+2,2*i:2*i+2] = M[2*i:2*i+2,2*i:2*i+2] - Mij M[2*j:2*j+2,2*j:2*j+2] = M[2*j:2*j+2,2*j:2*j+2] - Mij for i in range(N): r_now = 0.5*D0[i] if y0[i]<r_now: M[2*i+1, 2*i+1] = M[2*i+1, 2*i+1]+1/r_now**2 if y_up-y0[i]<r_now: M[2*i+1, 2*i+1] = M[2*i+1, 2*i+1]+1/r_now**2 M[2*N, 2*N] = M[2*N, 2*N]+1/r_now**2 M[2*i+1, 2*N] = M[2*i+1, 2*N]-1/r_now**2 M[2*N, 2*i+1] = M[2*N, 2*i+1]-1/r_now**2 m_sqrt = np.zeros((2*N+1, 2*N+1)) m_inv = np.zeros((2*N+1, 2*N+1)) for i in range(N): m_sqrt[2*i, 2*i] = 1/np.sqrt(m0[i]) m_sqrt[2*i+1, 2*i+1] = 1/np.sqrt(m0[i]) m_inv[2*i, 2*i] = 1/m0[i] m_inv[2*i+1, 2*i+1] = 1/m0[i] m_sqrt[2*N, 2*N] = 1/np.sqrt(m_up) m_inv[2*N, 2*N] = 1/m_up #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v def DM_mass_UpPlate_3D(N, x0, y0, z0, D0, m0, Lx, Ly, z_up, m_up): import numpy as np M = np.zeros((3*N+1, 3*N+1)) contactNum = 0 for i in range(N): for j in range(i): dij = 0.5*(D0[i]+D0[j]) dijsq = dij**2 dx = x0[i]-x0[j] dx = dx-round(dx/Lx)*Lx dy = y0[i]-y0[j] dy = dy-round(dy/Ly)*Ly dz = z0[i]-z0[j] rijsq = dx**2+dy**2+dz**2 if rijsq<dijsq: contactNum += 1 rijmat = np.array([[dx*dx, dx*dy, dx*dz], [dy*dx, dy*dy, dy*dz], [dz*dz, dz*dy, dz*dz]]) rij = np.sqrt(rijsq) Mij1 = -rijmat/rijsq/dijsq Mij2 = -(1-rij/dij)*(rijmat/rijsq-[[1,0,0],[0,1,0],[0,0,1]])/rij/dij Mij = Mij1+Mij2 M[3*i:3*i+3,3*j:3*j+3] = Mij M[3*j:3*j+3,3*i:3*i+3] = Mij M[3*i:3*i+3,3*i:3*i+3] = M[3*i:3*i+3,3*i:3*i+3] - Mij M[3*j:3*j+3,3*j:3*j+3] = M[3*j:3*j+3,3*j:3*j+3] - Mij for i in range(N): r_now = 0.5*D0[i] if z0[i]<r_now: M[3*i+2, 3*i+2] = M[3*i+2, 3*i+2]+1/r_now**2 if z_up-z0[i]<r_now: M[3*i+2, 3*i+2] = M[3*i+2, 3*i+2]+1/r_now**2 M[3*N, 3*N] = M[3*N, 3*N]+1/r_now**2 M[3*i+2, 3*N] = M[3*i+2, 3*N]-1/r_now**2 M[3*N, 3*i+2] = M[3*N, 3*i+2]-1/r_now**2 m_sqrt = np.zeros((3*N+1, 3*N+1)) m_inv = np.zeros((3*N+1, 3*N+1)) for i in range(N): m_sqrt[3*i, 3*i] = 1/np.sqrt(m0[i]) m_sqrt[3*i+1, 3*i+1] = 1/np.sqrt(m0[i]) m_sqrt[3*i+2, 3*i+2] = 1/np.sqrt(m0[i]) m_inv[3*i, 3*i] = 1/m0[i] m_inv[3*i+1, 3*i+1] = 1/m0[i] m_inv[3*i+2, 3*i+2] = 1/m0[i] m_sqrt[3*N, 3*N] = 1/np.sqrt(m_up) m_inv[3*N, 3*N] = 1/m_up #M = m_sqrt.dot(M).dot(m_sqrt) M = m_inv.dot(M) w,v = np.linalg.eig(M) return w,v

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gecco-2022

gecco-2022-main/RandomSearch/XOR-NESS/ConfigPlot.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Oct 10 21:01:26 2017 @author: Hightoutou """ import numpy as np def ConfigPlot_DiffSize(N, x, y, D, L, mark_print): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse Dmin = min(D) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] D_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) D_all.append(D[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_alpha(0.3) if D_all[i] > Dmin: e.set_facecolor('C1') else: e.set_facecolor('C0') i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass(N, x, y, D, L, m, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffMass2(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness(N, x, y, D, L, m, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C2') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness2(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C2') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffStiffness3(N, x, y, D, L, m, mark_print, hn, in1, in2, out): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) if i==in1: e.set_edgecolor((0, 1, 0)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='^', s=80, color=(0, 1, 0, 1)) elif i==in2: e.set_edgecolor((0, 0, 1)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='s', s=80, color=(0, 0, 1, 1)) elif i==out: e.set_edgecolor((1, 0, 0)) e.set_linewidth(4) plt.scatter(x_now, y_now, marker='*', s=100, color=(1, 0, 0, 1)) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('k') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) import matplotlib.lines as mlines red_star = mlines.Line2D([], [], color=(1, 0, 0), marker='*', linestyle='None', markersize=10, label='Output') blue_square = mlines.Line2D([], [], color=(0, 0, 1), marker='s', linestyle='None', markersize=10, label='Input 2') green_triangle = mlines.Line2D([], [], color=(0, 1, 0), marker='^', linestyle='None', markersize=10, label='Input 1') plt.legend(handles=[red_star, green_triangle, blue_square], bbox_to_anchor=(1.215, 1)) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_DiffMass_3D(N, x, y, z, D, L, m, mark_print): import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D m_min = min(m) m_max = max(m) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.set_aspect('equal') sphes = [] m_all = [] for i in range(int(N/2)): x_now = x[i]%L[0] y_now = y[i]%L[1] z_now = z[i]%L[2] r_now = 0.5*D[i] #alpha_now = 0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3 alpha_now = 0.3 pos1 = 0 pos2 = 1 for j in range(pos1, pos2): for k in range(pos1, pos2): for l in range(pos1, pos2): u = np.linspace(0, 2 * np.pi, 100) v = np.linspace(0, np.pi, 100) x_plot = x_now+j*L[0]+r_now * np.outer(np.cos(u), np.sin(v)) y_plot = y_now+k*L[1]+r_now * np.outer(np.sin(u), np.sin(v)) z_plot = z_now+l*L[2]+r_now * np.outer(np.ones(np.size(u)), np.cos(v)) ymin = y_plot[y_plot>0].min() ymax = y_plot[y_plot>0].max() print (i, ymin, ymax) ax.plot_surface(x_plot,y_plot,z_plot,rstride=4,cstride=4, color='C0',linewidth=0,alpha=alpha_now) #sphes.append(e) #m_all.append(m[i]) # i = 0 # for e in sphes: # ax.add_artist(e) # e.set_clip_box(ax.bbox) # e.set_facecolor('C0') # e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) # i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) ax.set_zlim(0, L[2]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_YFixed_rec(N, x, y, D, Lx, y_top, y_bot, m, mark_order): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_print = 0 m_min = min(m) m_max = max(m) if m_min == m_max: m_max *= 1.001 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) for k in range(-1, 2): e = Ellipse((x_now+k*Lx, y_now), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.3+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 #rect = Rectangle([0, y_top], Lx, 0.2*D[0], color='C0') #ax.add_patch(rect) for nn in np.arange(N): x1 = x[nn]%Lx d_up = y_top-y[nn] d_bot = y[nn]-y_bot r_now = 0.5*D[nn] if d_up<r_now: ax.plot([x1, x1], [y[nn], y[nn]+r_now], '-', color='w') if d_bot<r_now: ax.plot([x1, x1], [y[nn], y[nn]-r_now], '-', color='w') for mm in np.arange(nn+1, N): dy = y[mm]-y[nn] Dmn = 0.5*(D[mm]+D[nn]) if abs(dy) < Dmn: x2 = x[mm]%Lx if x2>x1: xl = x1 xr = x2 yl = y[nn] yr = y[mm] else: xl = x2 xr = x1 yl = y[mm] yr = y[nn] dx0 = xr-xl dx = dx0-round(dx0/Lx)*Lx dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: if dx0<Dmn: ax.plot([xl, xr], [yl, yr], '-', color='w') else: ax.plot([xl, xr-Lx], [yl, yr], '-', color='w') ax.plot([xl+Lx, xr], [yl, yr], '-', color='w') ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/plot_test/fig'+str(int(ind_nt+1e4))+'.png', dpi = 150) def ConfigPlot_DiffMass_SP(N, x, y, D, L, m, mark_print, ind_in, ind_out, ind_fix): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)*0.3) if i == ind_in: e.set_edgecolor('r') e.set_linewidth(width) if i == ind_out: e.set_edgecolor('b') e.set_linewidth(width) if i == ind_fix: e.set_edgecolor('k') e.set_linewidth(width) ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass_FixLx(N, x, y, D, L, m, mark_print, ind_wall): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i] y_now = y[i]%L[1] for l in range(-1, 2): e = Ellipse((x_now, y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) if ind_wall[i] > 0: e.set_edgecolor('k') e.set_linewidth(width) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) def ConfigPlot_DiffMass_SP_rec(N, x, y, D, L, m, mark_print, ind_in, ind_out, ind_fix): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] width = 2 for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m[i]-m_min)/(m_max-m_min)*0.3) if i == ind_in: e.set_edgecolor('r') e.set_linewidth(width) if i == ind_out: e.set_edgecolor('b') e.set_linewidth(width) if i == ind_fix: e.set_edgecolor('k') e.set_linewidth(width) Lx = L[0] Ly = L[1] for nn in np.arange(N): x1 = x[nn]%Lx y1 = y[nn]%Ly for mm in np.arange(nn+1, N): x2 = x[mm]%Lx y2 = y[mm]%Ly if x2>x1: xl = x1 xr = x2 yl = y1 yr = y2 else: xl = x2 xr = x1 yl = y2 yr = y1 dx0 = xr-xl dx = dx0-round(dx0/Lx)*Lx if y2>y1: xd = x1 xu = x2 yd = y1 yu = y2 else: xd = x2 xu = x1 yd = y2 yu = y1 dy0 = yu-yd dy = dy0-round(dy0/Ly)*Ly Dmn = 0.5*(D[mm]+D[nn]) dmn = np.sqrt(dx**2+dy**2) if dmn < Dmn: if dx0<Dmn and dy0<Dmn: ax.plot([xl, xr], [yl, yr], '-', color='w') else: if dx0>Dmn and dy0>Dmn: if yr>yl: ax.plot([xl, xr-Lx], [yl, yr-Ly], '-', color='w') ax.plot([xl+Lx, xr], [yl+Ly, yr], '-', color='w') else: ax.plot([xl, xr-Lx], [yl, yr+Ly], '-', color='w') ax.plot([xl+Lx, xr], [yl-Ly, yr], '-', color='w') else: if dx0>Dmn: ax.plot([xl, xr-Lx], [yl, yr], '-', color='w') ax.plot([xl+Lx, xr], [yl, yr], '-', color='w') if dy0>Dmn: ax.plot([xd, xu], [yd, yu-Ly], '-', color='w') ax.plot([xd, xu], [yd+Ly, yu], '-', color='w') ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig('/Users/Hightoutou/Desktop/fig.png', dpi = 300) return fig def ConfigPlot_EigenMode_DiffMass(N, x, y, D, L, m, em, mark_print, hn): import matplotlib.pyplot as plt from matplotlib.patches import Ellipse m_min = min(m) m_max = max(m) if m_min == m_max: m_max *= 1.001 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] m_all = [] for i in range(N): x_now = x[i]%L[0] y_now = y[i]%L[1] for k in range(-1, 2): for l in range(-1, 2): e = Ellipse((x_now+k*L[0], y_now+l*L[1]), D[i],D[i],0) ells.append(e) m_all.append(m[i]) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.3) i += 1 r_now = D[0]*0.5 dr = np.zeros(N) for i in range(N): dr[i] = np.sqrt(em[2*i]**2+em[2*i+1]**2) dr_max = max(dr) for i in range(N): ratio = dr[i]/dr_max*r_now/dr_max plt.arrow(x[i], y[i],em[2*i]*ratio, em[2*i+1]*ratio, head_width=0.005) ax.set_xlim(0, L[0]) ax.set_ylim(0, L[1]) plt.show() if mark_print == 1: fig.savefig(hn, dpi = 300) return fig def ConfigPlot_YFixed_SelfAssembly(N, Nl, x, y, theta, n, d1, d2, Lx, y_top, y_bot): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_order = 0 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] alpha_all = [] alpha1 = 0.6 alpha2 = 0.3 for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) alpha = alpha1 if i < Nl else alpha2 for k in range(-1, 2): e = Ellipse((x_now+k*Lx, y_now), d1,d1,0) ells.append(e) alpha_all.append(alpha) if i >= Nl: for ind in range(n): x_i = x_now+k*Lx+0.5*(d1+d2)*np.cos(theta[i]+ind*2*np.pi/n) y_i = y_now+0.5*(d1+d2)*np.sin(theta[i]+ind*2*np.pi/n) e = Ellipse((x_i, y_i), d2,d2,0) ells.append(e) alpha_all.append(alpha) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(alpha_all[i]) i += 1 ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show() def ConfigPlot_YFixed_SelfAssembly_BumpyBd(N, n_col, Nl, x, y, theta, n, d0, d1, d2, Lx, y_top, y_bot): import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Ellipse, Rectangle mark_order = 0 fig, ax = plt.subplots(subplot_kw={'aspect': 'equal'}) ells = [] alpha_all = [] alpha1 = 0.6 alpha2 = 0.3 for i in range(n_col+1): x_now = i*d0 e1 = Ellipse((x_now, y_bot), d0,d0,0) e2 = Ellipse((x_now, y_top), d0,d0,0) ells.append(e1) alpha_all.append(alpha1) ells.append(e2) alpha_all.append(alpha1) for i in range(N): x_now = x[i]%Lx y_now = y[i] if mark_order==1: plt.text(x_now, y_now, str(i)) alpha = alpha1 if i < Nl else alpha2 for k in range(-1, 2): e = Ellipse((x_now+k*Lx, y_now), d1,d1,0) ells.append(e) alpha_all.append(alpha) if i >= Nl: for ind in range(n): x_i = x_now+k*Lx+0.5*(d1+d2)*np.cos(theta[i]+ind*2*np.pi/n) y_i = y_now+0.5*(d1+d2)*np.sin(theta[i]+ind*2*np.pi/n) e = Ellipse((x_i, y_i), d2,d2,0) ells.append(e) alpha_all.append(alpha) i = 0 for e in ells: ax.add_artist(e) e.set_clip_box(ax.bbox) e.set_facecolor('C0') #e.set_alpha(0.2+(m_all[i]-m_min)/(m_max-m_min)*0.8) e.set_alpha(alpha_all[i]) i += 1 ax.set_xlim(0, Lx) ax.set_ylim(y_bot, y_top) plt.show()

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gecco-2022

gecco-2022-main/RandomSearch/XOR-NESS/genome_seed_binary.py

import constants as c from individual_seed_binary import INDIVIDUAL import pickle class GENOME: def __init__(self,ID,fitness=c.worstFitness): self.Set_ID(ID) self.indv = INDIVIDUAL(ID) self.age = 0 self.fitness = fitness def Age(self): self.age = self.age + 1 def Dominates(self,other): if self.Get_Fitness() <= other.Get_Fitness(): if self.Get_Age() <= other.Get_Age(): equalFitnesses = self.Get_Fitness() == other.Get_Fitness() equalAges = self.Get_Age() == other.Get_Age() if not equalFitnesses and equalAges: return True else: return self.Is_Newer_Than(other) else: return False else: return False def Evaluate(self): #self.indv.Start_Evaluation(True) f = self.indv.Compute_Fitness() # if f < 0: # self.fitness = c.worstFitness # else: # self.fitness = 1/(1+f) self.fitness = -f #print(f) #print(self.indv.genome) return self.fitness def Get_Age(self): return self.age def Get_Fitness(self): return self.fitness def Mutate(self): self.indv.Mutate() def Print(self): print(' fitness: ' , end = '' ) print(self.fitness , end = '' ) print(' age: ' , end = '' ) print(self.age , end = '' ) print() def Save(self,randomSeed): f = open('savedRobotsAfpoSeed.dat', 'ab') pickle.dump(self.indv , f) f.close() pass def Set_ID(self,ID): self.ID = ID def Show(self): #self.indv.Start_Evaluation(False, 40) self.indv.Compute_Fitness(True) # def __add__(self, other): # total_fitness = self.fitness + other.fitness # print("I've been called") # return GENOME(1, total_fitness) # def __radd__(self, other): # if other == 0: # return self # else: # return self.__add__(other) # -------------------- Private methods ---------------------- def Get_ID(self): return self.ID def Is_Newer_Than(self,other): return self.Get_ID() > other.Get_ID()

2,336

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gecco-2022

gecco-2022-main/RandomSearch/XOR-NESS/afpoPlots_seed_binary.py

import pickle import matplotlib.pyplot as plt from switch_binary import switch import constants as c import numpy runs = c.RUNS gens = c.numGenerations fitnesses = numpy.zeros([runs, gens]) temp = [] individuals = [] with open('savedRobotsAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): for g in range(1, gens+1): try: temp.append(pickle.load(f).fitness) except EOFError: break fitnesses[r-1] = temp temp = [] f.close() mean_f = numpy.mean(fitnesses, axis=0) std_f = numpy.std(fitnesses, axis=0) plt.figure(figsize=(6.4,4.8)) plt.plot(list(range(1, gens+1)), mean_f, color='blue') plt.fill_between(list(range(1, gens+1)), mean_f-std_f, mean_f+std_f, color='cornflowerblue', alpha=0.2) plt.xlabel("Generations") plt.ylabel("Best Fitness") plt.title("Fitness of the Best Individual in the Population - AFPO", fontsize='small') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.tight_layout() #plt.legend(['two robot', 'three robots'], loc='upper left') #plt.savefig("compare.pdf") plt.show() # running the best individuals m1 = 1 m2 = 10 N_light = 9 N = 30 bests = numpy.zeros([runs, gens]) temp = [] rubish = [] with open('savedRobotsLastGenAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): # population of the last generation temp = pickle.load(f) # best individual of last generation best = temp[0] switch.showPacking(m1, m2, N_light, best.indv.genome) print(switch.evaluate(m1, m2, N_light, best.indv.genome)) print(switch.evaluateAndPlot(m1, m2, N_light, best.indv.genome)) temp = [] f.close() # running all of the individuals of the last generation of each of the runs bests = numpy.zeros([runs, gens]) temp = [] rubish = [] with open('savedRobotsLastGenAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): print("run:") print(r) # population of the last generation temp = pickle.load(f) for g in range(0, gens): switch.showPacking(m1, m2, N_light, temp[g].indv.genome) print(switch.evaluateAndPlot(m1, m2, N_light, temp[g].indv.genome)) temp = [] f.close()

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28.972973

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gecco-2022

gecco-2022-main/RandomSearch/XOR-NESS/evolveAfpo_seed_binary.py

from afpo_seed_binary import AFPO import os import constants as c import random #cleaning up the data files try: os.remove("savedRobotsLastGenAfpoSeed.dat") except OSError: pass try: os.remove("avgFitnessAfpoSeed.dat") except OSError: pass try: os.remove("savedRobotsAfpoSeed.dat") except OSError: pass runs = c.RUNS for r in range(1, runs+1): print("*********************************************************", flush=True) print("run: "+str(r), flush=True) randomSeed = r random.seed(r) afpo = AFPO(randomSeed) afpo.Evolve() #afpo.Show_Best_Genome()

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gecco-2022-main/RandomSearch/XOR-NESS/individual_seed_binary.py

from switch_binary import switch import constants as c import random import math import numpy as np import sys import pickle class INDIVIDUAL: def __init__(self, i): # assuming curves have one control point, [Sx, Ex, Cx, Cy] for each fiber # assuming we have two planes, each with c.FIBERS of fibers on them self.m1 = 1 self.m2 = 10 #[2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000] #10 is what is in fig2 of the paper self.N_light = 9 #[25, 50, 75] #[9, 15, 21] self.N = 30 #self.low = 7 #self.high = 12 #indices = random.sample(range(0, N), N_light) self.genome = np.random.randint(low=0, high=2, size=self.N)#random.sample(range(0, self.N), self.N_light) #np.random.randint(0, high=c.GRID_SIZE-1, size=(c.FIBERS*2, 4), dtype='int') self.fitness = 0 self.ID = i def Compute_Fitness(self, show=False): # wait for the simulation to end and get the fitness self.fitness = switch.evaluate(self.m1, self.m2, self.N_light, self.genome)#, self.low, self.high) if show: switch.showPacking(self.m1, self.m2, self.N_light, self.genome)#, self.low, self.high) print("fitness is:") print(self.fitness) return self.fitness def Mutate(self): mutationRate = 0.05 probToMutate = np.random.choice([False, True], size=self.genome.shape, p=[1-mutationRate, mutationRate]) candidate = np.where(probToMutate, 1-self.genome, self.genome) self.genome = candidate def Print(self): print('[', self.ID, self.fitness, ']', end=' ') def Save(self): f = open('savedFitnessSeed.dat', 'ab') pickle.dump(self.fitness , f) f.close() def SaveBest(self): f = open('savedBestsSeed.dat', 'ab') pickle.dump(self.genome , f) f.close()

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gecco-2022

gecco-2022-main/RandomSearch/XOR-NESS/randomSearch2.py

from switch_binary import switch import matplotlib.pyplot as plt import random import numpy as np from joblib import Parallel, delayed import multiprocessing import pickle from scipy.stats import norm import operator from ConfigPlot import ConfigPlot_DiffStiffness3 with open('outs.dat', "rb") as f: outs = pickle.load(f) f.close() with open('samples.dat', "rb") as f: samples = pickle.load(f) f.close() # compute the cumulative sum #Nk_cum = np.cumsum(Nk) # go to log scale #log_Nk_cum = np.log10(Nk_cum) #log_k = np.log10(k) # plot the original data #fig = plt.figure() #ax = plt.gca() #ax.scatter(log_k, log_Nk_cum, s=5, alpha=0.3) #ax.set_title('CCDF in log-log scale') #ax.set_xlabel('$Log_{10}(k)$') #ax.set_ylabel('$Log_{10}(Nk_{>k})$') def showPacking(indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row m1=1 m2=10 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) # show packing ConfigPlot_DiffStiffness3(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf', ind_in1, ind_in2, ind_out) print("done", flush=True) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() n, bins, patches = plt.hist(x=outs, bins='auto', color='#0504aa', alpha=0.7, cumulative=False)#, grid=True) # fitting a normal distribution mu, std = norm.fit(outs) xmin, xmax = plt.xlim() x = np.linspace(xmin, xmax, 1000) p = norm.pdf(x, mu, std) #plt.plot(x, p, linewidth=2) myText = "Mean={:.3f}, STD={:.3f}".format(mu, std) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium', color='g') plt.xlabel('XOR-Ness') plt.ylabel('Counts') plt.title('Random Search', fontsize='medium') #plt.xlim([0, 8]) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) #plt.show() plt.savefig("histogram2.jpg", dpi=300) sortedList = list(zip(*sorted(zip(samples,outs), key=operator.itemgetter(1)))) showPacking(sortedList[0][0]) print(sortedList[1][0]) showPacking(sortedList[0][-1]) print(sortedList[1][-1]) showPacking(sortedList[0][-2]) print(sortedList[1][-2])

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gecco-2022-main/RandomSearch/XOR-NESS/plot_functions.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Oct 22 14:48:04 2017 @author: Hightoutou """ import matplotlib.pyplot as plt #import matplotlib #matplotlib.use('TkAgg') def Line_single(xdata, ydata, line_spec, xlabel, ylabel, mark_print, fn = '', xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.5*3/4) pos1 = ax1.get_position() pos2 = [pos1.x0 + 0.12, pos1.y0 + 0.05, pos1.width-0.1, pos1.height] ax1.set_position(pos2) #ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) plt.ylabel(ylabel, fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') ax1.plot(xdata, ydata, line_spec) if mark_print == 1: fig.savefig(fn, dpi = 300) fig.show() def Line_multi(xdata, ydata, line_spec, xlabel, ylabel, xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.5*3/4) ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) ax1.set_ylabel(ylabel, fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') for ii in range(len(xdata)): ax1.plot(xdata[ii], ydata[ii], line_spec[ii]) plt.show() def Line_yy(xdata, ydata, line_spec, xlabel, ylabel, xscale='linear', yscale='linear'): fig, ax1 = plt.subplots() fig.set_size_inches(3.5,3.5*3/4) ax1.tick_params(labelsize=10) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2) ax1.set_ylabel(ylabel[0], fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.xscale(xscale), plt.yscale(yscale) plt.style.use('default') ax1.plot(xdata[0], ydata[0], line_spec[0]) ax2 = ax1.twinx() ax2.set_ylabel(ylabel[1], fontsize=12) ax2.plot(xdata[1], ydata[1], line_spec[1]) plt.show()

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gecco-2022

gecco-2022-main/RandomSearch/XOR-NESS/FFT_functions.py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Oct 17 15:10:21 2017 @author: Hightoutou """ import numpy as np import matplotlib.pyplot as plt from plot_functions import Line_multi, Line_single #from numba import jit def FFT_Fup(Nt, F, dt, Freq_Vibr): sampling_rate = 1/dt t = np.arange(Nt)*dt fft_size = Nt xs = F[:fft_size] xf = np.absolute(np.fft.rfft(xs)/fft_size) freqs = (2*np.pi)*np.linspace(0, sampling_rate/2, fft_size//2+1) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 0: Line_multi([freqs[1:], [Freq_Vibr, Freq_Vibr]], [xf[1:], [min(xf[1:]), max(xf[1:])]], ['o', 'r--'], 'Frequency', 'FFT', 'linear', 'log') return freqs[1:], xf[1:] def FFT_Fup_RealImag(Nt, F, dt, Freq_Vibr): sampling_rate = 1/dt t = np.arange(Nt)*dt fft_size = Nt xs = F[:fft_size] xf = np.fft.rfft(xs)/fft_size freqs = (2*np.pi)*np.linspace(0, sampling_rate/2, fft_size/2+1) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] xf_real = xf.real xf_imag = xf.imag if 1 == 0: Line_multi([freqs[1:], [Freq_Vibr, Freq_Vibr]], [xf[1:], [min(xf[1:]), max(xf[1:])]], ['o', 'r--'], 'Frequency', 'FFT') return freqs[1:], xf_real[1:], xf_imag[1:] #@jit def vCorr_Cal(fft_size, Nt, y_raw): y_fft = np.zeros(fft_size) for jj in np.arange(fft_size): sum_vcf = 0 sum_tt = 0 count = 0 for kk in np.arange(Nt-jj): count = count+1 sum_vcf += y_raw[kk]*y_raw[kk+jj]; sum_tt = sum_tt+y_raw[kk]*y_raw[kk]; y_fft[jj] = sum_vcf/sum_tt; return y_fft def FFT_vCorr(Nt, N, vx_rec, vy_rec, dt): sampling_rate = 1/dt fft_size = Nt-1 freqs = (2*np.pi)*np.linspace(0, sampling_rate/2, fft_size/2+1) for ii in np.arange(2*N): #for ii in [0,4]: if np.mod(ii, 10) == 0: print('ii=%d\n' % (ii)) if ii >= N: y_raw = vy_rec[:, ii-N] else: y_raw = vx_rec[:, ii] y_fft = vCorr_Cal(fft_size, Nt, y_raw) if ii == 0: xf = np.absolute(np.fft.rfft(y_fft)/fft_size) else: xf += np.absolute(np.fft.rfft(y_fft)/fft_size) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 1: Line_single(freqs[1:], xf[1:], 'o', 'Frequency', 'FFT') return freqs[1:], xf[1:] def FFT_vCorr_3D(Nt, N, vx_rec, vy_rec, vz_rec, dt): sampling_rate = 1/dt fft_size = Nt-1 freqs = (2*np.pi)*np.linspace(0, sampling_rate/2, fft_size/2+1) for ii in np.arange(3*N): #for ii in [0,4]: if np.mod(ii, 10) == 0: print('ii=%d\n' % (ii)) if ii >= 2*N: y_raw = vz_rec[:, ii-2*N] elif ii < N: y_raw = vx_rec[:, ii] else: y_raw = vy_rec[:, ii-N] y_fft = vCorr_Cal(fft_size, Nt, y_raw) if ii == 0: xf = np.absolute(np.fft.rfft(y_fft)/fft_size) else: xf += np.absolute(np.fft.rfft(y_fft)/fft_size) ind = freqs<30 freqs = freqs[ind] xf = xf[ind] if 1 == 1: Line_single(freqs[1:], xf[1:], 'o', 'Frequency', 'FFT') return freqs[1:], xf[1:]

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gecco-2022

gecco-2022-main/RandomSearch/XOR-NESS/randomSearch.py

from switch_binary import switch import matplotlib.pyplot as plt import random import numpy as np from joblib import Parallel, delayed import multiprocessing import pickle m1 = 1 m2 = 10 N = 30 N_light = 9 samples = [] print("sampling", flush=True) for i in range(0, 5001): samples.append(np.random.randint(low=0, high=2, size=N)) print("sampling done", flush=True) num_cores = multiprocessing.cpu_count() outs = Parallel(n_jobs=num_cores)(delayed(switch.evaluate)(m1, m2, N_light, samples[i]) for i in range(0, 5001)) print("done", flush=True) f = open('outs.dat', 'ab') pickle.dump(outs , f) f.close() f = open('samples.dat', 'ab') pickle.dump(samples , f) f.close() n, bins, patches = plt.hist(x=outs, bins='auto', color='#0504aa', alpha=0.7, rwidth=0.85)#, grid=True) plt.xlabel('Andness') plt.ylabel('Counts') plt.title('Random Search') #plt.xlim([0, 8]) plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.show() plt.savefig("histogram.jpg", dpi=300)

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gecco-2022-main/RandomSearch/XOR-NESS/plotInOut.py

import constants as c import numpy as np from ConfigPlot import ConfigPlot_DiffStiffness2 from MD_functions import MD_VibrSP_ConstV_Yfixed_DiffK, FIRE_YFixed_ConstV_DiffK, MD_VibrSP_ConstV_Yfixed_DiffK2 from DynamicalMatrix import DM_mass_DiffK_Yfixed import random import matplotlib.pyplot as plt import pickle from os.path import exists from switch_binary import switch def showPacking(indices): k1 = 1. k2 = 10. n_col = 6 n_row = 5 N = n_col*n_row m1=1 m2=10 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) # show packing ConfigPlot_DiffStiffness2(N, x0, y0, D, [Lx,Ly], k_type, 0, '/Users/atoosa/Desktop/results/packing.pdf', ind_in1, ind_in2, ind_out) def plotInOut(indices): #%% Initial Configuration k1 = 1. k2 = 10. m1 = 1 m2 = 10 n_col = 6 n_row = 5 N = n_col*n_row Nt_fire = 1e6 dt_ratio = 40 Nt_SD = 1e5 Nt_MD = 1e5 dphi_index = -1 dphi = 10**dphi_index d0 = 0.1 d_ratio = 1.1 Lx = d0*n_col Ly = (n_row-1)*np.sqrt(3)/2*d0+d0 x0 = np.zeros(N) y0 = np.zeros(N) phi0 = N*np.pi*d0**2/4/(Lx*Ly) d_ini = d0*np.sqrt(1+dphi/phi0) D = np.zeros(N)+d_ini #D = np.zeros(N)+d0 x0 = np.zeros(N) y0 = np.zeros(N) for i_row in range(1, n_row+1): for i_col in range(1, n_col+1): ind = (i_row-1)*n_col+i_col-1 if i_row%2 == 1: x0[ind] = (i_col-1)*d0 else: x0[ind] = (i_col-1)*d0+0.5*d0 y0[ind] = (i_row-1)*np.sqrt(3)/2*d0 y0 = y0+0.5*d0 mass = np.zeros(N) + 1 k_list = np.array([k1, k2, k1 * k2 / (k1 + k2)]) k_type = indices #np.zeros(N, dtype=np.int8) #k_type[indices] = 1 # Steepest Descent to get energy minimum #x_ini, y_ini, p_now = MD_YFixed_ConstV_SP_SD_DiffK(Nt_SD, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) x_ini, y_ini, p_now = FIRE_YFixed_ConstV_DiffK(Nt_fire, N, x0, y0, D, mass, Lx, Ly, k_list, k_type) # skip the steepest descent for now to save time #x_ini = x0 #y_ini = y0 # calculating the bandgap - no need to do this in this problem w, v = DM_mass_DiffK_Yfixed(N, x_ini, y_ini, D, mass, Lx, 0.0, Ly, k_list, k_type) w = np.real(w) v = np.real(v) freq = np.sqrt(np.absolute(w)) ind_sort = np.argsort(freq) freq = freq[ind_sort] v = v[:, ind_sort] ind = freq > 1e-4 eigen_freq = freq[ind] eigen_mode = v[:, ind] w_delta = eigen_freq[1:] - eigen_freq[0:-1] index = np.argmax(w_delta) F_low_exp = eigen_freq[index] F_high_exp = eigen_freq[index+1] plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.scatter(np.arange(0, len(eigen_freq)), eigen_freq, marker='x', color='blue') plt.xlabel("Number") plt.ylabel("Frequency") plt.title("Vibrational Response", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) props = dict(facecolor='green', alpha=0.1) myText = 'f_low='+"{:.2f}".format(F_low_exp)+"\n"+'f_high='+"{:.2f}".format(F_high_exp)+"\n"+'band gap='+"{:.2f}".format(max(w_delta)) plt.text(0.85, 0.1, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='large', bbox=props) plt.tight_layout() plt.show() print("specs:") print(F_low_exp) print(F_high_exp) print(max(w_delta)) # specify the input ports and the output port SP_scheme = 0 digit_in = SP_scheme//2 digit_out = SP_scheme-2*digit_in ind_in1 = int((n_col+1)/2)+digit_in - 1 ind_in2 = ind_in1 + 2 ind_out = int(N-int((n_col+1)/2)+digit_out) ind_fix = int((n_row+1)/2)*n_col-int((n_col+1)/2) B = 1 Nt = 1e4 # it was 1e5 before, i reduced it to run faster # we are designing an and gait at this frequency Freq_Vibr = 7 # case 1, input [1, 1] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out1 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain1 = out1/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency") plt.ylabel("Amplitude of FFT") plt.title("Logic Gate Response - input = 11", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain1) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium') plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='solid') plt.plot(x_out, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps") plt.ylabel("Amplitude of Displacement") plt.title("Logic Gate Response - input = 11", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() plt.show() # case 2, input [1, 0] Amp_Vibr1 = 1e-2 Amp_Vibr2 = 0 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out2 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain2 = out2/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency") plt.ylabel("Amplitude of FFT") plt.title("Logic Gate Response - input = 10", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain2) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium') plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='solid') plt.plot(x_out, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps") plt.ylabel("Amplitude of Displacement") plt.title("Logic Gate Response - input = 10", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() plt.show() # case 3, input [0, 1] Amp_Vibr1 = 0 Amp_Vibr2 = 1e-2 # changed the resonator to one in MD_functions file and vibrations in x direction freq_fft, fft_in1, fft_in2, fft_x_out, fft_y_out, mean_cont, nt_rec, Ek_now, Ep_now, cont_now = MD_VibrSP_ConstV_Yfixed_DiffK(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) ind = np.where(freq_fft>Freq_Vibr) index_=ind[0][0] # fft of the output port at the driving frequency out3 = fft_x_out[index_-1] + (fft_x_out[index_]-fft_x_out[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input1 at driving frequency inp1 = fft_in1[index_-1] + (fft_in1[index_]-fft_in1[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) # fft of input2 at driving frequency inp2 = fft_in2[index_-1] + (fft_in2[index_]-fft_in2[index_-1])*((Freq_Vibr-freq_fft[index_-1])/(freq_fft[index_]-freq_fft[index_-1])) gain3 = out3/(inp1+inp2) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(freq_fft, fft_in1, color='green', label='Input1', linestyle='dotted') plt.plot(freq_fft, fft_in2, color='blue', label='Input2', linestyle=(0, (3, 5, 1, 5))) plt.plot(freq_fft, fft_x_out, color='red', label='Output', linestyle='dashed') plt.xlabel("Frequency") plt.ylabel("Amplitude of FFT") plt.title("Logic Gate Response - input = 01", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() #plt.axvline(x=Freq_Vibr, color='purple', linestyle='solid', alpha=0.5) myText = 'Gain='+"{:.3f}".format(gain3) plt.text(0.5, 0.9, myText, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, fontsize='medium') plt.show() x_in1, x_in2, x_out = MD_VibrSP_ConstV_Yfixed_DiffK2(k_list, k_type, B, Nt, N, x_ini, y_ini, D, mass, [Lx, Ly], Freq_Vibr, Amp_Vibr1, ind_in1, Amp_Vibr2, ind_in2, ind_out) fig = plt.figure(figsize=(6.4,4.8)) ax = plt.axes() plt.plot(x_in1, color='green', label='Input1', linestyle='solid') plt.plot(x_in2, color='blue', label='Input2', linestyle='solid') plt.plot(x_out, color='red', label='Output', linestyle='solid') plt.xlabel("Time Steps") plt.ylabel("Amplitude of Displacement") plt.title("Logic Gate Response - input = 01", fontsize='medium') plt.grid(color='skyblue', linestyle=':', linewidth=0.5) plt.legend(loc='upper right') plt.tight_layout() plt.show() print("gain1:") print(gain1) print("gain2:") print(gain2) print("gain3:") print(gain3) andness = 2*gain1/(gain2+gain3) return andness runs = c.RUNS gens = c.numGenerations # running the best individuals temp = [] rubish = [] with open('savedRobotsLastGenAfpoSeed.dat', "rb") as f: for r in range(1, runs+1): # population of the last generation temp = pickle.load(f) # best individual of last generation best = temp[0] showPacking(best.indv.genome) print(plotInOut(best.indv.genome)) temp = [] f.close()

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python-urx

python-urx-master/setup.py

from setuptools import setup setup( name="urx", version="0.11.0", description="Python library to control an UR robot", author="Olivier Roulet-Dubonnet", author_email="[email protected]", url='https://github.com/oroulet/python-urx', packages=["urx"], provides=["urx"], install_requires=["numpy", "math3d"], license="GNU Lesser General Public License v3", classifiers=[ "Programming Language :: Python", "Programming Language :: Python :: 3", "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Operating System :: OS Independent", "Topic :: System :: Hardware :: Hardware Drivers", "Topic :: Software Development :: Libraries :: Python Modules", ])

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python-urx

python-urx-master/make_deb.py

#!/usr/bin/python3 """ hackish file to crreate deb from setup.py """ import subprocess from email.utils import formatdate import urx DEBVERSION = urx.__version__ branch = subprocess.check_output("git rev-parse --abbrev-ref HEAD", shell=True) branch = branch.decode() branch = branch.strip() branch = str(branch).replace("'","") rev = subprocess.check_output("git log -1 --format=\'%ad--%h\' --date=short", shell=True) rev = rev.decode() rev = rev.strip() rev = rev.replace("'","") #rev = rev.replace(" ", "T", 1) #ev = rev.replace(" ", "Z", 1) vcsstring = "git-" + branch + "-" + rev def get_changelog(progname, version, changelog, date): """ return a dummy changelog acceptable by debian script engine """ return """%s (%s) unstable; urgency=low %s -- Olivier R-D <unknown@unknown> %s """ % (progname, version, changelog, date) def check_deb(name): print("checking if %s is installed" % name) subprocess.check_call("dpkg -s %s > /dev/null" % name, shell=True) if __name__ == "__main__": check_deb("build-essential") f = open("debian/changelog", "w") f.write(get_changelog("python-urx", DEBVERSION + vcsstring, "Updated to last changes in repository", formatdate())) f.close() #now build package #subprocess.check_call("dpkg-buildpackage -rfakeroot -uc -us -b", shell=True) subprocess.check_call("fakeroot dh binary --with python3,python2", shell=True) subprocess.check_call("dh clean", shell=True)

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python-urx

python-urx-master/release.py

import re import os def bump_version(): with open("setup.py") as f: s = f.read() m = re.search(r'version="(.*)\.(.*)\.(.*)",', s) v1, v2, v3 = m.groups() oldv = "{0}.{1}.{2}".format(v1, v2, v3) newv = "{0}.{1}.{2}".format(v1, v2, str(int(v3) + 1)) print("Current version is: {0}, write new version, ctrl-c to exit".format(oldv)) ans = input(newv) if ans: newv = ans s = s.replace(oldv, newv) with open("setup.py", "w") as f: f.write(s) return newv def release(): v = bump_version() ans = input("version bumped, commiting?(Y/n)") if ans in ("", "y", "yes"): os.system("git add setup.py") os.system("git commit -m 'new release'") os.system("git tag {0}".format(v)) ans = input("change committed, push to server?(Y/n)") if ans in ("", "y", "yes"): os.system("git push") os.system("git push --tags") ans = input("upload to pip?(Y/n)") if ans in ("", "y", "yes"): os.system("rm -rf dist/*") os.system("python setup.py sdist") os.system("twine upload dist/*") if __name__ == "__main__": release()

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python-urx

python-urx-master/tools/find_packet.py

from urx import ursecmon if __name__ == "__main__": f = open("packets.bin", "rb") s = open("packet.bin", "wb") data = f.read(99999) parser = ursecmon.ParserUtils() p, rest = parser.find_first_packet(data) print(len(p)) p, rest = parser.find_first_packet(rest) print(len(p)) s.write(p) p, rest = parser.find_first_packet(rest) print(len(p))

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python-urx

python-urx-master/tools/grabber.py

import socket import sys if __name__ == "__main__": host, port = "localhost", 30002 host, port = "192.168.1.8", 30002 if len(sys.argv) > 1: host = sys.argv[1] sock = socket.create_connection((host, port)) f = open("packets.bin", "wb") try: # Connect to server and send data for i in range(0, 20): data = sock.recv(1024) f.write(data) print("Got packet: ", i) finally: f.close() sock.close()

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python-urx

python-urx-master/tools/get_rob.py

#!/usr/bin/env python import sys import logging from math import pi from IPython import embed from urx import Robot import math3d if __name__ == "__main__": if len(sys.argv) > 1: host = sys.argv[1] else: host = 'localhost' try: robot = Robot(host) r = robot embed() finally: if "robot" in dir(): robot.close()

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python-urx

python-urx-master/tools/fakerobot.py

import socket import threading import socketserver import time class RequestHandler(socketserver.BaseRequestHandler): #def __init__(self, *args, **kwargs): #print(self, *args, **kwargs) #print("Got connection from {}".format( self.client_address[0]) ) #socketserver.BaseRequestHandler.__init__(self, *args, **kwargs) def handle(self): while True: data = str(self.request.recv(1024), 'ascii') cur_thread = threading.current_thread() print("{} received {} from {}".format(cur_thread.name, data, self.client_address) ) if data == "": return #when this methods returns, the connection to the client closes def setup(self): print("Got new connection from {}".format( self.client_address) ) self.server.handlers.append(self) def finish(self): print("Connection from {} lost".format( self.client_address) ) self.server.handlers.remove(self) class Server(socketserver.ThreadingMixIn, socketserver.TCPServer): def init(self): """ __init__ should not be overriden """ self.handlers = [] def close(self): for handler in self.handlers: handler.request.shutdown(socket.SHUT_RDWR) handler.request.close() self.shutdown() class FakeRobot(object): def run(self): host = "localhost" port = 30002 server = Server((host, port), RequestHandler) server.init() server_thread = threading.Thread(target=server.serve_forever) server_thread.daemon = True server_thread.start() print("Fake Universal robot running at ", host, port) try: f = open("packet.bin", "rb") packet = f.read() f.close() while True: time.sleep(0.09) #The real robot published data 10 times a second for handler in server.handlers: handler.request.sendall(packet) finally: print("Shutting down server") server.close() if __name__ == "__main__": r = FakeRobot() r.run()

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python-urx

python-urx-master/examples/test_movep.py

import time import urx import logging if __name__ == "__main__": rob = urx.Robot("192.168.1.6") try: l = 0.1 v = 0.07 a = 0.1 r = 0.05 pose = rob.getl() pose[2] += l rob.movep(pose, acc=a, vel=v, radius=r, wait=False) while True: p = rob.getl(wait=True) if p[2] > pose[2] - 0.05: break pose[1] += l rob.movep(pose, acc=a, vel=v, radius=r, wait=False) while True: p = rob.getl(wait=True) if p[1] > pose[1] - 0.05: break pose[2] -= l rob.movep(pose, acc=a, vel=v, radius=r, wait=False) while True: p = rob.getl(wait=True) if p[2] < pose[2] + 0.05: break pose[1] -= l rob.movep(pose, acc=a, vel=v, radius=0, wait=True) finally: rob.close()

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python-urx

python-urx-master/examples/get_robot.py

import urx from IPython import embed import logging if __name__ == "__main__": try: logging.basicConfig(level=logging.INFO) #robot = urx.Robot("192.168.1.6") robot = urx.Robot("192.168.1.100") #robot = urx.Robot("localhost") r = robot print("Robot object is available as robot or r") embed() finally: robot.close()

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python-urx

python-urx-master/examples/simple.py

import urx import logging if __name__ == "__main__": logging.basicConfig(level=logging.WARN) rob = urx.Robot("192.168.1.100") #rob = urx.Robot("localhost") rob.set_tcp((0,0,0,0,0,0)) rob.set_payload(0.5, (0,0,0)) try: l = 0.05 v = 0.05 a = 0.3 pose = rob.getl() print("robot tcp is at: ", pose) print("absolute move in base coordinate ") pose[2] += l rob.movel(pose, acc=a, vel=v) print("relative move in base coordinate ") rob.translate((0, 0, -l), acc=a, vel=v) print("relative move back and forth in tool coordinate") rob.translate_tool((0, 0, -l), acc=a, vel=v) rob.translate_tool((0, 0, l), acc=a, vel=v) finally: rob.close()

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python-urx

python-urx-master/examples/get_realtime_data.py

import urx import time import logging r = urx.Robot("192.168.111.134", use_rt=True, urFirm=5.1) if __name__ == "__main__": logging.basicConfig(level=logging.INFO) while 1: try: j_temp = r.get_joint_temperature() j_voltage = r.get_joint_voltage() j_current = r.get_joint_current() main_voltage = r.get_main_voltage() robot_voltage = r.get_robot_voltage() robot_current = r.get_robot_current() print("JOINT TEMPERATURE") print(j_temp) print("JOINT VOLTAGE") print(j_voltage) print("JOINT CURRENT") print(j_current) print("MAIN VOLTAGE") print(main_voltage) print("ROBOT VOLTAGE") print(robot_voltage) print("ROBOT CURRENT") print(robot_current) print("##########\t##########\t##########\t##########") time.sleep(1) except: pass r.close()

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python-urx

python-urx-master/examples/matrices.py

from math import pi import urx import logging if __name__ == "__main__": rob = urx.Robot("192.168.1.100") #rob = urx.Robot("localhost") rob.set_tcp((0,0,0,0,0,0)) rob.set_payload(0.5, (0,0,0)) try: l = 0.05 v = 0.05 a = 0.3 j = rob.getj() print("Initial joint configuration is ", j) t = rob.get_pose() print("Transformation from base to tcp is: ", t) print("Translating in x") rob.translate((l, 0, 0), acc=a, vel=v) pose = rob.getl() print("robot tcp is at: ", pose) print("moving in z") pose[2] += l rob.movel(pose, acc=a, vel=v) print("Translate in -x and rotate") t.orient.rotate_zb(pi/4) t.pos[0] -= l rob.set_pose(t, vel=v, acc=a) print("Sending robot back to original position") rob.movej(j, acc=0.8, vel=0.2) finally: rob.close()

937

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python-urx

python-urx-master/examples/spnav_control.py

from __future__ import division import spnav import time import math3d as m3d from math import pi import urx class Cmd(object): def __init__(self): self.reset() def reset(self): self.x = 0 self.y = 0 self.z = 0 self.rx = 0 self.ry = 0 self.rz = 0 self.btn0 = 0 self.btn1 = 0 def get_speeds(self): return [self.x, self.y, self.z, self.rx, self.ry, self.rz] class Service(object): def __init__(self, robot): self.robot = robot self.lin_coef = 5000 self.rot_coef = 5000 def loop(self): ts = 0 btn0_state = 0 btn_event = None cmd = Cmd() while True: time.sleep(0.01) cmd.reset() #spnav.spnav_remove_events(spnav.SPNAV_EVENT_ANY) # seems broken event = spnav.spnav_poll_event() if event: if type(event) is spnav.SpnavButtonEvent: btn_event = event if event.bnum == 0: btn0_state = event.press elif type(event) is spnav.SpnavMotionEvent: if abs(event.translation[0]) > 30: cmd.y = event.translation[0] / self.lin_coef if abs(event.translation[1]) > 30: cmd.z = -1 * event.translation[1] / self.lin_coef if abs(event.translation[2]) > 30: cmd.x = event.translation[2] / self.lin_coef if abs(event.rotation[0]) > 20: cmd.ry = event.rotation[0] / self.lin_coef if abs(event.rotation[1]) > 20: cmd.rz = -1 * event.rotation[1] / self.lin_coef if abs(event.rotation[2]) > 20: cmd.rx = event.rotation[2] / self.lin_coef if (time.time() - ts) > 0.12: ts = time.time() speeds = cmd.get_speeds() if btn0_state: self.robot.speedl_tool(speeds, acc=0.10, min_time=2) else: self.robot.speedl(speeds, acc=0.10, min_time=2) btn_event = None speeds = cmd.get_speeds() #if speeds != [0 for _ in speeds]: print(event) print("Sending", speeds) if __name__ == '__main__': spnav.spnav_open() robot = urx.Robot("192.168.0.90") #robot = urx.Robot("localhost") robot.set_tcp((0, 0, 0.27, 0, 0, 0)) trx = m3d.Transform() trx.orient.rotate_zb(pi/4) robot.set_csys("mycsys", trx) service = Service(robot) try: service.loop() finally: robot.close() spnav.spnav_close()

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python-urx

python-urx-master/examples/joystick_control.py

""" example program to control a universal robot with a joystick All joysticks are differens, so you will need to modify the script to work with your joystick """ import time import pygame import math3d as m3d from math import pi import urx class Cmd(object): def __init__(self): self.reset() def reset(self): self.axis0 = 0 self.axis1 = 0 self.axis2 = 0 self.axis3 = 0 self.axis4 = 0 self.axis5 = 0 self.btn0 = 0 self.btn1 = 0 self.btn2 = 0 self.btn3 = 0 self.btn4 = 0 self.btn5 = 0 self.btn6 = 0 self.btn7 = 0 self.btn8 = 0 self.btn9 = 0 class Service(object): def __init__(self, robot, linear_velocity=0.1, rotational_velocity=0.1, acceleration=0.1): self.joystick = None self.robot = robot #max velocity and acceleration to be send to robot self.linear_velocity = linear_velocity self.rotational_velocity = rotational_velocity self.acceleration = acceleration #one button send the robot to a preprogram position defined by this variable in join space self.init_pose = [-2.0782002408411593, -1.6628931459654561, 2.067930303382134, -1.9172217394630149, 1.5489023943220621, 0.6783171005488982] self.cmd = Cmd() def init_joystick(self): pygame.init() self.joystick = pygame.joystick.Joystick(0) self.joystick.init() print('Initialized Joystick : %s' % self.joystick.get_name()) def loop(self): print("Starting loop") air = False while True: self.cmd.reset() pygame.event.pump()#Seems we need polling in pygame... #get joystick state for i in range(0, self.joystick.get_numaxes()): val = self.joystick.get_axis(i) if i in (2, 5) and val != 0: val += 1 if abs(val) < 0.2: val = 0 tmp = "self.cmd.axis" + str(i) + " = " + str(val) if val != 0: print(tmp) exec(tmp) #get button state for i in range(0, self.joystick.get_numbuttons()): if self.joystick.get_button(i) != 0: tmp = "self.cmd.btn" + str(i) + " = 1" print(tmp) exec(tmp) if self.cmd.btn0: #toggle IO air = not air self.robot.set_digital_out(2, air) if self.cmd.btn9: print("moving to init pose") self.robot.movej(self.init_pose, acc=1, vel=0.1) #initalize speed array to 0 speeds = [0, 0, 0, 0, 0, 0] #get linear speed from joystick speeds[0] = -1 * self.cmd.axis0 * self.linear_velocity speeds[1] = self.cmd.axis1 * self.linear_velocity if self.cmd.btn4 and not self.cmd.axis2: speeds[2] = -self.linear_velocity if self.cmd.axis2 and not self.cmd.btn4: speeds[2] = self.cmd.axis2 * self.linear_velocity #get rotational speed from joystick speeds[4] = -1 * self.cmd.axis3 * self.rotational_velocity speeds[3] = -1 * self.cmd.axis4 * self.rotational_velocity if self.cmd.btn5 and not self.cmd.axis5: speeds[5] = self.rotational_velocity if self.cmd.axis5 and not self.cmd.btn5: speeds[5] = self.cmd.axis5 * -self.rotational_velocity #for some reasons everything is inversed speeds = [-i for i in speeds] #Now sending to robot. tol by default and base csys if btn2 is on if speeds != [0 for _ in speeds]: print("Sending ", speeds) if self.cmd.btn7: self.robot.speedl_tool(speeds, acc=self.acceleration, min_time=2) else: self.robot.speedl(speeds, acc=self.acceleration, min_time=2) def close(self): if self.joystick: self.joystick.quit() if __name__ == "__main__": robot = urx.Robot("192.168.1.100") r = robot #start joystick service with given max speed and acceleration service = Service(robot, linear_velocity=0.1, rotational_velocity=0.1, acceleration=0.1) service.init_joystick() try: service.loop() finally: robot.close() service.close()

4,580

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147

py

python-urx

python-urx-master/examples/test_all.py

""" Testing script that runs many of the urx methods, while attempting to keep robot pose around its starting pose """ from math import pi import time import sys import urx import logging if sys.version_info[0] < 3: # support python v2 input = raw_input def wait(): if do_wait: print("Click enter to continue") input() if __name__ == "__main__": logging.basicConfig(level=logging.INFO) do_wait = True if len(sys.argv) > 1: do_wait = False rob = urx.Robot("192.168.1.100") #rob = urx.Robot("localhost") rob.set_tcp((0, 0, 0, 0, 0, 0)) rob.set_payload(0.5, (0, 0, 0)) try: l = 0.05 v = 0.05 a = 0.3 r = 0.01 print("Digital out 0 and 1 are: ", rob.get_digital_out(0), rob.get_digital_out(1)) print("Analog inputs are: ", rob.get_analog_inputs()) initj = rob.getj() print("Initial joint configuration is ", initj) t = rob.get_pose() print("Transformation from base to tcp is: ", t) print("Translating in x") wait() rob.translate((l, 0, 0), acc=a, vel=v) pose = rob.getl() print("robot tcp is at: ", pose) print("moving in z") wait() pose[2] += l rob.movel(pose, acc=a, vel=v, wait=False) print("Waiting 2s for end move") time.sleep(2) print("Moving through several points with a radius") wait() pose[0] -= l p1 = pose[:] pose[2] -= l p2 = pose[:] rob.movels([p1, p2], vel=v, acc=a, radius=r) print("rotate tcp around around base z ") wait() t.orient.rotate_zb(pi / 8) rob.set_pose(t, vel=v, acc=a) print("moving in tool z") wait() rob.translate_tool((0, 0, l), vel=v, acc=a) print("moving in tool -z using speed command") wait() rob.speedl_tool((0, 0, -v, 0, 0, 0), acc=a, min_time=3) print("Waiting 2 seconds2") time.sleep(2) print("stop robot") rob.stopj() print("Test movec") wait() pose = rob.get_pose() via = pose.copy() via.pos[0] += l to = via.copy() to.pos[1] += l rob.movec(via, to, acc=a, vel=v) print("Sending robot back to original position") rob.movej(initj, acc=0.8, vel=0.2) finally: rob.close()

2,420

24.484211

110

py

python-urx

python-urx-master/docs/conf.py

# -*- coding: utf-8 -*- # # Python URx documentation build configuration file, created by # sphinx-quickstart on Mon May 11 21:37:43 2015. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os import shlex # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.coverage', 'sphinx.ext.viewcode', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'Python URx' copyright = u'2015, Olivier Roulet-Dubonnet' author = u'Olivier Roulet-Dubonnet' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '1.0' # The full version, including alpha/beta/rc tags. release = '1.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'alabaster' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' #html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # Now only 'ja' uses this config value #html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. #html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = 'PythonURxdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', # Latex figure (float) alignment #'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'PythonURx.tex', u'Python URx Documentation', u'Olivier Roulet-Dubonnet', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'pythonurx', u'Python URx Documentation', [author], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'PythonURx', u'Python URx Documentation', author, 'PythonURx', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False

9,300

31.183391

79

py

python-urx

python-urx-master/urx/urrtmon.py

''' Module for implementing a UR controller real-time monitor over socket port 30003. Confer http://support.universal-robots.com/Technical/RealTimeClientInterface Note: The packet lenght given in the web-page is 740. What is actually received from the controller is 692. It is assumed that the motor currents, the last group of 48 bytes, are not send. Originally Written by Morten Lind Parsing for Firmware 5.9 is added by Byeongdu Lee ''' import logging import socket import struct import time import threading from copy import deepcopy import numpy as np import math3d as m3d __author__ = "Morten Lind, Olivier Roulet-Dubonnet" __copyright__ = "Copyright 2011, NTNU/SINTEF Raufoss Manufacturing AS" __credits__ = ["Morten Lind, Olivier Roulet-Dubonnet"] __license__ = "LGPLv3" class URRTMonitor(threading.Thread): # Struct for revision of the UR controller giving 692 bytes rtstruct692 = struct.Struct('>d6d6d6d6d6d6d6d6d18d6d6d6dQ') # for revision of the UR controller giving 540 byte. Here TCP # pose is not included! rtstruct540 = struct.Struct('>d6d6d6d6d6d6d6d6d18d') rtstruct5_1 = struct.Struct('>d1d6d6d6d6d6d6d6d6d6d6d6d6d6d6d1d6d1d1d1d6d1d6d3d6d1d1d1d1d1d1d1d6d1d1d3d3d') rtstruct5_9 = struct.Struct('>d6d6d6d6d6d6d6d6d6d6d6d6d6d6d1d6d1d1d1d6d1d6d3d6d1d1d1d1d1d1d1d6d1d1d3d3d1d') def __init__(self, urHost, urFirm=None): threading.Thread.__init__(self) self.logger = logging.getLogger(self.__class__.__name__) self.daemon = True self._stop_event = True self._dataEvent = threading.Condition() self._dataAccess = threading.Lock() self._rtSock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self._rtSock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) self._urHost = urHost self.urFirm = urFirm # Package data variables self._timestamp = None self._ctrlTimestamp = None self._qActual = None self._qTarget = None self._tcp = None self._tcp_force = None self._joint_temperature = None self._joint_voltage = None self._joint_current = None self._main_voltage = None self._robot_voltage = None self._robot_current = None self._qdTarget = None self._qddTarget = None self._iTarget = None self._mTarget = None self._qdActual = None self._tcp_speed = None self._tcp = None self._robot_mode = None self._joint_modes = None self._digital_outputs = None self._program_state = None self._safety_status = None self.__recvTime = 0 self._last_ctrl_ts = 0 # self._last_ts = 0 self._buffering = False self._buffer_lock = threading.Lock() self._buffer = [] self._csys = None self._csys_lock = threading.Lock() def set_csys(self, csys): with self._csys_lock: self._csys = csys def __recv_bytes(self, nBytes): ''' Facility method for receiving exactly "nBytes" bytes from the robot connector socket.''' # Record the time of arrival of the first of the stream block recvTime = 0 pkg = b'' while len(pkg) < nBytes: pkg += self._rtSock.recv(nBytes - len(pkg)) if recvTime == 0: recvTime = time.time() self.__recvTime = recvTime return pkg def wait(self): with self._dataEvent: self._dataEvent.wait() def q_actual(self, wait=False, timestamp=False): """ Get the actual joint position vector.""" if wait: self.wait() with self._dataAccess: if timestamp: return self._timestamp, self._qActual else: return self._qActual getActual = q_actual def qd_actual(self, wait=False, timestamp=False): """ Get the actual joint velocity vector.""" if wait: self.wait() with self._dataAccess: if timestamp: return self._timestamp, self._qdActual else: return self._qdActual def q_target(self, wait=False, timestamp=False): """ Get the target joint position vector.""" if wait: self.wait() with self._dataAccess: if timestamp: return self._timestamp, self._qTarget else: return self._qTarget getTarget = q_target def tcf_pose(self, wait=False, timestamp=False, ctrlTimestamp=False): """ Return the tool pose values.""" if wait: self.wait() with self._dataAccess: tcf = self._tcp if ctrlTimestamp or timestamp: ret = [tcf] if timestamp: ret.insert(-1, self._timestamp) if ctrlTimestamp: ret.insert(-1, self._ctrlTimestamp) return ret else: return tcf getTCF = tcf_pose def tcf_force(self, wait=False, timestamp=False): """ Get the tool force. The returned tool force is a six-vector of three forces and three moments.""" if wait: self.wait() with self._dataAccess: # tcf = self._fwkin(self._qActual) tcf_force = self._tcp_force if timestamp: return self._timestamp, tcf_force else: return tcf_force getTCFForce = tcf_force def joint_temperature(self, wait=False, timestamp=False): """ Get the joint temperature.""" if wait: self.wait() with self._dataAccess: joint_temperature = self._joint_temperature if timestamp: return self._timestamp, joint_temperature else: return joint_temperature getJOINTTemperature = joint_temperature def joint_voltage(self, wait=False, timestamp=False): """ Get the joint voltage.""" if wait: self.wait() with self._dataAccess: joint_voltage = self._joint_voltage if timestamp: return self._timestamp, joint_voltage else: return joint_voltage getJOINTVoltage = joint_voltage def joint_current(self, wait=False, timestamp=False): """ Get the joint current.""" if wait: self.wait() with self._dataAccess: joint_current = self._joint_current if timestamp: return self._timestamp, joint_current else: return joint_current getJOINTCurrent = joint_current def main_voltage(self, wait=False, timestamp=False): """ Get the Safety Control Board: Main voltage.""" if wait: self.wait() with self._dataAccess: main_voltage = self._main_voltage if timestamp: return self._timestamp, main_voltage else: return main_voltage getMAINVoltage = main_voltage def robot_voltage(self, wait=False, timestamp=False): """ Get the Safety Control Board: Robot voltage (48V).""" if wait: self.wait() with self._dataAccess: robot_voltage = self._robot_voltage if timestamp: return self._timestamp, robot_voltage else: return robot_voltage getROBOTVoltage = robot_voltage def robot_current(self, wait=False, timestamp=False): """ Get the Safety Control Board: Robot current.""" if wait: self.wait() with self._dataAccess: robot_current = self._robot_current if timestamp: return self._timestamp, robot_current else: return robot_current getROBOTCurrent = robot_current def __recv_rt_data(self): head = self.__recv_bytes(4) # Record the timestamp for this logical package timestamp = self.__recvTime pkgsize = struct.unpack('>i', head)[0] self.logger.debug( 'Received header telling that package is %s bytes long', pkgsize) payload = self.__recv_bytes(pkgsize - 4) if self.urFirm is not None: if self.urFirm == 5.1: unp = self.rtstruct5_1.unpack(payload[:self.rtstruct5_1.size]) if self.urFirm == 5.9: unp = self.rtstruct5_9.unpack(payload[:self.rtstruct5_9.size]) else: if pkgsize >= 692: unp = self.rtstruct692.unpack(payload[:self.rtstruct692.size]) elif pkgsize >= 540: unp = self.rtstruct540.unpack(payload[:self.rtstruct540.size]) else: self.logger.warning( 'Error, Received packet of length smaller than 540: %s ', pkgsize) return with self._dataAccess: self._timestamp = timestamp # it seems that packet often arrives packed as two... maybe TCP_NODELAY is not set on UR controller?? # if (self._timestamp - self._last_ts) > 0.010: # self.logger.warning("Error the we did not receive a packet for {}s ".format( self._timestamp - self._last_ts)) # self._last_ts = self._timestamp self._ctrlTimestamp = np.array(unp[0]) if self._last_ctrl_ts != 0 and ( self._ctrlTimestamp - self._last_ctrl_ts) > 0.010: self.logger.warning( "Error the controller failed to send us a packet: time since last packet %s s ", self._ctrlTimestamp - self._last_ctrl_ts) self._last_ctrl_ts = self._ctrlTimestamp self._qActual = np.array(unp[31:37]) self._qdActual = np.array(unp[37:43]) self._qTarget = np.array(unp[1:7]) self._tcp_force = np.array(unp[67:73]) self._tcp = np.array(unp[73:79]) self._joint_current = np.array(unp[43:49]) if self.urFirm >= 3.1: self._joint_temperature = np.array(unp[86:92]) self._joint_voltage = np.array(unp[124:130]) self._main_voltage = unp[121] self._robot_voltage = unp[122] self._robot_current = unp[123] if self.urFirm>= 5.9: self._qdTarget = np.array(unp[7:13]) self._qddTarget = np.array(unp[13:19]) self._iTarget = np.array(unp[19:25]) self._mTarget = np.array(unp[25:31]) self._tcp_speed = np.array(unp[61:67]) self._joint_current = np.array(unp[49:55]) self._joint_voltage = np.array(unp[124:130]) self._robot_mode = unp[94] self._joint_modes = np.array(unp[95:101]) self._digital_outputs = unp[130] self._program_state = unp[131] self._safety_status = unp[138] if self._csys: with self._csys_lock: # might be a godd idea to remove dependancy on m3d tcp = self._csys.inverse * m3d.Transform(self._tcp) self._tcp = tcp.pose_vector if self._buffering: with self._buffer_lock: self._buffer.append( (self._timestamp, self._ctrlTimestamp, self._tcp, self._qActual)) # FIXME use named arrays of allow to configure what data to buffer with self._dataEvent: self._dataEvent.notifyAll() def start_buffering(self): """ start buffering all data from controller """ self._buffer = [] self._buffering = True def stop_buffering(self): self._buffering = False def try_pop_buffer(self): """ return oldest value in buffer """ with self._buffer_lock: if len(self._buffer) > 0: return self._buffer.pop(0) else: return None def pop_buffer(self): """ return oldest value in buffer """ while True: with self._buffer_lock: if len(self._buffer) > 0: return self._buffer.pop(0) time.sleep(0.001) def get_buffer(self): """ return a copy of the entire buffer """ with self._buffer_lock: return deepcopy(self._buffer) def get_all_data(self, wait=True): """ return all data parsed from robot as a dict """ if wait: self.wait() with self._dataAccess: return dict( timestamp=self._timestamp, ctrltimestamp=self._ctrlTimestamp, qActual=self._qActual, qTarget=self._qTarget, qdActual=self._qdActual, qdTarget=self._qdTarget, tcp=self._tcp, tcp_force=self._tcp_force, tcp_speed=self._tcp_speed, joint_temperature=self._joint_temperature, joint_voltage=self._joint_voltage, joint_current=self._joint_current, joint_modes=self._joint_modes, robot_modes=self._robot_mode, main_voltage=self._main_voltage, robot_voltage=self._robot_voltage, robot_current=self._robot_current, digital_outputs=self._digital_outputs, program_state=self._program_state, safety_status=self._safety_status) getALLData = get_all_data def stop(self): # print(self.__class__.__name__+': Stopping') self._stop_event = True def close(self): self.stop() self.join() def run(self): self._stop_event = False self._rtSock.connect((self._urHost, 30003)) while not self._stop_event: self.__recv_rt_data() self._rtSock.close()

14,305

34.410891

124

py

python-urx

python-urx-master/urx/robot.py

""" Python library to control an UR robot through its TCP/IP interface DOC LINK http://support.universal-robots.com/URRobot/RemoteAccess """ import math3d as m3d import numpy as np from urx.urrobot import URRobot __author__ = "Olivier Roulet-Dubonnet" __copyright__ = "Copyright 2011-2016, Sintef Raufoss Manufacturing" __license__ = "LGPLv3" class Robot(URRobot): """ Generic Python interface to an industrial robot. Compared to the URRobot class, this class adds the possibilty to work directly with matrices and includes support for setting a reference coordinate system """ def __init__(self, host, use_rt=False, urFirm=None): URRobot.__init__(self, host, use_rt, urFirm) self.csys = m3d.Transform() def _get_lin_dist(self, target): pose = URRobot.getl(self, wait=True) target = m3d.Transform(target) pose = m3d.Transform(pose) return pose.dist(target) def set_tcp(self, tcp): """ set robot flange to tool tip transformation """ if isinstance(tcp, m3d.Transform): tcp = tcp.pose_vector URRobot.set_tcp(self, tcp) def set_csys(self, transform): """ Set reference coordinate system to use """ self.csys = transform def set_orientation(self, orient, acc=0.01, vel=0.01, wait=True, threshold=None): """ set tool orientation using a orientation matric from math3d or a orientation vector """ if not isinstance(orient, m3d.Orientation): orient = m3d.Orientation(orient) trans = self.get_pose() trans.orient = orient self.set_pose(trans, acc, vel, wait=wait, threshold=threshold) def translate_tool(self, vect, acc=0.01, vel=0.01, wait=True, threshold=None): """ move tool in tool coordinate, keeping orientation """ t = m3d.Transform() if not isinstance(vect, m3d.Vector): vect = m3d.Vector(vect) t.pos += vect return self.add_pose_tool(t, acc, vel, wait=wait, threshold=threshold) def back(self, z=0.05, acc=0.01, vel=0.01): """ move in z tool """ self.translate_tool((0, 0, -z), acc=acc, vel=vel) def set_pos(self, vect, acc=0.01, vel=0.01, wait=True, threshold=None): """ set tool to given pos, keeping constant orientation """ if not isinstance(vect, m3d.Vector): vect = m3d.Vector(vect) trans = m3d.Transform(self.get_orientation(), m3d.Vector(vect)) return self.set_pose(trans, acc, vel, wait=wait, threshold=threshold) def movec(self, pose_via, pose_to, acc=0.01, vel=0.01, wait=True, threshold=None): """ Move Circular: Move to position (circular in tool-space) see UR documentation """ pose_via = self.csys * m3d.Transform(pose_via) pose_to = self.csys * m3d.Transform(pose_to) pose = URRobot.movec(self, pose_via.pose_vector, pose_to.pose_vector, acc=acc, vel=vel, wait=wait, threshold=threshold) if pose is not None: return self.csys.inverse * m3d.Transform(pose) def set_pose(self, trans, acc=0.01, vel=0.01, wait=True, command="movel", threshold=None): """ move tcp to point and orientation defined by a transformation UR robots have several move commands, by default movel is used but it can be changed using the command argument """ self.logger.debug("Setting pose to %s", trans.pose_vector) t = self.csys * trans pose = URRobot.movex(self, command, t.pose_vector, acc=acc, vel=vel, wait=wait, threshold=threshold) if pose is not None: return self.csys.inverse * m3d.Transform(pose) def add_pose_base(self, trans, acc=0.01, vel=0.01, wait=True, command="movel", threshold=None): """ Add transform expressed in base coordinate """ pose = self.get_pose() return self.set_pose(trans * pose, acc, vel, wait=wait, command=command, threshold=threshold) def add_pose_tool(self, trans, acc=0.01, vel=0.01, wait=True, command="movel", threshold=None): """ Add transform expressed in tool coordinate """ pose = self.get_pose() return self.set_pose(pose * trans, acc, vel, wait=wait, command=command, threshold=threshold) def get_pose(self, wait=False, _log=True): """ get current transform from base to to tcp """ pose = URRobot.getl(self, wait, _log) trans = self.csys.inverse * m3d.Transform(pose) if _log: self.logger.debug("Returning pose to user: %s", trans.pose_vector) return trans def get_orientation(self, wait=False): """ get tool orientation in base coordinate system """ trans = self.get_pose(wait) return trans.orient def get_pos(self, wait=False): """ get tool tip pos(x, y, z) in base coordinate system """ trans = self.get_pose(wait) return trans.pos def speedl(self, velocities, acc, min_time): """ move at given velocities until minimum min_time seconds """ v = self.csys.orient * m3d.Vector(velocities[:3]) w = self.csys.orient * m3d.Vector(velocities[3:]) vels = np.concatenate((v.array, w.array)) return self.speedx("speedl", vels, acc, min_time) def speedj(self, velocities, acc, min_time): """ move at given joint velocities until minimum min_time seconds """ return self.speedx("speedj", velocities, acc, min_time) def speedl_tool(self, velocities, acc, min_time): """ move at given velocities in tool csys until minimum min_time seconds """ pose = self.get_pose() v = pose.orient * m3d.Vector(velocities[:3]) w = pose.orient * m3d.Vector(velocities[3:]) self.speedl(np.concatenate((v.array, w.array)), acc, min_time) def movex(self, command, pose, acc=0.01, vel=0.01, wait=True, relative=False, threshold=None): """ Send a move command to the robot. since UR robotene have several methods this one sends whatever is defined in 'command' string """ t = m3d.Transform(pose) if relative: return self.add_pose_base(t, acc, vel, wait=wait, command=command, threshold=threshold) else: return self.set_pose(t, acc, vel, wait=wait, command=command, threshold=threshold) def movexs(self, command, pose_list, acc=0.01, vel=0.01, radius=0.01, wait=True, threshold=None): """ Concatenate several movex commands and applies a blending radius pose_list is a list of pose. This method is usefull since any new command from python to robot make the robot stop """ new_poses = [] for pose in pose_list: t = self.csys * m3d.Transform(pose) pose = t.pose_vector new_poses.append(pose) return URRobot.movexs(self, command, new_poses, acc, vel, radius, wait=wait, threshold=threshold) def movel_tool(self, pose, acc=0.01, vel=0.01, wait=True, threshold=None): """ move linear to given pose in tool coordinate """ return self.movex_tool("movel", pose, acc=acc, vel=vel, wait=wait, threshold=threshold) def movex_tool(self, command, pose, acc=0.01, vel=0.01, wait=True, threshold=None): t = m3d.Transform(pose) self.add_pose_tool(t, acc, vel, wait=wait, command=command, threshold=threshold) def getl(self, wait=False, _log=True): """ return current transformation from tcp to current csys """ t = self.get_pose(wait, _log) return t.pose_vector.tolist() def set_gravity(self, vector): if isinstance(vector, m3d.Vector): vector = vector.list return URRobot.set_gravity(self, vector) def new_csys_from_xpy(self): """ Restores and returns new coordinate system calculated from three points: X, Origin, Y based on math3d: Transform.new_from_xyp """ # Set coord. sys. to 0 self.csys = m3d.Transform() print("A new coordinate system will be defined from the next three points") print("Firs point is X, second Origin, third Y") print("Set it as a new reference by calling myrobot.set_csys(new_csys)") input("Move to first point and click Enter") # we do not use get_pose so we avoid rounding values pose = URRobot.getl(self) print("Introduced point defining X: {}".format(pose[:3])) px = m3d.Vector(pose[:3]) input("Move to second point and click Enter") pose = URRobot.getl(self) print("Introduced point defining Origo: {}".format(pose[:3])) p0 = m3d.Vector(pose[:3]) input("Move to third point and click Enter") pose = URRobot.getl(self) print("Introduced point defining Y: {}".format(pose[:3])) py = m3d.Vector(pose[:3]) new_csys = m3d.Transform.new_from_xyp(px - p0, py - p0, p0) self.set_csys(new_csys) return new_csys @property def x(self): return self.get_pos().x @x.setter def x(self, val): p = self.get_pos() p.x = val self.set_pos(p) @property def y(self): return self.get_pos().y @y.setter def y(self, val): p = self.get_pos() p.y = val self.set_pos(p) @property def z(self): return self.get_pos().z @z.setter def z(self, val): p = self.get_pos() p.z = val self.set_pos(p) @property def rx(self): return 0 @rx.setter def rx(self, val): p = self.get_pose() p.orient.rotate_xb(val) self.set_pose(p) @property def ry(self): return 0 @ry.setter def ry(self, val): p = self.get_pose() p.orient.rotate_yb(val) self.set_pose(p) @property def rz(self): return 0 @rz.setter def rz(self, val): p = self.get_pose() p.orient.rotate_zb(val) self.set_pose(p) @property def x_t(self): return 0 @x_t.setter def x_t(self, val): t = m3d.Transform() t.pos.x += val self.add_pose_tool(t) @property def y_t(self): return 0 @y_t.setter def y_t(self, val): t = m3d.Transform() t.pos.y += val self.add_pose_tool(t) @property def z_t(self): return 0 @z_t.setter def z_t(self, val): t = m3d.Transform() t.pos.z += val self.add_pose_tool(t) @property def rx_t(self): return 0 @rx_t.setter def rx_t(self, val): t = m3d.Transform() t.orient.rotate_xb(val) self.add_pose_tool(t) @property def ry_t(self): return 0 @ry_t.setter def ry_t(self, val): t = m3d.Transform() t.orient.rotate_yb(val) self.add_pose_tool(t) @property def rz_t(self): return 0 @rz_t.setter def rz_t(self, val): t = m3d.Transform() t.orient.rotate_zb(val) self.add_pose_tool(t)

11,388

29.94837

127

py

python-urx

python-urx-master/urx/robotiq_two_finger_gripper.py

#! /usr/bin/env python """ Python library to control Robotiq Two Finger Gripper connected to UR robot via Python-URX Tested using a UR5 Version CB3 and Robotiq 2-Finger Gripper Version 85 SETUP You must install the driver first and then power on the gripper from the gripper UI. The driver can be found here: http://support.robotiq.com/pages/viewpage.action?pageId=5963876 FAQ Q: Why does this class group all the commands together and run them as a single program as opposed to running each line seperately (like most of URX)? A: The gripper is controlled by connecting to the robot's computer (TCP/IP) and then communicating with the gripper via a socket (127.0.0.1:63352). The scope of the socket is at the program level. It will be automatically closed whenever a program finishes. Therefore it's important that we run all commands as a single program. DOCUMENTATION - This code was developed by downloading the gripper package "DCU-1.0.10" from http://support.robotiq.com/pages/viewpage.action?pageId=5963876. Or more directly from http://support.robotiq.com/download/attachments/5963876/DCU-1.0.10.zip - The file robotiq_2f_gripper_programs_CB3/rq_script.script was referenced to create this class FUTURE FEATURES Though I haven't developed it yet, if you look in robotiq_2f_gripper_programs_CB3/advanced_template_test.script and view function "rq_get_var" there is an example of how to determine the current state of the gripper and if it's holding an object. """ # noqa import logging import os import time from urx.urscript import URScript # Gripper Variables ACT = "ACT" GTO = "GTO" ATR = "ATR" ARD = "ARD" FOR = "FOR" SPE = "SPE" OBJ = "OBJ" STA = "STA" FLT = "FLT" POS = "POS" SOCKET_HOST = "127.0.0.1" SOCKET_PORT = 63352 SOCKET_NAME = "gripper_socket" class RobotiqScript(URScript): def __init__(self, socket_host=SOCKET_HOST, socket_port=SOCKET_PORT, socket_name=SOCKET_NAME): self.socket_host = socket_host self.socket_port = socket_port self.socket_name = socket_name super(RobotiqScript, self).__init__() # Reset connection to gripper self._socket_close(self.socket_name) self._socket_open(self.socket_host, self.socket_port, self.socket_name) def _import_rq_script(self): dir_path = os.path.dirname(os.path.realpath(__file__)) rq_script = os.path.join(dir_path, 'rq_script.script') with open(rq_script, 'rb') as f: rq_script = f.read() self.add_header_to_program(rq_script) def _rq_get_var(self, var_name, nbytes): self._socket_send_string("GET {}".format(var_name)) self._socket_read_byte_list(nbytes) def _get_gripper_fault(self): self._rq_get_var(FLT, 2) def _get_gripper_object(self): self._rq_get_var(OBJ, 1) def _get_gripper_status(self): self._rq_get_var(STA, 1) def _set_gripper_activate(self): self._socket_set_var(GTO, 1, self.socket_name) def _set_gripper_force(self, value): """ FOR is the variable range is 0 - 255 0 is no force 255 is full force """ value = self._constrain_unsigned_char(value) self._socket_set_var(FOR, value, self.socket_name) def _set_gripper_position(self, value): """ SPE is the variable range is 0 - 255 0 is no speed 255 is full speed """ value = self._constrain_unsigned_char(value) self._socket_set_var(POS, value, self.socket_name) def _set_gripper_speed(self, value): """ SPE is the variable range is 0 - 255 0 is no speed 255 is full speed """ value = self._constrain_unsigned_char(value) self._socket_set_var(SPE, value, self.socket_name) def _set_robot_activate(self): self._socket_set_var(ACT, 1, self.socket_name) class Robotiq_Two_Finger_Gripper(object): def __init__(self, robot, payload=0.85, speed=255, force=50, socket_host=SOCKET_HOST, socket_port=SOCKET_PORT, socket_name=SOCKET_NAME): self.robot = robot self.payload = payload self.speed = speed self.force = force self.socket_host = socket_host self.socket_port = socket_port self.socket_name = socket_name self.logger = logging.getLogger(u"robotiq") def _get_new_urscript(self): """ Set up a new URScript to communicate with gripper """ urscript = RobotiqScript(socket_host=self.socket_host, socket_port=self.socket_port, socket_name=self.socket_name) # Set input and output voltage ranges urscript._set_analog_inputrange(0, 0) urscript._set_analog_inputrange(1, 0) urscript._set_analog_inputrange(2, 0) urscript._set_analog_inputrange(3, 0) urscript._set_analog_outputdomain(0, 0) urscript._set_analog_outputdomain(1, 0) urscript._set_tool_voltage(0) urscript._set_runstate_outputs() # Set payload, speed and force urscript._set_payload(self.payload) urscript._set_gripper_speed(self.speed) urscript._set_gripper_force(self.force) # Initialize the gripper urscript._set_robot_activate() urscript._set_gripper_activate() # Wait on activation to avoid USB conflicts urscript._sleep(0.1) return urscript def gripper_action(self, value): """ Activate the gripper to a given value from 0 to 255 0 is open 255 is closed """ urscript = self._get_new_urscript() # Move to the position sleep = 2.0 urscript._set_gripper_position(value) urscript._sleep(sleep) # Send the script self.robot.send_program(urscript()) # sleep the code the same amount as the urscript to ensure that # the action completes time.sleep(sleep) def open_gripper(self): self.gripper_action(0) def close_gripper(self): self.gripper_action(255)

6,377

28.391705

86

py

python-urx

python-urx-master/urx/__init__.py

""" Python library to control an UR robot through its TCP/IP interface """ from urx.urrobot import RobotException, URRobot # noqa __version__ = "0.11.0" try: from urx.robot import Robot except ImportError as ex: print("Exception while importing math3d base robot, disabling use of matrices", ex) Robot = URRobot

327

24.230769

87

py

python-urx

python-urx-master/urx/urscript.py

#! /usr/bin/env python import logging # Controller Settings CONTROLLER_PORTS = [0, 1] CONTROLLER_VOLTAGE = [ 0, # 0-5V 2, # 0-10V ] # Tool Settings TOOL_PORTS = [2, 3] TOOL_VOLTAGE = [ 0, # 0-5V 1, # 0-10V 2, # 4-20mA ] OUTPUT_DOMAIN_VOLTAGE = [ 0, # 4-20mA 1, # 0-10V ] class URScript(object): def __init__(self): self.logger = logging.getLogger(u"urscript") # The header is code that is before and outside the myProg() method self.header = "" # The program is code inside the myProg() method self.program = "" def __call__(self): if(self.program == ""): self.logger.debug(u"urscript program is empty") return "" # Construct the program myprog = """def myProg():{}\nend""".format(self.program) # Construct the full script script = "" if self.header: script = "{}\n\n".format(self.header) script = "{}{}".format(script, myprog) return script def reset(self): self.header = "" self.program = "" def add_header_to_program(self, header_line): self.header = "{}\n{}".format(self.header, header_line) def add_line_to_program(self, new_line): self.program = "{}\n\t{}".format(self.program, new_line) def _constrain_unsigned_char(self, value): """ Ensure that unsigned char values are constrained to between 0 and 255. """ assert(isinstance(value, int)) if value < 0: value = 0 elif value > 255: value = 255 return value def _set_analog_inputrange(self, port, vrange): if port in CONTROLLER_PORTS: assert(vrange in CONTROLLER_VOLTAGE) elif port in TOOL_PORTS: assert(vrange in TOOL_VOLTAGE) msg = "set_analog_inputrange({},{})".format(port, vrange) self.add_line_to_program(msg) def _set_analog_output(self, input_id, signal_level): assert(input_id in [0, 1]) assert(signal_level in [0, 1]) msg = "set_analog_output({}, {})".format(input_id, signal_level) self.add_line_to_program(msg) def _set_analog_outputdomain(self, port, domain): assert(domain in OUTPUT_DOMAIN_VOLTAGE) msg = "set_analog_outputdomain({},{})".format(port, domain) self.add_line_to_program(msg) def _set_payload(self, mass, cog=None): msg = "set_payload({}".format(mass) if cog: assert(len(cog) == 3) msg = "{},{}".format(msg, cog) msg = "{})".format(msg) self.add_line_to_program(msg) def _set_runstate_outputs(self, outputs=None): if not outputs: outputs = [] msg = "set_runstate_outputs({})".format(outputs) self.add_line_to_program(msg) def _set_tool_voltage(self, voltage): assert(voltage in [0, 12, 24]) msg = "set_tool_voltage({})".format(voltage) self.add_line_to_program(msg) def _sleep(self, value): msg = "sleep({})".format(value) self.add_line_to_program(msg) def _socket_close(self, socket_name): msg = "socket_close(\"{}\")".format(socket_name) self.add_line_to_program(msg) def _socket_get_var(self, var, socket_name): msg = "socket_get_var(\"{}\",\"{}\")".format(var, socket_name) self.add_line_to_program(msg) self._sync() def _socket_open(self, socket_host, socket_port, socket_name): msg = "socket_open(\"{}\",{},\"{}\")".format(socket_host, socket_port, socket_name) self.add_line_to_program(msg) def _socket_read_byte_list(self, nbytes, socket_name): msg = "global var_value = socket_read_byte_list({},\"{}\")".format(nbytes, socket_name) # noqa self.add_line_to_program(msg) self._sync() def _socket_send_string(self, message, socket_name): msg = "socket_send_string(\"{}\",\"{}\")".format(message, socket_name) # noqa self.add_line_to_program(msg) self._sync() def _socket_set_var(self, var, value, socket_name): msg = "socket_set_var(\"{}\",{},\"{}\")".format(var, value, socket_name) # noqa self.add_line_to_program(msg) self._sync() def _socket_get_var2var(self, var, varout, socket_name, prefix = ''): msg = "{}{} = socket_get_var(\"{}\",\"{}\")".format(prefix, varout, var, socket_name) self.add_line_to_program(msg) def _socket_send_byte(self, byte, socket_name): msg = "socket_send_byte(\"{}\",\"{}\")".format(str(byte), socket_name) # noqa self.add_line_to_program(msg) self._sync() def _sync(self): msg = "sync()" self.add_line_to_program(msg)

4,900

30.216561

103

py

python-urx

python-urx-master/urx/urrobot.py

""" Python library to control an UR robot through its TCP/IP interface Documentation from universal robots: http://support.universal-robots.com/URRobot/RemoteAccess """ import logging import numbers try: from collections.abc import Sequence except ImportError: from collections import Sequence from urx import urrtmon from urx import ursecmon __author__ = "Olivier Roulet-Dubonnet" __copyright__ = "Copyright 2011-2015, Sintef Raufoss Manufacturing" __license__ = "LGPLv3" class RobotException(Exception): pass class URRobot(object): """ Python interface to socket interface of UR robot. programs are send to port 3002 data is read from secondary interface(10Hz?) and real-time interface(125Hz) (called Matlab interface in documentation) Since parsing the RT interface uses som CPU, and does not support all robots versions, it is disabled by default The RT interfaces is only used for the get_force related methods Rmq: A program sent to the robot i executed immendiatly and any running program is stopped """ def __init__(self, host, use_rt=False, urFirm=None): self.logger = logging.getLogger("urx") self.host = host self.urFirm = urFirm self.csys = None self.logger.debug("Opening secondary monitor socket") self.secmon = ursecmon.SecondaryMonitor(self.host) # data from robot at 10Hz self.rtmon = None if use_rt: self.rtmon = self.get_realtime_monitor() # precision of joint movem used to wait for move completion # the value must be conservative! otherwise we may wait forever self.joinEpsilon = 0.01 # It seems URScript is limited in the character length of floats it accepts self.max_float_length = 6 # FIXME: check max length!!! self.secmon.wait() # make sure we get data from robot before letting clients access our methods def __repr__(self): return "Robot Object (IP=%s, state=%s)" % (self.host, self.secmon.get_all_data()["RobotModeData"]) def __str__(self): return self.__repr__() def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() def is_running(self): """ Return True if robot is running (not necessary running a program, it might be idle) """ return self.secmon.running def is_program_running(self): """ check if program is running. Warning!!!!!: After sending a program it might take several 10th of a second before the robot enters the running state """ return self.secmon.is_program_running() def send_program(self, prog): """ send a complete program using urscript to the robot the program is executed immediatly and any runnning program is interrupted """ self.logger.info("Sending program: " + prog) self.secmon.send_program(prog) def get_tcp_force(self, wait=True): """ return measured force in TCP if wait==True, waits for next packet before returning """ return self.rtmon.getTCFForce(wait) def get_force(self, wait=True): """ length of force vector returned by get_tcp_force if wait==True, waits for next packet before returning """ tcpf = self.get_tcp_force(wait) force = 0 for i in tcpf: force += i**2 return force**0.5 def get_joint_temperature(self, wait=True): """ return measured joint temperature if wait==True, waits for next packet before returning """ return self.rtmon.getJOINTTemperature(wait) def get_joint_voltage(self, wait=True): """ return measured joint voltage if wait==True, waits for next packet before returning """ return self.rtmon.getJOINTVoltage(wait) def get_joint_current(self, wait=True): """ return measured joint current if wait==True, waits for next packet before returning """ return self.rtmon.getJOINTCurrent(wait) def get_main_voltage(self, wait=True): """ return measured Safety Control Board: Main voltage if wait==True, waits for next packet before returning """ return self.rtmon.getMAINVoltage(wait) def get_robot_voltage(self, wait=True): """ return measured Safety Control Board: Robot voltage (48V) if wait==True, waits for next packet before returning """ return self.rtmon.getROBOTVoltage(wait) def get_robot_current(self, wait=True): """ return measured Safety Control Board: Robot current if wait==True, waits for next packet before returning """ return self.rtmon.getROBOTCurrent(wait) def get_all_rt_data(self, wait=True): """ return all data parsed from robot real-time interace as a dict if wait==True, waits for next packet before returning """ return self.rtmon.getALLData(wait) def set_tcp(self, tcp): """ set robot flange to tool tip transformation """ prog = "set_tcp(p[{}, {}, {}, {}, {}, {}])".format(*tcp) self.send_program(prog) def set_payload(self, weight, cog=None): """ set payload in Kg cog is a vector x,y,z if cog is not specified, then tool center point is used """ if cog: cog = list(cog) cog.insert(0, weight) prog = "set_payload({}, ({},{},{}))".format(*cog) else: prog = "set_payload(%s)" % weight self.send_program(prog) def set_gravity(self, vector): """ set direction of gravity """ prog = "set_gravity(%s)" % list(vector) self.send_program(prog) def send_message(self, msg): """ send message to the GUI log tab on the robot controller """ prog = "textmsg(%s)" % msg self.send_program(prog) def set_digital_out(self, output, val): """ set digital output. val is a bool """ if val in (True, 1): val = "True" else: val = "False" self.send_program('digital_out[%s]=%s' % (output, val)) def get_analog_inputs(self): """ get analog input """ return self.secmon.get_analog_inputs() def get_analog_in(self, nb, wait=False): """ get analog input """ return self.secmon.get_analog_in(nb, wait=wait) def get_digital_in_bits(self): """ get digital output """ return self.secmon.get_digital_in_bits() def get_digital_in(self, nb, wait=False): """ get digital output """ return self.secmon.get_digital_in(nb, wait) def get_digital_out(self, val, wait=False): """ get digital output """ return self.secmon.get_digital_out(val, wait=wait) def get_digital_out_bits(self, wait=False): """ get digital output as a byte """ return self.secmon.get_digital_out_bits(wait=wait) def set_analog_out(self, output, val): """ set analog output, val is a float """ prog = "set_analog_out(%s, %s)" % (output, val) self.send_program(prog) def set_tool_voltage(self, val): """ set voltage to be delivered to the tool, val is 0, 12 or 24 """ prog = "set_tool_voltage(%s)" % (val) self.send_program(prog) def _wait_for_move(self, target, threshold=None, timeout=5, joints=False): """ wait for a move to complete. Unfortunately there is no good way to know when a move has finished so for every received data from robot we compute a dist equivalent and when it is lower than 'threshold' we return. if threshold is not reached within timeout, an exception is raised """ self.logger.debug("Waiting for move completion using threshold %s and target %s", threshold, target) start_dist = self._get_dist(target, joints) if threshold is None: threshold = start_dist * 0.8 if threshold < 0.001: # roboten precision is limited threshold = 0.001 self.logger.debug("No threshold set, setting it to %s", threshold) count = 0 while True: if not self.is_running(): raise RobotException("Robot stopped") dist = self._get_dist(target, joints) self.logger.debug("distance to target is: %s, target dist is %s", dist, threshold) if not self.secmon.is_program_running(): if dist < threshold: self.logger.debug("we are threshold(%s) close to target, move has ended", threshold) return count += 1 if count > timeout * 10: raise RobotException("Goal not reached but no program has been running for {} seconds. dist is {}, threshold is {}, target is {}, current pose is {}".format(timeout, dist, threshold, target, URRobot.getl(self))) else: count = 0 def _get_dist(self, target, joints=False): if joints: return self._get_joints_dist(target) else: return self._get_lin_dist(target) def _get_lin_dist(self, target): # FIXME: we have an issue here, it seems sometimes the axis angle received from robot pose = URRobot.getl(self, wait=True) dist = 0 for i in range(3): dist += (target[i] - pose[i]) ** 2 for i in range(3, 6): dist += ((target[i] - pose[i]) / 5) ** 2 # arbitraty length like return dist ** 0.5 def _get_joints_dist(self, target): joints = self.getj(wait=True) dist = 0 for i in range(6): dist += (target[i] - joints[i]) ** 2 return dist ** 0.5 def getj(self, wait=False): """ get joints position """ jts = self.secmon.get_joint_data(wait) return [jts["q_actual0"], jts["q_actual1"], jts["q_actual2"], jts["q_actual3"], jts["q_actual4"], jts["q_actual5"]] def speedx(self, command, velocities, acc, min_time): vels = [round(i, self.max_float_length) for i in velocities] vels.append(acc) vels.append(min_time) prog = "{}([{},{},{},{},{},{}], {}, {})".format(command, *vels) self.send_program(prog) def movej(self, joints, acc=0.1, vel=0.05, wait=True, relative=False, threshold=None): """ move in joint space """ if relative: l = self.getj() joints = [v + l[i] for i, v in enumerate(joints)] prog = self._format_move("movej", joints, acc, vel) self.send_program(prog) if wait: self._wait_for_move(joints[:6], threshold=threshold, joints=True) return self.getj() def movel(self, tpose, acc=0.01, vel=0.01, wait=True, relative=False, threshold=None): """ Send a movel command to the robot. See URScript documentation. """ return self.movex("movel", tpose, acc=acc, vel=vel, wait=wait, relative=relative, threshold=threshold) def movep(self, tpose, acc=0.01, vel=0.01, wait=True, relative=False, threshold=None): """ Send a movep command to the robot. See URScript documentation. """ return self.movex("movep", tpose, acc=acc, vel=vel, wait=wait, relative=relative, threshold=threshold) def servoc(self, tpose, acc=0.01, vel=0.01, wait=True, relative=False, threshold=None): """ Send a servoc command to the robot. See URScript documentation. """ return self.movex("servoc", tpose, acc=acc, vel=vel, wait=wait, relative=relative, threshold=threshold) def servoj(self, tjoints, acc=0.01, vel=0.01, t=0.1, lookahead_time=0.2, gain=100, wait=True, relative=False, threshold=None): """ Send a servoj command to the robot. See URScript documentation. """ if relative: l = self.getj() tjoints = [v + l[i] for i, v in enumerate(tjoints)] prog = self._format_servo("servoj", tjoints, acc=acc, vel=vel, t=t, lookahead_time=lookahead_time, gain=gain) self.send_program(prog) if wait: self._wait_for_move(tjoints[:6], threshold=threshold, joints=True) return self.getj() def _format_servo(self, command, tjoints, acc=0.01, vel=0.01, t=0.1, lookahead_time=0.2, gain=100, prefix=""): tjoints = [round(i, self.max_float_length) for i in tjoints] tjoints.append(acc) tjoints.append(vel) tjoints.append(t) tjoints.append(lookahead_time) tjoints.append(gain) return "{}({}[{},{},{},{},{},{}], a={}, v={}, t={}, lookahead_time={}, gain={})".format(command, prefix, *tjoints) def _format_move(self, command, tpose, acc, vel, radius=0, prefix=""): tpose = [round(i, self.max_float_length) for i in tpose] tpose.append(acc) tpose.append(vel) tpose.append(radius) return "{}({}[{},{},{},{},{},{}], a={}, v={}, r={})".format(command, prefix, *tpose) def movex(self, command, tpose, acc=0.01, vel=0.01, wait=True, relative=False, threshold=None): """ Send a move command to the robot. since UR robotene have several methods this one sends whatever is defined in 'command' string """ if relative: l = self.getl() tpose = [v + l[i] for i, v in enumerate(tpose)] prog = self._format_move(command, tpose, acc, vel, prefix="p") self.send_program(prog) if wait: self._wait_for_move(tpose[:6], threshold=threshold) return self.getl() def getl(self, wait=False, _log=True): """ get TCP position """ pose = self.secmon.get_cartesian_info(wait) if pose: pose = [pose["X"], pose["Y"], pose["Z"], pose["Rx"], pose["Ry"], pose["Rz"]] if _log: self.logger.debug("Received pose from robot: %s", pose) return pose def movec(self, pose_via, pose_to, acc=0.01, vel=0.01, wait=True, threshold=None): """ Move Circular: Move to position (circular in tool-space) see UR documentation """ pose_via = [round(i, self.max_float_length) for i in pose_via] pose_to = [round(i, self.max_float_length) for i in pose_to] prog = "movec(p%s, p%s, a=%s, v=%s, r=%s)" % (pose_via, pose_to, acc, vel, "0") self.send_program(prog) if wait: self._wait_for_move(pose_to, threshold=threshold) return self.getl() def movejs(self, joint_positions_list, acc=0.01, vel=0.01, radius=0.01, wait=True, threshold=None): """ Concatenate several movej commands and applies a blending radius joint_positions_list is a list of joint_positions. This method is usefull since any new command from python to robot make the robot stop """ return URRobot.movexs(self, "movej", joint_positions_list, acc, vel, radius, wait, threshold=threshold) def movels(self, pose_list, acc=0.01, vel=0.01, radius=0.01, wait=True, threshold=None): """ Concatenate several movel commands and applies a blending radius pose_list is a list of pose. This method is usefull since any new command from python to robot make the robot stop """ return self.movexs("movel", pose_list, acc, vel, radius, wait, threshold=threshold) def movexs(self, command, pose_list, acc=0.01, vel=0.01, radius=0.01, wait=True, threshold=None): """ Concatenate several movex commands and applies a blending radius pose_list is a list of pose. This method is usefull since any new command from python to robot make the robot stop """ header = "def myProg():\n" end = "end\n" prog = header # Check if 'vel' is a single number or a sequence. if isinstance(vel, numbers.Number): # Make 'vel' a sequence vel = len(pose_list) * [vel] elif not isinstance(vel, Sequence): raise RobotException( 'movexs: "vel" must be a single number or a sequence!') # Check for adequate number of speeds if len(vel) != len(pose_list): raise RobotException( 'movexs: "vel" must be a number or a list ' + 'of numbers the same length as "pose_list"!') # Check if 'radius' is a single number. if isinstance(radius, numbers.Number): # Make 'radius' a sequence radius = len(pose_list) * [radius] elif not isinstance(radius, Sequence): raise RobotException( 'movexs: "radius" must be a single number or a sequence!') # Ensure that last pose a stopping pose. radius[-1] = 0.0 # Require adequate number of radii. if len(radius) != len(pose_list): raise RobotException( 'movexs: "radius" must be a number or a list ' + 'of numbers the same length as "pose_list"!') prefix = '' if command in ['movel', 'movec']: prefix = 'p' for idx, pose in enumerate(pose_list): prog += self._format_move(command, pose, acc, vel[idx], radius[idx], prefix=prefix) + "\n" prog += end self.send_program(prog) if wait: if command == 'movel': self._wait_for_move(target=pose_list[-1], threshold=threshold, joints=False) elif command == 'movej': self._wait_for_move(target=pose_list[-1], threshold=threshold, joints=True) return self.getl() def stopl(self, acc=0.5): self.send_program("stopl(%s)" % acc) def stopj(self, acc=1.5): self.send_program("stopj(%s)" % acc) def stop(self): self.stopj() def close(self): """ close connection to robot and stop internal thread """ self.logger.info("Closing sockets to robot") self.secmon.close() if self.rtmon: self.rtmon.stop() def set_freedrive(self, val, timeout=60): """ set robot in freedrive/backdrive mode where an operator can jog the robot to wished pose. Freedrive will timeout at 60 seconds. """ if val: self.send_program("def myProg():\n\tfreedrive_mode()\n\tsleep({})\nend".format(timeout)) else: # This is a non-existant program, but running it will stop freedrive self.send_program("def myProg():\n\tend_freedrive_mode()\nend") def set_simulation(self, val): if val: self.send_program("set sim") else: self.send_program("set real") def get_realtime_monitor(self): """ return a pointer to the realtime monitor object usefull to track robot position for example """ if not self.rtmon: self.logger.info("Opening real-time monitor socket") self.rtmon = urrtmon.URRTMonitor(self.host, self.urFirm) # som information is only available on rt interface self.rtmon.start() self.rtmon.set_csys(self.csys) return self.rtmon def translate(self, vect, acc=0.01, vel=0.01, wait=True, command="movel"): """ move tool in base coordinate, keeping orientation """ p = self.getl() p[0] += vect[0] p[1] += vect[1] p[2] += vect[2] return self.movex(command, p, vel=vel, acc=acc, wait=wait) def up(self, z=0.05, acc=0.01, vel=0.01): """ Move up in csys z """ p = self.getl() p[2] += z self.movel(p, acc=acc, vel=vel) def down(self, z=0.05, acc=0.01, vel=0.01): """ Move down in csys z """ self.up(-z, acc, vel)

20,532

35.086116

231

py

python-urx

python-urx-master/urx/ursecmon.py

""" This file contains 2 classes: - ParseUtils containing utilies to parse data from UR robot - SecondaryMonitor, a class opening a socket to the robot and with methods to access data and send programs to the robot Both use data from the secondary port of the URRobot. Only the last connected socket on 3001 is the primary client !!!! So do not rely on it unless you know no other client is running (Hint the UR java interface is a client...) http://support.universal-robots.com/Technical/PrimaryAndSecondaryClientInterface """ from threading import Thread, Condition, Lock import logging import struct import socket from copy import copy import time __author__ = "Olivier Roulet-Dubonnet" __copyright__ = "Copyright 2011-2013, Sintef Raufoss Manufacturing" __credits__ = ["Olivier Roulet-Dubonnet"] __license__ = "LGPLv3" class ParsingException(Exception): def __init__(self, *args): Exception.__init__(self, *args) class Program(object): def __init__(self, prog): self.program = prog self.condition = Condition() def __str__(self): return "Program({})".format(self.program) __repr__ = __str__ class TimeoutException(Exception): def __init__(self, *args): Exception.__init__(self, *args) class ParserUtils(object): def __init__(self): self.logger = logging.getLogger("ursecmon") self.version = (0, 0) def parse(self, data): """ parse a packet from the UR socket and return a dictionary with the data """ allData = {} # print "Total size ", len(data) while data: psize, ptype, pdata, data = self.analyze_header(data) # print "We got packet with size %i and type %s" % (psize, ptype) if ptype == 16: allData["SecondaryClientData"] = self._get_data(pdata, "!iB", ("size", "type")) data = (pdata + data)[5:] # This is the total size so we resend data to parser elif ptype == 0: # this parses RobotModeData for versions >=3.0 (i.e. 3.0) if psize == 38: self.version = (3, 0) allData['RobotModeData'] = self._get_data(pdata, "!IBQ???????BBdd", ("size", "type", "timestamp", "isRobotConnected", "isRealRobotEnabled", "isPowerOnRobot", "isEmergencyStopped", "isSecurityStopped", "isProgramRunning", "isProgramPaused", "robotMode", "controlMode", "speedFraction", "speedScaling")) elif psize == 46: # It's 46 bytes in 3.2 self.version = (3, 2) allData['RobotModeData'] = self._get_data(pdata, "!IBQ???????BBdd", ("size", "type", "timestamp", "isRobotConnected", "isRealRobotEnabled", "isPowerOnRobot", "isEmergencyStopped", "isSecurityStopped", "isProgramRunning", "isProgramPaused", "robotMode", "controlMode", "speedFraction", "speedScaling", "speedFractionLimit")) elif psize == 47: self.version = (3, 5) allData['RobotModeData'] = self._get_data(pdata, "!IBQ???????BBddc", ("size", "type", "timestamp", "isRobotConnected", "isRealRobotEnabled", "isPowerOnRobot", "isEmergencyStopped", "isSecurityStopped", "isProgramRunning", "isProgramPaused", "robotMode", "controlMode", "speedFraction", "speedScaling", "speedFractionLimit", "reservedByUR")) else: allData["RobotModeData"] = self._get_data(pdata, "!iBQ???????Bd", ("size", "type", "timestamp", "isRobotConnected", "isRealRobotEnabled", "isPowerOnRobot", "isEmergencyStopped", "isSecurityStopped", "isProgramRunning", "isProgramPaused", "robotMode", "speedFraction")) elif ptype == 1: tmpstr = ["size", "type"] for i in range(0, 6): tmpstr += ["q_actual%s" % i, "q_target%s" % i, "qd_actual%s" % i, "I_actual%s" % i, "V_actual%s" % i, "T_motor%s" % i, "T_micro%s" % i, "jointMode%s" % i] allData["JointData"] = self._get_data(pdata, "!iB dddffffB dddffffB dddffffB dddffffB dddffffB dddffffB", tmpstr) elif ptype == 4: if self.version < (3, 2): allData["CartesianInfo"] = self._get_data(pdata, "iBdddddd", ("size", "type", "X", "Y", "Z", "Rx", "Ry", "Rz")) else: allData["CartesianInfo"] = self._get_data(pdata, "iBdddddddddddd", ("size", "type", "X", "Y", "Z", "Rx", "Ry", "Rz", "tcpOffsetX", "tcpOffsetY", "tcpOffsetZ", "tcpOffsetRx", "tcpOffsetRy", "tcpOffsetRz")) elif ptype == 5: allData["LaserPointer(OBSOLETE)"] = self._get_data(pdata, "iBddd", ("size", "type")) elif ptype == 3: if self.version >= (3, 0): fmt = "iBiibbddbbddffffBBb" # firmware >= 3.0 else: fmt = "iBhhbbddbbddffffBBb" # firmware < 3.0 allData["MasterBoardData"] = self._get_data(pdata, fmt, ("size", "type", "digitalInputBits", "digitalOutputBits", "analogInputRange0", "analogInputRange1", "analogInput0", "analogInput1", "analogInputDomain0", "analogInputDomain1", "analogOutput0", "analogOutput1", "masterBoardTemperature", "robotVoltage48V", "robotCurrent", "masterIOCurrent")) # , "masterSafetyState" ,"masterOnOffState", "euromap67InterfaceInstalled" )) elif ptype == 2: allData["ToolData"] = self._get_data(pdata, "iBbbddfBffB", ("size", "type", "analoginputRange2", "analoginputRange3", "analogInput2", "analogInput3", "toolVoltage48V", "toolOutputVoltage", "toolCurrent", "toolTemperature", "toolMode")) elif ptype == 9: continue # This package has a length of 53 bytes. It is used internally by Universal Robots software only and should be skipped. elif ptype == 8 and self.version >= (3, 2): allData["AdditionalInfo"] = self._get_data(pdata, "iB??", ("size", "type", "teachButtonPressed", "teachButtonEnabled")) elif ptype == 7 and self.version >= (3, 2): allData["ForceModeData"] = self._get_data(pdata, "iBddddddd", ("size", "type", "x", "y", "z", "rx", "ry", "rz", "robotDexterity")) # elif ptype == 8: # allData["varMessage"] = self._get_data(pdata, "!iBQbb iiBAcAc", ("size", "type", "timestamp", "source", "robotMessageType", "code", "argument", "titleSize", "messageTitle", "messageText")) # elif ptype == 7: # allData["keyMessage"] = self._get_data(pdata, "!iBQbb iiBAcAc", ("size", "type", "timestamp", "source", "robotMessageType", "code", "argument", "titleSize", "messageTitle", "messageText")) elif ptype == 20: tmp = self._get_data(pdata, "!iB Qbb", ("size", "type", "timestamp", "source", "robotMessageType")) if tmp["robotMessageType"] == 3: allData["VersionMessage"] = self._get_data(pdata, "!iBQbb bAbBBiAb", ("size", "type", "timestamp", "source", "robotMessageType", "projectNameSize", "projectName", "majorVersion", "minorVersion", "svnRevision", "buildDate")) elif tmp["robotMessageType"] == 6: allData["robotCommMessage"] = self._get_data(pdata, "!iBQbb iiAc", ("size", "type", "timestamp", "source", "robotMessageType", "code", "argument", "messageText")) elif tmp["robotMessageType"] == 1: allData["labelMessage"] = self._get_data(pdata, "!iBQbb iAc", ("size", "type", "timestamp", "source", "robotMessageType", "id", "messageText")) elif tmp["robotMessageType"] == 2: allData["popupMessage"] = self._get_data(pdata, "!iBQbb ??BAcAc", ("size", "type", "timestamp", "source", "robotMessageType", "warning", "error", "titleSize", "messageTitle", "messageText")) elif tmp["robotMessageType"] == 0: allData["messageText"] = self._get_data(pdata, "!iBQbb Ac", ("size", "type", "timestamp", "source", "robotMessageType", "messageText")) elif tmp["robotMessageType"] == 8: allData["varMessage"] = self._get_data(pdata, "!iBQbb iiBAcAc", ("size", "type", "timestamp", "source", "robotMessageType", "code", "argument", "titleSize", "messageTitle", "messageText")) elif tmp["robotMessageType"] == 7: allData["keyMessage"] = self._get_data(pdata, "!iBQbb iiBAcAc", ("size", "type", "timestamp", "source", "robotMessageType", "code", "argument", "titleSize", "messageTitle", "messageText")) elif tmp["robotMessageType"] == 5: allData["keyMessage"] = self._get_data(pdata, "!iBQbb iiAc", ("size", "type", "timestamp", "source", "robotMessageType", "code", "argument", "messageText")) else: self.logger.debug("Message type parser not implemented %s", tmp) else: self.logger.debug("Unknown packet type %s with size %s", ptype, psize) return allData def _get_data(self, data, fmt, names): """ fill data into a dictionary data is data from robot packet fmt is struct format, but with added A for arrays and no support for numerical in fmt names args are strings used to store values """ tmpdata = copy(data) fmt = fmt.strip() # space may confuse us d = dict() i = 0 j = 0 while j < len(fmt) and i < len(names): f = fmt[j] if f in (" ", "!", ">", "<"): j += 1 elif f == "A": # we got an array # first we need to find its size if j == len(fmt) - 2: # we are last element, size is the rest of data in packet arraysize = len(tmpdata) else: # size should be given in last element asn = names[i - 1] if not asn.endswith("Size"): raise ParsingException("Error, array without size ! %s %s" % (asn, i)) else: arraysize = d[asn] d[names[i]] = tmpdata[0:arraysize] # print "Array is ", names[i], d[names[i]] tmpdata = tmpdata[arraysize:] j += 2 i += 1 else: fmtsize = struct.calcsize(fmt[j]) # print "reading ", f , i, j, fmtsize, len(tmpdata) if len(tmpdata) < fmtsize: # seems to happen on windows raise ParsingException("Error, length of data smaller than advertized: ", len(tmpdata), fmtsize, "for names ", names, f, i, j) d[names[i]] = struct.unpack("!" + f, tmpdata[0:fmtsize])[0] # print names[i], d[names[i]] tmpdata = tmpdata[fmtsize:] j += 1 i += 1 return d def get_header(self, data): return struct.unpack("!iB", data[0:5]) def analyze_header(self, data): """ read first 5 bytes and return complete packet """ if len(data) < 5: raise ParsingException("Packet size %s smaller than header size (5 bytes)" % len(data)) else: psize, ptype = self.get_header(data) if psize < 5: raise ParsingException("Error, declared length of data smaller than its own header(5): ", psize) elif psize > len(data): raise ParsingException("Error, length of data smaller (%s) than declared (%s)" % (len(data), psize)) return psize, ptype, data[:psize], data[psize:] def find_first_packet(self, data): """ find the first complete packet in a string returns None if none found """ counter = 0 limit = 10 while True: if len(data) >= 5: psize, ptype = self.get_header(data) if psize < 5 or psize > 2000 or ptype != 16: data = data[1:] counter += 1 if counter > limit: self.logger.warning("tried %s times to find a packet in data, advertised packet size: %s, type: %s", counter, psize, ptype) self.logger.warning("Data length: %s", len(data)) limit = limit * 10 elif len(data) >= psize: self.logger.debug("Got packet with size %s and type %s", psize, ptype) if counter: self.logger.info("Remove %s bytes of garbage at begining of packet", counter) # ok we we have somehting which looks like a packet" return (data[:psize], data[psize:]) else: # packet is not complete self.logger.debug("Packet is not complete, advertised size is %s, received size is %s, type is %s", psize, len(data), ptype) return None else: # self.logger.debug("data smaller than 5 bytes") return None class SecondaryMonitor(Thread): """ Monitor data from secondary port and send programs to robot """ def __init__(self, host): Thread.__init__(self) self.logger = logging.getLogger("ursecmon") self._parser = ParserUtils() self._dict = {} self._dictLock = Lock() self.host = host secondary_port = 30002 # Secondary client interface on Universal Robots self._s_secondary = socket.create_connection((self.host, secondary_port), timeout=0.5) self._prog_queue = [] self._prog_queue_lock = Lock() self._dataqueue = bytes() self._trystop = False # to stop thread self.running = False # True when robot is on and listening self._dataEvent = Condition() self.lastpacket_timestamp = 0 self.start() try: self.wait() # make sure we got some data before someone calls us except TimeoutException as ex: self.close() raise ex def send_program(self, prog): """ send program to robot in URRobot format If another program is send while a program is running the first program is aborded. """ prog.strip() self.logger.debug("Enqueueing program: %s", prog) if not isinstance(prog, bytes): prog = prog.encode() data = Program(prog + b"\n") with data.condition: with self._prog_queue_lock: self._prog_queue.append(data) data.condition.wait() self.logger.debug("program sendt: %s", data) def run(self): """ check program execution status in the secondary client data packet we get from the robot This interface uses only data from the secondary client interface (see UR doc) Only the last connected client is the primary client, so this is not guaranted and we cannot rely on information to the primary client. """ while not self._trystop: with self._prog_queue_lock: if len(self._prog_queue) > 0: data = self._prog_queue.pop(0) self._s_secondary.send(data.program) with data.condition: data.condition.notify_all() data = self._get_data() try: tmpdict = self._parser.parse(data) with self._dictLock: self._dict = tmpdict except ParsingException as ex: self.logger.warning("Error parsing one packet from urrobot: %s", ex) continue if "RobotModeData" not in self._dict: self.logger.warning("Got a packet from robot without RobotModeData, strange ...") continue self.lastpacket_timestamp = time.time() rmode = 0 if self._parser.version >= (3, 0): rmode = 7 if self._dict["RobotModeData"]["robotMode"] == rmode \ and self._dict["RobotModeData"]["isRealRobotEnabled"] is True \ and self._dict["RobotModeData"]["isEmergencyStopped"] is False \ and self._dict["RobotModeData"]["isSecurityStopped"] is False \ and self._dict["RobotModeData"]["isRobotConnected"] is True \ and self._dict["RobotModeData"]["isPowerOnRobot"] is True: self.running = True else: if self.running: self.logger.error("Robot not running: " + str(self._dict["RobotModeData"])) self.running = False with self._dataEvent: # print("X: new data") self._dataEvent.notifyAll() def _get_data(self): """ returns something that looks like a packet, nothing is guaranted """ while True: # self.logger.debug("data queue size is: {}".format(len(self._dataqueue))) ans = self._parser.find_first_packet(self._dataqueue[:]) if ans: self._dataqueue = ans[1] # self.logger.debug("found packet of size {}".format(len(ans[0]))) return ans[0] else: # self.logger.debug("Could not find packet in received data") tmp = self._s_secondary.recv(1024) self._dataqueue += tmp def wait(self, timeout=0.5): """ wait for next data packet from robot """ tstamp = self.lastpacket_timestamp with self._dataEvent: self._dataEvent.wait(timeout) if tstamp == self.lastpacket_timestamp: raise TimeoutException("Did not receive a valid data packet from robot in {}".format(timeout)) def get_cartesian_info(self, wait=False): if wait: self.wait() with self._dictLock: if "CartesianInfo" in self._dict: return self._dict["CartesianInfo"] else: return None def get_all_data(self, wait=False): """ return last data obtained from robot in dictionnary format """ if wait: self.wait() with self._dictLock: return self._dict.copy() def get_joint_data(self, wait=False): if wait: self.wait() with self._dictLock: if "JointData" in self._dict: return self._dict["JointData"] else: return None def get_digital_out(self, nb, wait=False): if wait: self.wait() with self._dictLock: output = self._dict["MasterBoardData"]["digitalOutputBits"] mask = 1 << nb if output & mask: return 1 else: return 0 def get_digital_out_bits(self, wait=False): if wait: self.wait() with self._dictLock: return self._dict["MasterBoardData"]["digitalOutputBits"] def get_digital_in(self, nb, wait=False): if wait: self.wait() with self._dictLock: output = self._dict["MasterBoardData"]["digitalInputBits"] mask = 1 << nb if output & mask: return 1 else: return 0 def get_digital_in_bits(self, wait=False): if wait: self.wait() with self._dictLock: return self._dict["MasterBoardData"]["digitalInputBits"] def get_analog_in(self, nb, wait=False): if wait: self.wait() with self._dictLock: return self._dict["MasterBoardData"]["analogInput" + str(nb)] def get_analog_inputs(self, wait=False): if wait: self.wait() with self._dictLock: return self._dict["MasterBoardData"]["analogInput0"], self._dict["MasterBoardData"]["analogInput1"] def is_program_running(self, wait=False): """ return True if robot is executing a program Rmq: The refresh rate is only 10Hz so the information may be outdated """ if wait: self.wait() with self._dictLock: return self._dict["RobotModeData"]["isProgramRunning"] def close(self): self._trystop = True self.join() # with self._dataEvent: #wake up any thread that may be waiting for data before we close. Should we do that? # self._dataEvent.notifyAll() if self._s_secondary: with self._prog_queue_lock: self._s_secondary.close()

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CANTM-main/updateTopics_covid.py

import sys from GateMIcateLib import ModelUltiUpdateCAtopic as ModelUlti from GateMIcateLib import BatchIterBert, DictionaryProcess from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor from GateMIcateLib import ScholarPostProcessor as ReaderPostProcessor from GateMIcateLib.readers import WVmisInfoDataIter as dataIter from configobj import ConfigObj from torch.nn import init import argparse import json if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("testReaderargs", help="args for test reader") parser.add_argument("--configFile", help="config files if needed") parser.add_argument("--cachePath", help="save models") parser.add_argument("--randomSeed", type=int, help="randomSeed for reproduction") parser.add_argument("--num_epoches", type=int, default=5, help="num epoches") parser.add_argument("--patient", type=int, default=40, help="early_stop_patient") parser.add_argument("--earlyStopping", default='cls_loss', help="early stopping") parser.add_argument("--corpusType", default='wvmisinfo', help="corpus type, for select reader") parser.add_argument("--x_fields", help="x fileds", default='Claim,Explaination') parser.add_argument("--y_field", help="y filed", default='Label') args = parser.parse_args() testReaderargs = args.testReaderargs.split(',') x_fields = args.x_fields.split(',') config = ConfigObj(args.configFile) mUlti = ModelUlti(load_path=args.cachePath, gpu=True) trainable_weights = ['xy_topics.topic.weight', 'z_y_hidden.hidden1.weight', 'z2y_classifier.layer_output.weight', ] trainable_bias = [ 'xy_topics.topic.bias', 'z_y_hidden.hidden1.bias', 'z2y_classifier.layer_output.bias' ] trainable_no_init = [ 'mu_z2.weight', 'mu_z2.bias', 'log_sigma_z2.weight', 'log_sigma_z2.bias', 'x_y_hidden.hidden1.weight', 'x_y_hidden.hidden1.bias' ] for name, param in mUlti.net.named_parameters(): print(name) if name in trainable_weights: param.requires_grad = True param.data.uniform_(-1.0, 1.0) elif name in trainable_bias: param.requires_grad = True param.data.fill_(0) elif name in trainable_no_init: param.requires_grad = True else: param.requires_grad = False postProcessor = ReaderPostProcessor(config=config, word2id=True, remove_single_list=False, add_spec_tokens=True, x_fields=x_fields, y_field=args.y_field, max_sent_len=300) postProcessor.dictProcess = mUlti.bowdict testDataIter = dataIter(*testReaderargs, label_field=args.y_field, postProcessor=postProcessor, config=config, shuffle=True) testBatchIter = BatchIterBert(testDataIter, filling_last_batch=True, postProcessor=batchPostProcessor, batch_size=32) mUlti.train(testBatchIter, num_epohs=args.num_epoches, cache_path=args.cachePath)

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CANTM-main/getPerpare.py

import os import torch from transformers import * import nltk from pathlib import Path nltk.download('stopwords') nltk.download('punkt') model = BertModel.from_pretrained('bert-base-uncased') tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') script_path = os.path.abspath(__file__) print(script_path) parent = os.path.dirname(script_path) parent = os.path.join(parent, 'bert-base-uncased') print(parent) model_save_path = os.path.join(parent,'model') path = Path(model_save_path) path.mkdir(parents=True, exist_ok=True) model.save_pretrained(model_save_path) tokenizer_save_path = os.path.join(parent,'tokenizer') path = Path(tokenizer_save_path) path.mkdir(parents=True, exist_ok=True) tokenizer.save_pretrained(tokenizer_save_path)

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CANTM-main/raw2folds.py

import json import os import sys import random import math from pathlib import Path import copy def outputChunk(output_dir, chunkIdx, chunks): output_path = os.path.join(output_dir, str(chunkIdx)) path = Path(output_path) path.mkdir(parents=True, exist_ok=True) for i, item in enumerate(chunks): if i == chunkIdx: json_path = os.path.join(output_path, 'test.jsonlist') else: json_path = os.path.join(output_path, 'train.jsonlist') with open(json_path, 'a') as jsout: for line in item: jsout.write(json.dumps(line) + '\n') input_json = sys.argv[1] num_fold = int(sys.argv[2]) output_dir = sys.argv[3] with open(input_json, 'r') as inf: data = json.load(inf) random.shuffle(data) num_data = len(data) chunkSize = math.ceil(num_data/num_fold) chunks = [] for i in range(0, len(data), chunkSize): chunks.append(copy.deepcopy(data[i:i+chunkSize])) for idx, eachchunk in enumerate(chunks): outputChunk(output_dir, idx, chunks)

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CANTM-main/evaluation.py

import argparse import json import os from GateMIcateLib import EvaluationManager if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("trainReaderargs", help="args for train reader") parser.add_argument("--testReaderargs", help="args for test reader") parser.add_argument("--valReaderargs", help="args for val reader") parser.add_argument("--configFile", help="config files if needed") parser.add_argument("--cachePath", help="save models") parser.add_argument("--nFold", type=int, default=5, help="n fold") parser.add_argument("--randomSeed", type=int, help="randomSeed for reproduction") parser.add_argument("--preEmbd", default=False, action='store_true', help="calculate embedding before training") parser.add_argument("--dynamicSampling", default=False, action='store_true', help="sample based on class count") parser.add_argument("--model", default='clsTopic', help="model used for evaluation") parser.add_argument("--num_epoches", type=int, default=200, help="num epoches") parser.add_argument("--patient", type=int, default=4, help="early_stop_patient") parser.add_argument("--max_sent_len", type=int, default=300, help="maximum words in the sentence") parser.add_argument("--earlyStopping", default='cls_loss', help="early stopping") parser.add_argument("--corpusType", default='wvmisinfo', help="corpus type, for select reader") parser.add_argument("--splitValidation", type=float, help="split data from training for validation") parser.add_argument("--inspectTest", default=False, action='store_true', help="inspect testing data performance") parser.add_argument("--x_fields", help="x fileds", default='Claim,Explaination') parser.add_argument("--y_field", help="y filed", default='category') parser.add_argument("--trainOnly", help="only train the model, no split or test", default=False, action='store_true') parser.add_argument("--export_json", help="export json for scholar, need file path") parser.add_argument("--export_doc", help="export doc for npmi, need file path") parser.add_argument("--trainLDA", help="lda test", default=False, action='store_true') args = parser.parse_args() dictargs = vars(args) trainReaderargs = args.trainReaderargs.split(',') if args.testReaderargs: testReaderargs = args.testReaderargs.split(',') else: testReaderargs = None if args.valReaderargs: valReaderargs = args.valReaderargs.split(',') else: valReaderargs = None evaluaton = EvaluationManager(trainReaderargs, dictargs, testReaderargs=testReaderargs, valReaderargs=valReaderargs) if args.export_json: jsonData = evaluaton.get_covid_train_json_for_scholar() with open(args.export_json, 'w') as fo: json.dump(jsonData, fo) elif args.export_doc: all_doc = evaluaton.outputCorpus4NPMI() with open(args.export_doc, 'w') as fo: for item in all_doc: item = item.strip() fo.write(item+'\n') elif args.trainOnly: evaluaton.train_model_only() elif testReaderargs: evaluaton.train_test_evaluation() else: evaluaton.cross_fold_evaluation()

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CANTM-main/updateTopics.py

import sys from GateMIcateLib import ModelUltiUpdateCAtopic as ModelUlti from GateMIcateLib import BatchIterBert, DictionaryProcess from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor from GateMIcateLib import ScholarPostProcessor as ReaderPostProcessor from GateMIcateLib.readers import ACLimdbReader as dataIter from configobj import ConfigObj from torch.nn import init import argparse import json if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("testReaderargs", help="args for test reader") parser.add_argument("--configFile", help="config files if needed") parser.add_argument("--cachePath", help="save models") parser.add_argument("--randomSeed", type=int, help="randomSeed for reproduction") parser.add_argument("--num_epoches", type=int, default=5, help="num epoches") parser.add_argument("--patient", type=int, default=40, help="early_stop_patient") parser.add_argument("--earlyStopping", default='cls_loss', help="early stopping") parser.add_argument("--corpusType", default='wvmisinfo', help="corpus type, for select reader") parser.add_argument("--x_fields", help="x fileds", default='text') parser.add_argument("--y_field", help="y filed", default='selected_label') args = parser.parse_args() testReaderargs = args.testReaderargs.split(',') x_fields = args.x_fields.split(',') config = ConfigObj(args.configFile) mUlti = ModelUlti(load_path=args.cachePath, gpu=True) trainable_weights = ['xy_topics.topic.weight', 'z_y_hidden.hidden1.weight', 'z2y_classifier.layer_output.weight', ] trainable_bias = [ 'xy_topics.topic.bias', 'z_y_hidden.hidden1.bias', 'z2y_classifier.layer_output.bias' ] for name, param in mUlti.net.named_parameters(): if name in trainable_weights: param.requires_grad = True param.data.uniform_(-1.0, 1.0) elif name in trainable_bias: param.requires_grad = True param.data.fill_(0) else: param.requires_grad = False postProcessor = ReaderPostProcessor(config=config, word2id=True, remove_single_list=False, add_spec_tokens=True, x_fields=x_fields, y_field=args.y_field, max_sent_len=510) postProcessor.dictProcess = mUlti.bowdict testDataIter = dataIter(*testReaderargs, postProcessor=postProcessor, config=config, shuffle=True) testBatchIter = BatchIterBert(testDataIter, filling_last_batch=True, postProcessor=batchPostProcessor, batch_size=32) mUlti.train(testBatchIter, num_epohs=args.num_epoches, cache_path=args.cachePath)

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CANTM

CANTM-main/wvCovidData/update_wv_unique_id.py

import json import re import nltk import argparse import copy import os import sys script_path = os.path.abspath(__file__) print(script_path) global parent parent = os.path.dirname(script_path) print(parent) gatePython = os.path.join(parent, 'GATEpythonInterface') sys.path.append(gatePython) from GateInterface import * import string from pathlib import Path parser = argparse.ArgumentParser() parser.add_argument("inputJson", help="args for test reader") parser.add_argument("outputJson", help="args for test reader") args = parser.parse_args() input_json = args.inputJson output_json = args.outputJson with open(input_json, 'r') as fj: raw_data = json.load(fj) doc_id = 0 new_data = [] for item in raw_data: doc_id += 1 item['unique_wv_id'] = str(doc_id) new_data.append(copy.deepcopy(item)) print(doc_id) with open(output_json, 'w') as fo: json.dump(new_data, fo)

902

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CANTM

CANTM-main/wvCovidData/downloadIFCNSource.py

import sys from bs4 import BeautifulSoup import json from selenium import webdriver from selenium.webdriver.firefox.options import Options def download_source(url): options = Options() options.headless = True driver = webdriver.Firefox(options=options) driver.get(url) html = driver.page_source driver.close() soup = BeautifulSoup(html, 'html.parser') factcheck_Org = soup.find('p', attrs={'class':'entry-content__text entry-content__text--org'}).get_text()[17:] date = soup.find('p', attrs={'class':'entry-content__text entry-content__text--topinfo'}).get_text()[0:10] country = soup.find('p', attrs={'class':'entry-content__text entry-content__text--topinfo'}).get_text()[13:] ct = soup.find('h1', attrs={'class':'entry-title'}) for tag in ct.find_all('span'): tag.replaceWith('') claim = ct.get_text() explain = soup.find('p', attrs={'class':'entry-content__text entry-content__text--explanation'}).get_text()[13:] print(claim) print(explain) print(country) print(date) print(factcheck_Org) return claim,explain,country,date,factcheck_Org input_json = sys.argv[1] output_json = sys.argv[2] with open(input_json, 'r') as fin: data = json.load(fin) for i, each_data in enumerate(data): claim,explain,country,date,factcheck_Org = download_source(each_data['Link']) each_data['Claim'] = claim each_data['Explaination'] = explain each_data['Country'] = country each_data['Date'] = date each_data['Factcheck_Org'] = factcheck_Org print(i, len(data)) #break with open(output_json, 'w') as fo: json.dump(data, fo)

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CANTM

CANTM-main/wvCovidData/update_label.py

import json import re import nltk import argparse import copy import os import sys script_path = os.path.abspath(__file__) print(script_path) global parent parent = os.path.dirname(script_path) print(parent) gatePython = os.path.join(parent, 'GATEpythonInterface') sys.path.append(gatePython) from GateInterface import * import string from pathlib import Path parser = argparse.ArgumentParser() parser.add_argument("inputJson", help="args for test reader") parser.add_argument("outputJson", help="args for test reader") args = parser.parse_args() input_json = args.inputJson output_json = args.outputJson label_dict = { 'no_evidence':['NO EVIDENCE', 'No evidence', 'No Evidence', 'no evidence', 'Unproven', 'Unverified'], 'misleading': ['misleading', 'Misleading', 'MISLEADING', 'mislEADING', 'MiSLEADING'], 'false': ['Pants on Fire!', 'False', 'FALSE', 'Not true', 'false and misleading', 'false', 'PANTS ON FIRE', 'Fake news', 'Misleading/False', 'Fake', 'Incorrect'], 'partial_false': ['Partially correct', 'mostly false', 'HALF TRUTH', 'HALF TRUE', 'partly false', 'Mostly true', 'Partly true', 'Mixed', 'half true', 'True but', 'MOSTLY FALSE', 'Partially false', 'PARTLY FALSE', 'Partially true', 'MOSTLY TRUE', 'Partly false', 'Mostly False', 'Partly False', 'PARTLY TRUE', 'Mostly True', 'Half True', 'Mostly false'], } with open(input_json, 'r') as fj: raw_data = json.load(fj) doc_id = 0 new_data = [] for item in raw_data: item_keys = item.keys() print(item_keys) label = item['Label'] label_group = 'other' for each_label_group in label_dict: if label in label_dict[each_label_group]: label_group = each_label_group break doc_id += 1 item['ori_label'] = label item['Label'] = label_group new_data.append(copy.deepcopy(item)) print(doc_id) with open(output_json, 'w') as fo: json.dump(new_data, fo)

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CANTM

CANTM-main/wvCovidData/update_type_media.py

import json import re import nltk import argparse import copy import os import sys script_path = os.path.abspath(__file__) print(script_path) global parent parent = os.path.dirname(script_path) print(parent) gatePython = os.path.join(parent, 'GATEpythonInterface') sys.path.append(gatePython) from GateInterface import * import string from pathlib import Path source_out_folder = os.path.join(parent, 'test_text') Path(source_out_folder).mkdir(parents=True, exist_ok=True) printable = set(string.printable) ignore_explist=[ ['please', 'click', 'the', 'link', 'to', 'read', 'the', 'full', 'article'], ['please', 'read', 'the', 'full', 'article'], ] def check_ori_claim_type(claim_web_ori): media_type = set() image = ['image', 'photo', 'picture', 'images', 'pictures', 'photos', 'infograph'] video = ['video', 'videos', 'television'] audio = ['audio', 'radio'] text = ['text', 'articles', 'article','message', 'messages'] #print(claim_web_ori) for each_tok in claim_web_ori: if each_tok in image: media_type.add('image') if each_tok in video: media_type.add('video') if each_tok in audio: media_type.add('audio') if each_tok in text: media_type.add('text') return list(media_type) class JapeCheck: def __init__(self): self.gate = GateInterFace() self.gate.init() self.checkPipeline = GatePipeline('checkPipeline') self.checkPipeline.loadPipelineFromFile(os.path.join(parent, 'japeCheck.xgapp')) def check_jape(self, doc_name, claim_website): print(doc_name) document = GateDocument() document.loadDocumentFromFile(doc_name) #print(document) testCorpus = GateCorpus('testCorpus') testCorpus.addDocument(document) self.checkPipeline.setCorpus(testCorpus) self.checkPipeline.runPipeline() ats = document.getAnnotations('') media_type = ats.getType('mediaType') #print(media_type.annotationSet) media_type_dict = [] for item in media_type: #print(item.features) media_type_dict.append(item.features) mediaTypeMatchedList, mediaTypeUnMatchedList = self.match_type(media_type_dict, claim_website) loose_media_dict = [] loose_media = ats.getType('looseMedia') for item in loose_media: #print(item.features) loose_media_dict.append(item.features) looseMatchedList, looseUnMatchedList = self.match_type(loose_media_dict, claim_website) document.clearDocument() testCorpus.clearCorpus() return self.select_type(mediaTypeMatchedList, mediaTypeUnMatchedList, looseMatchedList, looseUnMatchedList) def count_match(self, matchedList): current_max_type = None current_max_count = 0 all_possible_types = set(matchedList) for possible_type in all_possible_types: current_possible_count = matchedList.count(possible_type) if current_possible_count > current_max_count: current_max_type = possible_type current_max_count = current_possible_count elif current_possible_count == current_max_count: if possible_type == 'video': current_max_type == 'video' source_media = [current_max_type] return source_media def select_type(self, mediaTypeMatchedList, mediaTypeUnMatchedList, looseMatchedList, looseUnMatchedList): source_media = [] if len(mediaTypeMatchedList) > 0: source_media = mediaTypeMatchedList elif len(looseMatchedList) > 0: source_media = self.count_match(looseMatchedList) #source_media = [looseMatchedList[0]] elif len(mediaTypeUnMatchedList) > 0: source_media = mediaTypeUnMatchedList elif len(looseUnMatchedList) > 0: source_media = self.count_match(looseUnMatchedList) #current_max_type = None #current_max_count = 0 #all_possible_types = set(looseUnMatchedList) #for possible_type in all_possible_types: # current_possible_count = looseUnMatchedList.count(possible_type) # if current_possible_count > current_max_count: # current_max_type = possible_type # current_max_count = current_possible_count #source_media = [current_max_type] return source_media def match_type(self,media_type_dict, webSite_list): matched_list = [] unmatched_list = [] for item in media_type_dict: try: current_web = item['oriWeb'] except: current_web = 'unknown' try: current_media = item['mediaType'] except: current_media = 'unknown_web' #print(current_web, current_media) current_web_list = current_web.split(',') #print(current_web_list) current_media_list_raw = current_media.split(',') current_media_list = [] for media_item in current_media_list_raw: if len(media_item) > 0: current_media_list.append(media_item) web_match = False for current_web_item in current_web_list: if current_web_item in webSite_list: #matched_list.append(current_media) web_match = True break #unmatched_list.append(current_media) if web_match: matched_list += current_media_list else: unmatched_list += current_media_list return matched_list, unmatched_list parser = argparse.ArgumentParser() parser.add_argument("inputJson", help="args for test reader") parser.add_argument("outputJson", help="args for test reader") parser.add_argument("--updateOnly", help="reverse selection criteria", default=False, action='store_true') args = parser.parse_args() input_json = args.inputJson output_json = args.outputJson with open(input_json, 'r') as fj: raw_data = json.load(fj) japeCheck = JapeCheck() num_video_source = 0 doc_id = 0 new_data = [] punct_chars = list(set(string.punctuation)-set("_")) punctuation = ''.join(punct_chars) pun_replace = re.compile('[%s]' % re.escape(punctuation)) for item in raw_data: update = True type_of_media = item['Type_of_media'] claim = item['Claim'] explanation = item['Explaination'] claim = claim.lower() claim = pun_replace.sub(' ', claim) claim_tok = nltk.word_tokenize(claim) explanation = explanation.lower() explanation = pun_replace.sub(' ', explanation) explanation_tok = nltk.word_tokenize(explanation) #print(type_of_media) if args.updateOnly: if len(type_of_media) > 0: update = False else: print(doc_id) update = True #print(update) if update: item_keys = item.keys() #print(item_keys) claim_website = item['Claim_Website'] #print(claim_website) claim_website_ori = item['Claim_web_ori'] if 'p_tag' in item: text_source = item['p_tag'] elif 'Source_PageTextEnglish' in item: text_source = item['Source_PageTextEnglish'] else: #print(doc_id) text_source = item['Source_PageTextOriginal'] source_media = [] if ('youtube' in claim_website) or ('tv' in claim_website) or ('tiktok' in claim_website): num_video_source += 1 source_media = ['video'] else: media_type = check_ori_claim_type(claim_website_ori) claim_media_type = check_ori_claim_type(claim_tok) if explanation_tok not in ignore_explist: exp_media_type = check_ori_claim_type(explanation_tok) else: print('!!!!!!!!', explanation_tok) exp_media_type = [] if len(media_type) > 0: num_video_source += 1 source_media = media_type elif len(claim_media_type) > 0: num_video_source += 1 source_media = claim_media_type #print(source_media, claim_tok) elif len(exp_media_type) > 0: num_video_source += 1 source_media = exp_media_type print(source_media, explanation_tok) else: print(claim_website_ori) text_out = os.path.join(parent, 'test_text') text_out = os.path.join(text_out, str(doc_id)+'.txt') filterd_text_source = ''.join(filter(lambda x: x in printable, text_source)) #print(filterd_text_source) if len(filterd_text_source) > 10: with open(text_out, 'w') as fo: fo.write(filterd_text_source) source_media = japeCheck.check_jape(text_out, claim_website) num_video_source += 1 doc_id += 1 print(source_media) item['Type_of_media'] = list(set(source_media)) new_data.append(copy.deepcopy(item)) print(num_video_source) with open(output_json, 'w') as fo: json.dump(new_data, fo)

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CANTM

CANTM-main/wvCovidData/mergeAnnos.py

import sys from WvLibs import WVdataIter import re from itertools import combinations import argparse import json import logging global logger class Wv_loger: def __init__(self, logging_level): self.logger = logging.getLogger('root') if logging_level == 'info': self.logger.setLevel(logging.INFO) elif logging_level == 'debug': self.logger.setLevel(logging.DEBUG) elif logging_level == 'warning': self.logger.setLevel(logging.WARNING) #self.logger.setLevel(logging.INFO) handler = logging.StreamHandler(sys.stdout) formatter = logging.Formatter('ROOT: %(message)s') handler.setFormatter(formatter) self.logger.addHandler(handler) #self.logger.info('info message') def logging(self, *args, logtype='debug', sep=' '): getattr(self.logger, logtype)(sep.join(str(a) for a in args)) def info(self, *args, sep=' '): getattr(self.logger, 'info')(sep.join(str(a) for a in args)) def debug(self, *args, sep=' '): getattr(self.logger, 'debug')(sep.join(str(a) for a in args)) def warning(self, *args, sep=' '): getattr(self.logger, 'warning')(sep.join(str(a) for a in args)) def clean_string(text): text = text.strip() text = text.replace('\t','') text = text.replace('\n','') return text def getList(dict): list = [] for key in dict.keys(): list.append(key) return list def check_pare(ann1, ann2, agreeDict): combine1 = ann1+'|'+ann2 combine2 = ann2+'|'+ann1 combines = [combine1, combine2] combineInDict = False for combine in combines: if combine in agreeDict: combineInDict = True break return combine, combineInDict def update_disagreement_dict(disagreeLabel1, disagreeLabel2, class_disagree_check_dict): if disagreeLabel1 not in class_disagree_check_dict: class_disagree_check_dict[disagreeLabel1] = {} if disagreeLabel2 not in class_disagree_check_dict[disagreeLabel1]: class_disagree_check_dict[disagreeLabel1][disagreeLabel2] = 0 class_disagree_check_dict[disagreeLabel1][disagreeLabel2] += 1 return class_disagree_check_dict def calAgreement(dataIter, numlabels=11): compare_list = [] pe = 1/numlabels ## get all paires for item in dataIter: tmp_list = [] anno_list = [] for annotation in item['annotations']: label = annotation['label'] confident = annotation['confident'] annotator = annotation['annotator'] if annotator not in anno_list: anno_list.append(annotator) tmp_list.append([annotator, label]) combine_list = list(combinations(tmp_list, 2)) compare_list += combine_list t=0 a=0 agreeDict = {} for compare_pair in compare_list: ann1 = compare_pair[0][0] label1 = compare_pair[0][1] ann2 = compare_pair[1][0] label2 = compare_pair[1][1] t+=1 combine, combineInDict = check_pare(ann1, ann2, agreeDict) if combineInDict: agreeDict[combine]['t'] += 1 else: agreeDict[combine] = {} agreeDict[combine]['t'] = 1 agreeDict[combine]['a'] = 0 agreeDict[combine]['disagree'] = {} agreeDict[combine]['disagree'][ann1] = [] agreeDict[combine]['disagree'][ann2] = [] if label1 == label2: a+=1 agreeDict[combine]['a'] += 1 else: agreeDict[combine]['disagree'][ann1].append(label1) agreeDict[combine]['disagree'][ann2].append(label2) pa = a/t #pe = 1/numlabels #print(pe) overall_kappa = (pa-pe)/(1-pe) logger.warning('overall agreement: ', pa) logger.warning('overall kappa: ', overall_kappa) logger.warning('total pair compareed: ', t) logger.warning('annotator pair agreement kappa num_compared') class_disagree_check_dict = {} for annPair in agreeDict: logger.info('\n') logger.info('============') num_compared = agreeDict[annPair]['t'] cpa = agreeDict[annPair]['a']/agreeDict[annPair]['t'] kappa = (cpa-pe)/(1-pe) logger.info(annPair, cpa, kappa, num_compared) keys = getList(agreeDict[annPair]['disagree']) logger.info(keys) for i in range(len(agreeDict[annPair]['disagree'][keys[0]])): disagreeLabel1 = agreeDict[annPair]['disagree'][keys[0]][i] disagreeLabel2 = agreeDict[annPair]['disagree'][keys[1]][i] logger.info(disagreeLabel1, disagreeLabel2) class_disagree_check_dict = update_disagreement_dict(disagreeLabel1, disagreeLabel2, class_disagree_check_dict) class_disagree_check_dict = update_disagreement_dict(disagreeLabel2, disagreeLabel1, class_disagree_check_dict) logger.info('\n') logger.info('=========================') logger.info('class level disagreement') logger.info('=========================') for item_label in class_disagree_check_dict: logging.info(item_label) logging.info(class_disagree_check_dict[item_label]) logging.info('===================') logging.info('\n') return pa, overall_kappa if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("raw_json_dir", help="Unannotated Json file dir") parser.add_argument("annoed_json_dir", help="Annotated Json file dir") parser.add_argument("merged_json", help="merged json") parser.add_argument("--ignoreLabel", help="ignore label, splited by using ,") parser.add_argument("--ignoreUser", help="ignore user, splited by using ,") parser.add_argument("--min_anno_filter", type=int, default=1, help="min annotation frequence") parser.add_argument("--min_conf_filter", type=int, default=-1, help="min confident") parser.add_argument("--output2csv", help="output to csv") parser.add_argument("--transfer_label", default=None, help="trasfer label to another category, in format: orilabel1:tranlabel1,orilabel2:tranlabel2") parser.add_argument("--cal_agreement", action='store_true',help="calculate annotation agreement") parser.add_argument("--logging_level", default='warning', help="logging level, default warning, other option inlcude info and debug") parser.add_argument("--user_conf", default=None, help="User level confident cutoff threshold, in format: user1:thres1,user2:thres2") parser.add_argument("--set_reverse", default=False, action='store_true', help="reverse the selection condition, to check what discared") #parser.add_argument("--logging_file", default='default.log',help="logging file") args = parser.parse_args() #logger = logging.getLogger() output2csv = None trans_dict = {} raw_json_dir = args.raw_json_dir annoed_json_dir = args.annoed_json_dir merged_json = args.merged_json min_frequence = args.min_anno_filter min_conf = args.min_conf_filter list2ignor = [] user2ignor = [] if args.ignoreLabel: list2ignor = args.ignoreLabel.split(',') if args.ignoreUser: user2ignor = args.ignoreUser.split(',') logging_level = args.logging_level logger = Wv_loger(logging_level) if args.output2csv: output2csv = args.output2csv if args.transfer_label: all_trans_labels = args.transfer_label.split(',') for label_pair in all_trans_labels: tokened_pair = label_pair.split(':') trans_dict[tokened_pair[0]] = tokened_pair[1] user_conf_dict = {} if args.user_conf: all_user_conf = args.user_conf.split(',') for conf_pair in all_user_conf: tokened_pair = conf_pair.split(':') user_conf_dict[tokened_pair[0]] = int(tokened_pair[1]) logger.warning(trans_dict) dataIter = WVdataIter(annoed_json_dir, raw_json_dir, min_anno_filter=min_frequence, ignoreLabelList=list2ignor, ignoreUserList=user2ignor, label_trans_dict=trans_dict, check_validation=False, confThres=min_conf, reverse_selection_condition=args.set_reverse, logging_level=logging_level, annotator_level_confident=user_conf_dict) data2merge = dataIter.getMergedData() print('num selected data:', len(data2merge)) with open(merged_json, 'w') as fj: json.dump(data2merge, fj) num_labels = 11 if output2csv: t=0 num_anno_dict = {} num_label_dict = {} with open(output2csv, 'w') as fo: outline = 'id\tclaim\texplaination\tselected_label\tlables_from_annotator\n' fo.write(outline) for item in data2merge: t += 1 num_annotation = len(item['annotations']) if num_annotation not in num_anno_dict: num_anno_dict[num_annotation] = 0 num_anno_dict[num_annotation] += 1 claim = clean_string(item['Claim']) explaination = clean_string(item['Explaination']) unique_id = item['unique_wv_id'].strip() if 'selected_label' in item: selected_label = item['selected_label'] else: selected_label = '' outline = unique_id+'\t'+claim+'\t'+explaination+'\t'+selected_label for annotation in item['annotations']: label = annotation['label'] confident = annotation['confident'] annotator = annotation['annotator'] if label not in num_label_dict: num_label_dict[label] = 0 num_label_dict[label] += 1 outline += '\t'+label+'\t'+confident+'\t'+annotator outline += '\n' fo.write(outline) print(num_anno_dict) print(num_label_dict) num_labels = len(num_label_dict) if args.cal_agreement: pa, kappa = calAgreement(dataIter, num_labels) print('agreement: ', pa) print('kappa: ', kappa)

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CANTM-main/wvCovidData/update_website.py

import json import re import nltk import sys import string import copy class MediaTypeOrg: def __init__(self): punct_chars = list(set(string.punctuation)-set("_")) print(punct_chars) punctuation = ''.join(punct_chars) self.replace = re.compile('[%s]' % re.escape(punctuation)) self.multi_word_dict = {'social media':'social_media'} self.facebook = ['facebook', 'fb', 'faceboos', 'facebok', 'faceboook', 'facebooks', 'facebbok','faacebook','facebookk'] self.twitter = ['twitter', 'tweets','tweet'] self.news = ['media', 'news', 'newspaper', 'newspapers', 'times', 'abcnews','cgtn'] self.whatsApp = ['whatsapp', 'wa'] self.email = ['email'] self.social_media = ['social_media', 'weibo', 'wechat'] ####### self.youtube = ['youtube', 'youtuber'] self.blog = ['blog', 'bloggers', 'blogs', 'blogger'] self.instagram = ['instagram', 'ig'] self.tv = ['tv', 'television'] self.line = ['line'] self.tiktok = ['tiktok'] self.chainMessage = ['chain', 'telegram'] self.type_dict={ 'facebook': self.facebook, 'twitter': self.twitter, 'news': self.news, 'whatsapp': self.whatsApp, 'email': self.email, 'youtube': self.youtube, 'blog': self.blog, 'instagram': self.instagram, 'tv': self.tv, 'line': self.line, 'chain_message': self.chainMessage, 'other_social_media': self.social_media, 'social_media': self.social_media+self.facebook+self.twitter+self.instagram, 'tiktok': self.tiktok } def type_org(self, data): addi_type_dict = {} ori_web_claim_dict = {} num_other = 0 new_data = [] for each_data in data: website_ori = each_data['Claim_web_ori'] included_type_list, tokened = self.get_type(website_ori) if len(included_type_list) == 0: num_other += 1 print(website_ori) included_type_list.append('other') each_data['Claim_Website'] = included_type_list new_data.append(copy.deepcopy(each_data)) return new_data def _check_item_in_list(self, tokened, check_list): for item in tokened: if item in check_list: return True return False def _get_multiword(self, lower_case_string): for item in self.multi_word_dict: lower_case_string = re.sub(item, self.multi_word_dict[item], lower_case_string) return lower_case_string def get_type(self, tokened): #print(tokened) #print(raw_media_type) #lc_raw_mt = raw_media_type.lower() #lc_raw_mt = self._get_multiword(lc_raw_mt) #lc_raw_mt_re = self.replace.sub(' ', lc_raw_mt) #tokened = nltk.word_tokenize(lc_raw_mt_re) included_type_list = [] for media_type in self.type_dict: check_list = self.type_dict[media_type] type_included = self._check_item_in_list(tokened, check_list) if type_included: included_type_list.append(media_type) ###check media #for search_string in self.social_media: # m = re.search(search_string, lc_raw_mt) # if m: # #print(raw_media_type) # included_type_list.append('social_media') #print(included_type_list) return included_type_list, tokened json_file = sys.argv[1] output_file = sys.argv[2] with open(json_file, 'r') as fj: data = json.load(fj) typeCheck = MediaTypeOrg() new_data = typeCheck.type_org(data) with open(output_file, 'w') as fo: json.dump(new_data, fo)

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CANTM-main/wvCovidData/getData.py

import wget import os import zipfile script_path = os.path.abspath(__file__) print(script_path) parent = os.path.dirname(script_path) print(parent) data_url = 'https://gate.ac.uk/g8/page/show/2/gatewiki/cow/covid19catedata/covidCateData.zip' wget.download(data_url, parent) zip_path = os.path.join(parent, 'covidCateData.zip') with zipfile.ZipFile(zip_path, 'r') as zip_ref: zip_ref.extractall(parent)

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CANTM

CANTM-main/wvCovidData/WvLibs/WVdataIter.py

from .DataReader import DataReader import random def disagreementSolver(annotations, local_logger=None): mergeMethodList = ['single_annotation', 'all_agreement', 'majority_agreement', 'majority_confidence', 'highest_confidence'] mergeMethod = None annotation_list = [] label_list = [] annotator_list = [] all_labels_include_duplicats = [] for annotation in annotations: label = annotation['label'] confident = annotation['confident'] annotator = annotation['annotator'] all_labels_include_duplicats.append([label, confident, annotator]) # for each annotator, we only consider once if annotator not in annotator_list: annotator_list.append(annotator) annotation_list.append([label, confident, annotator]) label_list.append(label) label_set = list(set(label_list)) set_count = [0]*len(label_set) conf_sum = [0]*len(label_set) for idx, current_label in enumerate(label_set): set_count[idx] = label_list.count(current_label) sorted_count = sorted(set_count, reverse=True) for each_annotation in annotation_list: label_set_idx = label_set.index(each_annotation[0]) try: conf_sum[label_set_idx] += int(each_annotation[1]) except: pass sorted_conf_sum = sorted(conf_sum, reverse=True) ## if only 1 label, or all annotator agree eachother if len(label_set) == 1: selected_label = label_set[0] if len(annotation_list) == 1: mergeMethod = 'single_annotation' else: mergeMethod = 'all_agreement' ## if have majority agreement elif sorted_count[0] > sorted_count[1]: selected_label_idx = set_count.index(sorted_count[0]) selected_label = label_set[selected_label_idx] mergeMethod = 'majority_agreement' ## if have higer summed confidence elif sorted_conf_sum[0] > sorted_conf_sum[1]: selected_label_idx = conf_sum.index(sorted_conf_sum[0]) selected_label = label_set[selected_label_idx] mergeMethod = 'majority_confidence' ## else pick the lable have highest confidence else: sorted_annotation_list = sorted(annotation_list, key=lambda s:s[1], reverse=True) selected_label = sorted_annotation_list[0][0] mergeMethod = 'highest_confidence' if local_logger: local_logger(selected_label, annotation_list, logtype='debug') return selected_label, mergeMethod class WVdataIter(DataReader): def __init__(self, annoed_json_dir, raw_json, min_anno_filter=-1, postProcessor=None, shuffle=False, check_validation=False, **kwargs): super().__init__(annoed_json_dir, raw_json, kwargs)#ignoreLabelList=ignoreLabelList, ignoreUserList=ignoreUserList, confThres=confThres, ignore_empty=ignore_empty) self.shuffle = shuffle self.check_validation = check_validation self.filterByMinAnno(min_anno_filter) self._reset_iter() self.postProcessor = postProcessor def filterByMinAnno(self, min_anno_filter): self.min_anno_filter = min_anno_filter all_links = [] for link in self.data_dict: num_annotations = len(self.data_dict[link]['annotations']) if num_annotations >= self.min_anno_filter: if self.check_validation: if self._check_annotation_valid(self.data_dict[link]['annotations']): all_links.append(link) else: all_links.append(link) self.all_links = all_links def _check_annotation_valid(self, annotation): at_least_one_ture = False for item in annotation: current_label = item['label'] current_confident = item['confident'] if len(current_label) > 0 and len(current_confident)>0: at_least_one_ture = True return at_least_one_ture def __iter__(self): if self.shuffle: random.shuffle(self.all_links) self._reset_iter() return self def __next__(self): if self.current_sample_idx < len(self.all_links): current_sample = self._readNextSample() self.current_sample_idx += 1 if self.postProcessor: return self.postProcessor(current_sample) else: return current_sample else: self._reset_iter() raise StopIteration def _readNextSample(self): current_link = self.all_links[self.current_sample_idx] current_sample = self.data_dict[current_link] return current_sample def __len__(self): return len(self.all_links) def _reset_iter(self): self.current_sample_idx = 0 def getMergedData(self, disagreementSolver=disagreementSolver): merge_method_dict = {} merged_data_list = [] merged_label_count = {} for item in self: if (len(item['annotations']) > 0) and disagreementSolver: annotations = item['annotations'] selected_label,merge_method = disagreementSolver(annotations, self.logging) if merge_method not in merge_method_dict: merge_method_dict[merge_method] = 0 if selected_label not in merged_label_count: merged_label_count[selected_label] = 0 item['selected_label'] = selected_label merge_method_dict[merge_method] += 1 merged_label_count[selected_label] += 1 merged_data_list.append(item) self.logging(merge_method_dict, logtype='info') self.logging(merged_label_count, logtype='info') return merged_data_list

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CANTM

CANTM-main/wvCovidData/WvLibs/__init__.py

from .DataReader import DataReader from .WVdataIter import WVdataIter

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22.666667

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CANTM

CANTM-main/wvCovidData/WvLibs/DataReader.py

import json import glob import os import re import hashlib import logging import sys class DataReader: def __init__(self, annoed_json_dir, raw_json_dir, kwargs): self._read_input_options(kwargs) print('label ignored: ', self.ignoreLabelList) print('user ignored: ', self.ignoreUserList) self._setReaderLogger() self._anno_user_regex() self._read_raw_json(raw_json_dir) self._read_annoed_data(annoed_json_dir) def _setReaderLogger(self): self.readerLogger = logging.getLogger('readerLogger') if self.logging_level == 'info': self.readerLogger.setLevel(logging.INFO) elif self.logging_level == 'debug': self.readerLogger.setLevel(logging.DEBUG) elif self.logging_level == 'warning': self.readerLogger.setLevel(logging.WARNING) handler = logging.StreamHandler(sys.stdout) formatter = logging.Formatter('DataReader: %(message)s') handler.setFormatter(formatter) self.readerLogger.addHandler(handler) def logging(self, *args, logtype='info', sep=' '): getattr(self.readerLogger, logtype)(sep.join(str(a) for a in args)) def _read_input_options(self, kwargs): self.ignoreLabelList = kwargs.get('ignoreLabelList', []) self.ignoreUserList = kwargs.get('ignoreUserList', []) self.confThres = kwargs.get('confThres', -1) self.filter_no_conf = kwargs.get('filter_no_conf', False) self.ignore_empty = kwargs.get('ignore_empty', False) self.label_trans_dict = kwargs.get('label_trans_dict', None) self.logging_level = kwargs.get('logging_level', 'warning') self.reverse_selection_condition = kwargs.get('reverse_selection_condition', False) self.annotator_level_confident = kwargs.get('annotator_level_confident', {}) def _anno_user_regex(self): self.label_field_regex = re.compile('ann\d*\_label') self.annotator_id_regex = re.compile('(?<=ann)\d*(?=\_label)') self.confident_field_regex = re.compile('ann\d*\_conf') self.remark_field_regex = re.compile('ann\d*\_remarks') def _read_annoed_data(self, annoed_json_dir): all_jsons = glob.glob(annoed_json_dir+'/*.json') for each_annoed_json_file in all_jsons: self._read_annoed_json(each_annoed_json_file) #print(all_jsons) def _read_annoed_json(self, annoed_json): with open(annoed_json, 'r') as f_json: all_json_data = json.load(f_json) for each_annoed_data in all_json_data: uniqueIdentifier = self._get_unique_identifier(each_annoed_data) annotation_info, include = self._get_annotation_info(each_annoed_data) if len(annotation_info['label']) < 0 and ignore_empty: include = False if self.reverse_selection_condition: if include == False: include = True else: include = False if include: self.data_dict[uniqueIdentifier]['annotations'].append(annotation_info) def _translabel(self, label): if label in self.label_trans_dict: transferd_label = self.label_trans_dict[label] else: transferd_label = label return transferd_label def _get_annotation_info(self, dict_data): annotation_info_dict = {} dict_keys = dict_data.keys() include = True annotator_id = None for current_key in dict_keys: m_label = self.label_field_regex.match(current_key) if m_label: raw_label_field = m_label.group() #print(raw_label_field) annotator_id = self.annotator_id_regex.search(raw_label_field).group() #print(annotator_id) annotation_info_dict['annotator'] = annotator_id label = dict_data[raw_label_field] if self.label_trans_dict: label = self._translabel(label) annotation_info_dict['label'] = label if label in self.ignoreLabelList: include = False if annotator_id in self.ignoreUserList: include = False m_conf = self.confident_field_regex.match(current_key) if m_conf: raw_conf_field = m_conf.group() confident = dict_data[raw_conf_field] annotation_info_dict['confident'] = confident if len(confident) < 1: if self.filter_no_conf: include = False elif annotator_id in self.annotator_level_confident: if int(confident) <= self.annotator_level_confident[annotator_id]: include = False elif (int(confident) <= self.confThres): include = False m_remark = self.remark_field_regex.match(current_key) if m_remark: raw_remark_field = m_remark.group() remark = dict_data[raw_remark_field] #print(remark) annotation_info_dict['remark'] = remark return annotation_info_dict, include def _get_unique_identifier(self, each_data): source_link = each_data['Source'].strip() claim = each_data['Claim'].strip() explaination = each_data['Explaination'].strip() sourceToken = source_link.split('/') top3Source = ' '.join(sourceToken[:3]) top200claim = claim[:200] top200expl = explaination[:200] uniqueString = top200claim+top200expl+top3Source #sourceQuesionToken = source_link.split('?') #uniqueString = sourceQuesionToken[0] #print(uniqueString) uniqueIdentifier = hashlib.sha224(uniqueString.encode('utf-8')).hexdigest() return uniqueIdentifier def _read_raw_json(self, raw_json_dir): self.data_dict = {} all_raw_jsons = glob.glob(raw_json_dir+'/*.json') duplicated = 0 total_data = 0 for each_raw_json in all_raw_jsons: ct=0 cd=0 with open(each_raw_json, 'r') as f_json: raw_data = json.load(f_json) for each_data in raw_data: #data_link = each_data['Link'] uniqueIdentifier = self._get_unique_identifier(each_data) if uniqueIdentifier not in self.data_dict: each_data['unique_wv_id'] = uniqueIdentifier each_data['annotations'] = [] self.data_dict[uniqueIdentifier] = each_data else: duplicated += 1 cd+=1 self.logging(uniqueIdentifier, logtype='debug') self.logging('id: ', self.data_dict[uniqueIdentifier]['unique_wv_id'], logtype='debug') self.logging(self.data_dict[uniqueIdentifier], logtype='debug') self.logging(each_data, logtype='debug') self.logging('\n', logtype='debug') total_data += 1 ct+=1 #print(ct, cd) #self.logging('Num selected data: ', len(self.data_dict), logtype='warning') self.logging('num duplicated: ', duplicated, logtype='info') self.logging('total num data: ', total_data, logtype='info')

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CANTM

CANTM-main/wvCovidData/GATEpythonInterface/example.py

from GateInterface import * #initialise gate gate= GateInterFace() # path to gate interface, for my case is /Users/xingyi/Gate/gateCodes/pythonInferface # this will open java tcp server (on port 7899) to execute GATE operation #gate.init('/Users/xingyi/Gate/gateCodes/pythonInferface') gate.init('./') # load document from url document = GateDocument() document.loadDocumentFromURL("https://gate.ac.uk") # load document from local file e.g. #document.loadDocumentFromFile("/Users/xingyi/Gate/gateCodes/pythonInferface/ft-airlines-27-jul-2001.xml") # get content from file content = document.getDocumentContent() print(content) # get annotation set name atsName = document.getAnnotationSetNames() print(atsName) #get Original markups annotation set ats = document.getAnnotations('Original markups') # load maven plugins gate.loadMvnPlugins("uk.ac.gate.plugins", "annie", "8.5") # load PRs gate.loadPRs('gate.creole.annotdelete.AnnotationDeletePR', 'anndeletePR') gate.loadPRs('gate.creole.tokeniser.DefaultTokeniser', 'defToken') # set initialise parameter for PR #prparameter['grammarURL'] = 'file:////Users/xingyi//Gate/ifpri/JAPE/main.jape' #gate.loadPRs('gate.creole.Transducer', prparameter) # create a pipeline testPipeLine = GatePipeline('testpipeline') testPipeLine.createPipeline() # add PRs to the pipeline testPipeLine.addPR('anndeletePR') testPipeLine.addPR('defToken') # get params print(testPipeLine.checkRunTimeParams('anndeletePR', 'setsToKeep')) # set run time params testPipeLine.setRunTimeParams('anndeletePR', 'keepOriginalMarkupsAS', 'false', 'Boolean') testPipeLine.setRunTimeParams('anndeletePR', 'setsToKeep', 'Key,Target', 'List') print(testPipeLine.checkRunTimeParams('anndeletePR', 'setsToKeep')) # create gate corpus testCorpus = GateCorpus('testCorpus') # add document to the corpus testCorpus.addDocument(document) # set corpus for pipeline testPipeLine.setCorpus(testCorpus) # run pipeline testPipeLine.runPipeline() # get default annotation set after run the pipeline, the annotation is stored in local python format defaultats = document.getAnnotations('') # load appilication from file #testPipeline2 = GatePipeline('testpipeline2') #testPipeLine.loadPipelineFromFile('/Users/xingyi/Gate/ifpri/ifpri.xgapp') # close the port gate.close()

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28.101266

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CANTM

CANTM-main/wvCovidData/GATEpythonInterface/GateInterface/GateInterface.py

import socket import json import re import os import subprocess import time import signal import pathlib class MyEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, np.integer): return int(obj) elif isinstance(obj, np.floating): return float(obj) elif isinstance(obj, np.ndarray): return obj.tolist() else: return super(MyEncoder, self).default(obj) class GateInterFace: def __init__(self): self.PORT = 7899 self.HOST = "localhost" self.maxSendChar = 512 self.maxRecvChar = 512 self.loadedPlugins = [] self.loadedPrs = [] self.pro = None self.logFile = "gpiterface"+str(self.PORT)+".log" self.interfaceJavaPath = None def init(self, interfaceJavaPath=None): if interfaceJavaPath: self.interfaceJavaPath = interfaceJavaPath else: interfaceJavaPath = pathlib.Path(__file__).parent.absolute() self.interfaceJavaPath = interfaceJavaPath.parent cwd = os.getcwd() runScript = os.path.join(self.interfaceJavaPath,'run.sh') os.chdir(self.interfaceJavaPath) if (os.path.isfile(self.logFile)): print("logfile existed, try to close previous session") try: with open(self.logFile,'r') as fin: line = fin.readline().strip() pid = int(line) os.killpg(os.getpgid(pid), signal.SIGTERM) except: print("can not kill process-"+str(pid)+"please close manully") print(runScript) #self.pro = subprocess.Popen(["bash", runScript]) hostportArg = "-Dexec.args=\""+str(self.PORT)+"\"" args=["mvn", "exec:java", "-Dexec.mainClass=uk.ac.gate.python.pythonInferface.GateServer",hostportArg] #self.pro = subprocess.Popen(["mvn", "exec:java -Dexec.mainClass=uk.ac.gate.python.pythonInferface.GateServer -Dexec.args="7899""]) self.pro = subprocess.Popen(args) with open(self.logFile,'w') as fo: fo.write(str(self.pro.pid)) os.chdir(cwd) time.sleep(5) def close(self): print(self.pro.pid) logFile = os.path.join(self.interfaceJavaPath, self.logFile) print(logFile) os.remove(logFile) os.killpg(os.getpgid(self.pro.pid), signal.SIGTERM) #try: # os.killpg(os.getpgid(self.pro.pid), signal.SIGTERM) # logFile = os.path.join(self.interfaceJavaPath, self.logFile) # print(logFile) # os.remove(logFile) #except: # print("logfile not correctly removed") def test(self): self._sendDoc2Java('test','this is test sent') def _sendDoc2Java(self, jsonKey, jsonValue): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.connect((self.HOST, self.PORT)) previ = 0 i = self.maxSendChar eov = False #if value length larger than self.maxChar then send in sepate packages to java valueLen = len(jsonValue) while(eov == False): subValue = jsonValue[previ:i] if i < valueLen: eov = False else: eov = True jsonSend = {'fromClient':{jsonKey:subValue, 'eov':eov}} previ = i i += self.maxSendChar sock.sendall((json.dumps(jsonSend, cls=MyEncoder)+"\n").encode('utf-8')) serverReturn = self._recvDocFromJava(sock) sock.close() #print(serverReturn) return serverReturn def _send2Java(self, jsonDict): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) #print(self.HOST,self.PORT) sock.connect((self.HOST, int(self.PORT))) #print(jsonDict) for jsonKey in jsonDict: #print(jsonKey) jsonValue = jsonDict[jsonKey] valueLen = len(jsonValue) previ = 0 i = self.maxSendChar eov = False #if value length larger than self.maxChar then send in sepate packages to java while(eov == False): subValue = jsonValue[previ:i] if i < valueLen: eov = False else: eov = True jsonSend = {'fromClient':{jsonKey:subValue, 'eov':False}} previ = i i += self.maxSendChar #print(jsonSend) sock.sendall((json.dumps(jsonSend, cls=MyEncoder)+"\n").encode('utf-8')) #print('finish') jsonSend = {'fromClient':{'eov':True}} sock.sendall((json.dumps(jsonSend, cls=MyEncoder)+"\n").encode('utf-8')) serverReturn = self._recvDocFromJava(sock) sock.close() #print(serverReturn) return serverReturn def _recvDocFromJava(self, sock): fullReturn = "" eov = False fullReturn = {} while(eov ==False): data_recv = sock.recv(self.maxRecvChar) data = json.loads(data_recv) eov = data['eov'] for item in data: partReturn = data[item] if item not in fullReturn: fullReturn[item] = partReturn else: fullReturn[item] += partReturn sock.sendall("success\n".encode('utf-8')) return fullReturn def loadMvnPlugins(self, group, artifact, version): jsonDict = {} jsonDict['plugin'] = 'maven' jsonDict['group'] = group jsonDict['artifact'] = artifact jsonDict['version'] = version response = self._send2Java(jsonDict) if response['message'] == 'success': self.loadedPlugins.append(response['pluginLoaded']) return response def loadPRs(self, resourcePath, name, features=None): jsonDict = {} if features == None: jsonDict['loadPR'] = 'withoutFeature' else: jsonDict['loadPR'] = 'withFeature' i = 0 for featureKey in features: keyName = 'prFeatureName'+str(i) valueName = 'prFeatureValue'+str(i) jsonDict[keyName] = featureKey jsonDict[valueName] = features[featureKey] i+=1 jsonDict['resourcePath'] = resourcePath jsonDict['name'] = name response = self._send2Java(jsonDict) if response['message'] == 'success': self.loadedPrs.append(response['PRLoaded']) return response def reinitPRs(self, name): jsonDict = {} jsonDict['reInitPR'] = 'none' jsonDict['name'] = name response = self._send2Java(jsonDict) if response['message'] != 'success': print('error unable to reinit pr', name) return response['message'] class AnnotationSet(GateInterFace): def __init__(self): GateInterFace.__init__(self) self.annotationSet = [] def __len__(self): return len(self.annotationSet) def __iter__(self): for node in self.annotationSet: yield node def getType(self, annotationType, startidx=None, endidx=None): newList = [] for annotation in self.annotationSet: if annotation.type == annotationType: if startidx and endidx: if annotation.startNode.offset >= startidx and annotation.endNode.offset <= endidx: newList.append(annotation) else: newList.append(annotation) subSet = AnnotationSet() subSet.annotationSet = newList return subSet def getbyRange(self, startidx=0, endidx=None): newList = [] for annotation in self.annotationSet: if annotation.startNode.offset >= startidx: if endidx: if annotation.endNode.offset <= endidx: newList.append(annotation) else: newList.append(annotation) subSet = AnnotationSet() subSet.annotationSet = newList return subSet def get(self,i): return self.annotationSet[i] def getbyId(self, annoId): returnAnno = None for annotation in self.annotationSet: if annotation.id == annoId: returnAnno = annotation break return returnAnno def append(self, annotation): self.annotationSet.append(annotation) def _getAnnotationFromResponse(self,response): annotationResponse = response['annotationSet'] annotationList = annotationResponse.split('\n, Anno') #print('getting annotation from response') #print(len(annotationList)) #print(annotationList[0]) idPattern = '(?<=tationImpl\: id\=)\d*(?=\; type\=.*\;)' typePattern = '(?<=; type\=).*(?=\; features\=)' featurePattern = '(?<=; features\=\{)[\S\s]*(?=\}\; start\=.*\;)' startNodePattern = '(?<=; start\=NodeImpl\: ).*(?= end\=NodeImpl)' endNodePattern = '(?<=; end\=NodeImpl\: ).*' fullPattern = 'AnnotationImpl\: id\=\d*\; type\=.*\; features\=\{.*\}\; start\=NodeImpl\: id\=\d*\; offset\=\d*\; end\=NodeImpl\: id\=\d*\; offset\=\d*' for rawAnnotationLine in annotationList: #print(rawAnnotationLine) #m = re.search(fullPattern, rawAnnotationLine) #print(m) #if m: # print('match') try: #if 1: #print(rawAnnotationLine) currentAnnotation = Annotation() currentAnnotation.id = int(re.search(idPattern,rawAnnotationLine).group(0)) currentAnnotation.type = re.search(typePattern,rawAnnotationLine).group(0) #print(len(re.search(featurePattern,rawAnnotationLine).group(0))) currentAnnotation._setFeatureFromRawLine(re.search(featurePattern,rawAnnotationLine).group(0)) #print(currentAnnotation.id) #print(currentAnnotation.type) #print(currentAnnotation.features) currentAnnotation._setStartNode(re.search(startNodePattern,rawAnnotationLine).group(0)) currentAnnotation._setEndNode(re.search(endNodePattern,rawAnnotationLine).group(0)) #print(currentAnnotation.startNode.id, currentAnnotation.startNode.offset) #print(currentAnnotation.endNode.id, currentAnnotation.endNode.offset) self.annotationSet.append(currentAnnotation) except: #print('bad line, ignore') #print(rawAnnotationLine) pass #currentAnnotation = Annotation() #currentAnnotation.id = int(re.search(idPattern,rawAnnotationLine).group(0)) #currentAnnotation.type = re.search(typePattern,rawAnnotationLine).group(0) #currentAnnotation._setFeatureFromRawLine(re.search(featurePattern,rawAnnotationLine).group(0)) #print(currentAnnotation.id) #print(currentAnnotation.type) #print(currentAnnotation.features) #currentAnnotation._setStartNode(re.search(startNodePattern,rawAnnotationLine).group(0)) #currentAnnotation._setEndNode(re.search(endNodePattern,rawAnnotationLine).group(0)) #print(currentAnnotation.startNode.id, currentAnnotation.startNode.offset) #print(currentAnnotation.endNode.id, currentAnnotation.endNode.offset) #self.annotationSet.append(currentAnnotation) class Annotation: def __init__(self): self.id = None self.type = None self.features = {} self.startNode = None self.endNode = None def overlap_set(self, compareSet): overlap = False for compare_annotation in compareSet: overlap = self.overlaps(compare_annotation) if overlap: break return overlap def matches(self, compareAnno): startNodeOffsetMatch = self.startNode.offset == compareAnno.startNode.offset endNodeOffsetMatch = self.endNode.offset == compareAnno.endNode.offset if startNodeOffsetMatch and endNodeOffsetMatch: return True def overlaps(self, compareAnno): selfStart = self.startNode.offset selfEnd = self.endNode.offset-1 compareStart = compareAnno.startNode.offset compareEnd = compareAnno.endNode.offset-1 if selfStart <= compareStart: if selfEnd >= compareStart: return True else: return False elif selfStart >= compareStart: if compareEnd >= selfStart: return True else: return False def _setFeatureFromRawLine(self, rawFeatureLine): #print(rawFeatureLine) if len(rawFeatureLine) > 0: #replace comma in list to ||| listPattern = '(?<=\=)\[[\w \,]+\]' listFeatures = re.findall(listPattern, rawFeatureLine) for listFeature in listFeatures: newPattern = re.sub(', ',' ||| ', listFeature) rawFeatureLine = rawFeatureLine.replace(listFeature, newPattern) splittedFeatures = re.split(', ',rawFeatureLine) #print(splittedFeatures) for splittedFeature in splittedFeatures: featureTok = splittedFeature.split('=') featureKey = featureTok[0] featureValue = featureTok[1] if self._isListFeature(featureValue): #self.features[featureKey] = [] listValues = featureValue[1:-1].split(' ||| ') self.features[featureKey] = listValues else: self.features[featureKey] = featureValue def _isListFeature(self, featureValue): pattern = '\[.*\]' m = re.match(pattern, featureValue) if m: return True else: return False def _setStartNode(self, rawLine): idPattern = '(?<=id\=)\d*(?=\;)' offSetPattern = '(?<=offset\=)\d*(?=(\;)|($))' nodeId = int(re.search(idPattern, rawLine).group(0)) offset = int(re.search(offSetPattern, rawLine).group(0)) #print(nodeId, offset) startNode = Node() startNode.id = nodeId startNode.offset = offset self.startNode = startNode def _setEndNode(self, rawLine): idPattern = '(?<=id\=)\d*(?=\;)' offSetPattern = '(?<=offset\=)\d*(?=(\;)|($))' nodeId = int(re.search(idPattern, rawLine).group(0)) offset = int(re.search(offSetPattern, rawLine).group(0)) #print(nodeId, offset) endNode = Node() endNode.id = nodeId endNode.offset = offset self.endNode = endNode class Node: def __init__(self): self.id = None self.offset = None class GateDocument(GateInterFace): def __init__(self): GateInterFace.__init__(self) self.documentName = None def loadDocumentFromURL(self, documentURL): documentName = documentURL serverReturn = self._sendDoc2Java('loadDocumentFromURL', documentURL) if serverReturn['message'] == 'success': self.documentName = documentName #print(serverReturn) def loadDocumentFromFile(self, documentPath): documentName = documentPath serverReturn = self._sendDoc2Java('loadDocumentFromFile', documentPath) #print(serverReturn) if serverReturn['message'] == 'success': self.documentName = documentName #print(serverReturn) def getDocumentContent(self): jsonDict = {} jsonDict['document'] = 'getDocumentContent' jsonDict['docName'] = self.documentName response = self._send2Java(jsonDict) docContent = response['docContent'] return docContent def getAnnotationSetNames(self): jsonDict = {} jsonDict['document'] = 'getAnnotationSetName' jsonDict['docName'] = self.documentName response = self._send2Java(jsonDict) astName = response['annotationSetName'] return astName def getAnnotations(self, annotationSetName): jsonDict = {} jsonDict['document'] = 'getAnnotations' jsonDict['docName'] = self.documentName jsonDict['annotationSetName'] = annotationSetName currentAnnotationSet = AnnotationSet() #print(jsonDict) response = self._send2Java(jsonDict) #print(response) currentAnnotationSet._getAnnotationFromResponse(response) return currentAnnotationSet def clearDocument(self): jsonDict = {} jsonDict['clearDocument'] = self.documentName response = self._send2Java(jsonDict) class GatePipeline(GateInterFace): def __init__(self, pipelineName): GateInterFace.__init__(self) self.pipelineName = pipelineName self.corpus = None self.prList = [] def loadPipelineFromFile(self, filePath): jsonDict = {} jsonDict['pipeline'] = 'loadPipelineFromFile' jsonDict['pipelineName'] = self.pipelineName jsonDict['filtPath'] = filePath response = self._send2Java(jsonDict) #print(response) def createPipeline(self): jsonDict = {} jsonDict['pipeline'] = 'createPipeline' jsonDict['pipelineName'] = self.pipelineName response = self._send2Java(jsonDict) #print(response) def addPR(self, prName): jsonDict = {} jsonDict['pipeline'] = 'addPR' jsonDict['pipelineName'] = self.pipelineName jsonDict['prName'] = prName response = self._send2Java(jsonDict) #print(response) self.prList.append(prName) def setCorpus(self, corpus): corpusName = corpus.corpusName jsonDict = {} jsonDict['pipeline'] = 'setCorpus' jsonDict['pipelineName'] = self.pipelineName jsonDict['corpusName'] = corpusName response = self._send2Java(jsonDict) #print(response) self.corpus = corpus def runPipeline(self): jsonDict = {} jsonDict['pipeline'] = 'runPipeline' jsonDict['pipelineName'] = self.pipelineName response = self._send2Java(jsonDict) #print(response) def checkRunTimeParams(self, prName, paramName): jsonDict = {} jsonDict['pipeline'] = 'checkParams' jsonDict['pipelineName'] = self.pipelineName jsonDict['resourceName'] = prName jsonDict['paramsName'] = paramName response = self._send2Java(jsonDict) #print(response) return response['message'] def setRunTimeParams(self, prName, paramName, paramValue, paramType): jsonDict = {} jsonDict['pipeline'] = 'setParams' jsonDict['pipelineName'] = self.pipelineName jsonDict['resourceName'] = prName jsonDict['paramsName'] = paramName jsonDict['paramsValue'] = paramValue jsonDict['paramsType'] = paramType response = self._send2Java(jsonDict) class GateCorpus(GateInterFace): def __init__(self, corpusName): GateInterFace.__init__(self) self.corpusName = corpusName self._createCorpus() self.documentList = [] def _createCorpus(self): jsonDict = {} jsonDict['corpus'] = 'createCorpus' jsonDict['corpusName'] = self.corpusName response = self._send2Java(jsonDict) #print(response) def clearCorpus(self): jsonDict = {} jsonDict['corpus'] = 'clearCorpus' jsonDict['corpusName'] = self.corpusName response = self._send2Java(jsonDict) def addDocument(self, document): documentName = document.documentName jsonDict = {} jsonDict['corpus'] = 'addDocument' jsonDict['corpusName'] = self.corpusName jsonDict['documentName'] = documentName response = self._send2Java(jsonDict) #print(response) self.documentList.append(document) #if __name__ == "__main__": # gate= GateInterFace() # gate.init('/Users/xingyi/Gate/gateCodes/pythonInferface') # #gate.test() # document = GateDocument() # document.loadDocumentFromURL("https://gate.ac.uk") # #document.loadDocumentFromFile("/Users/xingyi/Gate/gateCodes/pythonInferface/ft-airlines-27-jul-2001.xml") # #print(document.documentName) # #content = document.getDocumentContent() # #print(content) # #atsName = document.getAnnotationSetNames() # #print(atsName) # #ats = document.getAnnotations('') # #print(len(ats.annotationSet)) # #print(ats.annotationSet[0]) # response=gate.loadMvnPlugins("uk.ac.gate.plugins", "annie", "8.5") # print(response) # prparameter={} # response=gate.loadPRs('gate.creole.annotdelete.AnnotationDeletePR') # response=gate.loadPRs('gate.creole.tokeniser.DefaultTokeniser') # prparameter['grammarURL'] = 'file:////Users/xingyi//Gate/ifpri/JAPE/main.jape' # response=gate.loadPRs('gate.creole.Transducer', prparameter) # print(response) # print(gate.loadedPrs) # testPipeLine = GatePipeline('testpipeline') # testPipeLine.createPipeline() # testPipeLine.addPR('gate.creole.annotdelete.AnnotationDeletePR') # testPipeLine.addPR('gate.creole.tokeniser.DefaultTokeniser') # testCorpus = GateCorpus('testCorpus') # testCorpus.addDocument(document) # testPipeLine.setCorpus(testCorpus) # testPipeLine.runPipeline() # #ats = document.getAnnotations('Original markups') # ats = document.getAnnotations('') # print(len(ats.annotationSet)) # print(ats.annotationSet[0]) # testPipeline2 = GatePipeline('testpipeline2') # testPipeLine.loadPipelineFromFile('/Users/xingyi/Gate/ifpri/ifpri.xgapp') # gate.close() #

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CANTM-main/wvCovidData/GATEpythonInterface/GateInterface/__init__.py

from .GateInterface import GateInterFace, AnnotationSet, Annotation, Node, GateDocument, GatePipeline, GateCorpus

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CANTM-main/GateMIcateLib/modelUltiClassTopic.py

import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import numpy as np import copy import os from pathlib import Path import pickle import datetime from .modelUlti import modelUlti class ModelUltiClass(modelUlti): def __init__(self, net=None, gpu=False, load_path=None): super().__init__(net=net, gpu=gpu) if load_path: self.loadModel(load_path) if self.gpu: self.net.cuda() def train(self, trainBatchIter, num_epohs=100, valBatchIter=None, cache_path=None, earlyStopping='cls_loss', patience=5): pytorch_total_params = sum(p.numel() for p in self.net.parameters()) print('total_params: ',pytorch_total_params) pytorch_train_params = sum(p.numel() for p in self.net.parameters() if p.requires_grad) print('train_params: ',pytorch_train_params) self.bowdict = trainBatchIter.dataIter.postProcessor.dictProcess self.labels = trainBatchIter.dataIter.postProcessor.labelsFields if earlyStopping == 'None': earlyStopping = None self.cache_path = cache_path output_dict = {} output_dict['accuracy'] = 'no val iter' output_dict['perplexity'] = 'no val iter' output_dict['perplexity_x_only'] = 'no val iter' self.evaluation_history = [] self.optimizer = optim.Adam(self.net.parameters()) print(num_epohs) for epoch in range(num_epohs): begin_time = datetime.datetime.now() all_loss = [] all_elboz1 = [] all_elboz2 = [] all_bow = [] trainIter = self.pred(trainBatchIter, train=True) for current_prediction in trainIter: self.optimizer.zero_grad() pred = current_prediction['pred'] y = current_prediction['y'] atted = current_prediction['atted'] loss = pred['loss'] cls_loss = pred['cls_loss'].sum() elbo_z1 = pred['elbo_x'].to('cpu').detach().numpy() elbo_z2 = pred['elbo_xy'].to('cpu').detach().numpy() bow_x = current_prediction['x_bow'].to('cpu').detach().numpy() all_elboz1.append(elbo_z1) all_elboz2.append(elbo_z2) all_bow.append(bow_x) loss.backward() self.optimizer.step() loss_value = float(cls_loss.data.item()) all_loss.append(loss_value) all_elboz1 if epoch % 3 == 0: topics = self.getTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) #print('===========') x_only_topic = self.get_x_only_Topics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) #print('========================') self.getClassTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) if valBatchIter: output_dict = self.eval(valBatchIter, get_perp=True) avg_loss = sum(all_loss)/len(all_loss) output_dict['cls_loss'] = -avg_loss perplexity_z1, log_perp_z1 = self._get_prep(all_elboz1, all_bow) perplexity_z2, log_perp_z2 = self._get_prep(all_elboz2, all_bow) output_dict['train_ppl_loss'] = -perplexity_z1 if earlyStopping: stop_signal = self.earlyStop(output_dict, patience=patience, metric=earlyStopping, num_epoch=num_epohs) if stop_signal: print('stop signal received, stop training') cache_load_path = os.path.join(self.cache_path, 'best_net.model') print('finish training, load model from ', cache_load_path) self.loadWeights(cache_load_path) break end_time = datetime.datetime.now() timeused = end_time - begin_time print('epoch ', epoch, 'loss', avg_loss, ' val acc: ', output_dict['accuracy'], 'test_pplz2: ', output_dict['perplexity'], 'test_perpz1: ', output_dict['perplexity_x_only'], 'train_pplz2: ', perplexity_z2, 'train_perpz1: ', perplexity_z1, 'time: ', timeused) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) self.saveModel(self.cache_path) self.getTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) #print('===========') self.getClassTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) #print('===========') x_only_topic = self.get_x_only_Topics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) def getClassTopics(self, dictProcess, ntop=10, cache_path=None): termMatrix = self.net.get_class_topics() topicWordList = [] for each_topic in termMatrix: trans_list = list(enumerate(each_topic.cpu().numpy())) #print(trans_list) trans_list = sorted(trans_list, key=lambda k: k[1], reverse=True) #print(trans_list) topic_words = [dictProcess.get(item[0]) for item in trans_list[:ntop]] #print(topic_words) topicWordList.append(topic_words) if cache_path: save_path = os.path.join(cache_path, 'classtopics.txt') self.saveTopic(topicWordList, save_path) return topicWordList def saveModel(self, cache_path): model_path = os.path.join(cache_path, 'net.model') dict_path = os.path.join(cache_path, 'dict.pkl') label_path = os.path.join(cache_path, 'label.pkl') torch.save(self.net, model_path) with open(dict_path, 'wb') as fp: pickle.dump(self.bowdict, fp) with open(label_path, 'wb') as fp: pickle.dump(self.labels, fp) def loadModel(self, cache_path): model_path = os.path.join(cache_path, 'net.model') dict_path = os.path.join(cache_path, 'dict.pkl') label_path = os.path.join(cache_path, 'label.pkl') self.net = torch.load(model_path, map_location=torch.device("cpu")) self.net.eval() with open(dict_path, 'rb') as fp: self.bowdict = pickle.load(fp) with open(label_path, 'rb') as fp: self.labels = pickle.load(fp) def pred(self, batchGen, train=False, updateTopic=False): if train or updateTopic: self.net.train() #self.optimizer.zero_grad() else: self.net.eval() i=0 pre_embd = False for x, x_bow, y in batchGen: i+=1 print("processing batch", i, end='\r') if self.gpu: y = y.type(torch.cuda.LongTensor) x_bow = x_bow.type(torch.cuda.FloatTensor) x_bow.cuda() y.cuda() if batchGen.dataIter.postProcessor.embd_ready: pre_embd = True x = x.type(torch.cuda.FloatTensor).squeeze(1) x.cuda() else: x = x.type(torch.cuda.LongTensor) x.cuda() if train: one_hot_y = self.y2onehot(y) if batchGen.dataIter.label_weights_list: n_samples = self.get_num_samples(y, batchGen.dataIter.label_weights_list) else: n_samples = 10 #print(n_samples) pred, atted = self.net(x, bow=x_bow, train=True, true_y=one_hot_y, n_samples=n_samples, pre_embd=pre_embd, true_y_ids=y) elif updateTopic: pred, atted = self.net(x, bow=x_bow, pre_embd=pre_embd, update_catopic=True) else: pred, atted = self.net(x, bow=x_bow, pre_embd=pre_embd) #pred = pred['y_hat'] output_dict = {} output_dict['pred'] = pred output_dict['y'] = y output_dict['atted'] = atted output_dict['x_bow'] = x_bow yield output_dict def application_oneSent(self, x): if self.gpu: x = x.type(torch.cuda.LongTensor) x.cuda() pred, atted = self.net(x) output_dict = {} output_dict['pred'] = pred output_dict['atted'] = atted return output_dict def get_num_samples(self, y, weight_list): n_samples = 0 for y_item in y: n_samples += weight_list[y_item.item()] return n_samples def y2onehot(self, y): num_class = self.net.n_classes one_hot_y_list = [] for i in range(len(y)): current_one_hot = [0]*num_class current_one_hot[y[i].item()] = 1 one_hot_y_list.append(copy.deepcopy(current_one_hot)) tensor_one_hot_y = torch.tensor(one_hot_y_list) if self.gpu: tensor_one_hot_y = tensor_one_hot_y.type(torch.cuda.FloatTensor) tensor_one_hot_y = tensor_one_hot_y.cuda() return tensor_one_hot_y def getTopics(self, dictProcess, ntop=10, cache_path=None): termMatrix = self.net.get_topics() #print(termMatrix.shape) topicWordList = [] for each_topic in termMatrix: trans_list = list(enumerate(each_topic.cpu().numpy())) #print(trans_list) trans_list = sorted(trans_list, key=lambda k: k[1], reverse=True) #print(trans_list) topic_words = [dictProcess.get(item[0]) for item in trans_list[:ntop]] #print(topic_words) topicWordList.append(topic_words) if cache_path: save_path = os.path.join(cache_path, 'topics.txt') self.saveTopic(topicWordList, save_path) return topicWordList def get_x_only_Topics(self, dictProcess, ntop=10, cache_path=None): termMatrix = self.net.get_x_only_topics() #print(termMatrix.shape) topicWordList = [] for each_topic in termMatrix: trans_list = list(enumerate(each_topic.cpu().numpy())) #print(trans_list) trans_list = sorted(trans_list, key=lambda k: k[1], reverse=True) #print(trans_list) topic_words = [dictProcess.get(item[0]) for item in trans_list[:ntop]] #print(topic_words) topicWordList.append(topic_words) if cache_path: save_path = os.path.join(cache_path, 'x_only_topics.txt') self.saveTopic(topicWordList, save_path) return topicWordList def saveTopic(self, topics, save_path): with open(save_path, 'w') as fo: for each_topic in topics: topic_line = ' '.join(each_topic) fo.write(topic_line+'\n')

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CANTM-main/GateMIcateLib/PostprocessorBase.py

import nltk from nltk.corpus import stopwords import os import re class ReaderPostProcessorBase: def __init__(self, keep_case=False, label2id=True, config=None, word2id=False, exteralWord2idFunction=None, return_mask=False, remove_single_list=True, max_sent_len = 300, add_spec_tokens = False, add_CLS_token = False, ): self._init_defaults() self.add_CLS_token = add_CLS_token self.add_spec_tokens = add_spec_tokens self.max_sent_len = max_sent_len self.keep_case = keep_case self.label2id = label2id self.word2id = word2id self.exteralWord2idFunction = exteralWord2idFunction self.config = config self.return_mask = return_mask self.remove_single_list = remove_single_list def _init_defaults(self): self.labelsFields = None self.stop_words = set(stopwords.words('english')) self.dictProcess = None self.embd_ready = False def _remove_stop_words(self, tokened): remain_list = [] for token in tokened: contain_symbol, contain_number, contain_char, all_asii = self._check_string(token) keep = True if token in self.stop_words: keep = False elif len(token) == 1: keep = False elif token.isdigit(): keep = False elif not contain_char: keep = False elif not all_asii: keep = False elif len(token) > 18: keep = False if keep == True: remain_list.append(token) else: pass #print(token) return remain_list def _check_string(self, inputString): contain_symbol = False contain_number = False contain_char = False all_asii = True have_symbol = re.compile('[@_!#$%^&*()<>?/\|}{~:]') have_number = re.compile('\d') have_char = re.compile('[a-zA-Z]') ms = have_symbol.search(inputString) if ms: contain_symbol = True mn = have_number.search(inputString) if mn: contain_number = True mc = have_char.search(inputString) if mc: contain_char = True if contain_char and not contain_number and not contain_symbol: all_asii = all(ord(c) < 128 for c in inputString) return contain_symbol, contain_number, contain_char, all_asii def _removeSingleList(self, y): if len(y) == 1: return y[0] else: return y def _get_sample(self, sample, sample_field): current_rawx = sample[sample_field] if self.keep_case == False: current_rawx = current_rawx.lower() return current_rawx def label2ids(self, label): label_index = self.labelsFields.index(label) return label_index def x_pipeline(self, raw_x, add_special_tokens=True): raw_x = self.tokenizerProcessor(raw_x) if self.word2id: raw_x = self.word2idProcessor(raw_x, add_special_tokens=add_special_tokens) return raw_x def nltkTokenizer(self, text): return nltk.word_tokenize(text) def bertTokenizer(self, text): tokened = self.bert_tokenizer.tokenize(text) #print(tokened) #ided = self.bert_tokenizer.encode_plus(tokened, max_length=100, pad_to_max_length=True, is_pretokenized=True, add_special_tokens=True)['input_ids'] #print(ided) return tokened def bertWord2id(self,tokened, add_special_tokens=True): encoded = self.bert_tokenizer.encode_plus(tokened, max_length=self.max_sent_len, pad_to_max_length=True, is_pretokenized=True, add_special_tokens=add_special_tokens) #print(encoded) ided = encoded['input_ids'] if self.return_mask: mask = encoded['attention_mask'] return ided, mask else: return ided def get_label_desc_ids(self): label_desc_list = [] for label in self.labelsFields: label_desc = self.desctiptionDict[label] current_desc_ids = self.x_pipeline(label_desc, max_length=100) label_desc_list.append(current_desc_ids) label_ids = [s[0] for s in label_desc_list] label_mask_ids = [s[1] for s in label_desc_list] return label_ids, label_mask_ids

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CANTM-main/GateMIcateLib/modelUltiUpdateCATopic.py

import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import numpy as np import copy import os from pathlib import Path import pickle from .modelUltiClassTopic import ModelUltiClass class ModelUltiUpdateCAtopic(ModelUltiClass): def __init__(self, net=None, gpu=False, load_path=None): super().__init__(net=net, gpu=gpu, load_path=load_path) def train(self, trainBatchIter, num_epohs=100, valBatchIter=None, cache_path=None, earlyStopping='cls_loss', patience=5): self.bowdict = trainBatchIter.dataIter.postProcessor.dictProcess self.labels = trainBatchIter.dataIter.postProcessor.labelsFields if earlyStopping == 'None': earlyStopping = None self.cache_path = cache_path output_dict = {} output_dict['accuracy'] = 'no val iter' output_dict['perplexity'] = 'no val iter' output_dict['perplexity_x_only'] = 'no val iter' self.evaluation_history = [] self.optimizer = optim.Adam(self.net.parameters()) print(num_epohs) for epoch in range(num_epohs): all_loss = [] all_elboz1 = [] all_elboz2 = [] all_bow = [] trainIter = self.pred(trainBatchIter, train=False, updateTopic=True) for current_prediction in trainIter: self.optimizer.zero_grad() pred = current_prediction['pred'] atted = current_prediction['atted'] loss = pred['loss'] bow_x = current_prediction['x_bow'].to('cpu').detach().numpy() all_bow.append(bow_x) loss.backward() self.optimizer.step() topics = self.getTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) topics = self.getTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) print('finish epoch ', epoch) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) self.saveModel(self.cache_path) self.getTopics(trainBatchIter.dataIter.postProcessor.dictProcess, cache_path=self.cache_path)

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CANTM-main/GateMIcateLib/batchPostProcessors.py

import torch def xonlyBatchProcessor(x, y): ss = [s[1] for s in x] return ss[0] def bowBertBatchProcessor(raw_x, y): x = [s[0] for s in raw_x] idded_words = [s[1] for s in raw_x] y_class = y return torch.tensor(x), torch.tensor(idded_words), torch.tensor(y_class) def xyOnlyBertBatchProcessor(raw_x, y): x = [s[0] for s in raw_x] y_class = y return torch.tensor(x), torch.tensor(y_class) def singleProcessor_noy(raw_x): x = [raw_x[0]] return torch.tensor(x)

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CANTM-main/GateMIcateLib/modelUltiVAEtm_noatt.py

import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import numpy as np import copy import os from pathlib import Path from .modelUlti import modelUlti class ModelUltiVAEtmNOatt(modelUlti): def __init__(self, net=None, gpu=False): super().__init__(net=net, gpu=gpu) def train(self, trainBatchIter, num_epohs=100, valBatchIter=None, cache_path=None, earlyStopping='cls_loss', patience=5): self.cache_path = cache_path output_dict = {} output_dict['accuracy'] = 'no val iter' self.evaluation_history = [] classifier_paramters = list(self.net.wv_hidden.parameters()) + list(self.net.wv_classifier.parameters()) topic_model_paramters = list(self.net.mu.parameters())+ list(self.net.log_sigma.parameters()) + list(self.net.topics.parameters()) self.optimizer_classifier = optim.Adam(classifier_paramters) self.optimizer_topic_modelling = optim.Adam(topic_model_paramters) #self.optimizer = optim.Adam(self.net.parameters()) self.criterion = nn.CrossEntropyLoss() if self.gpu: self.criterion.cuda() for epoch in range(num_epohs): all_loss = [] trainIter = self.pred(trainBatchIter, train=True) for current_prediction in trainIter: #self.optimizer.zero_grad() self.optimizer_classifier.zero_grad() self.optimizer_topic_modelling.zero_grad() pred = current_prediction['pred'] y = current_prediction['y'] atted = current_prediction['atted'] #y_desc_representation = current_prediction['y_desc_representation'] #class_loss = self.criterion(pred['pred'], y) #topic_loss = pred['loss'] #print(class_loss) #desc_loss = self.desc_criterion(input=atted, target=y_desc_representation) loss = pred['loss'] cls_loss = pred['cls_loss'].sum() loss.backward() #self.optimizer.step() self.optimizer_classifier.step() self.optimizer_topic_modelling.step() #loss_value = float(loss.data.item()) loss_value = float(cls_loss.data.item()) all_loss.append(loss_value) if epoch % 20 == 0: self.getTopics(trainBatchIter.dataIter.postProcessor.dictProcess) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) print("Finish Epoch ", epoch) if valBatchIter: output_dict = self.eval(valBatchIter) avg_loss = sum(all_loss)/len(all_loss) output_dict['cls_loss'] = -avg_loss if earlyStopping: stop_signal = self.earlyStop(output_dict, patience=patience, metric=earlyStopping, num_epoch=num_epohs) if stop_signal: print('stop signal received, stop training') cache_load_path = os.path.join(self.cache_path, 'best_net.model') print('finish training, load model from ', cache_load_path) self.loadWeights(cache_load_path) break print('epoch ', epoch, 'loss', avg_loss, ' val acc: ', output_dict['accuracy']) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) #cache_load_path = os.path.join(self.cache_path, 'best_net.model') #print('finish training, load model from ', cache_load_path) #self.loadWeights(cache_load_path) def pred(self, batchGen, train=False): if train: self.net.train() #self.optimizer.zero_grad() else: self.net.eval() i=0 pre_embd = False for x, x_bow, y in batchGen: i+=1 print("processing batch", i, end='\r') if self.gpu: y = y.type(torch.cuda.LongTensor) x_bow = x_bow.type(torch.cuda.FloatTensor) x_bow.cuda() y.cuda() if batchGen.dataIter.postProcessor.embd_ready: pre_embd = True x = x.type(torch.cuda.FloatTensor).squeeze(1) x.cuda() else: x = x.type(torch.cuda.LongTensor) x.cuda() if train: one_hot_y = self.y2onehot(y) if batchGen.dataIter.label_weights_list: n_samples = self.get_num_samples(y, batchGen.dataIter.label_weights_list) else: n_samples = 10 #print(n_samples) pred, atted = self.net(x, bow=x_bow, train=True, true_y=one_hot_y, n_samples=n_samples, pre_embd=pre_embd, true_y_ids=y) else: pred, atted = self.net(x, bow=x_bow, pre_embd=pre_embd) output_dict = {} output_dict['pred'] = pred output_dict['y'] = y output_dict['atted'] = atted yield output_dict def application_oneSent(self, x): if self.gpu: x = x.type(torch.cuda.LongTensor) x.cuda() pred, atted = self.net(x) output_dict = {} output_dict['pred'] = pred output_dict['atted'] = atted return output_dict def get_num_samples(self, y, weight_list): n_samples = 0 for y_item in y: n_samples += weight_list[y_item.item()] return n_samples def y2onehot(self, y): num_class = self.net.n_classes one_hot_y_list = [] for i in range(len(y)): current_one_hot = [0]*num_class current_one_hot[y[i].item()] = 1 one_hot_y_list.append(copy.deepcopy(current_one_hot)) tensor_one_hot_y = torch.tensor(one_hot_y_list) if self.gpu: tensor_one_hot_y = tensor_one_hot_y.type(torch.cuda.FloatTensor) tensor_one_hot_y = tensor_one_hot_y.cuda() return tensor_one_hot_y def getTopics(self, dictProcess, ntop=10): termMatrix = self.net.get_topics() #print(termMatrix.shape) topicWordList = [] for each_topic in termMatrix: trans_list = list(enumerate(each_topic.cpu().numpy())) #print(trans_list) trans_list = sorted(trans_list, key=lambda k: k[1], reverse=True) #print(trans_list) topic_words = [dictProcess.get(item[0]) for item in trans_list[:ntop]] #print(topic_words) topicWordList.append(topic_words) return topicWordList

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CANTM-main/GateMIcateLib/ScholarPostProcessor.py

import nltk from nltk.corpus import stopwords import os import re from .WVPostProcessor import WVPostProcessor from transformers import BertTokenizer import glob import string from collections import Counter from nltk.tokenize import sent_tokenize punct_chars = list(set(string.punctuation) - set("'")) punct_chars.sort() punctuation = ''.join(punct_chars) replace = re.compile('[%s]' % re.escape(punctuation)) punct_chars_more = list(set(string.punctuation) - set(["'",","])) punct_chars_more.sort() punctuation_more = ''.join(punct_chars_more) replace_more = re.compile('[%s]' % re.escape(punctuation_more)) alpha = re.compile('^[a-zA-Z_]+$') alpha_or_num = re.compile('^[a-zA-Z_]+|[0-9_]+$') alphanum = re.compile('^[a-zA-Z0-9_]+$') def tokenize(text, strip_html=False, lower=True, keep_emails=False, keep_at_mentions=False, keep_numbers=False, keep_alphanum=False, min_length=3, stopwords=None, vocab=None, keep_pun=False): text = clean_text(text, strip_html, lower, keep_emails, keep_at_mentions, keep_pun=keep_pun) tokens = text.split() if stopwords is not None: tokens = ['_' if t in stopwords else t for t in tokens] # remove tokens that contain numbers if not keep_alphanum and not keep_numbers: tokens = [t if alpha.match(t) else '_' for t in tokens] # or just remove tokens that contain a combination of letters and numbers elif not keep_alphanum: tokens = [t if alpha_or_num.match(t) else '_' for t in tokens] # drop short tokens if min_length > 0: tokens = [t if len(t) >= min_length else '_' for t in tokens] counts = Counter() unigrams = [t for t in tokens if t != '_'] counts.update(unigrams) if vocab is not None: tokens = [token for token in unigrams if token in vocab] else: tokens = unigrams return tokens, counts def clean_text(text, strip_html=False, lower=True, keep_emails=False, keep_at_mentions=False, keep_pun=False): # remove html tags if strip_html: text = re.sub(r'<[^>]+>', '', text) else: # replace angle brackets text = re.sub(r'<', '(', text) text = re.sub(r'>', ')', text) # lower case if lower: text = text.lower() # eliminate email addresses if not keep_emails: text = re.sub(r'\S+@\S+', ' ', text) # eliminate @mentions if not keep_at_mentions: text = re.sub(r'\s@\S+', ' ', text) # replace underscores with spaces text = re.sub(r'_', ' ', text) # break off single quotes at the ends of words text = re.sub(r'\s\'', ' ', text) text = re.sub(r'\'\s', ' ', text) if not keep_pun: # remove periods text = re.sub(r'\.', '', text) # replace all other punctuation (except single quotes) with spaces text = replace.sub(' ', text) else: # remove periods text = re.sub(r'\.', '', text) # replace all other punctuation (except single quotes) with spaces text = replace_more.sub(' ', text) # remove single quotes text = re.sub(r'\'', '', text) # replace all whitespace with a single space text = re.sub(r'\s', ' ', text) # strip off spaces on either end text = text.strip() return text class ScholarPostProcessor(WVPostProcessor): def __init__(self, stopwords_source=['snowball'], min_token_length=3, **kwargs): super().__init__(**kwargs) script_path = os.path.abspath(__file__) parent = os.path.dirname(script_path) self.stopwords_source = stopwords_source self.min_token_length = min_token_length stop_list_dir = os.path.join(parent, 'stopwords') self._get_stop_words(stop_list_dir) print(self.stop_words) def _get_stop_words(self, stop_list_dir): self.stop_words = set() snowball_stopwords_list_file = os.path.join(stop_list_dir, 'snowball_stopwords.txt') mallet_stopwords_list_file = os.path.join(stop_list_dir, 'mallet_stopwords.txt') scholar_stopwords_list_file = os.path.join(stop_list_dir, 'custom_stopwords.txt') if 'snowball' in self.stopwords_source: with open(snowball_stopwords_list_file, 'r') as fin: for line in fin: stop_word = line.strip() self.stop_words.add(stop_word) def clean_source(self, source_text): #split_lines = source_text.split('\n | _') split_lines = re.split('\n|_|=|\*|\||\/', source_text) added_sent = [] for splited_line in split_lines: all_sents_split = sent_tokenize(splited_line) for each_sent in all_sents_split: keep = True line_tok, _= tokenize(splited_line, stopwords=self.stop_words) if len(line_tok) < 3: keep=False if keep: added_sent.append(splited_line) return ' '.join(added_sent) def postProcess(self, sample): split_x = [] for x_field in self.x_fields: current_rawx = self._get_sample(sample, x_field) split_x.append(current_rawx) current_rawx = ' '.join(split_x) current_rawx_tokened, _ = tokenize(current_rawx, keep_numbers=True, keep_alphanum=True, min_length=1, keep_pun=True) current_rawx = ' '.join(current_rawx_tokened) #print(current_rawx) ## Bert toknise for hidden layers. add_special_tokens not added, additional attention will be applied on token level (CLS not used) if self.embd_ready: current_x = sample['embd'] else: #cleaned_raw_x = self.clean_source(current_rawx) #print(cleaned_raw_x) cleaned_raw_x = current_rawx if len(cleaned_raw_x) > 10: current_x = self.x_pipeline(cleaned_raw_x, add_special_tokens=self.add_spec_tokens) else: current_x = self.x_pipeline(current_rawx, add_special_tokens=self.add_spec_tokens) ## NLTK tokenise and remove stopwords for topic modelling #current_x_nltk_tokened = self.nltkTokenizer(current_rawx) #current_x_nltk_tokened = self._remove_stop_words(current_x_nltk_tokened) current_x_nltk_tokened,_ = tokenize(current_rawx, stopwords=self.stop_words) if self.dictProcess: current_x_nltk_tokened = self.dictProcess.doc2countHot(current_x_nltk_tokened) x=[current_x, current_x_nltk_tokened] y = sample[self.y_field] if self.label2id: y = self.label2ids(y) if self.remove_single_list: x = self._removeSingleList(x) y = self._removeSingleList(y) return x, y def _remove_stop_words(self, tokened): remain_list = [] for token in tokened: contain_symbol, contain_number, contain_char, all_asii = self._check_string(token) keep = True if token in self.stop_words: keep = False elif len(token) < self.min_token_length: keep = False elif token.isdigit(): keep = False elif not contain_char: keep = False elif not all_asii: keep = False elif contain_number: keep = False if keep == True: remain_list.append(token) else: pass #print(token) return remain_list

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CANTM-main/GateMIcateLib/BatchIter.py

import math import random class BatchIterBert: def __init__(self, dataIter, batch_size=32, filling_last_batch=False, postProcessor=None): self.dataIter = dataIter self.batch_size = batch_size self.num_batches = self._get_num_batches() self.filling_last_batch = filling_last_batch self.postProcessor = postProcessor self.fillter = [] self._reset_iter() def _get_num_batches(self): num_batches = math.ceil(len(self.dataIter)/self.batch_size) return num_batches def _reset_iter(self): self.current_batch_idx = 0 def __iter__(self): self._reset_iter() return self def __next__(self): if self.current_batch_idx < self.num_batches: current_batch_x, current_batch_y = self._readNextBatch() self.current_batch_idx += 1 if self.postProcessor: return self.postProcessor(current_batch_x, current_batch_y) else: return current_batch_x, current_batch_y else: self._reset_iter() raise StopIteration def __len__(self): return self.num_batches def _readNextBatch(self): i = 0 batch_list_x = [] batch_list_y = [] while i < self.batch_size: try: x, y = next(self.dataIter) if self.filling_last_batch: self._update_fillter(x, y) batch_list_x.append(x) batch_list_y.append(y) i+=1 except StopIteration: if self.filling_last_batch: batch_list_x, batch_list_y = self._filling_last_batch(batch_list_x, batch_list_y) i = self.batch_size return batch_list_x, batch_list_y def _filling_last_batch(self, batch_list_x, batch_list_y): num_current_batch = len(batch_list_x) num_filling = self.batch_size - num_current_batch random.shuffle(self.fillter) filler_x = [s[0] for s in self.fillter[:num_filling]] filler_y = [s[1] for s in self.fillter[:num_filling]] batch_list_x += filler_x batch_list_y += filler_y return batch_list_x, batch_list_y def _update_fillter(self, x, y): r = random.random() if len(self.fillter) < self.batch_size: self.fillter.append([x, y]) elif r>0.9: self.fillter.pop(0) self.fillter.append([x, y])

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CANTM-main/GateMIcateLib/DictionaryProcess.py

class DictionaryProcess: def __init__(self, common_dictionary): self.common_dictionary = common_dictionary self.num_vocab = len(self.common_dictionary) def doc2bow(self, input_doc): gensim_bow_doc = self.common_dictionary.doc2bow(input_doc) return gensim_bow_doc def doc2countHot(self, input_doc): gensim_bow_doc = self.doc2bow(input_doc) doc_vec = [0] * self.num_vocab for item in gensim_bow_doc: vocab_idx = item[0] vovab_counts = item[1] doc_vec[vocab_idx] = vovab_counts return doc_vec def get(self, wordidx): return self.common_dictionary[wordidx] def __len__(self): return self.num_vocab

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CANTM-main/GateMIcateLib/ScholarProcessor.py

import nltk from nltk.corpus import stopwords import os import re from .PostprocessorBase import ReaderPostProcessorBase from transformers import BertTokenizer def tokenize(text, strip_html=False, lower=True, keep_emails=False, keep_at_mentions=False, keep_numbers=False, keep_alphanum=False, min_length=3, stopwords=None, vocab=None): text = clean_text(text, strip_html, lower, keep_emails, keep_at_mentions) tokens = text.split() if stopwords is not None: tokens = ['_' if t in stopwords else t for t in tokens] # remove tokens that contain numbers if not keep_alphanum and not keep_numbers: tokens = [t if alpha.match(t) else '_' for t in tokens] # or just remove tokens that contain a combination of letters and numbers elif not keep_alphanum: tokens = [t if alpha_or_num.match(t) else '_' for t in tokens] # drop short tokens if min_length > 0: tokens = [t if len(t) >= min_length else '_' for t in tokens] counts = Counter() unigrams = [t for t in tokens if t != '_'] counts.update(unigrams) if vocab is not None: tokens = [token for token in unigrams if token in vocab] else: tokens = unigrams return tokens, counts def clean_text(text, strip_html=False, lower=True, keep_emails=False, keep_at_mentions=False): # remove html tags if strip_html: text = re.sub(r'<[^>]+>', '', text) else: # replace angle brackets text = re.sub(r'<', '(', text) text = re.sub(r'>', ')', text) # lower case if lower: text = text.lower() # eliminate email addresses if not keep_emails: text = re.sub(r'\S+@\S+', ' ', text) # eliminate @mentions if not keep_at_mentions: text = re.sub(r'\s@\S+', ' ', text) # replace underscores with spaces text = re.sub(r'_', ' ', text) # break off single quotes at the ends of words text = re.sub(r'\s\'', ' ', text) text = re.sub(r'\'\s', ' ', text) # remove periods text = re.sub(r'\.', '', text) # replace all other punctuation (except single quotes) with spaces text = replace.sub(' ', text) # remove single quotes text = re.sub(r'\'', '', text) # replace all whitespace with a single space text = re.sub(r'\s', ' ', text) # strip off spaces on either end text = text.strip() return text class ScholarProcessor(ReaderPostProcessorBase): def __init__(self, x_fields=['Claim', 'Explaination'], y_field='selected_label', **kwargs): super().__init__(**kwargs) self.x_fields = x_fields self.y_field = y_field self.initProcessor() def initProcessor(self): bert_tokenizer_path = os.path.join(self.config['BERT'].get('bert_path'), 'tokenizer') self.bert_tokenizer = BertTokenizer.from_pretrained(bert_tokenizer_path) self.tokenizerProcessor = self.bertTokenizer self.word2idProcessor = self.bertWord2id if 'TARGET' in self.config: self.labelsFields = self.config['TARGET'].get('labels') else: self.labelsFields = ['PubAuthAction', 'CommSpread', 'GenMedAdv', 'PromActs', 'Consp', 'VirTrans', 'VirOrgn', 'PubPrep', 'Vacc', 'Prot', 'None'] #print(self.labelsFields) def postProcess(self, sample): split_x = [] for x_field in self.x_fields: current_rawx = self._get_sample(sample, x_field) split_x.append(current_rawx) current_rawx = ' '.join(split_x) ## Bert toknise for hidden layers. add_special_tokens not added, additional attention will be applied on token level (CLS not used) if self.embd_ready: current_x = sample['embd'] else: current_x = self.x_pipeline(current_rawx, add_special_tokens=self.add_spec_tokens) ## NLTK tokenise and remove stopwords for topic modelling #current_x_nltk_tokened = self.nltkTokenizer(current_rawx) #current_x_nltk_tokened = self._remove_stop_words(current_x_nltk_tokened) current_x_nltk_tokened = tokenize(current_rawx) if self.dictProcess: current_x_nltk_tokened = self.dictProcess.doc2countHot(current_x_nltk_tokened) x=[current_x, current_x_nltk_tokened] y = sample[self.y_field] if self.label2id: y = self.label2ids(y) if self.remove_single_list: x = self._removeSingleList(x) y = self._removeSingleList(y) return x, y

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CANTM-main/GateMIcateLib/__init__.py

from .BatchIter import BatchIterBert from .DictionaryProcess import DictionaryProcess from .WVPostProcessor import WVPostProcessor from .ScholarPostProcessor import ScholarPostProcessor from .modelUltiClassTopic import ModelUltiClass from .modelUlti import modelUlti as ModelUlti from .EvaluationManager import EvaluationManager from .modelUltiUpdateCATopic import ModelUltiUpdateCAtopic

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CANTM-main/GateMIcateLib/modelUlti.py

import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import numpy as np import os from pathlib import Path class modelUlti: def __init__(self, net=None, gpu=False): if net: self.net = net self.gpu = gpu if self.gpu and net: self.net.cuda() def train(self, trainBatchIter, num_epohs=100, valBatchIter=None, cache_path=None, patience=15, earlyStopping='cls_loss'): self.cache_path = cache_path output_dict = {} output_dict['accuracy'] = 'no val iter' self.evaluation_history = [] self.optimizer = optim.Adam(self.net.parameters()) self.criterion = nn.CrossEntropyLoss() if self.gpu: self.criterion.cuda() for epoch in range(num_epohs): all_loss = [] trainIter = self.pred(trainBatchIter, train=True) for current_prediction in trainIter: pred = current_prediction['pred']['y_hat'] y = current_prediction['y'] self.optimizer.zero_grad() loss = self.criterion(pred, y) loss.backward() self.optimizer.step() loss_value = float(loss.data.item()) all_loss.append(loss_value) print("Finish batch") if valBatchIter: output_dict = self.eval(valBatchIter) avg_loss = sum(all_loss)/len(all_loss) output_dict['cls_loss'] = -avg_loss if earlyStopping: stop_signal = self.earlyStop(output_dict, num_epoch=num_epohs, patience=patience, metric=earlyStopping) if stop_signal: print('stop signal received, stop training') cache_load_path = os.path.join(self.cache_path, 'best_net.model') print('finish training, load model from ', cache_load_path) self.loadWeights(cache_load_path) break print('epoch ', epoch, 'loss', avg_loss, ' val acc: ', output_dict['accuracy']) if epoch % 20 == 0: cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) cache_last_path = os.path.join(self.cache_path, 'last_net.model') self.saveWeights(cache_last_path) def earlyStop(self, output_dict, metric='accuracy', patience=40, num_epoch=None): result = output_dict[metric] stop_signal = False self.evaluation_history.append(result) num_epochs = len(self.evaluation_history) max_result = max(self.evaluation_history) max_epoch = self.evaluation_history.index(max_result) max_passed = num_epochs - max_epoch if max_passed >= patience: stop_signal = True if num_epoch: #print('num epoch passed: ', len(self.evaluation_history)) #print('max_epoches:', num_epoch) if len(self.evaluation_history) == num_epoch: stop_signal = True if max_passed == 1: print('caching best ') cache_path = os.path.join(self.cache_path, 'best_net.model') self.saveWeights(cache_path) return stop_signal def pred(self, batchGen, train=False): pre_embd = False if train: self.net.train() else: self.net.eval() i=0 for x, y in batchGen: i+=1 print("processing batch", i, end='\r') if self.gpu: y = y.type(torch.cuda.LongTensor) y.cuda() if batchGen.dataIter.postProcessor.embd_ready: pre_embd = True x = x.type(torch.cuda.FloatTensor).squeeze(1) x.cuda() else: x = x.type(torch.cuda.LongTensor) x.cuda() pred = self.net(x, pre_embd=pre_embd) output_dict = {} output_dict['pred'] = pred output_dict['y'] = y yield output_dict def eval(self, batchGen, get_perp=False): output_dict = {} all_prediction = [] all_true_label = [] all_elbo_x = [] all_elbo_xy = [] all_bow_x = [] #print(len(batchGen)) #print(len(batchGen.dataIter)) for current_prediction in self.pred(batchGen): pred = current_prediction['pred']['y_hat'] y = current_prediction['y'] current_batch_out = F.softmax(pred, dim=-1) label_prediction = torch.max(current_batch_out, -1)[1] current_batch_out_list = current_batch_out.to('cpu').detach().numpy() label_prediction_list = label_prediction.to('cpu').detach().numpy() y_list = y.to('cpu').detach().numpy() all_prediction.append(label_prediction_list) all_true_label.append(y_list) if get_perp: elbo_x = current_prediction['pred']['elbo_x'].to('cpu').detach().numpy() elbo_xy = current_prediction['pred']['elbo_xy'].to('cpu').detach().numpy() bow_x = current_prediction['x_bow'].to('cpu').detach().numpy() #print(elbo_xy) all_elbo_x.append(elbo_x) all_elbo_xy.append(elbo_xy) all_bow_x.append(bow_x) if get_perp: perplexity, log_perp = self._get_prep(all_elbo_xy, all_bow_x) output_dict['perplexity'] = perplexity output_dict['log_perplexity'] = log_perp perplexity_x_only, log_perp_x_only = self._get_prep(all_elbo_x, all_bow_x) output_dict['perplexity_x_only'] = perplexity_x_only output_dict['log_perplexity_x_only'] = log_perp_x_only all_prediction = np.concatenate(all_prediction) all_true_label = np.concatenate(all_true_label) #print(len(all_true_label)) num_correct = (all_prediction == all_true_label).sum() accuracy = num_correct / len(all_prediction) output_dict['accuracy'] = accuracy output_dict['f-measure'] = {} num_classes = len(batchGen.dataIter.postProcessor.labelsFields) for class_id in list(range(num_classes)): f_measure_score = self.fMeasure(all_prediction, all_true_label, class_id) output_dict['f-measure']['class '+str(class_id)] = f_measure_score return output_dict def _get_prep(self, all_elbo_list, all_bow_list): all_elbo = np.concatenate(all_elbo_list) all_bow = np.concatenate(all_bow_list) ############################################### ##num_token = all_bow.sum(axis=1) ##print(num_token) ##print(num_token.shape) #log_perp = np.mean(all_elbo / all_bow.sum(axis=1)) #print(log_perp) ############################################# num_token = all_bow.sum() log_perp = all_elbo.sum() / num_token ############################################# #print(log_perp) perplexity = np.exp(log_perp) #print(perplexity) return perplexity, log_perp def saveWeights(self, save_path): torch.save(self.net.state_dict(), save_path) def loadWeights(self, load_path, cpu=True): if cpu: self.net.load_state_dict(torch.load(load_path, map_location=torch.device('cpu')), strict=False) else: self.net.load_state_dict(torch.load(load_path), strict=False) self.net.eval() def fMeasure(self, all_prediction, true_label, class_id, ignoreid=None): #print(class_id) mask = [class_id] * len(all_prediction) mask_arrary = np.array(mask) pred_mask = np.argwhere(all_prediction==class_id) #print(pred_mask) true_mask = np.argwhere(true_label==class_id) #print(true_mask) #print(len(true_mask)) total_pred = 0 total_true = 0 pc = 0 for i in pred_mask: if all_prediction[i[0]] == true_label[i[0]]: pc+=1 if true_label[i[0]] != ignoreid: total_pred += 1 rc = 0 for i in true_mask: if all_prediction[i[0]] == true_label[i[0]]: rc+=1 if true_label[i[0]] != ignoreid: total_true += 1 if total_pred == 0: precision = 0 else: precision = float(pc)/total_pred if total_true == 0: recall = 0 else: recall = float(rc)/total_true if (precision+recall)==0: f_measure = 0 else: f_measure = 2*((precision*recall)/(precision+recall)) #print(total_true) return precision, recall, f_measure, total_pred, total_true, pc, rc

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CANTM

CANTM-main/GateMIcateLib/EvaluationManager.py

import sys import nltk import math from GateMIcateLib import BatchIterBert, DictionaryProcess #from GateMIcateLib import WVPostProcessor as ReaderPostProcessor from configobj import ConfigObj import torch import argparse import copy from sklearn.model_selection import KFold import random import os from pathlib import Path from gensim.corpora.dictionary import Dictionary from gensim.models import LdaModel import numpy as np def get_average_fmeasure_score(results_dict, field): t=0 score = 0 for class_field in results_dict['f-measure']: score += sum(results_dict['f-measure'][class_field][field]) t += len(results_dict['f-measure'][class_field][field]) return score/t def get_micro_fmeasure(results_dict, num_field, de_field): score = 0 for class_field in results_dict['f-measure']: numerator = sum(results_dict['f-measure'][class_field][num_field]) denominator = sum(results_dict['f-measure'][class_field][de_field]) if denominator != 0: score += numerator/denominator t = len(results_dict['f-measure']) return score/t class EvaluationManager: def __init__(self, trainReaderargs, envargs, testReaderargs=None, valReaderargs=None): self._initParams(envargs) self.trainReaderargs = trainReaderargs self.testReaderargs = testReaderargs self.valReaderargs = valReaderargs self.getLibs() self._get_train_DataIter() def get_covid_train_json_for_scholar(self): current_traindataIter=dataIter(*self.trainReaderargs, config=self.config, shuffle=False) all_json = [] for item in current_traindataIter: claim = item['Claim'] explaination = item['Explaination'] label = item['selected_label'] sample_id = item['unique_wv_id'] text = claim+' '+explaination current_dict = {} current_dict['text'] = text current_dict['sentiment'] = label current_dict['id'] = sample_id all_json.append(current_dict) return all_json def outputCorpus4NPMI(self): all_doc = [] token_count = [] current_traindataIter=dataIter(*self.trainReaderargs, config=self.config, shuffle=False) for item in current_traindataIter: alltext=[] for field in self.x_fields: current_text = nltk.word_tokenize(item[field]) token_count.append(len(current_text)) alltext.append(' '.join(current_text)) all_doc.append(' '.join(alltext)) if self.testReaderargs: self.testDataIter = dataIter(*self.testReaderargs, config=self.config, shuffle=False) for item in current_traindataIter: alltext=[] for field in self.x_fields: current_text = nltk.word_tokenize(item[field]) token_count.append(len(current_text)) alltext.append(' '.join(current_text)) all_doc.append(' '.join(alltext)) print(sum(token_count)/len(token_count)) return all_doc def _get_train_DataIter(self): self.postProcessor = ReaderPostProcessor(config=self.config, word2id=True, remove_single_list=False, add_spec_tokens=True, x_fields=self.x_fields, y_field=self.y_field, max_sent_len=self.max_sent_len) print(*self.trainReaderargs) self.trainDataIter = dataIter(*self.trainReaderargs, postProcessor=self.postProcessor, config=self.config, shuffle=True) if self.testReaderargs: self.testDataIter = dataIter(*self.testReaderargs, postProcessor=self.postProcessor, config=self.config, shuffle=False) print(self.get_dict) if self.get_dict: print('building dict') self.buildDict() if self.preEmbd: print('pre calculating embedding') net = Model(self.config, vocab_dim=self.vocab_dim) mUlti = modelUlti(net, gpu=self.gpu) self.trainDataIter.preCalculateEmbed(mUlti.net.bert_embedding, 0) if not self.testReaderargs: self.all_ids = copy.deepcopy(self.trainDataIter.all_ids) random.shuffle(self.all_ids) ## deep copy train reader to test reader self.testDataIter = copy.deepcopy(self.trainDataIter) self.valDataIter = None def _initParams(self,envargs): print(envargs) self.get_perp = False self.get_dict = False self.vocab_dim = None self.have_dict = False self.config_file = envargs.get('configFile',None) self.config = ConfigObj(self.config_file) self.cache_path = envargs.get('cachePath',None) self.n_fold = envargs.get('nFold',5) self.randomSeed = envargs.get('randomSeed',None) self.preEmbd = envargs.get('preEmbd',False) self.dynamicSampling = envargs.get('dynamicSampling',False) self.modelType = envargs.get('model', 'clsTopic') self.corpusType = envargs.get('corpusType', 'wvmisinfo') self.max_sent_len = envargs.get('max_sent_len', '300') self.num_epoches = envargs.get('num_epoches', 150) self.patient = envargs.get('patient', 40) self.batch_size = envargs.get('batch_size', 32) self.earlyStopping = envargs.get('earlyStopping', 'cls_loss') self.x_fields = envargs.get('x_fields', 'Claim,Explaination') self.x_fields = self.x_fields.split(',') print(self.x_fields) self.y_field = envargs.get('y_field', 'selected_label') self.dict_no_below = envargs.get('dict_no_below', 0) self.dict_no_above = envargs.get('dict_no_above', 1.0) self.dict_keep_n = envargs.get('dict_keep_n', 5000) self.splitValidation = envargs.get('splitValidation',None) self.inspectTest = envargs.get('inspectTest', True) self.trainLDA = envargs.get('trainLDA', False) self.gpu = envargs.get('gpu', True) self.envargs = envargs def train_lda(self, cache_path): print(cache_path) trainBatchIter = BatchIterBert(self.trainDataIter, filling_last_batch=False, postProcessor=batchPostProcessor, batch_size=1) bow_list = [] for item in trainBatchIter: bow = item[1].squeeze().detach().numpy().tolist() bow_list.append(self.bow_2_gensim(bow)) print(len(bow_list)) #print(self.dictProcess.common_dictionary.id2token) lda = LdaModel(np.array(bow_list), num_topics=50, passes=200, chunksize=len(bow_list), id2word=self.dictProcess.common_dictionary) #print(lda.show_topic(1, topn=10)) output_topic_line = '' for topic_id in range(50): current_topic_list = [] current_topic = lda.show_topic(topic_id, topn=10) for topic_tuple in current_topic: current_topic_list.append(topic_tuple[0]) output_topic_line += ' '.join(current_topic_list)+'\n' #print(current_topic_list) topic_file = os.path.join(cache_path, 'ldatopic.txt') with open(topic_file, 'w') as fo: fo.write(output_topic_line) testBatchIter = BatchIterBert(self.testDataIter, filling_last_batch=False, postProcessor=batchPostProcessor, batch_size=1) test_bow_list = [] word_count = 0 for item in testBatchIter: bow = item[1].squeeze().detach().numpy().tolist() word_count += sum(bow) test_bow_list.append(self.bow_2_gensim(bow)) print(word_count) ppl = lda.log_perplexity(test_bow_list, len(test_bow_list)) print(ppl) bound = lda.bound(test_bow_list) print(bound/word_count) print(np.exp2(-bound/word_count)) def bow_2_gensim(self, bow): gensim_format = [] for idx, count in enumerate(bow): if count > 0: gensim_format.append((idx,count)) return gensim_format def train(self, cache_path=None): if self.inspectTest and (not self.splitValidation): print('inspecting test, please dont use val acc as early stoping') self.valDataIter = self.testDataIter elif self.inspectTest and self.splitValidation: print('inspectTest and splitValidation can not use same time') print('deset inspectTest') self.inspectTest = False if self.splitValidation: print('splitting test for validation') self.valDataIter = copy.deepcopy(self.trainDataIter) train_val_ids = copy.deepcopy(self.trainDataIter.all_ids) random.shuffle(train_val_ids) split_4_train = 1-self.splitValidation top_n_4_train = math.floor(len(train_val_ids) * split_4_train) id_4_train = train_val_ids[:top_n_4_train] id_4_val = train_val_ids[top_n_4_train:] self.trainDataIter.all_ids = id_4_train self.valDataIter.all_ids = id_4_val assert self.inspectTest != self.splitValidation, 'splitValidation will overwrite inspectTest, dont use at the same time' if self.dynamicSampling: print('get training data sample weights') trainDataIter.cal_sample_weights() self.trainDataIter._reset_iter() trainBatchIter = BatchIterBert(self.trainDataIter, filling_last_batch=True, postProcessor=batchPostProcessor, batch_size=self.batch_size) if self.valDataIter: self.valDataIter._reset_iter() valBatchIter = BatchIterBert(self.valDataIter, filling_last_batch=False, postProcessor=batchPostProcessor, batch_size=self.batch_size) else: valBatchIter = None print(self.vocab_dim) net = Model(self.config, vocab_dim=self.vocab_dim) self.mUlti = modelUlti(net, gpu=self.gpu) #print(next(trainBatchIter)) self.mUlti.train(trainBatchIter, cache_path=cache_path, num_epohs=self.num_epoches, valBatchIter=valBatchIter, patience=self.patient, earlyStopping=self.earlyStopping) def train_test_evaluation(self): path = Path(self.cache_path) path.mkdir(parents=True, exist_ok=True) self.train(cache_path=self.cache_path) testBatchIter = BatchIterBert(self.testDataIter, filling_last_batch=False, postProcessor=batchPostProcessor, batch_size=self.batch_size) results = self.mUlti.eval(testBatchIter, get_perp=self.get_perp) print(results) def train_model_only(self): path = Path(self.cache_path) path.mkdir(parents=True, exist_ok=True) self.train(cache_path=self.cache_path) def cross_fold_evaluation(self): kf = KFold(n_splits=self.n_fold) fold_index = 1 results_dict = {} results_dict['accuracy'] = [] results_dict['perplexity'] = [] results_dict['log_perplexity'] = [] results_dict['perplexity_x_only'] = [] results_dict['f-measure'] = {} for each_fold in kf.split(self.all_ids): train_ids, test_ids = self.reconstruct_ids(each_fold) self.trainDataIter.all_ids = train_ids self.testDataIter.all_ids = test_ids self.testDataIter._reset_iter() fold_cache_path = os.path.join(self.cache_path, 'fold'+str(fold_index)) path = Path(fold_cache_path) path.mkdir(parents=True, exist_ok=True) if self.trainLDA: self.train_lda(cache_path=fold_cache_path) else: self.train(cache_path=fold_cache_path) testBatchIter = BatchIterBert(self.testDataIter, filling_last_batch=False, postProcessor=batchPostProcessor, batch_size=self.batch_size) results = self.mUlti.eval(testBatchIter, get_perp=self.get_perp) print(results) results_dict['accuracy'].append(results['accuracy']) if 'perplexity' in results: results_dict['perplexity'].append(results['perplexity']) results_dict['log_perplexity'].append(results['log_perplexity']) results_dict['perplexity_x_only'].append(results['perplexity_x_only']) for f_measure_class in results['f-measure']: if f_measure_class not in results_dict['f-measure']: results_dict['f-measure'][f_measure_class] = {'precision':[], 'recall':[], 'f-measure':[], 'total_pred':[], 'total_true':[], 'matches':[]} results_dict['f-measure'][f_measure_class]['precision'].append(results['f-measure'][f_measure_class][0]) results_dict['f-measure'][f_measure_class]['recall'].append(results['f-measure'][f_measure_class][1]) results_dict['f-measure'][f_measure_class]['f-measure'].append(results['f-measure'][f_measure_class][2]) results_dict['f-measure'][f_measure_class]['total_pred'].append(results['f-measure'][f_measure_class][3]) results_dict['f-measure'][f_measure_class]['total_true'].append(results['f-measure'][f_measure_class][4]) results_dict['f-measure'][f_measure_class]['matches'].append(results['f-measure'][f_measure_class][5]) fold_index += 1 print(results_dict) overall_accuracy = sum(results_dict['accuracy'])/len(results_dict['accuracy']) if len(results_dict['perplexity']) >0: overall_perplexity = sum(results_dict['perplexity'])/len(results_dict['perplexity']) print('perplexity: ', overall_perplexity) overall_log_perplexity = sum(results_dict['log_perplexity'])/len(results_dict['log_perplexity']) print('log perplexity: ', overall_log_perplexity) overall_perplexity_x = sum(results_dict['perplexity_x_only'])/len(results_dict['perplexity_x_only']) print('perplexity_x_only: ', overall_perplexity_x) macro_precision = get_average_fmeasure_score(results_dict, 'precision') macro_recall = get_average_fmeasure_score(results_dict, 'recall') macro_fmeasure = get_average_fmeasure_score(results_dict, 'f-measure') micro_precision = get_micro_fmeasure(results_dict, 'matches', 'total_pred') micro_recall = get_micro_fmeasure(results_dict, 'matches', 'total_true') micro_fmeasure = 2*((micro_precision*micro_recall)/(micro_precision+micro_recall)) print('accuracy: ', overall_accuracy) print('micro_precision: ', micro_precision) print('micro_recall: ', micro_recall) print('micro_f-measure: ', micro_fmeasure) print('macro_precision: ', macro_precision) print('macro_recall: ', macro_recall) print('macro_f-measure: ', macro_fmeasure) def reconstruct_ids(self, each_fold): output_ids = [[],[]] #[train_ids, test_ids] for sp_id in range(len(each_fold)): current_output_ids = output_ids[sp_id] current_fold_ids = each_fold[sp_id] for doc_id in current_fold_ids: current_output_ids.append(self.all_ids[doc_id]) return output_ids def buildDict(self): batchiter = BatchIterBert(self.trainDataIter, filling_last_batch=False, postProcessor=xonlyBatchProcessor, batch_size=1) common_dictionary = Dictionary(batchiter) print(len(common_dictionary)) if self.testReaderargs: print('update vocab from test set') batchiter = BatchIterBert(self.testDataIter, filling_last_batch=False, postProcessor=xonlyBatchProcessor, batch_size=1) common_dictionary.add_documents(batchiter) print(len(common_dictionary)) common_dictionary.filter_extremes(no_below=self.dict_no_below, no_above=self.dict_no_above, keep_n=self.dict_keep_n) self.dictProcess = DictionaryProcess(common_dictionary) self.postProcessor.dictProcess = self.dictProcess self.vocab_dim = len(self.dictProcess) self.have_dict = True if 1: count_list = [] self.trainDataIter._reset_iter() batchiter = BatchIterBert(self.trainDataIter, filling_last_batch=False, postProcessor=xonlyBatchProcessor, batch_size=1) for item in batchiter: current_count = sum(item) count_list.append(current_count) #print(current_count) print(sum(count_list)/len(count_list)) def getModel(self): self.net = Model(config, vocab_dim=vocab_dim) def getLibs(self): print('getting libs') print(self.modelType) global modelUlti global Model global xonlyBatchProcessor global batchPostProcessor global dataIter global ReaderPostProcessor if self.modelType == 'clsTopic': from GateMIcateLib import ModelUltiClass as modelUlti from GateMIcateLib.models import CLSAW_TopicModel as Model from GateMIcateLib.batchPostProcessors import xonlyBatchProcessor from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor self.get_dict = True self.get_perp = True elif self.modelType == 'clsTopicSL': from GateMIcateLib import ModelUltiClass as modelUlti from GateMIcateLib.models import CLSAW_TopicModelSL as Model from GateMIcateLib.batchPostProcessors import xonlyBatchProcessor from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor self.get_dict = True self.get_perp = True elif self.modelType == 'baselineBert': from GateMIcateLib import ModelUlti as modelUlti from GateMIcateLib.models import BERT_Simple as Model from GateMIcateLib.batchPostProcessors import xyOnlyBertBatchProcessor as batchPostProcessor elif self.modelType == 'nvdm': from GateMIcateLib import ModelUltiClass as modelUlti from GateMIcateLib.models import NVDM as Model from GateMIcateLib.batchPostProcessors import xonlyBatchProcessor from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor self.get_dict = True self.get_perp = True elif self.modelType == 'orinvdm': from GateMIcateLib import ModelUltiClass as modelUlti from GateMIcateLib.models import ORINVDM as Model from GateMIcateLib.batchPostProcessors import xonlyBatchProcessor from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor self.get_dict = True self.get_perp = True elif self.modelType == 'clsTopicBE': from GateMIcateLib import ModelUltiClass as modelUlti from GateMIcateLib.models import CLSAW_TopicModel_BERTEN as Model from GateMIcateLib.batchPostProcessors import xonlyBatchProcessor from GateMIcateLib.batchPostProcessors import bowBertBatchProcessor as batchPostProcessor self.get_dict = True self.get_perp = True print(self.corpusType) if self.corpusType == 'wvmisinfo': from GateMIcateLib.readers import WVmisInfoDataIter as dataIter from GateMIcateLib import WVPostProcessor as ReaderPostProcessor self.dict_no_below = 3 self.dict_no_above = 0.7 elif self.corpusType == 'wvmisinfoScholar': from GateMIcateLib.readers import WVmisInfoDataIter as dataIter from GateMIcateLib import ScholarPostProcessor as ReaderPostProcessor self.dict_keep_n = 2000 elif self.corpusType == 'aclIMDB': from GateMIcateLib.readers import ACLimdbReader as dataIter from GateMIcateLib import ScholarPostProcessor as ReaderPostProcessor elif self.corpusType == 'tsvBinary': from GateMIcateLib.readers import TsvBinaryFolderReader as dataIter from GateMIcateLib import WVPostProcessor as ReaderPostProcessor self.dict_no_below = 3 self.dict_no_above = 0.7

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CANTM

CANTM-main/GateMIcateLib/WVPostProcessor.py

import nltk from nltk.corpus import stopwords import os import re from .PostprocessorBase import ReaderPostProcessorBase from transformers import BertTokenizer class WVPostProcessor(ReaderPostProcessorBase): def __init__(self, x_fields=['Claim', 'Explaination'], y_field='category', **kwargs): super().__init__(**kwargs) self.x_fields = x_fields self.y_field = y_field self.initProcessor() def initProcessor(self): bert_tokenizer_path = os.path.join(self.config['BERT'].get('bert_path'), 'tokenizer') self.bert_tokenizer = BertTokenizer.from_pretrained(bert_tokenizer_path) self.tokenizerProcessor = self.bertTokenizer self.word2idProcessor = self.bertWord2id if 'TARGET' in self.config: self.labelsFields = self.config['TARGET'].get('labels') else: self.labelsFields = ['PubAuthAction', 'CommSpread', 'GenMedAdv', 'PromActs', 'Consp', 'VirTrans', 'VirOrgn', 'PubPrep', 'Vacc', 'Prot', 'None'] #print(self.labelsFields) def postProcess(self, sample): split_x = [] for x_field in self.x_fields: current_rawx = self._get_sample(sample, x_field) split_x.append(current_rawx) current_rawx = ' '.join(split_x) ## Bert toknise for hidden layers. add_special_tokens not added, additional attention will be applied on token level (CLS not used) if self.embd_ready: current_x = sample['embd'] else: current_x = self.x_pipeline(current_rawx, add_special_tokens=self.add_spec_tokens) ## NLTK tokenise and remove stopwords for topic modelling current_x_nltk_tokened = self.nltkTokenizer(current_rawx) current_x_nltk_tokened = self._remove_stop_words(current_x_nltk_tokened) if self.dictProcess: current_x_nltk_tokened = self.dictProcess.doc2countHot(current_x_nltk_tokened) x=[current_x, current_x_nltk_tokened] y = sample[self.y_field] if self.label2id: y = self.label2ids(y) if self.remove_single_list: x = self._removeSingleList(x) y = self._removeSingleList(y) return x, y

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CANTM-main/GateMIcateLib/models/CLSAW_TopicModel_simple_loss.py

import torch from torch import nn from torch.nn import init from torch.nn import functional as F import math from .miscLayer import BERT_Embedding, WVHidden, WVClassifier, Identity, Topics, kld, CLSAW_TopicModel_Base class CLSAW_TopicModelSL(CLSAW_TopicModel_Base): def __init__(self, config, vocab_dim=None): super().__init__(config=config) default_config = {} self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim if self.banlance_loss: self.banlance_lambda = float(math.ceil(vocab_dim/self.n_classes)) else: self.banlance_lambda = 1 #self.wv_hidden = WVHidden(bert_dim, self.hidden_dim) self.hidden_dim = bert_dim ##############M1########################################### self.mu_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.x_only_topics = Topics(self.z_dim, vocab_dim) self.xy_classifier = WVClassifier(self.z_dim, self.n_classes) self.class_criterion = nn.CrossEntropyLoss() #############M2############################################ self.hidden_y_dim = self.hidden_dim + self.n_classes self.z_y_dim = self.z_dim + self.n_classes self.x_y_hidden = WVHidden(self.hidden_y_dim, self.hidden_dim) self.z_y_hidden = WVHidden(self.z_y_dim, self.ntopics) self.mu_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.xy_topics = Topics(self.ntopics, vocab_dim) self.z2y_classifier = WVClassifier(self.ntopics, self.n_classes) ############################################################ self.h_to_z = Identity() self.class_topics = Topics(self.n_classes, vocab_dim) self.reset_parameters() def forward(self,x, mask=None, n_samples=1, bow=None, train=False, true_y=None, pre_embd=False, true_y_ids=None): #print(true_y.shape) if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] atted = bert_rep[:,0] #hidden = self.wv_hidden(atted) hidden = atted mu_z1 = self.mu_z1(hidden) log_sigma_z1 = self.log_sigma_z1(hidden) kldz1 = kld(mu_z1, log_sigma_z1) rec_loss_z1 = 0 classifier_loss = 0 kldz2 = 0 rec_loss_z2 = 0 log_y_hat_rec_loss = 0 class_topic_rec_loss = 0 if not train: ### for discriminator, we only use mean z1 = mu_z1 y_hat_logis = self.xy_classifier(z1) log_probz_1 = self.x_only_topics(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) log_prob_class_topic = self.class_topics(y_hat) #y = y_hat_logis for i in range(n_samples): if train: z1 = torch.zeros_like(mu_z1).normal_() * torch.exp(log_sigma_z1) + mu_z1 z1 = self.h_to_z(z1) log_probz_1 = self.x_only_topics(z1) y_hat_logis = self.xy_classifier(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) log_prob_class_topic = self.class_topics(y_hat) classifier_loss += self.class_criterion(y_hat_logis, true_y_ids) y_hat_h = torch.cat((hidden, y_hat), dim=-1) x_y_hidden = self.x_y_hidden(y_hat_h) mu_z2 = self.mu_z2(x_y_hidden) log_sigma_z2 = self.log_sigma_z2(x_y_hidden) z2 = torch.zeros_like(mu_z2).normal_() * torch.exp(log_sigma_z2) + mu_z2 topic = z2 log_prob_z2 = self.xy_topics(topic) #y_hat_rec = self.z2y_classifier(topic) #log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) rec_loss_z1 = rec_loss_z1-(log_probz_1 * bow).sum(dim=-1) kldz2 += kld(mu_z2, log_sigma_z2) rec_loss_z2 = rec_loss_z2 - (log_prob_z2 * bow).sum(dim=-1) #log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_rec*true_y).sum(dim=-1) #log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_rec*y_hat).sum(dim=-1) class_topic_rec_loss = class_topic_rec_loss - (log_prob_class_topic*bow).sum(dim=-1) rec_loss_z1 = rec_loss_z1/n_samples #print(rec_loss_z1.shape) classifier_loss = classifier_loss/n_samples kldz2 = kldz2/n_samples rec_loss_z2 = rec_loss_z2/n_samples log_y_hat_rec_loss = log_y_hat_rec_loss/n_samples class_topic_rec_loss = class_topic_rec_loss/n_samples elbo_z1 = kldz1 + rec_loss_z1 #print(elbo_z1.shape) #elbo_z1 = elbo_z1.sum() elbo_z2 = kldz2 + rec_loss_z2# + log_y_hat_rec_loss #print(elbo_z2) #elbo_z2 = elbo_z2.sum() #class_topic_rec_loss = class_topic_rec_loss.sum() classifier_loss = classifier_loss total_loss = elbo_z1.sum() + elbo_z2.sum() + class_topic_rec_loss.sum() + classifier_loss*self.banlance_lambda*self.classification_loss_lambda y = { 'loss': total_loss, 'elbo_xy': elbo_z2, 'rec_loss': rec_loss_z2, 'kld': kldz2, 'cls_loss': classifier_loss, 'class_topic_loss': class_topic_rec_loss, 'y_hat': y_hat_logis, 'elbo_x': elbo_z1 } #################################################################################################################################################### # else: # z1 = mu_z1 # y_hat_logis = self.xy_classifier(z1) # y_hat = torch.softmax(y_hat_logis, dim=-1) # y = y_hat_logis # # # y_hat_h = torch.cat((hidden, y_hat), dim=-1) # x_y_hidden = self.x_y_hidden(y_hat_h) # mu_z2 = self.mu_z2(x_y_hidden) # log_sigma_z2 = self.log_sigma_z2(x_y_hidden) # z2 = torch.zeros_like(mu_z2).normal_() * torch.exp(log_sigma_z2) + mu_z2 # # kldz2 = kld(mu_z2, log_sigma_z2) # log_prob_z2 = self.xy_topics(z2) # y_hat_rec = self.z2y_classifier(z2) # log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) # # return y, None def reset_parameters(self): init.zeros_(self.log_sigma_z1.weight) init.zeros_(self.log_sigma_z1.bias) init.zeros_(self.log_sigma_z2.weight) init.zeros_(self.log_sigma_z2.bias) def get_topics(self): return self.xy_topics.get_topics() def get_class_topics(self): return self.class_topics.get_topics() def get_x_only_topics(self): return self.x_only_topics.get_topics()

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CANTM-main/GateMIcateLib/models/CLSAW_TopicModel.py

import torch from torch import nn from torch.nn import init from torch.nn import functional as F import math from .miscLayer import BERT_Embedding, WVHidden, WVClassifier, Identity, Topics, kld, CLSAW_TopicModel_Base class CLSAW_TopicModel(CLSAW_TopicModel_Base): def __init__(self, config, vocab_dim=None): super().__init__(config=config) default_config = {} self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim if self.banlance_loss: self.banlance_lambda = float(math.ceil(vocab_dim/self.n_classes)) else: self.banlance_lambda = 1 #self.wv_hidden = WVHidden(bert_dim, self.hidden_dim) self.hidden_dim = bert_dim ##############M1########################################### self.mu_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.x_only_topics = Topics(self.z_dim, vocab_dim) self.xy_classifier = WVClassifier(self.z_dim, self.n_classes) self.class_criterion = nn.CrossEntropyLoss() #############M2############################################ self.hidden_y_dim = self.hidden_dim + self.n_classes self.z_y_dim = self.z_dim + self.n_classes self.x_y_hidden = WVHidden(self.hidden_y_dim, self.hidden_dim) self.z_y_hidden = WVHidden(self.z_y_dim, self.ntopics) self.mu_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.xy_topics = Topics(self.ntopics, vocab_dim) self.z2y_classifier = WVClassifier(self.ntopics, self.n_classes) ############################################################ self.h_to_z = Identity() self.class_topics = Topics(self.n_classes, vocab_dim) self.reset_parameters() def forward(self,x, mask=None, n_samples=1, bow=None, train=False, true_y=None, pre_embd=False, true_y_ids=None, update_catopic=False): #print(true_y.shape) if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] atted = bert_rep[:,0] #hidden = self.wv_hidden(atted) hidden = atted mu_z1 = self.mu_z1(hidden) log_sigma_z1 = self.log_sigma_z1(hidden) kldz1 = kld(mu_z1, log_sigma_z1) rec_loss_z1 = 0 classifier_loss = 0 kldz2 = 0 rec_loss_z2 = 0 log_y_hat_rec_loss = 0 class_topic_rec_loss = 0 #if not train: # ### for discriminator, we only use mean # z1 = mu_z1 # y_hat_logis = self.xy_classifier(z1) # log_probz_1 = self.x_only_topics(z1) # y_hat = torch.softmax(y_hat_logis, dim=-1) # log_prob_class_topic = self.class_topics(y_hat) # #y = y_hat_logis for i in range(n_samples): z1 = torch.zeros_like(mu_z1).normal_() * torch.exp(log_sigma_z1) + mu_z1 z1 = self.h_to_z(z1) log_probz_1 = self.x_only_topics(z1) #if train or update_catopic: # z1 = torch.zeros_like(mu_z1).normal_() * torch.exp(log_sigma_z1) + mu_z1 # z1 = self.h_to_z(z1) # log_probz_1 = self.x_only_topics(z1) # y_hat_logis = self.xy_classifier(z1) # y_hat = torch.softmax(y_hat_logis, dim=-1) # log_prob_class_topic = self.class_topics(y_hat) if train or update_catopic: y_hat_logis = self.xy_classifier(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) #print(y_hat.shape) else: y_hat_logis = self.xy_classifier(mu_z1) y_hat = torch.softmax(y_hat_logis, dim=-1) if train: classifier_loss += self.class_criterion(y_hat_logis, true_y_ids) log_prob_class_topic = self.class_topics(y_hat) y_hat_h = torch.cat((hidden, y_hat), dim=-1) x_y_hidden = self.x_y_hidden(y_hat_h) mu_z2 = self.mu_z2(x_y_hidden) log_sigma_z2 = self.log_sigma_z2(x_y_hidden) z2 = torch.zeros_like(mu_z2).normal_() * torch.exp(log_sigma_z2) + mu_z2 y_hat_z = torch.cat((z2, y_hat), dim=-1) topic = self.z_y_hidden(y_hat_z) log_prob_z2 = self.xy_topics(topic) y_hat_rec = self.z2y_classifier(topic) log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) rec_loss_z1 = rec_loss_z1-(log_probz_1 * bow).sum(dim=-1) kldz2 += kld(mu_z2, log_sigma_z2) rec_loss_z2 = rec_loss_z2 - (log_prob_z2 * bow).sum(dim=-1) #log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_rec*true_y).sum(dim=-1) log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_rec*y_hat).sum(dim=-1) class_topic_rec_loss = class_topic_rec_loss - (log_prob_class_topic*bow).sum(dim=-1) rec_loss_z1 = rec_loss_z1/n_samples #print(rec_loss_z1.shape) classifier_loss = classifier_loss/n_samples kldz2 = kldz2/n_samples rec_loss_z2 = rec_loss_z2/n_samples log_y_hat_rec_loss = log_y_hat_rec_loss/n_samples class_topic_rec_loss = class_topic_rec_loss/n_samples elbo_z1 = kldz1 + rec_loss_z1 #print(elbo_z1.shape) #elbo_z1 = elbo_z1.sum() elbo_z2 = kldz2 + rec_loss_z2 + log_y_hat_rec_loss #print(elbo_z2) #elbo_z2 = elbo_z2.sum() #class_topic_rec_loss = class_topic_rec_loss.sum() classifier_loss = classifier_loss total_loss = elbo_z1.sum() + elbo_z2.sum() + class_topic_rec_loss.sum() + classifier_loss*self.banlance_lambda*self.classification_loss_lambda if update_catopic: total_loss = elbo_z2.sum() y = { 'loss': total_loss, 'elbo_xy': elbo_z2, 'rec_loss': rec_loss_z2, 'kld': kldz2, 'cls_loss': classifier_loss, 'class_topic_loss': class_topic_rec_loss, 'y_hat': y_hat_logis, 'elbo_x': elbo_z1 } #################################################################################################################################################### # else: # z1 = mu_z1 # y_hat_logis = self.xy_classifier(z1) # y_hat = torch.softmax(y_hat_logis, dim=-1) # y = y_hat_logis # # # y_hat_h = torch.cat((hidden, y_hat), dim=-1) # x_y_hidden = self.x_y_hidden(y_hat_h) # mu_z2 = self.mu_z2(x_y_hidden) # log_sigma_z2 = self.log_sigma_z2(x_y_hidden) # z2 = torch.zeros_like(mu_z2).normal_() * torch.exp(log_sigma_z2) + mu_z2 # # kldz2 = kld(mu_z2, log_sigma_z2) # log_prob_z2 = self.xy_topics(z2) # y_hat_rec = self.z2y_classifier(z2) # log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) # # return y, None def reset_parameters(self): init.zeros_(self.log_sigma_z1.weight) init.zeros_(self.log_sigma_z1.bias) init.zeros_(self.log_sigma_z2.weight) init.zeros_(self.log_sigma_z2.bias) def get_topics(self): return self.xy_topics.get_topics() def get_class_topics(self): return self.class_topics.get_topics() def get_x_only_topics(self): return self.x_only_topics.get_topics()

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CANTM-main/GateMIcateLib/models/CLSAW_TopicModelBertEnrich.py

import torch from torch import nn from torch.nn import init from torch.nn import functional as F import math from .miscLayer import BERT_Embedding, WVHidden, WVClassifier, Identity, Topics, kld, CLSAW_TopicModel_Base class CLSAW_TopicModel_BERTEN(CLSAW_TopicModel_Base): def __init__(self, config, vocab_dim=None): super().__init__(config=config) default_config = {} self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim if self.banlance_loss: self.banlance_lambda = float(math.ceil(vocab_dim/self.n_classes)) else: self.banlance_lambda = 1 self.hidden_dim = 500 self.bow_hidden = WVHidden(vocab_dim, 500) self.mix_bert = WVHidden(500+ bert_dim, self.hidden_dim) ##############M1########################################### self.mu_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.x_only_topics = Topics(self.z_dim, vocab_dim) self.xy_classifier = WVClassifier(self.z_dim, self.n_classes) self.class_criterion = nn.CrossEntropyLoss() #############M2############################################ self.hidden_y_dim = self.hidden_dim + self.n_classes self.z_y_dim = self.z_dim + self.n_classes self.x_y_hidden = WVHidden(self.hidden_y_dim, self.hidden_dim) self.z_y_hidden = WVHidden(self.z_y_dim, self.ntopics) self.mu_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.xy_topics = Topics(self.ntopics, vocab_dim) self.z2y_classifier = WVClassifier(self.ntopics, self.n_classes) ############################################################ self.h_to_z = Identity() self.class_topics = Topics(self.n_classes, vocab_dim) self.reset_parameters() def forward(self,x, mask=None, n_samples=1, bow=None, train=False, true_y=None, pre_embd=False, true_y_ids=None): #print(true_y.shape) if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] bow_hidden = self.bow_hidden(bow) atted = bert_rep[:,0] bert_bow = torch.cat((atted, bow_hidden), dim=-1) hidden = self.mix_bert(bert_bow) #hidden = atted mu_z1 = self.mu_z1(hidden) log_sigma_z1 = self.log_sigma_z1(hidden) kldz1 = kld(mu_z1, log_sigma_z1) rec_loss_z1 = 0 classifier_loss = 0 kldz2 = 0 rec_loss_z2 = 0 log_y_hat_rec_loss = 0 class_topic_rec_loss = 0 if not train: ### for discriminator, we only use mean z1 = mu_z1 y_hat_logis = self.xy_classifier(z1) log_probz_1 = self.x_only_topics(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) log_prob_class_topic = self.class_topics(y_hat) #y = y_hat_logis for i in range(n_samples): if train: z1 = torch.zeros_like(mu_z1).normal_() * torch.exp(log_sigma_z1) + mu_z1 z1 = self.h_to_z(z1) log_probz_1 = self.x_only_topics(z1) y_hat_logis = self.xy_classifier(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) log_prob_class_topic = self.class_topics(y_hat) classifier_loss += self.class_criterion(y_hat_logis, true_y_ids) y_hat_h = torch.cat((hidden, y_hat), dim=-1) x_y_hidden = self.x_y_hidden(y_hat_h) mu_z2 = self.mu_z2(x_y_hidden) log_sigma_z2 = self.log_sigma_z2(x_y_hidden) z2 = torch.zeros_like(mu_z2).normal_() * torch.exp(log_sigma_z2) + mu_z2 y_hat_z = torch.cat((z2, y_hat), dim=-1) topic = self.z_y_hidden(y_hat_z) log_prob_z2 = self.xy_topics(topic) y_hat_rec = self.z2y_classifier(topic) log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) rec_loss_z1 = rec_loss_z1-(log_probz_1 * bow).sum(dim=-1) kldz2 += kld(mu_z2, log_sigma_z2) rec_loss_z2 = rec_loss_z2 - (log_prob_z2 * bow).sum(dim=-1) #log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_rec*true_y).sum(dim=-1) log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_rec*y_hat).sum(dim=-1) class_topic_rec_loss = class_topic_rec_loss - (log_prob_class_topic*bow).sum(dim=-1) rec_loss_z1 = rec_loss_z1/n_samples #print(rec_loss_z1.shape) classifier_loss = classifier_loss/n_samples kldz2 = kldz2/n_samples rec_loss_z2 = rec_loss_z2/n_samples log_y_hat_rec_loss = log_y_hat_rec_loss/n_samples class_topic_rec_loss = class_topic_rec_loss/n_samples elbo_z1 = kldz1 + rec_loss_z1 #print(elbo_z1.shape) #elbo_z1 = elbo_z1.sum() elbo_z2 = kldz2 + rec_loss_z2 + log_y_hat_rec_loss #print(elbo_z2) #elbo_z2 = elbo_z2.sum() #class_topic_rec_loss = class_topic_rec_loss.sum() classifier_loss = classifier_loss total_loss = elbo_z1.sum() + elbo_z2.sum() + class_topic_rec_loss.sum() + classifier_loss*self.banlance_lambda*self.classification_loss_lambda y = { 'loss': total_loss, 'elbo_xy': elbo_z2, 'rec_loss': rec_loss_z2, 'kld': kldz2, 'cls_loss': classifier_loss, 'class_topic_loss': class_topic_rec_loss, 'y_hat': y_hat_logis, 'elbo_x': elbo_z1 } #################################################################################################################################################### # else: # z1 = mu_z1 # y_hat_logis = self.xy_classifier(z1) # y_hat = torch.softmax(y_hat_logis, dim=-1) # y = y_hat_logis # # # y_hat_h = torch.cat((hidden, y_hat), dim=-1) # x_y_hidden = self.x_y_hidden(y_hat_h) # mu_z2 = self.mu_z2(x_y_hidden) # log_sigma_z2 = self.log_sigma_z2(x_y_hidden) # z2 = torch.zeros_like(mu_z2).normal_() * torch.exp(log_sigma_z2) + mu_z2 # # kldz2 = kld(mu_z2, log_sigma_z2) # log_prob_z2 = self.xy_topics(z2) # y_hat_rec = self.z2y_classifier(z2) # log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) # # return y, None def reset_parameters(self): init.zeros_(self.log_sigma_z1.weight) init.zeros_(self.log_sigma_z1.bias) init.zeros_(self.log_sigma_z2.weight) init.zeros_(self.log_sigma_z2.bias) def get_topics(self): return self.xy_topics.get_topics() def get_class_topics(self): return self.class_topics.get_topics() def get_x_only_topics(self): return self.x_only_topics.get_topics()

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CANTM

CANTM-main/GateMIcateLib/models/miscLayer.py

from transformers import BertModel import math import os import torch.nn.functional as F import torch import torch.nn as nn class SingleHeadAttention(nn.Module): def __init__(self, d_model, d_output, dropout = 0.1): super().__init__() self.q = nn.Parameter(torch.randn([d_output, 1]).float()) self.v_linear = nn.Linear(d_model, d_output) self.dropout_v = nn.Dropout(dropout) self.k_linear = nn.Linear(d_model, d_output) self.dropout_k = nn.Dropout(dropout) self.softmax_simi = nn.Softmax(dim=1) self.dropout = nn.Dropout(dropout) #self.out = nn.Linear(d_output, d_output) def forward(self, x, mask=None): k = self.k_linear(x) k = F.relu(k) k = self.dropout_k(k) v = self.v_linear(x) v = F.relu(v) v = self.dropout_v(v) dotProducSimi = k.matmul(self.q) normedSimi = self.softmax_simi(dotProducSimi) attVector = v.mul(normedSimi) weightedSum = torch.sum(attVector, dim=1) #output = self.out(weightedSum) return weightedSum class Norm(nn.Module): def __init__(self, d_model, eps = 1e-6): super().__init__() self.size = d_model # create two learnable parameters to calibrate normalisation self.alpha = nn.Parameter(torch.ones(self.size)) self.bias = nn.Parameter(torch.zeros(self.size)) self.eps = eps def forward(self, x): norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) \ / (x.std(dim=-1, keepdim=True) + self.eps) + self.bias return norm def attention(q, k, v, d_k, mask=None, dropout=None): scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k) if mask is not None: mask = mask.unsqueeze(1) scores = scores.masked_fill(mask == 0, -1e9) scores = F.softmax(scores, dim=-1) if dropout is not None: scores = dropout(scores) output = torch.matmul(scores, v) return output class EncoderLayer(nn.Module): def __init__(self, d_model, heads, dropout=0.1): super().__init__() self.norm_1 = Norm(d_model) self.norm_2 = Norm(d_model) self.attn = MultiHeadAttention(heads, d_model, dropout=dropout) self.ff = FeedForward(d_model, dropout=dropout) self.dropout_1 = nn.Dropout(dropout) self.dropout_2 = nn.Dropout(dropout) def forward(self, x, mask): x2 = self.norm_1(x) x = x + self.dropout_1(self.attn(x2,x2,x2,mask)) x2 = self.norm_2(x) x = x + self.dropout_2(self.ff(x2)) return x class MultiHeadAttention(nn.Module): def __init__(self, heads, d_model, dropout = 0.1): super().__init__() self.d_model = d_model self.d_k = d_model // heads self.h = heads self.q_linear = nn.Linear(d_model, d_model) self.v_linear = nn.Linear(d_model, d_model) self.k_linear = nn.Linear(d_model, d_model) self.dropout = nn.Dropout(dropout) self.out = nn.Linear(d_model, d_model) def forward(self, q, k, v, mask=None): bs = q.size(0) # perform linear operation and split into N heads # bs, sl, d_model --> bs, sl, heads, sub_d_model # d_model = heads * sub_d_model k = self.k_linear(k).view(bs, -1, self.h, self.d_k) q = self.q_linear(q).view(bs, -1, self.h, self.d_k) v = self.v_linear(v).view(bs, -1, self.h, self.d_k) # transpose to get dimensions bs * N * sl * d_model k = k.transpose(1,2) q = q.transpose(1,2) v = v.transpose(1,2) # calculate attention using function we will define next scores = attention(q, k, v, self.d_k, mask, self.dropout) # concatenate heads and put through final linear layer concat = scores.transpose(1,2).contiguous()\ .view(bs, -1, self.d_model) output = self.out(concat) return output class FeedForward(nn.Module): def __init__(self, d_model, d_ff=1024, dropout = 0.1): super().__init__() # We set d_ff as a default to 2048 self.linear_1 = nn.Linear(d_model, d_ff) self.dropout = nn.Dropout(dropout) self.linear_2 = nn.Linear(d_ff, d_model) def forward(self, x): x = self.dropout(F.relu(self.linear_1(x))) x = self.linear_2(x) return x class BERT_Embedding(nn.Module): def __init__(self, config): super().__init__() bert_model_path = os.path.join(config['BERT'].get('bert_path'), 'model') self.bert_dim = int(config['BERT'].get('bert_dim')) self.trainable_layers = config['BERT'].get('trainable_layers') self.bert = BertModel.from_pretrained(bert_model_path) if self.trainable_layers: #print(self.trainable_layers) #self.bert = BertModel.from_pretrained(bert_model_path) for name, param in self.bert.named_parameters(): if name in self.trainable_layers: param.requires_grad = True #print(name, param) else: param.requires_grad = False else: for p in self.bert.parameters(): p.requires_grad = False def forward(self, x, mask=None): if mask == None: mask = x != 0 mask.type(x.type()) bert_rep = self.bert(x, attention_mask=mask) return bert_rep class Dense(nn.Module): def __init__(self, input_dim, out_dim, non_linear=None): super().__init__() self.dense = nn.Linear(input_dim, out_dim) self.non_linear = non_linear def forward(self, x): output = self.dense(x) if self.non_linear: output = self.non_linear(output) return output class Topics(nn.Module): def __init__(self, k, vocab_size, bias=True): super(Topics, self).__init__() self.k = k self.vocab_size = vocab_size self.topic = nn.Linear(k, vocab_size, bias=bias) def forward(self, logit): # return the log_prob of vocab distribution return torch.log_softmax(self.topic(logit), dim=-1) def get_topics(self): #print('hey') #print(self.topic.weight) return torch.softmax(self.topic.weight.data.transpose(0, 1), dim=-1) def get_topic_word_logit(self): """topic x V. Return the logits instead of probability distribution """ return self.topic.weight.transpose(0, 1) class Identity(nn.Module): def __init__(self): super(Identity, self).__init__() def forward(self, *input): if len(input) == 1: return input[0] return input def kld(mu, log_sigma): """log q(z) || log p(z). mu: batch_size x dim log_sigma: batch_size x dim """ return -0.5 * (1 - mu ** 2 + 2 * log_sigma - torch.exp(2 * log_sigma)).sum(dim=-1) class BERT_Mapping_mapping(nn.Module): def __init__(self, bert_dim): super().__init__() self.att = SingleHeadAttention(bert_dim, bert_dim) def forward(self,x): atted = self.att(x) return atted class WVHidden(nn.Module): def __init__(self, input_dim, hidden_dim): super().__init__() self.hidden1 = nn.Linear(input_dim, hidden_dim) def forward(self, x): hidden = F.leaky_relu(self.hidden1(x)) return hidden class WVClassifier(nn.Module): def __init__(self, n_hidden, n_classes): super().__init__() self.layer_output = torch.nn.Linear(n_hidden, n_classes) def forward(self, x): out = self.layer_output(x) return out class CLSAW_TopicModel_Base(nn.Module): def __init__(self, config=None): super().__init__() self._init_params() if config: self._read_config(config) def _init_params(self): self.hidden_dim = 300 self.z_dim = 100 self.ntopics = 50 self.class_topic_loss_lambda = 1 self.classification_loss_lambda = 1 self.banlance_loss = False def _read_config(self, config): self.n_classes = len(config['TARGET'].get('labels')) if 'MODEL' in config: if 'hidden_dim' in config['MODEL']: self.hidden_dim = int(config['MODEL'].get('hidden_dim')) if 'z_dim' in config['MODEL']: self.z_dim = int(config['MODEL'].get('z_dim')) if 'ntopics' in config['MODEL']: self.ntopics = int(config['MODEL'].get('ntopics')) if 'class_topic_loss_lambda' in config['MODEL']: self.class_topic_loss_lambda = float(config['MODEL'].get('class_topic_loss_lambda')) if 'classification_loss_lambda' in config['MODEL']: self.class_topic_loss_lambda = float(config['MODEL'].get('classification_loss_lambda')) if 'banlance_loss' in config['MODEL']: self.banlance_loss = config['MODEL'].as_bool('banlance_loss') self.n_class_topics = self.z_dim+self.n_classes

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CANTM-main/GateMIcateLib/models/CLSAW_TopicModel_linear.py

import torch from torch import nn from torch.nn import init from torch.nn import functional as F import math from .miscLayer import BERT_Embedding, WVHidden, WVClassifier, Identity, Topics, kld, CLSAW_TopicModel_Base class CLSAW_TopicModel(CLSAW_TopicModel_Base): def __init__(self, config, vocab_dim=None): super().__init__(config=config) default_config = {} self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim if self.banlance_loss: self.banlance_lambda = float(math.ceil(vocab_dim/self.n_classes)) else: self.banlance_lambda = 1 #self.wv_hidden = WVHidden(bert_dim, self.hidden_dim) self.hidden_dim = bert_dim ##############M1########################################### self.mu_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.x_only_topics = Topics(self.z_dim, vocab_dim) self.xy_classifier = WVClassifier(self.z_dim, self.n_classes) self.class_criterion = nn.CrossEntropyLoss() #############M2############################################ self.hidden_y_dim = self.hidden_dim + self.n_classes self.z_y_dim = self.z_dim + self.n_classes self.x_y_hidden = WVHidden(self.hidden_y_dim, self.hidden_dim) self.z_y_hidden = WVHidden(self.z_y_dim, self.ntopics) self.mu_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z2 = nn.Linear(self.hidden_dim, self.z_dim) self.xy_topics = Topics(self.ntopics, vocab_dim) self.z2y_classifier = WVClassifier(self.ntopics, self.n_classes) ############################################################ self.h_to_z = Identity() self.class_topics = Topics(self.n_classes, vocab_dim) self.reset_parameters() def forward(self,x, mask=None, n_samples=1, bow=None, train=False, true_y=None, pre_embd=False, true_y_ids=None): #print(true_y.shape) if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] atted = bert_rep[:,0] #hidden = self.wv_hidden(atted) hidden = atted mu_z1 = self.mu_z1(hidden) log_sigma_z1 = self.log_sigma_z1(hidden) kldz1 = kld(mu_z1, log_sigma_z1) rec_loss_z1 = 0 classifier_loss = 0 kldz2 = 0 rec_loss_z2 = 0 log_y_hat_rec_loss = 0 class_topic_rec_loss = 0 if not train: ### for discriminator, we only use mean z1 = mu_z1 y_hat_logis = self.xy_classifier(z1) log_probz_1 = self.x_only_topics(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) log_prob_class_topic = self.class_topics(y_hat) #y = y_hat_logis for i in range(n_samples): if train: z1 = torch.zeros_like(mu_z1).normal_() * torch.exp(log_sigma_z1) + mu_z1 z1 = self.h_to_z(z1) log_probz_1 = self.x_only_topics(z1) y_hat_logis = self.xy_classifier(z1) y_hat = torch.softmax(y_hat_logis, dim=-1) log_prob_class_topic = self.class_topics(y_hat) classifier_loss += self.class_criterion(y_hat_logis, true_y_ids) y_hat_h = torch.cat((hidden, y_hat), dim=-1) x_y_hidden = self.x_y_hidden(y_hat_h) mu_z2 = self.mu_z2(x_y_hidden) log_sigma_z2 = self.log_sigma_z2(x_y_hidden) z2 = torch.zeros_like(mu_z2).normal_() * torch.exp(log_sigma_z2) + mu_z2 y_hat_z = torch.cat((z2, y_hat), dim=-1) topic = self.z_y_hidden(y_hat_z) log_prob_z2 = self.xy_topics(topic) y_hat_rec = self.z2y_classifier(topic) log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) rec_loss_z1 = rec_loss_z1-(log_probz_1 * bow).sum(dim=-1) kldz2 += kld(mu_z2, log_sigma_z2) rec_loss_z2 = rec_loss_z2 - (log_prob_z2 * bow).sum(dim=-1) #log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_rec*true_y).sum(dim=-1) log_y_hat_rec_loss = log_y_hat_rec_loss - (log_y_hat_rec*y_hat).sum(dim=-1) class_topic_rec_loss = class_topic_rec_loss - (log_prob_class_topic*bow).sum(dim=-1) rec_loss_z1 = rec_loss_z1/n_samples #print(rec_loss_z1.shape) classifier_loss = classifier_loss/n_samples kldz2 = kldz2/n_samples rec_loss_z2 = rec_loss_z2/n_samples log_y_hat_rec_loss = log_y_hat_rec_loss/n_samples class_topic_rec_loss = class_topic_rec_loss/n_samples elbo_z1 = kldz1 + rec_loss_z1 #print(elbo_z1.shape) #elbo_z1 = elbo_z1.sum() elbo_z2 = kldz2 + rec_loss_z2 + log_y_hat_rec_loss #print(elbo_z2) #elbo_z2 = elbo_z2.sum() #class_topic_rec_loss = class_topic_rec_loss.sum() classifier_loss = classifier_loss total_loss = elbo_z1.sum() + elbo_z2.sum() + class_topic_rec_loss.sum() + classifier_loss*self.banlance_lambda*self.classification_loss_lambda y = { 'loss': total_loss, 'elbo_xy': elbo_z2, 'rec_loss': rec_loss_z2, 'kld': kldz2, 'cls_loss': classifier_loss, 'class_topic_loss': class_topic_rec_loss, 'y_hat': y_hat_logis, 'elbo_x': elbo_z1 } #################################################################################################################################################### # else: # z1 = mu_z1 # y_hat_logis = self.xy_classifier(z1) # y_hat = torch.softmax(y_hat_logis, dim=-1) # y = y_hat_logis # # # y_hat_h = torch.cat((hidden, y_hat), dim=-1) # x_y_hidden = self.x_y_hidden(y_hat_h) # mu_z2 = self.mu_z2(x_y_hidden) # log_sigma_z2 = self.log_sigma_z2(x_y_hidden) # z2 = torch.zeros_like(mu_z2).normal_() * torch.exp(log_sigma_z2) + mu_z2 # # kldz2 = kld(mu_z2, log_sigma_z2) # log_prob_z2 = self.xy_topics(z2) # y_hat_rec = self.z2y_classifier(z2) # log_y_hat_rec = torch.log_softmax(y_hat_rec, dim=-1) # # return y, None def reset_parameters(self): init.zeros_(self.log_sigma_z1.weight) init.zeros_(self.log_sigma_z1.bias) init.zeros_(self.log_sigma_z2.weight) init.zeros_(self.log_sigma_z2.bias) def get_topics(self): return self.xy_topics.get_topics() def get_class_topics(self): return self.class_topics.get_topics() def get_x_only_topics(self): return self.x_only_topics.get_topics()

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CANTM

CANTM-main/GateMIcateLib/models/bertSimple.py

from transformers import BertModel from .miscLayer import BERT_Embedding, CLSAW_TopicModel_Base, WVHidden import os import torch.nn.functional as F import torch import torch.nn as nn class BERT_Simple(CLSAW_TopicModel_Base): def __init__(self, config, **kwargs): super().__init__(config=config) self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim self.hidden_dim = bert_dim self.hidden2 = WVHidden(self.hidden_dim, self.z_dim) self.layer_output = torch.nn.Linear(self.z_dim, self.n_classes) #self.layer_output = torch.nn.Linear(bert_dim, self.n_classes) def forward(self, x, mask=None, pre_embd=False): if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] bert_rep = bert_rep[:,0] #hidden = self.hidden1(bert_rep) hidden = bert_rep hidden = self.hidden2(hidden) out = self.layer_output(hidden) y = { 'y_hat':out } return y

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CANTM

CANTM-main/GateMIcateLib/models/bertPure.py

from transformers import BertModel from .miscLayer import BERT_Embedding, CLSAW_TopicModel_Base, WVHidden import os import torch.nn.functional as F import torch import torch.nn as nn class BERT_Simple(CLSAW_TopicModel_Base): def __init__(self, config, **kwargs): super().__init__(config=config) self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim self.hidden_dim = bert_dim #self.hidden2 = WVHidden(self.hidden_dim, self.z_dim) #self.layer_output = torch.nn.Linear(self.z_dim, self.n_classes) self.layer_output = torch.nn.Linear(bert_dim, self.n_classes) def forward(self, x, mask=None, pre_embd=False): if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] bert_rep = bert_rep[:,0] #hidden = self.hidden1(bert_rep) hidden = bert_rep #hidden = self.hidden2(hidden) out = self.layer_output(hidden) y = { 'y_hat':out } return y

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CANTM

CANTM-main/GateMIcateLib/models/NVDM.py

import torch from torch import nn from torch.nn import init from torch.nn import functional as F import math from .miscLayer import BERT_Embedding, WVHidden, WVClassifier, Identity, Topics, kld, CLSAW_TopicModel_Base class NVDM(CLSAW_TopicModel_Base): def __init__(self, config, vocab_dim=None): super().__init__(config=config) default_config = {} self.bert_embedding = BERT_Embedding(config) bert_dim = self.bert_embedding.bert_dim if self.banlance_loss: self.banlance_lambda = float(math.ceil(vocab_dim/self.n_classes)) else: self.banlance_lambda = 1 #self.wv_hidden = WVHidden(bert_dim, self.hidden_dim) self.hidden_dim = bert_dim ##############M1########################################### self.mu_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.log_sigma_z1 = nn.Linear(self.hidden_dim, self.z_dim) self.x_only_topics = Topics(self.z_dim, vocab_dim) #self.xy_classifier = WVClassifier(self.z_dim, self.n_classes) #self.class_criterion = nn.CrossEntropyLoss() #############M2############################################ #self.hidden_y_dim = self.hidden_dim + self.n_classes #self.z_y_dim = self.z_dim + self.n_classes #self.x_y_hidden = WVHidden(self.hidden_y_dim, self.hidden_dim) #self.z_y_hidden = WVHidden(self.z_y_dim, self.ntopics) #self.mu_z2 = nn.Linear(self.hidden_dim, self.z_dim) #self.log_sigma_z2 = nn.Linear(self.hidden_dim, self.z_dim) #self.xy_topics = Topics(self.ntopics, vocab_dim) #self.z2y_classifier = WVClassifier(self.ntopics, self.n_classes) ############################################################ self.h_to_z = Identity() #self.class_topics = Topics(self.n_classes, vocab_dim) self.reset_parameters() def forward(self,x, mask=None, n_samples=1, bow=None, train=False, true_y=None, pre_embd=False, true_y_ids=None): #print(true_y.shape) if pre_embd: bert_rep = x else: bert_rep = self.bert_embedding(x, mask) bert_rep = bert_rep[0] atted = bert_rep[:,0] #hidden = self.wv_hidden(atted) hidden = atted mu_z1 = self.mu_z1(hidden) log_sigma_z1 = self.log_sigma_z1(hidden) kldz1 = kld(mu_z1, log_sigma_z1) rec_loss_z1 = 0 classifier_loss = 0 kldz2 = 0 rec_loss_z2 = 0 log_y_hat_rec_loss = 0 class_topic_rec_loss = 0 for i in range(n_samples): z1 = torch.zeros_like(mu_z1).normal_() * torch.exp(log_sigma_z1) + mu_z1 z1 = self.h_to_z(z1) log_probz_1 = self.x_only_topics(z1) rec_loss_z1 = rec_loss_z1-(log_probz_1 * bow).sum(dim=-1) rec_loss_z1 = rec_loss_z1/n_samples elbo_z1 = kldz1 + rec_loss_z1 total_loss = elbo_z1.sum() y_hat_logis = torch.zeros(x.shape[0], self.n_classes) elbo_z2 = torch.zeros_like(elbo_z1) classifier_loss = torch.tensor(0) y = { 'loss': total_loss, 'elbo_xy': elbo_z2, 'rec_loss': rec_loss_z2, 'kld': kldz2, 'cls_loss': classifier_loss, 'class_topic_loss': class_topic_rec_loss, 'y_hat': y_hat_logis, 'elbo_x': elbo_z1 } return y, None def reset_parameters(self): init.zeros_(self.log_sigma_z1.weight) init.zeros_(self.log_sigma_z1.bias) def get_topics(self): return self.x_only_topics.get_topics() def get_class_topics(self): return self.x_only_topics.get_topics() def get_x_only_topics(self): return self.x_only_topics.get_topics()

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