%matplotlib inline
import matplotlib.pyplot as plt

import numpy as np
from astropy.table import QTable

"""
Explanation: Plotting and Fitting with Python
End of explanation
"""


t = np.linspace(0,2,100)               # 100 points linearly spaced between 0.0 and 2.0
s = np.cos(2*np.pi*t) * np.exp(-t)     # s if a function of t

plt.plot(t,s)

"""
Explanation: Simple Plotting
End of explanation
"""


plt.style.available

plt.style.use('ggplot')

plt.plot(t,s)

plt.xlabel('time (s)')
plt.ylabel('voltage (mV)')
plt.title('This is a title')

plt.ylim(-1.5,1.5)

plt.plot(t, s, color='b', marker='None', linestyle='--');   # adding the ';' at then suppresses the Out[] line

mask1 = np.where((s>-0.4) & (s<0))

plt.plot(t, s, color='b', marker='None', linestyle='--')

plt.plot(t[mask1],s[mask1],color="g",marker="o",linestyle="None",markersize=8);

"""
Explanation: Simple plotting - with style
The default style of matplotlib is a bit lacking in style. Some would term it ugly. The new version of matplotlib has added some new styles that you can use in place of the default. Changing the style will effect all of the rest of the plots on the notebook.
End of explanation
"""


from astropy import units as u

from astropy.visualization import quantity_support
quantity_support()

v = 10 * u.m / u.s
t2 = np.linspace(0,10,1000) * u.s
y = v * t2

plt.plot(t2,y)

"""
Explanation: In addition, you can specify colors in many different ways:

Grayscale intensities: color = '0.8'
RGB triplets: color = (0.3, 0.1, 0.9)
Hex strings: color = '#7ff00'
HTML color names: color = 'Chartreuse'

matplotlib will work with Astropy units
End of explanation
"""


#Histogram of "h" with 20 bins

np.random.seed(42)
h = np.random.randn(500)

plt.hist(h, bins=20, facecolor='MediumOrchid');

mask2 = np.where(h>0.0)

np.random.seed(42)
j = np.random.normal(2.0,1.0,300)  # normal dist, ave = 2.0, std = 1.0

plt.hist(h[mask2], bins=20, facecolor='#b20010', histtype='stepfilled')
plt.hist(j,        bins=20, facecolor='#0200b0', histtype='stepfilled', alpha = 0.30);

"""
Explanation: Simple Histograms
End of explanation
"""


fig,ax = plt.subplots(1,1)                    # One window
fig.set_size_inches(11,8.5)                   # (width,height) - letter paper landscape

fig.tight_layout()                          # Make better use of space on plot

ax.set_xlim(0.0,1.5)

ax.spines['bottom'].set_position('zero')    # Move the bottom axis line to x = 0

ax.set_xlabel("This is X")
ax.set_ylabel("This is Y")

ax.plot(t, s, color='b', marker='None', linestyle='--')

ax.text(0.8, 0.6, 'Bad Wolf', color='green', fontsize=36)            # You can place text on the plot

ax.vlines(0.4, -0.4, 0.8, color='m', linewidth=3)                    # vlines(x, ymin, ymax)
ax.hlines(0.8,  0.2, 0.6, color='y', linewidth=5)                    # hlines(y, xmin, xmax)

fig.savefig('fig1.png', bbox_inches='tight')

!ls *.png

!display fig1.png

"""
Explanation: You have better control of the plot with the object oriented interface.
While most plt functions translate directly to ax methods (such as plt.plot() → ax.plot(), plt.legend() → ax.legend(), etc.), this is not the case for all commands.
In particular, functions to set limits, labels, and titles are slightly modified.
For transitioning between matlab-style functions and object-oriented methods, make the following changes:

plt.xlabel()  → ax.set_xlabel()
plt.ylabel() → ax.set_ylabel()
plt.xlim()  → ax.set_xlim()
plt.ylim() → ax.set_ylim()
plt.title() → ax.set_title()
End of explanation
"""


fig, ax = plt.subplots(2,2)                                               # 2 rows 2 columns
fig.set_size_inches(11,8.5)                                               # width, height

fig.tight_layout()                                                        # Make better use of space on plot

ax[0,0].plot(t, s, color='b', marker='None', linestyle='--')              # Plot at [0,0]

ax[0,1].hist(h, bins=20, facecolor='MediumOrchid')                        # Plot at [0,1]

ax[1,0].hist(j,bins=20, facecolor='HotPink', histtype='stepfilled')       # Plot at [1,0]

ax[1,1].set_xscale('log')                                                 # Plot at [1,1] - x-axis set to log
ax[1,1].plot(t, s, color='r', marker='None', linestyle='--'); 

"""
Explanation: Subplots

subplot(rows,columns)
Access each subplot like a matrix. [x,y]
For example: subplot(2,2) makes four panels with the coordinates:
End of explanation
"""


T = QTable.read('M15_Bright.csv', format='ascii.csv')
T[0:3]

fig, ax = plt.subplots(1,2)                 # 1 row, 2 colums
fig.set_size_inches(15,5)

fig.tight_layout()

# The plot for [0,0]

ax[0].set_xlim(-40,140)
ax[0].set_ylim(-120,120)

ax[0].set_aspect('equal')                    # Force intervals in x = intervals in y
ax[0].invert_xaxis()                         # RA increases to the left!

ax[0].set_xlabel("$\Delta$RA [sec]")
ax[0].set_ylabel("$\Delta$Dec [sec]")

ax[0].plot(T['RA'], T['Dec'],color="g",marker="o",linestyle="None",markersize=5);

# The plot for [0,1]

BV = T['Bmag'] - T['Vmag']
V = T['Vmag']

ax[1].set_xlim(-0.25,1.5)
ax[1].set_ylim(12,19)

ax[1].set_aspect(1/6)         # Make 1 unit in X = 6 units in Y
ax[1].invert_yaxis()          # Magnitudes increase to smaller values

ax[1].set_xlabel("B-V")
ax[1].set_ylabel("V")

ax[1].plot(BV,V,color="b",marker="o",linestyle="None",markersize=5);

# overplotting

maskC = np.where((V < 16.25) & (BV < 0.55))     

ax[0].plot(T['RA'][maskC], T['Dec'][maskC],color="r",marker="o",linestyle="None",markersize=4, alpha=0.5)
ax[1].plot(BV[maskC], V[maskC],color="r",marker="o",linestyle="None",markersize=4, alpha=0.5);

"""
Explanation: An Astronomical Example - Color Magnitude Diagrams
End of explanation
"""


D1 = QTable.read('data1.csv', format='ascii.csv')
D1[0:2]

plt.plot(D1['x'],D1['y'],marker="o",linestyle="None",markersize=5);

# 1-D fit y = ax + b

Fit1 = np.polyfit(D1['x'],D1['y'],1)

Fit1       # The coefficients of the fit (a,b)

Yfit = np.polyval(Fit1,D1['x'])   # The polynomial of Fit1 applied to the points D1['x']

plt.plot(D1['x'], D1['y'], marker="o", linestyle="None", markersize=5)

plt.plot(D1['x'], Yfit, linewidth=4, color='c', linestyle='--')

D2 = QTable.read('data2.csv', format='ascii.csv')

plt.plot(D2['x'],D2['y'],marker="o",linestyle="None",markersize=5);

# 2-D fit y = ax**2 + bx + c

Fit2 = np.polyfit(D2['x'],D2['y'],2)

Fit2

Yfit = np.polyval(Fit2,D2['x'])

plt.plot(D2['x'], D2['y'], marker="o", linestyle="None", markersize=5)

plt.plot(D2['x'], Yfit, linewidth=3, color='y', linestyle='--');

# Be careful, very high-order fits may be garbage

Fit3 = np.polyfit(D1['x'],D1['y'],20)

xx = np.linspace(0,10,200)

Yfit = np.polyval(Fit3,xx)

plt.plot(D1['x'], D1['y'], marker="o", linestyle="None", markersize=8)
plt.plot(xx, Yfit, linewidth=3, color='m', linestyle='--');

plt.ylim(-20,120)

"""
Explanation: Curve Fitting
End of explanation
"""


D3 = QTable.read('data3.csv', format='ascii.csv')

plt.plot(D3['x'],D3['y'],marker="o",linestyle="None",markersize=5);

from scipy.optimize import curve_fit

"""
Explanation: Fitting a specific function
End of explanation
"""


def ringo(x,a,b):
    return a*np.sin(b*x)

Aguess = 75
Bguess = 1.0/5.0

fitpars, error = curve_fit(ringo,D3['x'],D3['y'],p0=[Aguess,Bguess])

# Function to fit = ringo
# X points to fit = D3['x']
# Y points to fit = D3['y']
# Initial guess at values for a,b = [Aguess,Bguess]

print(fitpars)

Z = np.linspace(0,100,1000)

plt.plot(Z, ringo(Z, *fitpars), 'r-')
plt.plot(Z, ringo(Z,Aguess,Bguess), 'g--')

plt.plot(D3['x'],D3['y'],marker="o",linestyle="None",markersize=5);

"""
Explanation: $$ \Large f(x) = a \sin(bx) $$
End of explanation
"""


Aguess = 35
Bguess = 1.0

fitpars, error = curve_fit(ringo,D3['x'],D3['y'],p0=[Aguess,Bguess])

print(fitpars)

plt.plot(Z, ringo(Z, *fitpars), 'r-')
plt.plot(Z, ringo(Z,Aguess,Bguess), 'g--')

plt.plot(D3['x'],D3['y'],marker="o",linestyle="None",markersize=5);

"""
Explanation: Bad initial guesses can lead to very bad fits
End of explanation
"""


theta = np.linspace(0.0,2*np.pi,300)

y1 = abs(np.sin(theta))
y2 = -abs(np.cos(theta))

plt.plot(theta, y1, color="b", label="|Sin|")
plt.plot(theta, y2, color="r", label="-|Cos|")

plt.legend(loc=0,shadow=True);

"""
Explanation: Plotting Extras
Adding Legends
End of explanation
"""


plt.polar(theta,theta/5.0,label="spiral")
plt.polar(theta,np.cos(4*theta),label="flower")

plt.legend(loc=2, frameon=False);

"""
Explanation: Polar Plots
End of explanation
"""


plt.axis('equal')

labels = ('John', 'Paul' ,'George' ,'Ringo')     # Name of slices
sizes = [0.3, 0.15, 0.45, 0.10]                  # Size of slices
colors = ['r', 'g', 'b', 'c']                    # Color of Slices
explode = (0, 0, 0.1, 0)                         # Offset slide 3

plt.pie(sizes,explode=explode,labels=labels,colors=colors,shadow=True);

"""
Explanation: Everyone likes Pie
End of explanation
"""


from mpl_toolkits.mplot3d import Axes3D

ax = plt.axes(projection='3d')
xx = np.cos(3*theta)
yy = np.sin(2*theta)

ax.plot(theta,xx,yy);
ax.view_init(azim = -140,elev = 15)

"""
Explanation: Pseudo 3D plots
End of explanation
"""


