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kats/tests/models/test_harmonic_regression.py | ottertune/ottertune-kats | 0 | 12787551 | # Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import unittest
from unittest import TestCase
import pkgutil
import io
import numpy as np
import pandas as pd
from kats.consts import TimeSeriesData
from kats.models.harmonic_regression import (
HarmonicRegressionModel,
HarmonicRegressionParams,
)
def load_data(file_name):
ROOT = "kats"
if "kats" in os.getcwd().lower():
path = "data/"
else:
path = "kats/data/"
data_object = pkgutil.get_data(ROOT, path + file_name)
return pd.read_csv(io.BytesIO(data_object), encoding="utf8")
class testHarmonicRegression(TestCase):
def setUp(self):
times = pd.to_datetime(
np.arange(start=1576195200, stop=1577836801, step=60 * 60), unit="s"
)
self.series_times = pd.Series(times)
harms = HarmonicRegressionModel.fourier_series(self.series_times, 24, 3)
self.harms_sum = np.sum([1, 1, 1, 1, 1, 1] * harms, axis=1)
self.data = TimeSeriesData(
pd.DataFrame({"time": self.series_times, "values": self.harms_sum})
)
self.params = HarmonicRegressionParams(24, 3)
def test_fit_and_predict(self) -> None:
hrm = HarmonicRegressionModel(self.data, self.params)
hrm.fit()
self.assertIsNotNone(hrm.params)
# pyre-fixme[16]: `HarmonicRegressionModel` has no attribute `harms`.
self.assertIsNotNone(hrm.harms)
# pyre-fixme[6]: Expected `Series` for 1st param but got
# `Union[pd.core.frame.DataFrame, pd.core.series.Series]`.
preds = hrm.predict(self.series_times.head(1))
self.assertAlmostEqual(preds["fcst"][0], self.harms_sum[0], delta=0.0001)
if __name__ == "__main__":
unittest.main()
| 2.546875 | 3 |
erdospy/test/__init__.py | NiMlr/erdospy | 0 | 12787552 | <gh_stars>0
from .tests import testall
| 1.015625 | 1 |
src/mlspace/scripts/__init__.py | abhishekkrthakur/mlspace | 283 | 12787553 | from .base import script as base_script
from .install_docker import script as install_docker
from .install_nvidia_docker import script as install_nvidia_docker
from .install_nvidia_drivers import script as install_nvidia_drivers
| 1.007813 | 1 |
scripts/optimize_latent/optimize_cifar10.py | bg459/gan-ensembling-loader | 86 | 12787554 | import argparse
import torch
import numpy as np
import os
import data
from networks import domain_generator, domain_classifier
from utils import util
def optimize(opt):
dataset_name = 'cifar10'
generator_name = 'stylegan2-cc' # class conditional stylegan
transform = data.get_transform(dataset_name, 'imval')
dset = data.get_dataset(dataset_name, opt.partition,
load_w=False, transform=transform)
total = len(dset)
if opt.indices is None:
start_idx = 0
end_idx = total
else:
start_idx = opt.indices[0]
end_idx = opt.indices[1]
generator = domain_generator.define_generator(
generator_name, dataset_name, load_encoder=False)
util.set_requires_grad(False, generator.generator)
resnet = domain_classifier.define_classifier(dataset_name,
'imageclassifier')
### iterate ###
for i in range(start_idx, end_idx):
img, label = dset[i]
print("Running img %d/%d" % (i, len(dset)))
filename = os.path.join(opt.w_path, '%s_%06d.npy' %
(opt.partition, i))
if os.path.isfile(filename):
print(filename + ' found... skipping')
continue
img = img[None].cuda()
with torch.no_grad():
pred_logit = resnet(img)
_, pred_label = pred_logit.max(1)
pred_label = pred_label.item()
print("True label %d prd label %d" % (label, pred_label))
ckpt, loss = generator.optimize(img, pred_label)
current_z = ckpt['current_z'].detach().cpu().numpy()
np.save(filename, current_z)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--partition', type=str, required=True,
help='specify train, val, or test partition')
parser.add_argument('--w_path', type=str, required=True,
help='directory to save the optimized latents')
parser.add_argument('--indices', type=int, nargs=2,
help='optimize latents for specific image range')
opt = parser.parse_args()
print(opt)
os.makedirs(opt.w_path, exist_ok=True)
optimize(opt)
| 2.296875 | 2 |
ramda/zip_with_test.py | Rafi993/pyramda | 56 | 12787555 | from .zip_with import zip_with
from ramda.private.asserts import assert_iterables_equal
def add(a, b):
return a + b
def uniq_nocurry_test():
assert_iterables_equal(zip_with(add, [1, 1, 1], [1, 2, 3]), [2, 3, 4])
def take_curry_test():
assert_iterables_equal(zip_with(add)([1, 1, 1], [1, 2, 3]), [2, 3, 4])
| 2.578125 | 3 |
regMRAToMRI.py | KitwareMedical/TubeTK-pypbm | 6 | 12787556 | <filename>regMRAToMRI.py
"""regMRAToMRI.py
"""
__license__ = "Apache License, Version 2.0"
__author__ = "<NAME>, Kitware Inc., 2013"
__email__ = "E-Mail: <EMAIL>"
__status__ = "Development"
import os
import sys
import subprocess
from optparse import OptionParser
from core import regtools
import pickle
def usage():
"""Print usage information"""
print("""
Run a RIGID registration between MRA and MRI images (in a
pairwise manner).
USAGE:
{0} [OPTIONS]
{0} -h
OPTIONS (Overview):
-l FILE0 FILE1
-d FILE0 FILE1
-c FILE
-x
OPTIONS (Detailed):
-x
Set this flag to FORCE the recomputation of all intermediate
results.
-c FILE
FILE is a configuration FILE in JSON format that contains the
absolute path's to all binaries that are used during the
registration process. For help, see the examplary configuration
file config.json.example.
-l FILE0 FILE1
FILE0 is an ASCII file that contains the absolute path of
each TARGET image, e.g.,
/tmp/target1.mha
/tmp/target2.mha
....
FILE1 is an ASCII file that contains the absolute path of
each MOVING image, e.g.,
/tmp/moving1.mha
/tmp/moving2.mha
....
-d FILE0 FILE1
Upon completion of the script, these files will contain
lists of various files produced during the RIGID registration
process. The format of these files is compatible with
Python's pickle functionality.
FILE0 will contain a list of all moving images, resampled
in the space of the corresponding fixed image.
FILE1 will contain a list of all RIGID transformation
files that were generated during the registration process.
AUTHOR: <NAME>, Kitware Inc., 2013
(<EMAIL>)
""".format(sys.argv[0]))
if __name__ == "__main__":
parser = OptionParser(add_help_option=False)
parser.add_option("-d", dest="dFiles", action="store", nargs=2)
parser.add_option("-l", dest="lFiles", action="store", nargs=2)
parser.add_option("-h", dest="doHelp", action="store_true", default=False)
parser.add_option("-x", dest="recomp", action="store_true", default=False)
parser.add_option("-c", dest="config")
options, args = parser.parse_args()
if options.doHelp:
usage()
sys.exit(-1)
if (options.lFiles is None or
options.dFiles is None or
options.config is None):
usage()
sys.exit(-1)
helper = regtools.regtools(options.config)
tListFile, mListFile = options.lFiles
iListFile, xListFile = options.dFiles
tList = open(tListFile).readlines()
mList = open(mListFile).readlines()
tList = [c.strip() for c in tList]
mList = [c.strip() for c in mList]
# register moving images to target images (pairwise)
iFileList, xFileList = helper.rReg2(tList,
mList,
"RigidMRAToMRI",
options.recomp)
# dump the list files to HDD
pickle.dump(iFileList, open(iListFile, "w"))
pickle.dump(xFileList, open(xListFile, "w")) | 2.453125 | 2 |
anprx/plot.py | ppintosilva/anprx | 6 | 12787557 | ################################################################################
# Module: plot.py
# Description: Plot functions
# License: Apache v2.0
# Author: <NAME>
# Web: https://github.com/pedroswits/anprx
################################################################################
import math
import adjustText
import osmnx as ox
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.colorbar as colorbar
from .utils import save_fig
from .core import Edge
from .constants import Units
from .constants import deg2distance
#
#
#
def plot_camera(
camera,
bbox_side = 100,
show_camera = True,
camera_color = "#FFFFFF",
camera_marker = "*",
camera_markersize = 10,
annotate_camera = True,
draw_radius = False,
#
fig_height = 6,
fig_width = None,
margin = 0.02,
bgcolor='k',
node_color='#999999',
node_edgecolor='none',
node_zorder=2,
node_size=50,
node_alpha = 1,
edge_color='#555555',
edge_linewidth=1.5,
edge_alpha=1,
#
probability_cmap = plt.cm.Oranges,
show_colorbar_label = True,
draw_colorbar = True,
draw_arrow = False,
#
color_near_nodes = True,
color_candidate_edges = True,
nn_color = '#66B3BA',
nedge_color = '#D0CE7C',
labels_color = "white",
annotate_nn_id = False,
annotate_nn_distance = True,
adjust_text = True,
#
save = False,
file_format = 'png',
filename = None,
dpi = 300):
"""
Plot the camera on a networkx spatial graph.
Parameters
----------
bbox_side : int
half the length of one side of the bbox (a square) in which to plot the camera. This value should usually be kept within small scales (hundreds of meters), otherwise near nodes and candidate edges become imperceptible.
camera_color : string
the color of the point representing the location of the camera
camera_marker : string
marker used to represent the camera
camera_markersize: int
the size of the marker representing the camera
annotate_camera : True
whether to annotate the camera or not using its id
draw_radius : bool
whether to draw (kind of) a circle representing the range of the camera
bgcolor : string
the background color of the figure and axis - passed to osmnx's plot_graph
node_color : string
the color of the nodes - passed to osmnx's plot_graph
node_edgecolor : string
the color of the node's marker's border - passed to osmnx's plot_graph
node_zorder : int
zorder to plot nodes, edges are always 2, so make node_zorder 1 to plot nodes beneath them or 3 to plot nodes atop them - passed to osmnx's plot_graph
node_size : int
the size of the nodes - passed to osmnx's plot_graph
node_alpha : float
the opacity of the nodes - passed to osmnx's plot_graph
edge_color : string
the color of the edges' lines - passed to osmnx's plot_graph
edge_linewidth : float
the width of the edges' lines - passed to osmnx's plot_graph
edge_alpha : float
the opacity of the edges' lines - passed to osmnx's plot_graph
probability_cmap : matplotlib colormap
Colormap used to color candidate edges by probability of observation.
show_colorbar_label : bool
whether to set the label of the colorbar or not
draw_colorbar : bool
whether to plot a colorbar as a legend for probability_cmap
nn_color : string
the color of near nodes - these are not necessarily in range of the camera, but they are part of edges that do
nedge_color : string
the color of candidate edges - nearby edges filtered by address or other condition
labels_color : string
the color of labels used to annotate nearby nodes
annotate_nn_id : bool
whether the text annotating near nodes should include their id
annotate_nn_distance : bool
whether the text annotating near nodes should include their distance from the camera
adjust_text : bool
whether to optimise the location of the annotations, using adjustText.adjust_text, so that overlaps are avoided. Notice that this incurs considerable computational cost. Turning this feature off will result in much faster plotting.
save : bool
whether to save the figure in the app folder's images directory
file_format : string
format of the image
filename : string
filename of the figure to be saved. The default value is the camera's id.
dpi : int
resolution of the image
Returns
-------
fig, ax : tuple
"""
if filename is None:
filename = camera.id
bbox = ox.bbox_from_point(point = camera.point,
distance = bbox_side)
# Set color of near nodes by index
nodes_colors = [node_color] * len(camera.network.nodes())
if color_near_nodes:
i = 0
for node in camera.network.nodes(data = False):
if node in camera.lsystem['nnodes']:
nodes_colors[i] = nn_color
i = i + 1
# Color near edges
edges_colors = [edge_color] * len(camera.network.edges())
if color_candidate_edges:
norm = colors.Normalize(vmin=0, vmax=1)
cmap = plt.cm.ScalarMappable(norm=norm, cmap=probability_cmap)
pcolor = { edge : cmap.to_rgba(p)
for edge, p in camera.p_cedges.items() }
j = 0
for u,v,k in camera.network.edges(keys = True, data = False):
edge = Edge(u,v,k)
if edge in camera.lsystem['cedges']:
edges_colors[j] = pcolor[edge]
j = j + 1
# Plot it
fig, axis = \
ox.plot_graph(
camera.network,
bbox = bbox,
margin = margin,
bgcolor = bgcolor,
node_color = nodes_colors,
node_edgecolor = node_edgecolor,
node_zorder = node_zorder,
edge_color = edges_colors,
node_alpha = node_alpha,
edge_linewidth = edge_linewidth,
edge_alpha = edge_alpha,
node_size = node_size,
save = False,
show = False,
close = False,
axis_off = True,
fig_height = fig_height,
fig_width = fig_width)
if draw_colorbar:
axis2 = fig.add_axes([0.3, 0.15, 0.15, 0.02])
cb = colorbar.ColorbarBase(
axis2,
cmap=probability_cmap,
norm=norm,
orientation='horizontal')
cb.set_ticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])
if show_colorbar_label:
cb.set_label("Probability of edge", color = labels_color, size = 9)
cb.ax.xaxis.set_tick_params(pad=0,
color = labels_color,
labelcolor = labels_color,
labelsize = 8)
# Plot Camera
if show_camera:
camera_point = axis.plot(
camera.point.lng,
camera.point.lat,
marker = camera_marker,
color = camera_color,
markersize = camera_markersize)
if show_camera and draw_radius:
radius_circle = \
plt.Circle((camera.point.lng, camera.point.lat),
radius = camera.radius/deg2distance(unit = Units.m),
color=camera_color,
fill=False)
axis.add_artist(radius_circle)
if show_camera and annotate_camera:
camera_text = axis.annotate(
str(camera.id),
xy = (camera.point.lng, camera.point.lat),
color = labels_color)
if draw_arrow:
base_x = camera.network.nodes[camera.edge.u]['x']
base_y = camera.network.nodes[camera.edge.u]['y']
end_x = camera.network.nodes[camera.edge.v]['x']
end_y = camera.network.nodes[camera.edge.v]['y']
color = pcolor[camera.edge]
axis.annotate('',
xytext = (base_x, base_y),
xy = (end_x, end_y),
arrowprops=dict(arrowstyle="->", color=color),
size = 15)
if color_near_nodes and (annotate_nn_id or annotate_nn_distance):
# Annotate nearest_neighbors
texts = []
for id in camera.lsystem['nnodes']:
distance_x = camera.lsystem['lnodes'][id][0]
distance_y = camera.lsystem['lnodes'][id][1]
distance = math.sqrt(distance_x ** 2 + distance_y ** 2)
if distance < bbox_side:
s1 = ""
s2 = ""
if annotate_nn_id:
s1 = "{}: ".format(id)
if annotate_nn_distance:
s2 = "{:,.1f}m".format(distance)
text = axis.text(camera.network.node[id]['x'],
camera.network.node[id]['y'],
s = s1 + s2,
color = labels_color)
texts.append(text)
if show_camera and annotate_camera:
texts.append(camera_text)
if adjust_text:
additional_obj = []
if draw_radius:
additional_obj.append(radius_circle)
if annotate_camera:
additional_obj.append(camera_text)
adjustText.adjust_text(
texts,
x = [ camera.network.node[id]['x'] for id in camera.lsystem['nnodes'] ],
y = [ camera.network.node[id]['y'] for id in camera.lsystem['nnodes'] ],
ax = axis,
add_objects = camera_point + additional_obj,
force_points = (0.5, 0.6),
expand_text = (1.2, 1.4),
expand_points = (1.4, 1.4))
if save:
save_fig(fig, axis, filename, file_format, dpi)
return fig, axis
| 2.25 | 2 |
1.py | gewplay/e5 | 9 | 12787558 | <filename>1.py
# -*- coding: UTF-8 -*-
from os import lseek
import requests as req
import json,sys,time
#先注册azure应用,确保应用有以下权限:
#files: Files.Read.All、Files.ReadWrite.All、Sites.Read.All、Sites.ReadWrite.All
#user: User.Read.All、User.ReadWrite.All、Directory.Read.All、Directory.ReadWrite.All
#mail: Mail.Read、Mail.ReadWrite、MailboxSettings.Read、MailboxSettings.ReadWrite
#注册后一定要再点代表xxx授予管理员同意,否则outlook api无法调用
num1 = 0
def gettoken(refresh_token,secret,cid):
headers={'Content-Type':'application/x-www-form-urlencoded'
}
data={'grant_type': 'refresh_token',
'refresh_token': refresh_token,
'client_id':cid,
'client_secret':secret,
'redirect_uri':'http://localhost:53682/'
}
html = req.post('https://login.microsoftonline.com/common/oauth2/v2.0/token',data=data,headers=headers)
jsontxt = json.loads(html.text)
refresh_token = jsontxt['refresh_token']
access_token = jsontxt['access_token']
global refresh
refresh = refresh_token
return access_token
def main(refresh_token,secret,cid):
global num1
localtime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(int(time.time())))
access_token=gettoken(refresh_token,secret,cid)
headers={
'Authorization':access_token,
'Content-Type':'application/json'
}
try:
if req.get(r'https://graph.microsoft.com/v1.0/me/drive/root',headers=headers).status_code == 200:
num1+=1
print("1调用成功"+str(num1)+'次')
if req.get(r'https://graph.microsoft.com/v1.0/me/drive',headers=headers).status_code == 200:
num1+=1
print("2调用成功"+str(num1)+'次')
if req.get(r'https://graph.microsoft.com/v1.0/drive/root',headers=headers).status_code == 200:
num1+=1
print('3调用成功'+str(num1)+'次')
if req.get(r'https://graph.microsoft.com/v1.0/users ',headers=headers).status_code == 200:
num1+=1
print('4调用成功'+str(num1)+'次')
if req.get(r'https://graph.microsoft.com/v1.0/me/messages',headers=headers).status_code == 200:
num1+=1
print('5调用成功'+str(num1)+'次')
if req.get(r'https://graph.microsoft.com/v1.0/me/mailFolders/inbox/messageRules',headers=headers).status_code == 200:
num1+=1
print('6调用成功'+str(num1)+'次')
if req.get(r'https://graph.microsoft.com/v1.0/me/mailFolders/Inbox/messages/delta',headers=headers).status_code == 200:
num1+=1
print('7调用成功'+str(num1)+'次')
if req.get(r'https://graph.microsoft.com/v1.0/me/drive/root/children',headers=headers).status_code == 200:
num1+=1
print('8调用成功'+str(num1)+'次')
if req.get(r'https://api.powerbi.com/v1.0/myorg/apps',headers=headers).status_code == 200:
num1+=1
print('8调用成功'+str(num1)+'次')
if req.get(r'https://graph.microsoft.com/v1.0/me/mailFolders',headers=headers).status_code == 200:
num1+=1
print('9调用成功'+str(num1)+'次')
if req.get(r'https://graph.microsoft.com/v1.0/me/outlook/masterCategories',headers=headers).status_code == 200:
num1+=1
print('10调用成功'+str(num1)+'次')
print('此次运行结束时间为 :', localtime)
except:
print("pass2")
pass
for fo in refresh_token:
try:
refresh_token = fo['text']
sid = fo['id']
secret = fo['key']
cid = fo['cid']
for _ in range(3):
main(refresh_token,secret,cid)
refresh_token = refresh
fox(refresh_token,sid)
except:
print('pass1')
pass
| 2.40625 | 2 |
pytrol/control/Ananke.py | mothguib/pytrol | 0 | 12787559 | <filename>pytrol/control/Ananke.py<gh_stars>0
# -*- coding: utf-8 -*-
import pickle
import networkx as nx
import numpy as np
import pytrol.util.misc as misc
from pytrol.control.Communicating import Communicating
from pytrol.control.agent.Agent import Agent
from pytrol.control.agent.MAPTrainerModelAgent import MAPTrainerModelAgent
from pytrol.model.Data import Data
from pytrol.model.action.Action import Action
from pytrol.model.action.Actions import Actions
from pytrol.model.knowledge.EnvironmentKnowledge import EnvironmentKnowledge
from pytrol.model.network.Network import Network
from pytrol.util.SimPreprocessor import SimPreprocessor
from pytrol.util.net.Connection import Connection
from pytrol.util.net.SimulatedConnection import SimulatedConnection
from pytrol.model.AgentTypes import AgentTypes
class Ananke(Communicating):
r"""`Ananke` is the core of the simulator, i.e. the structure that
concretely handles the simulation running. It is also a *communicating*.
In the ancient Greek religion, the goddess Ananke is a personification
of inevitability, compulsion and necessity. She is often depicted as
holding a spindle.
The life cycle of `Ananke` starts with the mission's initialisation which
takes place in `Ananke.__init__` , where it loads the graph, all
information relative to the current mission, as well as the agents.
Then, in `Ananke.run` the main loop of simulation over the time steps is
executed. There is as many iterations in this loop as the mission's
duration `duration` . This loop stands for the running of simulation: at
each period the strategy of agents simulated herein is deployed. More
precisely, at each iteration Ananke executes the main procedure of the
strategy by calling, for every agent, the methods described above that
constitutes their life cycle.
Args:
cnt: the connection used by Ananke to communicate
exec_path: path of the execution file defining a particular
instanciation of a MAP configuration. An instanciation of a MAP
configuration consists in an initial position for every patrolling
agent
archivist_addr: archivist's address
duration: duration of the simulation run
depth: range of communication
ntw_name: name of the network, i.e. the graph, to patrol
nagts: number of agents
variant: simulated strategy's variant
trace_agent: `True` if the archivist logs also the individual idlenesses
of the agents, `False` otherwise
trace_agents_estms: `True` if the archivist logs also the estimated
idlenesses of every agent, in the case of patrolling strategies
using
estimators,:
interaction: `True` if interaction is enabled, `False` otherwise
"""
def __init__(self,
cnt: Connection,
exec_path: str,
archivist_addr,
duration: int,
depth: float,
ntw_name: str = '',
nagts: int = 0,
variant: str = '',
trace_agents: bool = False,
trace_agents_estms: bool = False,
interaction: bool = False):
Communicating.__init__(self, cnt)
self.duration = duration
self.t = 0
self.archivist_addr = archivist_addr
self.depth = depth
self.trace_agents_estms = trace_agents_estms
self.trace_agents = trace_agents
# I. Configuration
# I.1. Environment
# I.1.1. Network initialisation
exec_path = exec_path
self.graph, fl_edges_lgts, vertices, edges, edges_to_vertices, \
locations, edge_activations, idls, socs, speeds_societies, \
agts_pos_societies, socs_to_ids = \
SimPreprocessor.load_run(exec_path)
edges_lgts = np.round(fl_edges_lgts)
for s in speeds_societies:
if s["id"] != "InactiveSociety":
self.speeds = s["speeds"]
self.network = Network(self.graph, edges_to_vertices, edges_lgts,
edge_activations, vertices, edges, locations)
# I.2. Agents
# I.2.1 Total number of agents calculation
# Total number of agents takes also into account the coordinator in the
# context of coordinated or multi-society strategies
self.t_nagts = 0
for s in socs:
self.t_nagts += len(s['agents'])
# I.2.2 Agent generation
self.agts_addrs, self.agts_pos = self. \
load_societies(socs=socs,
idls=idls,
agts_pos_societies=agts_pos_societies,
nagts=nagts,
t_nagts=self.t_nagts,
depth=depth,
ntw_name=ntw_name,
variant=variant,
interaction=interaction)
# Raising an exception if forbidden values are set
if len(self.agts_pos[self.agts_pos[:, 2] < 0]) > 0:
raise ValueError("Negative unit of edge forbidden")
edges_with_agts = self.agts_pos[self.agts_pos[:, 1] < 0]
if len(edges_with_agts[edges_with_agts[:, 2] > 0]) > 0:
raise ValueError("Forbidden value(s) for position("
"s) ", edges_with_agts[edges_with_agts[:, 2] > 0],
"of `agts_pos`. Some positions have units that "
"are not equal to zero whereas they correspond "
"to positions not in an edge (edge `-1`).")
self.pcvd_agt_matrix = np.zeros(self.agts_pos.shape)
# I.3 Idlenesses
# I.3.1 Real idlenesses
self.idls = idls
# I.3.2 Idlenesses of agents
self.idls_agts = np.zeros((len(self.agts_pos), len(self.graph)),
dtype=np.int16)
self.agts_estm_idls = np.zeros((len(self.agts_pos), len(self.graph)),
dtype=np.float32)
def run(self):
r"""Runs the main simulation loop over the time steps. There is as many
iterations in this loop as the mission's duration `self.duration` . This
loop stands for the running of simulation: at each period the strategy
of agents simulated herein is deployed. More precisely, at each
iteration Ananke the main procedure of the strategy is executed by
calling, for every agent, the methods that constitutes their life cycle.
"""
print("{}: Ananke: starts".format(misc.timestamp()))
self.start_archivist()
print("{}: Archivist: starts".format(misc.timestamp()))
while not self.stop_working:
while self.t < self.duration:
# Creation of the Completed List which is a dictionary
c_list = np.zeros(self.t_nagts, dtype=np.uint8)
if self.trace_agents:
self.collect_idls_agts()
# Archiving the current period
self.send_to_archivist()
# TDP
# print("Cycle ", self.t, ':\n\n')
# print('Agents position:', self.agts_pos, '\n')
# Preparation of agent position perception,
# `pcp_mat`: perception matrix
pcp_mat = self.compute_pos_pcp(
self.compute_range_pos_pcp_matrix())
for a in self.agts_addrs:
a.prepare()
for a in self.agts_addrs:
# Generating and flowing perceptions to agents
# Agents' perception
agts_pos = self.gen_prcved_agts_pos(a, pcp_mat)
# Subgraph' perception if needed
# subgraph = self.build_subgraph(a)
# Agent's own position perception if needed
# pos = self.build_current_vertex(a)
# Perceiving
a.perceive(agts_pos)
while np.sum(c_list) != self.t_nagts:
# Main procedure of agents
# 1. Communications when needed at the beginning of the
# strategy
for a in self.agts_addrs:
a.communicate()
# 2. Analysing received messages as long as the current
# cycle is not complete (`c_list` is not empty)
for a in self.agts_addrs:
a.analyse()
# 3. Deciding
for a in self.agts_addrs:
a.decide()
# 4. Acting as long as the current cycle is not complete (
# `c_list` is not empty)
for a in self.agts_addrs:
action = a.act() # After each
# procedure `act`, the agent must activate its
# attribute `ac`
self.process_action(action, a)
# 5. Checking the cycle termination
for a in self.agts_addrs:
if len(a.messages) != 0:
a.c = True
for a in self.agts_addrs:
# Testing whether a change must be done in the
# Completed List
if a.ac and not a.c: # and c_list[a.id] == 0:
c_list[a.id] = 1
# Agents reactivated if needed, for example to
# process a received message
elif not a.ac or a.c: # and c_list[a.id] == 1:
c_list[a.id] = 0
# TDP
# print("Real idlenesses ", self.idls)
for a in self.agts_addrs:
a.update_knowledge()
for a in self.agts_addrs:
a.post_process()
# Environment updating for the next time step
self.update_environment()
'''
# Last call of `send_to_archivist` to log the last idlenesses
# and next position resulting from the last move of agents
# self.send_to_archivist()
'''
# The simulation is over, other communicatings are then stopped
'''
if self.display_addr is not None:
self.send("stop_working", self.display_addr)
print("Display: stops to work")
'''
print("{}: Latest idlenesses:\n {}" \
.format(misc.timestamp(), self.idls))
# The log is saved before stopping the archivist
self.stop_archivist()
self.stop_agts()
self.stop_working = True
print("{}: Ananke: stops to work".format(misc.timestamp()))
@staticmethod
def compute_pos_pcp(am: np.ndarray) -> np.ndarray:
r"""The binary relation that represents whether agents are within range
is called *neighbouring relation*, and its transitive closure, which
represents whether agents are transitively within range using one
another as relay, is called *connection relation*. With connection
relation, two agents are regarded as connected iff. there exists a path
between them.
`compute_pos_pcp` converts the matrix of the neighbouring relation
into that of the connection relation.
Args:
am (np.ndarray): an `np.ndarray` representing the matrix of the
neighbouring relation
Returns:
m: an `np.ndarray` representing the matrix of the connection
relation
"""
m = np.zeros(am.shape, dtype=np.uint8)
graph = nx.from_numpy_matrix(am)
subgraphs = nx.connected_component_subgraphs(graph)
for g in subgraphs:
# Submatrix
sm = Ananke.expand_nx_subgraphs(g, graph.number_of_nodes())
for n in g.nodes():
m[n] = sm[n]
# TDP
# print("Ananke.compute_pos_pcp:\n",
# "am:\n", am,
# "m:\n", m)
return m
def compute_range_pos_pcp_matrix(self) -> np.ndarray:
r"""Computes the adjacent matrix of agents that perceive each others
according to the range of perception `depth` .
Returns:
m: an `np.ndarray` standing for the adjacent matrix of agents
that perceive each others according to the range of perception
`depth`
"""
# if self.agts_addrs[0].name == "coordinator":
nagts = len(self.agts_pos)
# `m`: the adjacent matrix of agents perceiving each others
# according to the range of perception `depth`
m = np.zeros((nagts, nagts), dtype=np.uint8)
for i, p_1 in enumerate(self.agts_pos):
for j, p_2 in enumerate(self.agts_pos):
if self.network.eucl_dist(p_1, p_2) <= self.depth:
m[i][j] = 1
return m
@staticmethod
def expand_nx_subgraphs(g: nx.Graph, nvtcs: int) -> np.ndarray:
r"""Expands a NetworkX subgraph so that it have the same size as its
parent graph. The new components are populated with 0.
Args:
g (nx.Graph): subgraph
nvtcs (int): number of vertices in the parent graph
Returns:
an `np.ndarray` representing subgraph connection adjacent matrix
"""
m = np.zeros((nvtcs, nvtcs), dtype=np.uint8)
k = 1
for u in g.nodes:
m[u][u] = 1
for v in list(g.nodes)[k:]:
m[u][v] = 1
m[v][u] = 1
k += 1
return m
def gen_prcved_agts_pos(self, a: Agent, pcp_mat: np.ndarray) -> np.ndarray:
r"""Generates all agents' perceived positions for the current agent
`a` , except the perceiving agent itself
Args:
a (Agent): current agent
pcp_mat (np.ndarray): matrix of perception
Returns:
a_agts_pos: an `np.ndarray` populated with the perceived
positions of the other agents
"""
# Perceived agents' positions
p_agts_pos = np.ones(self.agts_pos.shape, dtype=np.int16) * -1
for i in np.where(pcp_mat[a.id] == 1):
p_agts_pos[i] = self.agts_pos[i]
# The agent does not perceive itself
p_agts_pos[a.id] = np.ones(3, dtype=np.int16) * -1
# TDP
# print("Ananke.gen_prcved_agts_pos: ",
# "Agent id: " + str(a.id) + " p_agts_pos:\n",
# p_agts_pos, '\n')
return p_agts_pos
def collect_idls_agts(self):
for i in range(len(self.agts_addrs)):
self.idls_agts[i] = self.agts_addrs[i].env_knl.idls
if self.trace_agents_estms:
for i in range(len(self.agts_addrs)):
self.agts_estm_idls[i] = self.agts_addrs[i].model_estm_idls
def build_subgraph(self, a: Agent):
r"""
Args:
a (Agent):
"""
return self.graph
def build_current_vertex(self, a: Agent):
r"""
Args:
a (Agent):
"""
return self.agts_pos[a.id]
def process_action(self, action: Action, a: Agent):
r"""
Args:
action (Action):
a (Agent):
"""
if action.type == Actions.Moving_to:
if not np.array_equal(self.agts_pos[a.id], action.frm):
print(self.agts_pos[a.id], action.frm)
raise ValueError(
"Inconsistent start vertex in this MovingTo "
"Action")
self.agts_pos[a.id] = action.to
def update_environment(self):
self.t += 1
self.idls += 1
for a in self.agts_addrs:
self.idls = np.where(a.env_knl.idls < self.idls,
a.env_knl.idls, self.idls)
def load_societies(self,
socs: dict,
idls: np.ndarray,
agts_pos_societies: dict,
nagts: int,
t_nagts: int,
depth: float,
ntw_name: str = '',
variant: str = '',
interaction: bool = True) -> (list, np.ndarray):
r"""
Args:
socs (dict):
idls (np.ndarray):
agts_pos_societies (dict):
nagts (int):
t_nagts (int):
depth (float):
ntw_name (str):
variant (str):
interaction (bool):
"""
agts_addrs = []
agts_pos = np.empty([0, 3], dtype=np.int16)
# I.2.1. Agent Position
# Initial agents positions
for s in agts_pos_societies:
if s["id"] != "InactiveSociety":
agts_pos = np.append(agts_pos, s["agts_pos"], axis=0)
# TODO: Filling env_knl.vertices_to_agents and
# env_knl.edges_to_agents with -1 before updating it
for s in socs:
if s["id"] != "InactiveSociety":
for a in s['agents']:
# Check if tracing agents' idlenesses' estimates is
# performed only with MAPTrainerModelAgent agents
if self.trace_agents_estms and \
AgentTypes.id_to_class_name[a["type"]] == \
MAPTrainerModelAgent:
raise ValueError("Tracing agents' idlenesses' "
"estimates is only available for "
"MAPTrainerModelAgent agents")
# Here, all agents and Ananke share the same network object
# in order to accelerate the initialisation
env_knl = EnvironmentKnowledge(ntw=self.network,
idls=idls.copy(),
speeds=self.speeds.copy(),
agts_pos=agts_pos.copy(),
ntw_name=ntw_name,
nagts=nagts,
t_nagts=t_nagts)
cnt = SimulatedConnection()
# Agents
# Initializing agents, retrieving their address to put them
# into a list to send it to the archivist. In order to
# keep idlenesses and agents position personal, passing
# graph and agent perception by copy except for the
# archivist
agt = AgentTypes. \
id_to_class_name[a["type"]](id_=a["id"],
original_id=
a["original_id"],
env_knl=env_knl,
connection=cnt,
agts_addrs=agts_addrs,
variant=variant,
depth=depth,
interaction=interaction)
agts_addrs.append(agt)
# Agents' address is set for each agent after instantiating all
# agents in order to have retrieved all their addresses
for agt in agts_addrs:
agt.set_agts_addrs(agts_addrs)
return agts_addrs, agts_pos
def start_archivist(self):
r"""Tasks to perform before starting to archive."""
if self.trace_agents:
self.archivist_addr.prepare_idlss_agts(len(self.agts_addrs))
def send_to_archivist(self, agts_pos: np.ndarray = None,
idls: np.ndarray = None):
r"""
Args:
agts_pos (np.ndarray):
idls (np.ndarray):
"""
if agts_pos is None or idls is None:
self.archivist_addr.log(self.t, self.agts_pos.tolist(),
self.idls.tolist(),
self.idls_agts.tolist(),
self.agts_estm_idls.tolist())
else:
self.send(pickle.dumps({Data.Cycle: self.t,
Data.Agts_pos: agts_pos.tolist(),
Data.Idls: idls.tolist()}),
self.archivist_addr)
def stop_archivist(self):
self.archivist_addr.save_log()
def stop_agts(self):
for a in self.agts_addrs:
a.stop()
| 2.71875 | 3 |
tests/test_urls.py | adriangrepo/mathsonmars | 0 | 12787560 | #! ../env/bin/python
# -*- coding: utf-8 -*-
import sys
print("TestURLs sys.path: {0}".format(sys.path))
import unittest
from mathsonmars.models import db, User, Role
from mathsonmars import create_app
from mathsonmars.constants.modelconstants import RoleTypes, DefaultUserName
import logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
create_user = False
class TestURLs(unittest.TestCase):
def setUp(self):
#admin._views = []
#rest_api.resources = []
app = create_app('mathsonmars.settings.TestConfig')
self.client = app.test_client()
self.app = app
db.app = app
db.create_all()
def tearDown(self):
db.session.remove()
db.drop_all()
def test_home(self):
""" Tests if the home page loads """
rv = self.client.get('/')
assert rv.status_code == 200
def test_login(self):
""" Tests if the login page loads """
rv = self.client.get('/login')
assert rv.status_code == 200
def test_logout(self):
""" Tests if the logout page loads """
rv = self.client.get('/logout')
assert rv.status_code == 302
def test_restricted_logged_out(self):
""" Tests if the restricted page returns a 302
if the user is logged out
"""
rv = self.client.get('/restricted')
assert rv.status_code == 302
def test_restricted_logged_in(self):
""" Tests if the restricted page returns a 200
if the user is logged in
"""
with self.app.app_context():
admin_role = Role(role_name = RoleTypes.ADMIN)
db.session.add(admin_role)
db.session.flush()
admin = User(role_id = admin_role.id, user_name='admin', password='<PASSWORD>')
db.session.add(admin)
db.session.commit()
self.client.get('/login', data=dict(
username='admin',
password="<PASSWORD>"
), follow_redirects=True)
rv = self.client.get('/restricted')
logger.debug("--test_restricted_logged_in() code:{0}, rv:{1}".format(rv.status_code, rv))
assert rv.status_code == 302
if __name__ == "__main__":
unittest.main() | 2.328125 | 2 |
Pacote Dowload/Pyhton3/Maiores de Idade.py | JoaoVictorDeCastro/Python3 | 0 | 12787561 | from datetime import date
maior = 0
menor = 0
for c in range(1, 8):
ano = int(input('Digite o ano de nascimento: '))
if date.today().year - ano >= 18:
maior += 1
else:
menor += 1
print('Das sete pessoas digitadas {} são MAIORES DE IDADE.' .format(maior))
print('As outras {} pessoas são MENORES DE IDADE.' .format(menor))
| 4 | 4 |
resources.py | ostash-group/GeneticPlotter | 3 | 12787562 | # -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.9.1)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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qt_resource_name = b"\
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qt_resource_struct_v1 = b"\
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qt_resource_struct_v2 = b"\
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\x00\x00\x01\x5e\x0e\xdf\x33\x6b\
"
qt_version = QtCore.qVersion().split('.')
if qt_version < ['5', '8', '0']:
rcc_version = 1
qt_resource_struct = qt_resource_struct_v1
else:
rcc_version = 2
qt_resource_struct = qt_resource_struct_v2
def qInitResources():
QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
| 0.980469 | 1 |
server/tests/test_leds.py | jeffeb3/sandypi | 0 | 12787563 | <reponame>jeffeb3/sandypi
from server.hw_controller.leds.leds_driver import LedsDriver
def test_led_driver_error():
try:
ld = LedsDriver()
except:
assert(True)
def test_led_driver():
try:
ld = LedsDriver((30,20))
assert(True)
except:
assert(False)
| 2.203125 | 2 |
Warm Up: Machine Learning with a Heart/Python approach (Random Forest).py | RenXie/Drivendata | 0 | 12787564 | <gh_stars>0
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns #for plotting
from sklearn.ensemble import RandomForestClassifier #for the model
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz #plot tree
from sklearn.metrics import roc_curve, auc #for model evaluation
from sklearn.metrics import classification_report #for model evaluation
from sklearn.metrics import confusion_matrix #for model evaluation
from sklearn.model_selection import train_test_split #for data splitting
import eli5 #for purmutation importance
from eli5.sklearn import PermutationImportance
import shap #for SHAP values
from pdpbox import pdp, info_plots #for partial plots
np.random.seed(123) #ensure reproducibility
dt = pd.read_csv("train_values.csv")
dt.head(10)
dtr = pd.read_csv("train_labels.csv")
dt = pd.concat([dt, dtr.loc[:,'heart_disease_present']], axis=1, sort=False)
del dt['patient_id']
dt['thal'][dt['thal'] == 'normal'] = 1
dt['thal'][dt['thal'] == 'fixed_defect'] = 2
dt['thal'][dt['thal'] == 'reversible_defect'] = 3
dt.dtypes
dt['sex'] = dt['sex'].astype('object')
dt['chest_pain_type'] = dt['chest_pain_type'].astype('object')
dt['fasting_blood_sugar_gt_120_mg_per_dl'] = dt['fasting_blood_sugar_gt_120_mg_per_dl'].astype('object')
dt['resting_ekg_results'] = dt['resting_ekg_results'].astype('object')
dt['exercise_induced_angina'] = dt['exercise_induced_angina'].astype('object')
dt['slope_of_peak_exercise_st_segment'] = dt['slope_of_peak_exercise_st_segment'].astype('object')
#Random Forest
X_train, X_test, y_train, y_test = train_test_split(dt.drop('heart_disease_present', 1), dt['heart_disease_present'], test_size = .2, random_state=10)
model = RandomForestClassifier(max_depth=5)
model.fit(X_train, y_train)
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=5, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,
oob_score=False, random_state=None, verbose=0,
warm_start=False)
#Result evaluation
y_predict = model.predict(X_test)
y_pred_quant = model.predict_proba(X_test)[:, 1]
y_pred_bin = model.predict(X_test)
confusion_matrix = confusion_matrix(y_test, y_pred_bin)
confusion_matrix
total=sum(sum(confusion_matrix))
sensitivity = confusion_matrix[0,0]/(confusion_matrix[0,0]+confusion_matrix[1,0])
print('Sensitivity : ', sensitivity )
specificity = confusion_matrix[1,1]/(confusion_matrix[1,1]+confusion_matrix[0,1])
print('Specificity : ', specificity)
#ROC
fpr, tpr, thresholds = roc_curve(y_test, y_pred_quant)
fig, ax = plt.subplots()
ax.plot(fpr, tpr)
ax.plot([0, 1], [0, 1], transform=ax.transAxes, ls="--", c=".3")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.rcParams['font.size'] = 12
plt.title('ROC curve for diabetes classifier')
plt.xlabel('False Positive Rate (1 - Specificity)')
plt.ylabel('True Positive Rate (Sensitivity)')
plt.grid(True)
#AUC
auc(fpr, tpr)
#Submission
dtt = pd.read_csv('test_values.csv')
dtt['thal'][dtt['thal'] == 'normal'] = 1
dtt['thal'][dtt['thal'] == 'fixed_defect'] = 2
dtt['thal'][dtt['thal'] == 'reversible_defect'] = 3
del dtt['patient_id']
dtt.dtypes
dtt['sex'] = dtt['sex'].astype('object')
dtt['chest_pain_type'] = dtt['chest_pain_type'].astype('object')
dtt['fasting_blood_sugar_gt_120_mg_per_dl'] = dtt['fasting_blood_sugar_gt_120_mg_per_dl'].astype('object')
dtt['resting_ekg_results'] = dtt['resting_ekg_results'].astype('object')
dtt['exercise_induced_angina'] = dtt['exercise_induced_angina'].astype('object')
dtt['slope_of_peak_exercise_st_segment'] = dtt['slope_of_peak_exercise_st_segment'].astype('object')
test_pred_quant = model.predict_proba(dtt)[:, 1]
submission_p = pd.read_csv('submission_format.csv')
submission_p.loc[:,'heart_disease_present'] = test_pred_quant
export_csv = submission_p.to_csv ('submission.csv', index = None, header=True)
| 2.484375 | 2 |
couchbase_mapping/tests/design.py | hdmessaging/couchbase-mapping-python | 1 | 12787565 | # -*- coding: utf-8 -*-
#
# Copyright (C) 2008 <NAME>
# All rights reserved.
#
# This software is licensed as described in the file COPYING, which
# you should have received as part of this distribution.
import doctest
import unittest
from couchbase_mapping import design
from couchbase_mapping.tests import testutil
class DesignTestCase(testutil.TempDatabaseMixin, unittest.TestCase):
def test_options(self):
options = {'collation': 'raw'}
view = design.ViewDefinition(
'foo', 'foo',
'function(doc) {emit(doc._id, doc._rev)}',
options=options)
_, db = self.temp_db()
view.sync(db)
design_doc = db['_design/foo'].ddoc
self.assertTrue(design_doc['views']['foo']['options'] == options)
def test_multiple_views(self):
map_by_name = 'function(doc, meta) {emit(doc.name, null)}'
view1 = design.ViewDefinition(
'test_multiple_views',
'by_name',
map_by_name)
map_by_id = 'function(doc, meta) {emit(meta.id, null)}'
view2 = design.ViewDefinition(
'test_multiple_views',
'by_id',
map_by_id)
_, db = self.temp_db()
view1.sync(db)
view2.sync(db)
design_doc = db['_design/test_multiple_views'].ddoc
self.assertEqual(design_doc['views']['by_name']['map'], map_by_name)
self.assertEqual(design_doc['views']['by_id']['map'], map_by_id)
def suite():
suite = unittest.TestSuite()
suite.addTest(unittest.makeSuite(DesignTestCase))
suite.addTest(doctest.DocTestSuite(design))
return suite
if __name__ == '__main__':
unittest.main(defaultTest='suite')
| 2.171875 | 2 |
src/algorithms/main/sorts/heap_sort.py | JoelGRod/Algorithms-py | 0 | 12787566 | <filename>src/algorithms/main/sorts/heap_sort.py<gh_stars>0
"""
Heapsort
A comparison-based sorting algorithm. Heapsort can be thought
of as an improved selection sort: like that algorithm, it divides its
input into a sorted and an unsorted region, and it iteratively shrinks
the unsorted region by extracting the largest element and moving that
to the sorted region. The improvement consists of the use of a heap data
structure rather than a linear-time search to find the maximum.
Best Average Worst
n log(n) n log(n) n log(n)
""" | 3.484375 | 3 |
tkinter/tk-stopwatch.pyw | rsmith-nl/python-examples | 0 | 12787567 | <filename>tkinter/tk-stopwatch.pyw
#!/usr/bin/env python3
# file: tk-stopwatch.pyw
# vim:fileencoding=utf-8:fdm=marker:ft=python
#
# Copyright © 2020 <NAME> <<EMAIL>>.
# SPDX-License-Identifier: MIT
# Created: 2020-04-16T22:14:50+0200
# Last modified: 2022-02-04T16:37:22+0100
"""Example tkinter script showing use of the “after” timeout."""
import os
import sys
import time
import tkinter as tk
import tkinter.font as tkfont
import types
__version__ = "2022.02.03"
def start():
state.starttime = time.time()
state.run = True
display()
root.after(1000, update)
def stop():
state.run = False
state.starttime = None
box.delete(0, tk.END)
def update():
if state.run:
display()
root.after(1000, update)
def display():
difference = int(time.time() - state.starttime)
minutes, seconds = divmod(difference, 60)
hours, minutes = divmod(minutes, 60)
display = "{:02d}:{:02d}:{:02d}".format(hours, minutes, seconds)
box.delete(0, tk.END)
box.insert(0, display)
if __name__ == "__main__":
# Detach from the command line on UNIX systems.
if os.name == "posix":
if os.fork():
sys.exit()
# Create program state
state = types.SimpleNamespace()
state.starttime = None
state.run = False
# Create widgets.
root = tk.Tk()
root.title("Stopwatch v" + __version__)
root.resizable(False, False)
# Set the font
default_font = tkfont.nametofont("TkDefaultFont")
default_font.configure(size=12)
root.option_add("*Font", default_font)
# box to display stopwatch
box = tk.Entry(root, width=20, borderwidth=5)
box.grid(row=0, column=0)
start_button = tk.Button(root, text="Start", command=start)
stop_button = tk.Button(root, text="Stop", command=stop)
start_button.grid(row=1, column=0)
stop_button.grid(row=2, column=0)
# Run the GUI
root.mainloop()
| 3.75 | 4 |
absynthe/cfg/graph.py | chaturv3di/absynthe | 6 | 12787568 | <filename>absynthe/cfg/graph.py
from __future__ import print_function
from sys import stderr
from typing import TextIO, List, Tuple
from .node import Node
from random import randint
class Graph(object):
"""
The Graph class. Can have multiple root nodes; and it suffices
for objects of this class to only keep track of the root nodes.
The actual graph is defined by recursively following the
successors of all the roots.
NOTE: The nodes in the graph must all have unique IDs, otherwise
it will result in unintended behaviour. For instance, the
size() method will not work properly since it relies on
unique IDs to count the number of nodes.
"""
def __init__(self, id: str, numRoots: int = 1) -> None:
"""
Constructor for the class.
Args:
id(str): A unique ID for the graph
numRoots(int): The number of roots that this graph contains.
"""
self._id = id
self._numRoots = numRoots
self._roots = []
return
def addRoot(self, root: Node):
"""
Adds a root to this graph.
Args:
root(Node): A Node object to be added to the list of roots.
Raises:
IndexError: In case the list of roots already contains
_numRoots nodes in it.
"""
if (len(self._roots) == self._numRoots):
raise IndexError("Cannot accommodate any more root nodes.")
self._roots.append(root)
return
def getRootAt(self, index: int = 0) -> Node:
"""
Args:
index(int): The index of the desired root, mindful of the order
in which roots were added to this graph.
Returns:
Node: The root at the given index.
Raises:
IndexError: If the index is out of bounds.
"""
try:
return self._roots[index]
except IndexError as error:
print("Illegal argument for index:", index,
"Number of root nodes:", len(self._roots),
"Expected number of root nodes:", self._numRoots,
file=stderr)
raise error
return None
def getRootAtRandom(self) -> Node:
"""
Returns:
Node: One of the root nodes of this graph, selecte uniformly
at random.
"""
randomIndex = randint(0, len(self._roots) - 1)
return self._roots[randomIndex]
def getID(self) -> str:
return self._id
def _bfsAndCount(self, node: Node) -> int:
"""
Search the tree located below 'node' in a breadth-first manner,
and return the count of this sub-tree, incl. 'node'in the count.
Args:
node(Node): The node, the size of whose sub-tree is to be
computed.
Returns:
int: The number of nodes in the sub-tree rooted at 'node',
including 'node'.
"""
nodeID = node.getID()
try:
_ = self._nodeDict[nodeID]
return 0
except KeyError:
self._nodeDict[nodeID] = True
pass
count = 1
for i in range(node.getNumSuccessors()):
count += self._bfsAndCount(node.getSuccessorAt(i))
return count
def size(self) -> int:
"""
Returns:
int: The total number of nodes (with unique IDs) in this graph.
If there are multiple Node objects with identical IDs,
then this method will not produce expected result.
"""
size = 0
self._nodeDict = dict()
for r in self._roots:
if r is not None:
size += self._bfsAndCount(r)
return size
def _bfsAndAdd(self, node: Node, transitionList: List[Tuple[str, str]]) -> None:
nodeID = node.getID()
try:
_ = self._nodeDict[nodeID]
return
except KeyError:
self._nodeDict[nodeID] = True
pass
for i in range(node.getNumSuccessors()):
succNode: Node = node.getSuccessorAt(i)
succNodeID = succNode.getID()
transitionList.append((nodeID, succNodeID))
self._bfsAndAdd(succNode, transitionList)
return
def dumpDotFile(self, fp: TextIO) -> None:
"""
Creates a file that could be visualised using graphviz's DOT program.
Args:
fp(typing.TextIO): A text file stream that can be written to.
"""
self._nodeDict = dict()
transitionList: List[Tuple[str, str]] = list()
for r in self._roots:
if r is not None:
self._bfsAndAdd(r, transitionList)
fileContent: List[str] = ["digraph \""]
fileContent.append(self._id)
fileContent.append("\" {\n")
for transition in transitionList:
fileContent.append("\t\"")
fileContent.append(transition[0])
fileContent.append("\" -> \"")
fileContent.append(transition[1])
fileContent.append("\";\n")
fileContent.append("}\n")
fileText = "".join(fileContent)
fp.write(fileText)
fp.flush()
return
| 3.859375 | 4 |
tests/examples/test_nwchem_pymatgen.py | nkeilbart/aiida-nwchem | 1 | 12787569 | #!/usr/bin/env runaiida
# -*- coding: utf-8 -*-
###########################################################################
# Copyright (c), The AiiDA team. All rights reserved. #
# This file is part of the AiiDA code. #
# #
# The code is hosted on GitHub at https://github.com/aiidateam/aiida_core #
# For further information on the license, see the LICENSE.txt file #
# For further information please visit http://www.aiida.net #
###########################################################################
import sys
import os
from aiida.common.example_helpers import test_and_get_code
from aiida.common.exceptions import NotExistent
################################################################
ParameterData = DataFactory('parameter')
StructureData = DataFactory('structure')
try:
dontsend = sys.argv[1]
if dontsend == "--dont-send":
submit_test = True
elif dontsend == "--send":
submit_test = False
else:
raise IndexError
except IndexError:
print >> sys.stderr, ("The first parameter can only be either "
"--send or --dont-send")
sys.exit(1)
try:
codename = sys.argv[2]
except IndexError:
codename = None
queue = None
#queue = "Q_aries_free"
settings = None
#####
code = test_and_get_code(codename, expected_code_type='nwchem.pymatgen')
calc = code.new_calc()
calc.label = "Test NWChem"
calc.description = "Test calculation with the NWChem SCF code"
calc.set_max_wallclock_seconds(30*60) # 30 min
calc.set_resources({"num_machines": 1})
if queue is not None:
calc.set_queue_name(queue)
parameters = ParameterData(dict={
'tasks': [ { 'theory': 'scf',
'basis_set': {'O': '6-31g','H': '6-31g'},
'charge': 0,
'spin_multiplicity': None,
'title' : None,
'operation': '',
'theory_directives': [],
'alternate_directives': {} } ],
'directives': [],
'geometry_options': ['units','angstroms'],
'symmetry_options': [],
'memory_options': [],
'mol': {
'sites': [
{ 'species': [ { 'element': 'O', 'occu': 1 } ],
'xyz': [ 0, 0, 0 ] },
{ 'species': [ { 'element': 'H', 'occu': 1 } ],
'xyz': [ 0, 1.43042809, -1.107152660 ] },
{ 'species': [ { 'element': 'H', 'occu': 1 } ],
'xyz': [ 0, -1.43042809, -1.107152660 ] },
]
}
})
calc.use_parameters(parameters)
if submit_test:
subfolder, script_filename = calc.submit_test()
print "Test_submit for calculation (uuid='{}')".format(
calc.uuid)
print "Submit file in {}".format(os.path.join(
os.path.relpath(subfolder.abspath),
script_filename
))
else:
calc.store_all()
print "created calculation; calc=Calculation(uuid='{}') # ID={}".format(
calc.uuid,calc.dbnode.pk)
calc.submit()
print "submitted calculation; calc=Calculation(uuid='{}') # ID={}".format(
calc.uuid,calc.dbnode.pk)
| 1.726563 | 2 |
bot_group_only.py | PushDotGame/telegram-forward-bot | 1 | 12787570 | <reponame>PushDotGame/telegram-forward-bot<gh_stars>1-10
from libs import shell
from libs import config_bot as config
from libs.MQBot import MQBot
from libs.group import handlers
from telegram.ext import Updater
def main():
# bot
bot = MQBot(token=config.BOT_TOKEN)
# updater
updater = Updater(
bot=bot,
use_context=True,
request_kwargs=config.REQUEST_KWARGS,
)
# dispatcher
dp = updater.dispatcher
# private: `/start`
handlers.private_command_start.attach(dp)
# group: `/serve`
handlers.group_command_serve.attach(dp)
# group: commands
handlers.group_command_kick.attach(dp)
# handlers.group_command_rules.attach(dp)
# handlers.group_command_game.attach(dp)
handlers.group_command_resp.attach(dp)
# group: new, left
handlers.new_chat_members.attach(dp)
handlers.left_chat_member.attach(dp)
# group: text
handlers.group_text.attach(dp)
def start():
updater.start_webhook(listen=config.SERVER_LISTEN,
port=config.BOT_PORT,
url_path=config.BOT_URL_PATH,
key=config.PATH_TO_KEY,
cert=config.PATH_TO_CERT,
webhook_url=config.BOT_WEBHOOK_URL)
print('updater started.')
return
def stop():
bot.stop_mq()
updater.stop()
print('updater stopped.')
exit('Goodbye.')
return
start()
if config.DEBUG_MODE:
shell.embed()
if __name__ == "__main__":
main()
| 2.453125 | 2 |
realsense_examples/launch/template.launch.py | getsomatic/ros2_intel_realsense | 0 | 12787571 | <reponame>getsomatic/ros2_intel_realsense<filename>realsense_examples/launch/template.launch.py
import os
from ament_index_python.packages import get_package_share_directory
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument
from launch_ros.actions import Node
from launch.substitutions import LaunchConfiguration
from launch.actions import IncludeLaunchDescription
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import ThisLaunchFileDir
def generate_launch_description():
rviz_config_dir = os.path.join(get_package_share_directory('realsense_ros'), 'config', 'rs_cartographer.rviz')
return LaunchDescription([
Node(
package='rviz2',
node_executable='rviz2',
node_name='rviz2',
arguments=['-d', rviz_config_dir],
parameters=[{'use_sim_time': 'false'}]),
Node(
package='realsense_node',
node_executable='realsense_node',
node_namespace="/t265",
output='screen',
parameters=[get_package_share_directory('realsense_ros')+'/config/t265.yaml'],
remappings=[('/t265/camera/odom/sample', '/odom')],
),
Node(
package='realsense_node',
node_executable='realsense_node',
node_namespace="/d435",
output='screen',
parameters=[get_package_share_directory('realsense_ros')+'/config/d435.yaml']
),
])
| 2.078125 | 2 |
chatbot_nlu/extractors/bert_bilstm_crf_entity_extractor.py | charlesXu86/Chatbot_NLU | 6 | 12787572 | # -*- coding: utf-8 -*-
'''
@Author : Xu
@Software: PyCharm
@File : bert_bilstm_crf_entity_extractor.py
@Time : 2019-09-26 11:09
@Desc :
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
import os
import re
import io
import typing
import pickle
from builtins import str
from typing import Any, Dict, List, Optional, Text, Tuple
from rasa.nlu.extractors import EntityExtractor
from rasa.nlu.model import Metadata
from rasa.nlu.training_data import Message
from chatbot_nlu.utils.bilstm_utils import \
char_mapping, tag_mapping, prepare_dataset, BatchManager, iob_iobes, \
iob2, save_model, create_model, input_from_line
from chatbot_nlu.models.model import Model
from multiprocessing import cpu_count
import jieba
logger = logging.getLogger(__name__)
if typing.TYPE_CHECKING:
import numpy as np
import tensorflow as tf
import tensorflow.contrib
try:
import tensorflow as tf
except ImportError:
tf = None | 1.882813 | 2 |
lib/pipefunc.py | kei-iketani/plex | 153 | 12787573 | <reponame>kei-iketani/plex<gh_stars>100-1000
#*********************************************************************
# content = common functions
# version = 0.1.0
# date = 2019-12-01
#
# license = MIT <https://github.com/alexanderrichtertd>
# author = <NAME> <<EMAIL>>
#*********************************************************************
import os
import glob
import json
import time
import webbrowser
# NO logging since it will break the init
#*********************************************************************
# FUNCTIONS
def help(name=''):
from tank import Tank
if not name and os.getenv('SOFTWARE'):
name = os.getenv('SOFTWARE')
project_help = Tank().data_project['HELP']
if name in project_help:
webbrowser.open(project_help[name])
else:
webbrowser.open(project_help['default'])
# GET all (sub) keys in dict
def get_all_keys(key_list, dictonary=[]):
for key, items in key_list.iteritems():
dictonary.append(key)
if isinstance(items, dict):
get_all_keys(items, dictonary)
return dictonary
# decorator: return function duration time
def get_duration(func):
def timed(*args, **kw):
startTime = time.time()
resultTime = func(*args, **kw)
endTime = time.time()
printResult = '%r (%r, %r) %2.2f sec' % (func.__name__, args, kw, endTime-startTime)
print(printResult)
return resultTime
return timed
def find_inbetween(text, first, last):
try:
start = text.index(first) + len(first)
end = text.index(last, start)
except ValueError: return ""
return text[start:end]
#*********************************************************************
# FOLDER
# @BRIEF creates a folder, checks if it already exists,
# creates the folder above if the path is a file
def create_folder(path):
if len(path.split('.')) > 1: path = os.path.dirname(path)
if not os.path.exists(path):
try: os.makedirs(path)
except: print('CANT create folder: {}'.format(path))
# @BRIEF opens folder even if file is given
def open_folder(path):
path = os.path.normpath(path)
if os.path.exists(path):
if len(path.split('.')) > 1: path = os.path.dirname(path)
webbrowser.open(path)
else: print('UNVALID path: {}'.format(path))
return path
#*********************************************************************
# FILES
# @BRIEF get a file/folder list with specifics
#
# @PARAM path string.
# file_type string/string[]. '*.py'
# extension bool. True:[name.py] False:[name]
# exclude string /string[]. '__init__.py' | '__init__' | ['btnReport48', 'btnHelp48']
#
# @RETURN strint[].
def get_file_list(path, file_type='*', extension=False, exclude='*', add_path=False):
if(os.path.exists(path)):
getFile = []
try: os.chdir(path)
except: print('Invalid dir: {}'.format(path))
for file_name in glob.glob(file_type):
if exclude in file_name: continue
if add_path: file_name = os.path.normpath(('/').join([path,file_name]))
if extension: getFile.append(file_name)
else: getFile.append((file_name.split('.')[0]))
return getFile
##
# @BRIEF GET ALL subfolders in the path
def get_deep_folder_list(path, add_path=False):
if add_path: getFile = map(lambda x: x[0], os.walk(path))
else: getFile = map(lambda x: os.path.basename(x[0]), os.walk(path))
try: getFile.pop(0)
except: print('CANT pop file. Path: {}'.format(path))
return getFile
#*********************************************************************
# REPOSITORY
def make_github_issue(title, body=None, assignee='', milestone=None, labels=None):
import requests
from tank import Tank
REPO_DATA = Tank().user.data_user_path
if not assignee: assignee = REPO_DATA['username']
# Our url to create issues via POST
url = 'https://api.github.com/repos/%s/%s/issues' % (REPO_DATA['owner'], REPO_DATA['repository'])
# Create an authenticated session to create the issue
session = requests.Session()
session.auth = (REPO_DATA['username'], REPO_DATA['password'])
issue = {'title': title,
'body': body,
'assignee': assignee,
'milestone': milestone,
'labels': labels}
# Add the issue to our repository
repo = session.post(url, json.dumps(issue))
if repo.status_code == 201:
LOG.info('Successfully created Issue {}'.format(title))
else:
LOG.warning('Could not create Issue {}.\nResponse:{}'.format(title, repo.content))
#*********************************************************************
# TEST
# make_github_issue(title='Login Test', body='Body text', milestone=None, labels=['bug'])
| 2.40625 | 2 |
torchimagefilter/filter.py | pmeier/torchimagefilter | 0 | 12787574 | <gh_stars>0
from typing import Optional, Tuple, List
from copy import copy
import numpy as np
import torch
from torch import nn
from torch.nn.functional import conv2d, pad
from .kernel import *
class ImageFilter(nn.Module):
def __init__(
self,
kernel: torch.Tensor,
output_shape: str = "same",
padding_mode: str = "constant",
constant_padding_value: float = 0.0,
):
super().__init__()
self.register_buffer("kernel", kernel)
if output_shape not in ("valid", "same", "full"):
raise ValueError(
"'output_shape' should be one of " "{'valid', 'same', 'full'}"
)
self.output_shape = output_shape
self.padding_size = self._calculate_padding_size()
if output_shape not in ("valid", "same", "full"):
raise ValueError(
"'padding_mode' should be one of "
"{'constant', 'reflect', 'replicate', 'circular'}"
)
self.padding_mode = padding_mode
self.constant_padding_value = constant_padding_value
@property
def kernel_size(self) -> Tuple[int, int]:
return self.kernel.size()
def _calculate_padding_size(self) -> List[int]:
kernel_height, kernel_width = self.kernel_size
if self.output_shape == "valid":
pad_top = pad_bot = pad_left = pad_right = 0
elif self.output_shape == "same":
pad_vert, pad_horz = kernel_height - 1, kernel_width - 1
pad_top, pad_left = pad_vert // 2, pad_horz // 2
pad_bot, pad_right = pad_vert - pad_top, pad_horz - pad_left
else: # self.output_shape == "full":
pad_top = pad_bot = kernel_height - 1
pad_left = pad_right = kernel_width - 1
return [pad_left, pad_right, pad_top, pad_bot]
def forward(self, image: torch.Tensor) -> torch.Tensor:
is_batched = image.dim() == 4
if not is_batched:
image = image.unsqueeze(0)
image = self._pad_image(image)
image = self._filter_image(image)
if not is_batched:
image = image.squeeze(0)
return image
def _filter_image(self, image: torch.Tensor) -> torch.Tensor:
num_channels = image.size()[1]
weight = self.kernel.view(1, 1, *self.kernel.size())
weight = weight.repeat(num_channels, 1, 1, 1)
return conv2d(
image,
weight,
bias=None,
groups=num_channels,
stride=1,
padding=0,
dilation=1,
)
def _pad_image(self, image: torch.Tensor) -> torch.Tensor:
if self.output_shape == "valid":
return image
return pad(
image,
self.padding_size,
mode=self.padding_mode,
value=self.constant_padding_value,
)
def extra_repr(self) -> str:
extra_repr = self.image_filter_extra_repr()
if extra_repr:
extra_repr += ", "
extra_repr += "output_shape={output_shape}"
if self.output_shape != "valid":
extra_repr += ", padding_mode={padding_mode}"
if self.padding_mode == "constant":
extra_repr += ", constant_padding_value={constant_padding_value}"
return extra_repr.format(**self.__dict__)
def image_filter_extra_repr(self) -> str:
return ""
class BoxFilter(ImageFilter):
def __init__(self, radius: int, normalize: bool = True, **kwargs):
self.radius = radius
kernel = box_kernel(radius, normalize=normalize)
super().__init__(kernel, **kwargs)
def image_filter_extra_repr(self) -> str:
return "radius={radius}".format(**self.__dict__)
class GaussFilter(ImageFilter):
def __init__(
self,
std: Optional[float] = None,
radius: Optional[int] = None,
normalize: bool = True,
**kwargs
):
if std is None and radius is None:
msg = (
"One argument of 'std' and 'radius' has to have a value different "
"from None. The respective other is subsequently calculated."
)
raise ValueError(msg)
if std is None:
std = radius / 3.0
self.std = std
if radius is None:
radius = int(np.ceil(3.0 * std))
self.radius = radius
kernel = gauss_kernel(std, radius, normalize=normalize)
super().__init__(kernel, **kwargs)
def image_filter_extra_repr(self) -> str:
dct = copy(self.__dict__)
dct["std"] = "{:.2g}".format(self.std)
return "std={std}, radius={radius}".format(**dct)
| 2.453125 | 2 |
SigProfilerExtractor/version.py | david-a-parry/SigProfilerExtractor | 1 | 12787575 | <reponame>david-a-parry/SigProfilerExtractor
# THIS FILE IS GENERATED FROM SIGPROFILEREXTRACTOR SETUP.PY
short_version = '1.1.4'
version = '1.1.4'
Update = 'Heirarchy option deleted, clustering deleted and signatures orders by the mutation burden'
| 0.695313 | 1 |
tests/services/conftest.py | javi-cortes/gigaupload | 0 | 12787576 | <gh_stars>0
import pathlib
import uuid
import pytest
from app.schemas import FileQuery
from app.services.file import FileService
@pytest.fixture()
def file_service(db):
return FileService(db)
@pytest.fixture(scope="session")
def file():
return open("tests/files_stub/test_file", "wb")
@pytest.fixture(scope="session", autouse=True)
def cleanup(file):
"""Cleanup a testing file once we are finished."""
file.close()
file_to_rem = pathlib.Path("tests/files_stub/test_file")
file_to_rem.unlink(missing_ok=True)
@pytest.fixture(scope="session")
def file_query():
return FileQuery(uri=uuid.uuid4())
| 2.09375 | 2 |
umb/admin/forms.py | LuckyWirasakti/umb | 0 | 12787577 | <reponame>LuckyWirasakti/umb
from umb.admin.models import Master, Weight
from django import forms
class WeightForm(forms.ModelForm):
class Meta:
model = Weight
fields = '__all__'
class MasterForm(forms.ModelForm):
class Meta:
model = Master
fields = '__all__'
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
for visible in self.visible_fields():
visible.field.widget.attrs['class'] = 'form-control' | 1.914063 | 2 |
fast/FAST/FAST.py | yagweb/pyfast | 0 | 12787578 | <filename>fast/FAST/FAST.py
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 01 20:49:10 2014
@author: YangWenguang
Issues contact with: <EMAIL>
os.popen： 返回值是一个开放的文件对象连接到管道，可读写模式是根据是否为'r'（预设）或'w'。
"""
import os
import numpy as np
import matplotlib.pylab as plt
import scipy.io as spio
import scipy.signal as spsignal
from scipy.linalg import det, eig, norm, eigh
from mpldatacursor import datacursor
#import MBMP.FAST.pyCAE
#import MBMP.FAST._pyCAE
#import MBMP.FAST.FASTCore
#from MBMP.FAST.FASTCore import fast
rads2rmp = 30/np.pi
rad2deg = 180.0/np.pi
class output(object):
def __init__(self):
u_srvd = wtdyn.SrvD.u
y_srvd = wtdyn.SrvD.y #t时刻，控制系统的约束状态
y_ad = wtdyn.AD.y
y_ed = wtdyn.ED.y
#控制系统
self.ElecPwr = y_srvd.elecpwr
self.BlPitchCom = y_srvd.blpitchcom *rad2deg #相当于copy()
# self.BlPitch = u_srvd.blpitch *rad2deg #copy()
# self.BlPitch = OtherSt_ED.blpitch *rad2deg #copy()
# self.BlPitch = OtherSt_ED.allouts[280:283].copy()
#空气动力学
self.Velocity = y_ad.ifw_outputs.velocity
#弹性动力学：结构、转子
self.BlPitch = y_ed.blpitch *rad2deg
# self.BlPitch4 = getBladePitch() *rad2deg
self.Yaw = y_ed.yaw
self.YawRate = y_ed.yawrate
self.LSS_Spd = y_ed.lss_spd * rads2rmp
self.HSS_Spd = y_ed.hss_spd * rads2rmp
self.RotSpeed = y_ed.rotspeed * rads2rmp
self.TwrAccel = y_ed.twraccel
self.YawAngle = y_ed.yawangle
def getBladePitch():
import ctypes
temp = _pyCAE.w_ed_outputtype__array__blpitch(pyCAE.wtdyn.y_ed._handle)
dloc = temp[3]
value = (ctypes.c_float *3).from_address(dloc)
value = np.array(value[:])
return value
class pitchCtrlInput():
def __init__(self):
t = fast.m_FAST.t_global #目前的仿真时间秒
u = fast.SrvD.u #t时刻，控制系统的输入
p = fast.SrvD.p #控制系统参数
x = fast.SrvD.x #t时刻，控制系统的连续状态
xd = fast.SrvD.xd #t时刻，控制系统的连续状态
z = fast.SrvD.z #t时刻，控制系统的约束状态
y = fast.SrvD.y #t时刻，控制系统的输出
self.BlPitch = u.blpitch
self.ElecPwr = y.elecpwr
self.LSS_Spd = u.lss_spd
self.TwrAccel = u.twraccel
self.BlPitchCom = y.blpitchcom
self.NumBl = p.numbl
self.t = t
self.DT = p.dt
self.RootName = p.rootname
class pitchCtrlBase(object):
def calc(self):
paras = pitchCtrlInput()
print(paras.BlPitch*180/np.pi)
print(paras.BlPitchCom)
y = fast.SrvD.y #t时刻，控制系统的约束状态
self.setvalue([1,2,3])
return y.blpitchcom
def setvalue(self, values):
y = fast.SrvD.y #t时刻，控制系统的约束状态
y.blpitchcom = values
class yawCtrlInput():
def __init__(self):
t = fast.m_FAST.t_global #目前的仿真时间秒
u = fast.SrvD.u #t时刻，控制系统的输入
p = fast.SrvD.p #控制系统参数
x = fast.SrvD.x #t时刻，控制系统的连续状态
xd = fast.SrvD.xd #t时刻，控制系统的连续状态
z = fast.SrvD.z #t时刻，控制系统的约束状态
y = fast.SrvD.y #t时刻，控制系统的约束状态
self.Yaw = u.yaw
self.YawRate = u.yawrate
self.WindDir = u.winddir
self.YawErr = u.yawerr
self.NumBl = p.numbl
self.t = t
self.DT = p.dt
self.RootName = p.rootname
class yawCtrlBase(object):
def calc(self):
paras = yawCtrlInput()
print(paras.Yaw, paras.YawRate)
y = fast.SrvD.y #t时刻，控制系统的约束状态
self.setvalue(1,2)
return y.externalyawposcom, y.externalyawratecom
def setvalue(self, pos, rate):
y = fast.SrvD.y #t时刻，控制系统的约束状态
y.externalyawposcom = pos
y.externalyawratecom = rate
class TorqueCtrlInput():
def __init__(self):
t = fast.m_FAST.t_global #目前的仿真时间秒
u = fast.SrvD.u #t时刻，控制系统的输入
p = fast.SrvD.p #控制系统参数
x = fast.SrvD.x #t时刻，控制系统的连续状态
xd = fast.SrvD.xd #t时刻，控制系统的连续状态
z = fast.SrvD.z #t时刻，控制系统的约束状态
y = fast.SrvD.y #t时刻，控制系统的约束状态
self.HSS_Spd = u.hss_spd
self.LSS_Spd = u.lss_spd
self.GenEff = p.geneff
self.GenTrq = y.gentrq
self.ElecPwr = y.elecpwr
self.NumBl = p.numbl
self.t = t
self.DT = p.dt
self.RootName = p.rootname
class TorqueCtrlBase(object):
def calc(self):
paras = TorqueCtrlInput()
print(paras.GenTrq, paras.ElecPwr, paras.HSS_Spd)
y = fast.SrvD.y #t时刻，控制系统的约束状态
self.setvalue(1,2)
return y.gentrq, y.elecpwr
def setvalue(self, torque, pwr):
'''
只要给定需要的扭矩，转速通过动力学去平衡
'''
y = fast.SrvD.y #t时刻，控制系统的约束状态
y.gentrq = torque
y.elecpwr = pwr
def fft(datas, samplingRate):
samplingN = len(datas)
temp = np.fft.fft(datas)
amps = (np.abs(temp)*2/samplingN)[0:samplingN/2]
amps[0] = amps[0]/2.0
df = samplingRate/samplingN
flist = np.arange(len(amps))*df
return flist, amps
def fast_exe(path):
path = os.path.abspath(path)
cmd = '%s %s' % (os.path.join(os.path.dirname(__file__), 'FAST_win32.exe'), path)
result = os.system(cmd)
return result
def turbsim(path):
path = os.path.abspath(path)
cmd = '%s %s' % (os.path.join(os.path.dirname(__file__), 'turbsim.exe'), path)
result = os.system(cmd)
return result
def IECWind(path, destfolder=None):
import pyPreFast
# import subprocess
# subprocess.Popen(['IECWind.exe',], shell=False)
if destfolder == None:
destfolder = os.path.dirname(path) + '\\'
pyPreFast.startiecwind(path, destfolder)
def modes(path):
'''
path不能带有后缀！
'''
cmd = '%s %s' % (os.path.join(os.path.dirname(__file__), 'modes.exe'), path)
result = os.system(cmd)
return result
def simulink(path):
pass
def test_IECWind():
destfolder = '..\\Data\\FASTV8_demo_a\\demo_a\\IECWind\\'
path = '..\\Data\\FASTV8_demo_a\\demo_a\\Wind\\IEC.ipt'
IECWind(path, destfolder)
def test_turbsim(cfg_path, speed, turb, dest):
turbsim(cfg_path)
if dest is None:
dest = os.path.dirname(cfg_path)
old = '%s.sum' % cfg_path[:-4]
new = os.path.join(dest, '%s_%dmps.sum' % (turb, speed))
if os.path.exists(new):
os.remove(new)
os.rename(old, new)
old = '%s.wnd' % cfg_path[:-4]
new = os.path.join(dest, '%s_%dmps.wnd' % (turb, speed))
if os.path.exists(new):
os.remove(new)
os.rename(old, new)
def test_modes():
# path = r'..\Data\FASTV8_demo_a\pre\modes_Blade'
path = r'..\Data\FASTV8_demo_a\pre\modes_Tower'
modes(path)
def test_pyCAE():
addr = _pyCAE.fast_data.m_FAST.ctypes.data
obj = pyCAE.FAST_MiscVarType(addr)
print(obj.t_global)
# print(_pyCAE.fast_data.n_t_global)
# pyCAE.wtdyn.n_t_global += 4
# print(pyCAE.fast_data.n_t_global)
# print(pyCAE.startwtdyn.__doc__)
path = r'../Data/FASTV8_demo_a/Test11.fst'
path = os.path.abspath(path)
# pyCAE.fast_data.wtdyn_start(path)
# pyCAE.fast_data.wtdyn_init(path)
# for i in range(pyCAE.wtdyn.n_TMax_m1):
# pyCAE.stepwtdyn()
# pyCAE.fast_data.n_t_global += 1
# pyCAE.endwtdyn()
def test_ctrlbase():
path = r'../Data/FASTV8_demo_a/Test11.fst'
fast.init(path)
fast.step()
# import pdb
# pdb.set_trace()
print('pitch-test')
a = pitchCtrlBase()
print(a.calc())
print('yaw-test')
a = yawCtrlBase()
print(a.calc())
print('torque-test')
a = TorqueCtrlBase()
print(a.calc())
def test_ctrlbase2():
path = r'../Data/FASTV8_demo_a/Test11.fst'
path = os.path.abspath(path)
pyCAE.wtdyn.wtdyn_init(path)
values = []
for i in range(200*10):
pyCAE.wtdyn.wtdyn_step()
_pyCAE.wtdyn.n_t_global += 1
if i>0 and i%10 == 0:
paras = output()
# b = [paras.BlPitch[0], paras.BlPitchCom[0], paras.BlPitch1[0], paras.BlPitch2[0], paras.BlPitch3[0]]
b = [paras.BlPitch[0], paras.BlPitchCom[0], paras.RotSpeed, paras.LSS_Spd, paras.HSS_Spd, paras.ElecPwr]
values.append(b)
values = np.array(values)
fig = plt.figure(facecolor='white')
for i in range(5):
fig.add_subplot(5,1,i+1)
plt.plot(values[:,i])
plt.show()
# _pyCAE.endwtdyn()
pyCAE.wtdyn.wtdyn_end()
#from Transient import MPT, slaveNode
#class test_planet_transient_bat_task(slaveNode):
## def to_Write(self, msg):
## print msg,
# def process(self, pnum=3,
# speed_c = 120,
def test_fast_default():
# fast.result_root = 'result'
fast.result_name = 'rr1'
# fast.windfilename = 'Wind\A_16mps.wnd'
fast.windfilename = 'Wind\A_8mps.wnd'
import time
# path = r'../Data/FASTV8_WP1.5/Test11.fst'
# path = r'../Data/FASTV8_demo_a/Test11.fst'
path = r'../Data/FASTV8_demo_hr/Test11.fst'
# fast.simulate(path, maxStep=2)
start = time.time()
# fast.simulate(path)
fast.simulate(path, maxStep=200*20)
# fast.init(path, maxStep=-1)
elapsed = time.time() - start
print('time consumed: %.1f' % elapsed)
# fast_exe(path)
path = '%s.out' % fast.outfileroot
print(path)
from FASTPost import test_plot1
test_plot1(path)
plt.gcf().canvas.set_window_title(fast.windfilename)
plt.show()
def test_fast_default2():
# path = r'../Data/FASTV8_WP1.5/Test11.fst'
path = r'../Data/FASTV8_demo_a/Test11.fst'
path = os.path.abspath(path)
# wtdyn.simulate(path, maxStep=2)
os.system(r'E:\Project\EIS\FAST\FASTV8.10.00a\bin_raw\FAST_win32.exe %s' % path)
if __name__ == '__main__':
print('start')
# import f90wrap.runtime
# print f90wrap.runtime.sizeof_fortran_t
# laoxu_modal()
# laoxu_transient()
# test_pyCAE()
# test_pyCAE_element()
# test_ctrlbase()
# test_ctrlbase2()
# test_fast_default()
# test_fast_default2()
# test_modes()
# test_IECWind()
cfg_path = '../../Data/FASTV8_demo_hr/demo_a/Wind/60_84m_mps.inp'
test_turbsim(cfg_path, 24, 'A', '.')
print('end') | 1.914063 | 2 |
model.py | GuanLab/sepsis | 7 | 12787579 | <filename>model.py
import lightgbm as lgb
import numpy as np
def lightgbm_train(train_data, train_label):
"""
params:
train_data: Numpy array
train_label: Numpy array
yields:
gbm
"""
lgb_train = lgb.Dataset(np.asarray(train_data), np.asarray(train_label))
params = {
'boosting_type': 'gbdt',
'objective': 'regression',
'num_leaves': 150,
'learning_rate': 0.05,
'verbose': 0,
'n_estimators': 400,
'reg_alpha': 2.0,
}
gbm = lgb.train(params,
lgb_train,
num_boost_round=1000
)
return gbm
#print('Saving model...')
#filename = 'finalized_model.sav.'+sys.argv[1]
#pickle.dump(gbm, open(filename, 'wb'))
| 2.859375 | 3 |
pacote-download/Python/modulo01/python01ex/ex049.py | fabiosabariego/curso-python | 0 | 12787580 | <reponame>fabiosabariego/curso-python
"""
Refaça o desafio 9, mostrando a tabuada do numero que o usuário escolher, só que faça com for
"""
res = 0
num = int(input('Digite um numero: '))
print('=' * 20)
print('A Tabuada do {} é:'.format(num))
print('=' * 20)
for c in range(1, 11):
res = num * c
print('{} x {} = {}'.format(num, c, res))
| 4.03125 | 4 |
flexlab/drivers/primary_chiller.py | rongxinyin/HIL-Sim | 0 | 12787581 | <gh_stars>0
from driver import Driver, pd, np, datetime
import logging
from flexlab.drivers.modbus_driver import Modbus_Driver
class Primary_Chiller(Driver):
def __init__(self, config_file="read_data_config.yaml"):
logging.basicConfig(format='%(asctime)s %(levelname)s:%(message)s', level=logging.INFO)
self.logger = logging.getLogger(__name__)
try:
super(Primary_Chiller, self).__init__(config_file=config_file)
except Exception as e:
self.logger.error("error reading config file={0} error={1}".format(config_file, str(e)))
self.primary_chiller_config = self.config.get('primary_chiller')
self.table_name = self.primary_chiller_config.get('db_table_name')
self.chiller = Modbus_Driver(config_file, config_section='primary_chiller')
self.chiller.initialize_modbus()
def write_to_db(self):
time_now = self.get_utc_time_now()
measurements = self.chiller.get_data(unit=self.chiller.UNIT_ID)
df = pd.DataFrame.from_records(measurements, index=[time_now])
df.index.name = 'time'
self.chiller.kill_modbus()
if not df.empty:
self.push_to_db(df, self.table_name)
else:
print("nothing to push to {0}".format(self.table_name))
if __name__ == "__main__":
obj = Primary_Chiller(config_file='read_data_config.yaml')
obj.write_to_db() | 2.375 | 2 |
ssi.py | NingAnMe/extraterrestrial-solar-radiation | 0 | 12787582 | <gh_stars>0
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2020-04-01 15:10
# @Author : NingAnMe <<EMAIL>>
from dateutil.relativedelta import relativedelta
import numpy as np
G0_Correct = 0.75 # 使用G0订正Itol的系数
ER_TXT = 'er.txt'
EP_TXT = 'ep.txt'
def cos(x):
return np.cos(np.radians(x))
def sin(x):
return np.sin(np.radians(x))
def isleap(y):
y = int(y)
return (y % 4 == 0 and y % 100 != 0) or y % 400 == 0
def calDoy(y, m, d):
y = int(y)
m = int(m)
d = int(d)
Doy = 0
a = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
if isleap(y):
a[1] = 29
for x in a[0:m - 1]:
Doy += x
return Doy + d
def calDelta(Doy):
# print "360/365*(284 + Doy) is %f" % (360.0/365*(284 + Doy))
return 23.45 * sin(360.0 / 365 * (284 + Doy))
def calOmega(hr, min, lon, E):
print(hr, min, lon, E)
TT = hr + min / 60.0 + 4 * (lon - 120) / 60.0 + E / 60.0
print('TT', TT)
return (TT - 12) * 15
def calCosThetaz(lat, Delta, Omega):
print('lat, Delta, Omega', lat, Delta, Omega)
return cos(lat) * cos(Delta) * cos(Omega) + sin(lat) * sin(Delta)
def calEDNI(Doy):
return 1366.1 * (1 + 0.033 * cos(360.0 / 365 * Doy))
def calG0(Doy, CosThetaz):
EDNI = calEDNI(Doy)
print(EDNI)
return EDNI * CosThetaz
def calRb(lat, Beta, Delta, Omega, CosThetaz):
return (cos(lat - Beta) * cos(Delta) * cos(Omega) + sin(lat - Beta) * sin(Delta)) / CosThetaz
def calGt(Ib, Id, Ai, Rb, Beta, Itol):
return (Ib + Id * Ai) * Rb + Id * (1 - Ai) * (1 + cos(Beta)) / 2.0 + Itol * 0.2 * (1 - cos(Beta)) / 2.0
def assignTime(date):
"""
DEBUG函数assignTime，原来的函数直接在hour加8可能超过24
:param date:
:return:
"""
date += relativedelta(hours=8) # 修改时间为北京时
print(date)
datestrf = date.strftime('%Y-%m-%d-%H-%M-%S')
y, m, d, h, mm, s = datestrf.split('-')
return [int(i) for i in (y, m, d, h, mm)]
def assignE(y, m, d):
"""
assignE
:param y:
:param m:
:param d:
:return:
"""
y = int(y)
m = int(m)
d = int(d)
if isleap(y):
e_file = ER_TXT
else:
e_file = EP_TXT
e_data = np.loadtxt(e_file)
md = int('{:02d}{:02d}'.format(m, d))
index = np.where(e_data == md)
row = index[0]
if row.size != 0:
return e_data[row, 1]
else:
raise ValueError('没有找到E值： {}'.format((y, m, d)))
def cal_tol(date_time, lons, lats):
"""
计算理论太阳能辐照度
:param date_time: 世界时时间
:return:
"""
y, m, d, hr, minus = assignTime(date_time)
e = assignE(y, m, d)
doy = calDoy(y, m, d)
delta = calDelta(doy)
print('delta', delta)
Omega = calOmega(hr, minus, lons, e)
print('Omega', Omega)
cos_the_taz = calCosThetaz(lats, delta, Omega)
print('cos_the_taz', cos_the_taz)
G0 = calG0(doy, cos_the_taz)
print('G0', G0)
itol = G0_Correct * G0
return itol
if __name__ == '__main__':
from datetime import datetime
date_time = datetime.strptime('19990101060000', '%Y%m%d%H%M%S')
print(date_time)
print(cal_tol(date_time, 116.55, 40.12)) | 2.84375 | 3 |
tests/broken_pickle.py | Kyle-Kyle/angr | 6,132 | 12787583 | <reponame>Kyle-Kyle/angr<gh_stars>1000+
import pickle
import angr
import nose
def test_pickle_state():
b = angr.Project("/home/angr/angr/angr/tests/blob/x86_64/fauxware")
p = b.path_generator.entry_point()
p.state.inspect.make_breakpoint('mem_write')
nose.tools.assert_true('inspector' in p.state.plugins)
s_str = pickle.dumps(p.state)
s2 = pickle.loads(s_str)
nose.tools.assert_is(p.state, s2)
del p
del s2
import gc; gc.collect()
s2 = pickle.loads(s_str)
nose.tools.assert_true('inspector' not in s2.plugins)
if __name__ == '__main__':
test_pickle_state()
| 2.09375 | 2 |
Baselines/OLTR/Codes/algorithms/basiconlineranker.py | nju-websoft/TRAVERS | 55 | 12787584 | <gh_stars>10-100
# -*- coding: utf-8 -*-
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
import numpy as np
import utils.rankings as rnk
class BasicOnlineRanker(object):
def __init__(self, n_results, n_features):
self.n_features = n_features
self.n_results = n_results
self.n_interactions = 0
self.model_updates = 0
self._messages = {}
self._default_messages = {}
self._train_features = None
self._train_query_ranges = None
@staticmethod
def default_parameters():
'''Return all parameter values for this ranker.
used for logging purposes.'''
return {}
def add_message(self, name, default_value=0):
self._default_messages[name] = default_value
def remove_message(self, name):
del self._default_messages[name]
def set_message(self, name, value):
self._messages[name] = value
def get_messages(self):
messages = self._default_messages.copy()
messages.update(self._messages)
return messages
def reset_messages(self):
self._messages.clear()
def setup(self, train_features, train_query_ranges):
self._train_features = train_features
self._train_query_ranges = train_query_ranges
def clean(self):
del self._train_features
del self._train_query_ranges
def get_test_rankings(self, features,
query_ranges, inverted=True):
return rnk.rank_multiple_queries(
np.zeros(features.shape[0]),
query_ranges,
inverted=inverted,
n_results=self.n_results)
def get_query_features(self, query_id, features,
query_ranges):
start_i = query_ranges[query_id]
end_i = query_ranges[query_id + 1]
return features[start_i:end_i, :]
def get_query_label(self, query_id, label_vector,
query_ranges):
start_i = query_ranges[query_id]
end_i = query_ranges[query_id + 1]
return label_vector[start_i:end_i]
def get_query_size(self, query_id, query_ranges):
return query_ranges[query_id+1] - query_ranges[query_id]
def get_train_query_ranking(self, query_id, inverted=False):
self._last_query_id = query_id
query_feat = self.get_query_features(query_id,
self._train_features,
self._train_query_ranges)
self._last_ranking = self._create_train_ranking(
query_id,
query_feat,
inverted)[:self.n_results]
return self._last_ranking
def _create_train_ranking(self, query_id, query_feat, inverted):
n_docs = self.get_query_size(query_id,
self._train_query_ranges)
return rnk.rank_single_query(np.zeros(n_docs),
inverted=inverted,
n_results=self.n_results)[:self.n_results]
def process_clicks(self, clicks):
self.update_to_interaction(clicks)
self.n_interactions += 1
def update_to_interaction(self, clicks):
pass | 2.28125 | 2 |
Dataset/Leetcode/train/98/408.py | kkcookies99/UAST | 0 | 12787585 | <filename>Dataset/Leetcode/train/98/408.py
class Solution:
def XXX(self, root: TreeNode, min_=float("-inf"), max_=float("inf")) -> bool:
return (not root) or ((min_ < root.val < max_) and self.XXX(root.left, min_, root.val) and self.XXX(root.right, root.val, max_))
| 3.203125 | 3 |
panrep/encoders.py | amazon-research/panrep | 10 | 12787586 | from classifiers import BaseRGCN
from dgl.nn.pytorch import RelGraphConv
from functools import partial
import torch
import torch.nn.functional as F
import torch.nn as nn
from dgl.nn import RelGraphConv
from layers import RelGraphConvHetero, EmbeddingLayer, RelGraphAttentionHetero,MiniBatchRelGraphEmbed
class EncoderRGCN(BaseRGCN):
def create_features(self):
features = torch.arange(self.num_nodes)
if self.use_cuda:
features = features.cuda()
return features
def build_input_layer(self):
return RelGraphConv(self.inp_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
dropout=self.dropout)
# TODO different layers may have different number of hidden units current implementation prevents
def build_hidden_layer(self, idx):
return RelGraphConv(self.h_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
dropout=self.dropout)
def build_class_output_layer(self):
return RelGraphConv(self.h_dim, self.out_dim, self.num_rels, "basis",
self.num_bases, activation=partial(F.softmax, dim=1),
self_loop=self.use_self_loop)
def build_reconstruct_output_layer(self):
return RelGraphConv(self.h_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=partial(F.softmax, dim=1),
self_loop=self.use_self_loop)
def build_output_layer(self):
return self.build_reconstruct_output_layer()
class EncoderRelGraphAttentionHetero(nn.Module):
def __init__(self,
h_dim,
in_size_dict,
etypes,
ntypes,
num_hidden_layers=1,
dropout=0,
use_self_loop=False):
super(EncoderRelGraphAttentionHetero, self).__init__()
self.h_dim = h_dim
self.rel_names = list(set(etypes))
self.rel_names.sort()
self.num_hidden_layers = num_hidden_layers
self.dropout = dropout
self.use_self_loop = use_self_loop
self.in_size_dict = in_size_dict
self.embed_layer = EmbeddingLayer(self.in_size_dict, h_dim, ntypes)
self.layers = nn.ModuleList()
# h2h
for i in range(self.num_hidden_layers):
self.layers.append(RelGraphAttentionHetero(
self.h_dim, self.h_dim, etypes, activation=F.relu, self_loop=self.use_self_loop,
dropout=self.dropout))
def forward(self,G, corrupt=False):
if corrupt:
# create local variable do not permute the original graph
g = G.local_var()
for key in self.in_size_dict:
# TODO possibly high complexity here??
# The following implements the permutation of features within each node class.
# for the negative sample in the information maximization step
perm = torch.randperm(g.nodes[key].data['features'].shape[0])
g.nodes[key].data['features'] = g.nodes[key].data['features'][perm]
else:
g = G
h = self.embed_layer(g)
for layer in self.layers:
h = layer(g, h)
return h
class EncoderRelGraphConvHomo(nn.Module):
def __init__(self,
device,
num_nodes,
h_dim,
num_rels,
num_bases=None,
num_hidden_layers=1,
dropout=0,
use_self_loop=False,
low_mem=False,
layer_norm=False):
super(EncoderRelGraphConvHomo, self).__init__()
self.device = torch.device(device)
self.num_nodes = num_nodes
self.h_dim = h_dim
self.num_rels = num_rels
self.num_bases = None if num_bases < 0 else num_bases
self.num_hidden_layers = num_hidden_layers
self.dropout = dropout
self.use_self_loop = use_self_loop
self.low_mem = low_mem
self.layer_norm = layer_norm
self.layers = nn.ModuleList()
# i2h
self.layers.append(RelGraphConv(
self.h_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
low_mem=self.low_mem, dropout=self.dropout, layer_norm=layer_norm))
# h2h
for idx in range(self.num_hidden_layers):
self.layers.append(RelGraphConv(
self.h_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
low_mem=self.low_mem, dropout=self.dropout, layer_norm=layer_norm))
# h2o
#self.layers.append(RelGraphConv(
# self.h_dim, self.out_dim, self.num_rels, "basis",
# self.num_bases, activation=None,
# self_loop=self.use_self_loop,
# low_mem=self.low_mem, layer_norm=layer_norm))
def forward(self, blocks, feats, corrupt=False, norm=None):
h = feats
if corrupt:
perm = torch.randperm(len(feats))
h = h[perm]
if blocks is None:
# full graph training
blocks = [self.g] * len(self.layers)
for layer, block in zip(self.layers, blocks):
block = block.to(self.device)
h = layer(block, h, block.edata['etype'], block.edata['norm'])
return h
class EncoderRelGraphConvHetero(nn.Module):
def __init__(self,
h_dim,
etypes,
ntypes,
device,
g,
num_bases=-1,
num_hidden_layers=1,
dropout=0,
use_self_loop=False):
super(EncoderRelGraphConvHetero, self).__init__()
self.h_dim = h_dim
self.rel_names = list(set(etypes))
self.rel_names.sort()
self.num_bases = None if num_bases < 0 else num_bases
self.num_hidden_layers = num_hidden_layers
self.dropout = dropout
self.use_self_loop = use_self_loop
self.embed_layer = MiniBatchRelGraphEmbed(g=g,device=device,embed_size=h_dim)
self.layers = nn.ModuleList()
# h2h
for i in range(self.num_hidden_layers):
self.layers.append(RelGraphConvHetero(
self.h_dim, self.h_dim, self.rel_names, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
dropout=self.dropout))
def forward(self,G, corrupt=False):
if corrupt:
# create local variable do not permute the original graph
g = G.local_var()
for key in self.g.ntypes:
# TODO possibly high complexity here??
# The following implements the permutation of features within each node class.
# for the negative sample in the information maximization step
perm = torch.randperm(g.nodes[key].data['h_f'].shape[0])
g.nodes[key].data['h_f'] = g.nodes[key].data['h_f'][perm]
else:
g = G
h = self.embed_layer(g,full=True)
for layer in self.layers:
h = layer(g, h)
return h
def forward_mb(self,blocks,permute=False):
h = self.embed_layer(blocks[0])
if permute:
for key in h.keys():
perm = torch.randperm(h[key].shape[0])
h[key] = h[key][perm]
for layer, block in zip(self.layers, blocks):
# print(h)
h = layer.forward_mb(block, h)
return h
class HGTLayer(nn.Module):
def __init__(self,
in_dim,
out_dim,
node_dict,
edge_dict,
n_heads,
dropout=0.2,
use_norm=False):
super(HGTLayer, self).__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.node_dict = node_dict
self.edge_dict = edge_dict
self.num_types = len(node_dict)
self.num_relations = len(edge_dict)
self.total_rel = self.num_types * self.num_relations * self.num_types
self.n_heads = n_heads
self.d_k = out_dim // n_heads
self.sqrt_dk = math.sqrt(self.d_k)
self.att = None
self.k_linears = nn.ModuleList()
self.q_linears = nn.ModuleList()
self.v_linears = nn.ModuleList()
self.a_linears = nn.ModuleList()
self.norms = nn.ModuleList()
self.use_norm = use_norm
for t in range(self.num_types):
self.k_linears.append(nn.Linear(in_dim, out_dim))
self.q_linears.append(nn.Linear(in_dim, out_dim))
self.v_linears.append(nn.Linear(in_dim, out_dim))
self.a_linears.append(nn.Linear(out_dim, out_dim))
if use_norm:
self.norms.append(nn.LayerNorm(out_dim))
self.relation_pri = nn.Parameter(torch.ones(self.num_relations, self.n_heads))
self.relation_att = nn.Parameter(torch.Tensor(self.num_relations, n_heads, self.d_k, self.d_k))
self.relation_msg = nn.Parameter(torch.Tensor(self.num_relations, n_heads, self.d_k, self.d_k))
self.skip = nn.Parameter(torch.ones(self.num_types))
self.drop = nn.Dropout(dropout)
nn.init.xavier_uniform_(self.relation_att)
nn.init.xavier_uniform_(self.relation_msg)
def edge_attention(self, edges):
etype = edges.data['id'][0]
'''
Step 1: Heterogeneous Mutual Attention
'''
relation_att = self.relation_att[etype]
relation_pri = self.relation_pri[etype]
key = torch.bmm(edges.src['k'].transpose(1, 0), relation_att).transpose(1, 0)
att = (edges.dst['q'] * key).sum(dim=-1) * relation_pri / self.sqrt_dk
'''
Step 2: Heterogeneous Message Passing
'''
relation_msg = self.relation_msg[etype]
val = torch.bmm(edges.src['v'].transpose(1, 0), relation_msg).transpose(1, 0)
return {'a': att, 'v': val}
def message_func(self, edges):
return {'v': edges.data['v'], 'a': edges.data['a']}
def reduce_func(self, nodes):
'''
Softmax based on target node's id (edge_index_i).
NOTE: Using DGL's API, there is a minor difference with this softmax with the original one.
This implementation will do softmax only on edges belong to the same relation type, instead of for all of the edges.
'''
att = F.softmax(nodes.mailbox['a'], dim=1)
h = torch.sum(att.unsqueeze(dim=-1) * nodes.mailbox['v'], dim=1)
return {'t': h.view(-1, self.out_dim)}
def forward(self, G, h):
with G.local_scope():
node_dict, edge_dict = self.node_dict, self.edge_dict
for srctype, etype, dsttype in G.canonical_etypes:
k_linear = self.k_linears[node_dict[srctype]]
v_linear = self.v_linears[node_dict[srctype]]
q_linear = self.q_linears[node_dict[dsttype]]
G.nodes[srctype].data['k'] = k_linear(h[srctype]).view(-1, self.n_heads, self.d_k)
G.nodes[srctype].data['v'] = v_linear(h[srctype]).view(-1, self.n_heads, self.d_k)
G.nodes[dsttype].data['q'] = q_linear(h[dsttype]).view(-1, self.n_heads, self.d_k)
G.apply_edges(func=self.edge_attention, etype=etype)
G.multi_update_all({etype: (self.message_func, self.reduce_func) \
for etype in edge_dict}, cross_reducer='mean')
new_h = {}
for ntype in G.ntypes:
'''
Step 3: Target-specific Aggregation
x = norm( W[node_type] * gelu( Agg(x) ) + x )
'''
n_id = node_dict[ntype]
alpha = torch.sigmoid(self.skip[n_id])
trans_out = self.drop(self.a_linears[n_id](G.nodes[ntype].data['t']))
trans_out = trans_out * alpha + h[ntype] * (1 - alpha)
if self.use_norm:
new_h[ntype] = self.norms[n_id](trans_out)
else:
new_h[ntype] = trans_out
return new_h
class EncoderHGT(nn.Module):
def __init__(self, G, node_dict, edge_dict, n_inp, n_hid, n_out, n_layers, n_heads, use_norm=True):
super(EncoderHGT, self).__init__()
self.node_dict = node_dict
self.edge_dict = edge_dict
self.gcs = nn.ModuleList()
self.n_inp = n_inp
self.n_hid = n_hid
self.n_out = n_out
self.n_layers = n_layers
self.adapt_ws = nn.ModuleList()
for t in range(len(node_dict)):
self.adapt_ws.append(nn.Linear(n_inp, n_hid))
for _ in range(n_layers):
self.gcs.append(HGTLayer(n_hid, n_hid, node_dict, edge_dict, n_heads, use_norm=use_norm))
self.out = nn.Linear(n_hid, n_out)
def forward(self, G, out_key):
h = {}
for ntype in G.ntypes:
n_id = self.node_dict[ntype]
h[ntype] = F.gelu(self.adapt_ws[n_id](G.nodes[ntype].data['inp']))
for i in range(self.n_layers):
h = self.gcs[i](G, h)
return self.out(h[out_key])
| 2.5625 | 3 |
hseling_api_autotutor/app/recommendation_logic/get_new_knowledge_db.py | NatalieIsupova/hseling-repo-autotutor | 0 | 12787587 | <gh_stars>0
from gensim.models.keyedvectors import FastTextKeyedVectors
#fasttext = FastTextKeyedVectors.load("D:/fasttext_word2vec/araneum_none_fasttextcbow_300_5_2018/araneum_none_fasttextcbow_300_5_2018.model")
#fasttext = FastTextKeyedVectors.load("/Users/nigula/input/araneum_none_fasttextcbow_300_5_2018/araneum_none_fasttextcbow_300_5_2018.model")
from text_processing import get_text_map
from os import listdir
from os.path import join
import random
import json
from collections import OrderedDict
import numpy as np
import pandas as pd
from collections import OrderedDict
texts_lenta = pd.read_csv("./articles/music_lenta.csv")
with open("text_map_improved_example.json", "r") as f:
text_map = json.load(f)
shan = {0: OrderedDict([(0, True), (2, False), (5, False)]),
17: OrderedDict([(0, False),
(2, False),
(4, False),
(8, True),
(10, False),
(12, False)]),
26: OrderedDict([(0, False), (5, True), (7, True), (13, False)]),
32: OrderedDict([(3, True), (5, True), (14, False), (18, False)]),
98: OrderedDict([(0, True), (4, True), (5, True), (8, False)]),
121: OrderedDict([#(0, True),вопрос проебан просто не вставлен в тест!!!!
(1, True),
(3, False),
(4, True),
(6, False),
(11, True)]),
130: OrderedDict([(0, True),
(1, False),
# (3, True),вопрос проебан просто не вставлен в тест!!!!
(4, False),
(5, False),
(8, False),
(9, True),
(16, True)]),
133: OrderedDict([(2, False),
(6, False),
(8, False),
(9, False),
(18, False),
(20, False),
(40, True),
(49, False)]),
200: OrderedDict([(0, False),(2, False), (4, False), (10, True), (12, True)]),
231: OrderedDict([(2, False), (4, True), (7, False), (12, False)]),
240: OrderedDict([(1, True), (11, False), (15, False)]),
316: OrderedDict([(0, True), (5, False), (7, False), (11, True)]),
331: OrderedDict([(2, False), (4, False), (10, False)]),
334: OrderedDict([(1, False), (2, False), (5, True), (8, False)]),
336: OrderedDict([(1, True), (7, True), (10, True), (13, True)]),
366: OrderedDict([(0, True),
(1, False),
(5, False),
(8, False),
(10, True),
(11, True),
(15, False)]),
371: OrderedDict([(0, False),
(2, False),
(3, False),
(6, False),
(12, False),
(21, False)])}#!
def get_user_db(ans_dict, ans_dict_name):
DEBUG = False
user = user_vector(debug = DEBUG)
for text in text_maps_json_dict.keys():
print("text_index", text)
text_map = text_maps_json_dict[text]
user.start_new_text()
for question_sentence_index in question_text_sent_collocind_dict[str(text)].keys():
user.update_vector_with_answer_sentence(text_map['sentences_map'][int(question_sentence_index)],
effected_collocations_start_indexes_list = question_text_sent_collocind_dict[str(text)][str(question_sentence_index)],
correct_answer = ans_dict[int(text)][int(question_sentence_index)])
user.end_text(text_map)
user.export_user_db(ans_dict_name)
get_user_db(shan, "shan_big_musician") | 2.203125 | 2 |
textmyself.py | ryanrobertson21/Morning-Surf-Report | 0 | 12787588 | #!/usr/bin/env python
#textMyself.py - Defines the textmyself() function that texts a message passed to it as a string
from twilio.rest import TwilioRestClient
# Read in account information
with open('/Users/RyanRobertson21/PycharmProjects/CoolProjects/twilioAccountInfo') as f:
info=f.read().splitlines()
# Preset Values
accountSID = info[0]
authToken = info[1]
myNumber = info[2]
twilioNumber = info[3]
# Send message from twilio to my number
def textmyself(message):
twilioCli = TwilioRestClient(accountSID, authToken)
twilioCli.messages.create(body=message, from_=twilioNumber, to=myNumber)
| 3.15625 | 3 |
proc/model.py | nisarkhanatwork/rocksample_deeprl | 0 | 12787589 | <filename>proc/model.py
import keras
from keras import backend as K
from keras.layers import Dense
from keras.models import Sequential
from support import *
import keras.losses
INPUT_SIZE = 56
def get_model(data_type):
# Model parameters and other things based upon the type of data
op_dims = 4
op_activ = 'softmax'
# krnl_init = keras.initializers.RandomNormal(mean = 0.0, stddev = 0.05, seed = 11211)
krnl_init = keras.initializers.GlorotUniform(seed = 11211)
bias_init = keras.initializers.Zeros()
if (data_type == 'encoded'):
# NOT IN USE!!!!
ip_dims = 6
l1_units = 200
l2_units = 200
activ = 'relu'
lrn_rt = 1.0e-06
#https://stackoverflow.com/questions/59737875/keras-change-learning-rate
opt = keras.optimizers.Adam(lr = lrn_rt)
elif (data_type == 'sparse'):
ip_dims = INPUT_SIZE
l1_units = 400
l2_units = 90
activ = 'relu'
lrn_rt = 1.0e-04
# Adagrad
#opt = keras.optimizers.Adagrad(
# learning_rate=lrn_rt, initial_accumulator_value=0.1, epsilon=1e-07,
# name='Adagrad')
# Adadelta optimizer
#opt = keras.optimizers.Adadelta(
# lr = lrn_rt, rho = 0.95, epsilon = 1e-07)
# RMSprop optimizer
#https://stackoverflow.com/questions/59737875/keras-change-learning-rate
# opt = keras.optimizers.RMSprop(
# learning_rate = lrn_rt, rho = 0.9, momentum = 0.0, epsilon = 1e-07,
# centered = False, name = 'RMSprop')
# Adam w/wo amsgrad
opt = keras.optimizers.Adam(lr = lrn_rt, amsgrad=True)
# Nadam
# opt = keras.optimizers.Nadam(
# lr=lrn_rt, beta_1=0.9, beta_2=0.999, epsilon=1e-07, name = 'nadam')
#opt = keras.optimizers.SGD(
# learning_rate=lrn_rt, momentum=0.001, nesterov=True, name='SGD'
# )
# Loss
# loss = keras.losses.KLDivergence()
loss = keras.losses.MeanSquaredError(reduction="auto", name="mean_squared_error")
else:
import sys
sys.exit(0)
# creates a generic neural network architecture
model = Sequential()
model.add(Dense(units = l1_units,
input_dim = ip_dims,
activation = activ,
kernel_initializer = krnl_init,
bias_initializer = bias_init))
model.add(Dense(units = l1_units,
input_dim = ip_dims,
activation = activ,
kernel_initializer = krnl_init,
bias_initializer = bias_init))
model.add(Dense(units = l2_units,
activation= activ,
kernel_initializer = krnl_init,
bias_initializer = bias_init))
model.add(Dense(units = l2_units,
activation= activ,
kernel_initializer = krnl_init,
bias_initializer = bias_init))
# output layer
model.add(Dense(units = op_dims,
activation = op_activ,
kernel_initializer = krnl_init,
bias_initializer = bias_init))
# compile the model using traditional Machine Learning losses and optimizers
model.compile(loss=loss, optimizer=opt, metrics=['accuracy'])
return model
def get_pre_proc_info(data_type):
if (data_type == 'encoded'):
get_features = get_pos
from sklearn.preprocessing import RobustScaler
pre_proc_features = RobustScaler()
else:
get_features = get_same
from sklearn.preprocessing import OrdinalEncoder
pre_proc_features = OrdinalEncoder()
from sklearn.preprocessing import MultiLabelBinarizer
pre_proc_features = MultiLabelBinarizer()
from sklearn.preprocessing import PolynomialFeatures
pre_proc_features = PolynomialFeatures(4)
from sklearn.preprocessing import RobustScaler
pre_proc_features = RobustScaler()
from sklearn.preprocessing import StandardScaler
pre_proc_features = StandardScaler()
return get_features, pre_proc_features
| 2.5625 | 3 |
examples/kiva/service/kiva.py | aashish24/tangelo | 1 | 12787590 | <reponame>aashish24/tangelo
import bson.json_util
import datetime
import pymongo
import json
import cherrypy
import tangelo
def run(servername, dbname, type, datatype, querydata = None,
collection = None, by = None, datemin = None, datemax = None, count = 100):
# Check if we have valid count value if not default to 100
try:
count = int(count)
except exceptions.ValueError:
print "Unable to parse count value setting default to 100"
# Construct an empty response object.
response = tangelo.empty_response();
# Establish a connection to the MongoDB server.
try:
conn = pymongo.Connection(servername)
except pymongo.errors.AutoReconnect as e:
response['error'] = "error: %s" % (e.message)
return bson.json_util.dumps(response)
# Extract the requested database and collection.
db = conn[dbname]
# TODO (Chaudhary) Clean up this code. Currently the goal
# is to get the data in the right format for the visualization.
if type == "find":
# Current we support three types of datatypes / collections: loans,
# lenders, and lender-loan-links
# Perform find operation on loans
if datatype == "loans":
cherrypy.log("Processing find query for datatype " + datatype)
# Check if we have valid datemin and max. Currently only
# the loas has time entries
if (datemin is not None and datemax is not None):
timeConstraint = { "loans:posted_date" : { "$gte" : datemin,
"$lte" : datemax } }
else:
timeConstraint = {}
# cherrypy.log("timeConstraint " + str(timeConstraint))
coll = db["kiva.loans"]
# Assumption: that only certain fields are required
result = coll.find(timeConstraint, { "_id": 0, "loans:id": 1,
"loans:location:geo:pairs": 1,
"loans:loan_amount": 1,
"loans:sector": 1 }).limit(count)
# Sort result by loan amount
result.sort("loans:loan_amount", -1)
response = [["%s" % d["loans:id"], [float(x)
for x in d["loans:location:geo:pairs"].split()],
float(d["loans:loan_amount"]),
str(d["loans:sector"]),
str(d["loans:id"])] for d in result if d["loans:id"] != None]
# Perform find operation on lenders
if datatype == "lenders":
cherrypy.log("Processing find query for datatype " + datatype)
# Use time if available
if (datemin is not None and datemax is not None):
timeConstraint = { "lenders:member_since" : { "$gte" : datemin,
"$lte" : datemax } }
else:
timeConstraint = {}
coll = db["kiva.lenders"]
result = coll.find(
{ "$and" : [{"lenders:location:geo:pairs": { "$exists": "true" } }, timeConstraint]}, {
"_id": 0, "lenders:loan_count": 1,
"lenders:lender_id":1,
"lenders:location:geo:pairs":1}).limit(count)
# Sort result by loan count
result.sort("lenders:loan_count", -1)
response = [["%s" % d["lenders:lender_id"],
[float(x) for x in d["lenders:location:geo:pairs"].split()],
float(d["lenders:loan_count"])] for d in result if d["lenders:lender_id"] != None]
# Pefrom find operation on lender loan links data
if datatype == "lender-loan-links":
# TODO For now find the lenders and then using the result of the query
# find relevant lender-loan links
cherrypy.log("Processing find query for datatype " + datatype)
coll = db["kiva.lenders"]
lenders = coll.find({ "lenders:location:geo:pairs": { "$exists": "true" } }, {
"_id": 0, "lenders:lender_id":1 }).limit(count)
lenders = ["%s" % d["lenders:lender_id"] for d in lenders if d["lenders:lender_id"] != None]
# Use time if available
if (datemin is not None and datemax is not None):
timeConstraint = { "loans:posted_date" : { "$gte" : datemin,
"$lte" : datemax } }
else:
timeConstraint = {}
coll = db["kiva.lender.loan.links"]
result = coll.find({"$and": [{ "id" : {"$in" : list(lenders)}}, timeConstraint]}, {
"_id": 0, "id": 1,
"loans:id": 1,
"loans:borrower_count": 1,
"loans:loan_amount": 1})
response = [["%s" % d["id"], "%s" % d["loans:id"],
float(d["loans:borrower_count"]),
float(d["loans:loan_amount"])] for d in result if d["id"] != None]
# Perform aggregation type queries
# NOTE The code below is not tested / used.
elif type == "aggregate":
if datatype == "lenders":
coll = db["kiva.lenders"]
conditions = [{"member_since": {"$ne": None}}];
if datemin != None and datemax != None:
date_min = datetime.datetime.strptime(datemin, "%Y-%m-%d")
date_max = datetime.datetime.strptime(datemax, "%Y-%m-%d")
conditions.append({"date": {"$gte": date_min}})
conditions.append({"date": {"$lt": date_max}})
if lenders != None:
conditions.append({"lenders_id": int(lenders)})
pipeline = []
if len(conditions) > 0:
pipeline.append({"$match": {"$and": conditions}})
if by == "month":
group = {"year": {"$year": "$date"}, "month": {"$month": "$member_since"}}
else:
group = "$country_code"
pipeline.append({"$group": {"_id": group, "amount": {"$sum": "$loan_count"}}})
result = coll.aggregate(pipeline)
if by == "month":
response = [[d["_id"], float(d["loan_count"])] for d in result["result"] if d["_id"] != None]
else:
response = [["%s" % d["_id"], float(d["amount"])] for d in result["result"] if d["_id"] != None]
elif datatype == "population":
coll = db["census"]
response = [[d["_id"], int(d["pop2010"])] for d in coll.find()]
elif datatype == "charities":
coll = db["charitynet.normalized.transactions"]
result = coll.aggregate([{"$group": {"_id": "$charity_id", "count": {"$sum": 1}}}, {"$sort": {"count": -1}}])
response = [[d["_id"], d["_id"], d["count"]] for d in result["result"]]
else:
response['error'] = "error: unknown datatype requested"
# Convert to JSON and return the result.
return bson.json_util.dumps(response)
| 2.703125 | 3 |
materials/migrations/0024_auto_20180321_1512.py | mgovoni-devel/MatD3 | 7 | 12787591 | <filename>materials/migrations/0024_auto_20180321_1512.py
# Generated by Django 2.0.1 on 2018-03-21 15:12
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('materials', '0023_merge_20180320_1842'),
]
operations = [
migrations.AddField(
model_name='idinfo',
name='data_extraction_method',
field=models.CharField(default='TBD', max_length=500),
),
migrations.AddField(
model_name='idinfo',
name='source',
field=models.CharField(default='TBD', max_length=500),
),
]
| 1.539063 | 2 |
Complexity 01/00002_list_filtering.py | alexudovichenko/python-solutions | 0 | 12787592 | from random import randint
class Solution:
'''
TASK DESCRIPTION
Преобразуйте список целых чисел: оставьте только кратные пяти.
Примечание, ввод производится в синтаксисе списка
EXAMPLES:
Sample Input:
[4, 5, 7, 237895, 32, 432, 45, 0]
Sample Output:
5 237895 45 0
'''
def eval_list_filter(self, line: str):
# или from ast import literal_eval
number_list = eval(line)
return tuple(filter(lambda number: not number % 5, number_list))
def core_list_filter(self, line: str) -> tuple:
number_list = list(map(int, line.strip('[]').split(', ')))
return tuple(filter(lambda number: not number % 5, number_list))
if __name__ == '__main__':
line = input()
result = Solution()
print(*result.core_list_filter(line))
# заменить на юнит тесты
for i in range(100):
temporary_list = [randint(-500, 500) for _ in range(randint(5, 30))]
temporary_line = str(temporary_list)
assert result.core_list_filter(temporary_line) == result.eval_list_filter(temporary_line), 'Test failed'
| 3.71875 | 4 |
instagram_get_fans/model/instagram_user.py | comtong/instagram_graw_fans | 1 | 12787593 | <reponame>comtong/instagram_graw_fans
class InstagramUser(object):
def __init__(self,user_id, username,timestamp):
self.user_id = user_id
self.username = username
self.timestamp = timestamp
| 2.25 | 2 |
atomate/lammps/firetasks/__init__.py | dongsenfo/atomate | 1 | 12787594 | from __future__ import unicode_literals
# from .write_inputs import *
# from .run_calc import *
# from .parse_outputs import *
# from .glue_tasks import *
| 1.023438 | 1 |
under construction/tictactoe/client.py | LucasEmmes/pythonScripts | 0 | 12787595 | <filename>under construction/tictactoe/client.py
import socket
import os
import threading
import random
import time
PORT = 5050
FORMAT = "utf-8"
DISCONNECT_MESSAGE = "!DISCONNECT"
# SERVER = input("Please enter server IP: ")
SERVER = "10.18.0.2"
ADDR = (SERVER, PORT)
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client.connect(ADDR)
connected = True
logged_in = False
in_game = False
opponent = False
my_turn = False
username = False
user_id = False
game_active = False
valid_move = False
game_board = [[" ", " ", " "], [" ", " ", " "], [" ", " ", " "]]
def clear():
os.system('cls')
os.system('mode con: cols=39 lines=20')
def print_board(arr):
clear()
print("\n")
print(f"{arr[0]}\n{arr[1]}\n{arr[2]}")
print("\n")
print("\n")
def startup():
clear()
print("---------------------------------------")
print("\n")
print("Welcome to TicTacToes".rjust(30, " "))
print("\n")
print("---------------------------------------")
print("\n")
print("\n")
print("Enter a username between 3 and 20 chars")
while True:
username = input(">> ")
if len(username) < 3 or len(username) > 20:
print("I said please enter a username between 3 and 20 characters inclusive you fuck")
elif " " in username:
print("NO SPACES U CUNT")
else:
break
return username
def get_in_game():
clear()
print("---------------------------------------")
print("\n")
print("You may create a new room,".rjust(7, " "))
print("or join a friend via code".rjust(7, " "))
print("\n")
print("---------------------------------------")
print("\n")
print("\n")
print("Type CREATE or JOIN")
while True:
decision = input(">> ")
if decision.upper() == "CREATE":
send("CREATE")
time.sleep(1)
if not in_game:
print("Failed to create room")
else:
return 1
elif decision.upper() == "JOIN":
code = input("\nPlease enter the room code\n>> ")
if len(code) > 4:
print("The code is too long (like my dic)\n")
else:
send(f"JOIN{code}")
time.sleep(1)
if not in_game:
print("Failed to connect to room")
print("It may be full or the code may be wrong")
else:
return 1
else:
print("Listen here you schmuck >:^O")
def make_move():
print("Make your move")
move = input(">> ")
send(f"MOVE{move}")
while True:
time.sleep(1)
if not valid_move:
print("Not a valid move")
return 0
else:
return 1
def send(msg):
message = msg.encode(FORMAT)
client.send(message)
def receive():
global logged_in
global in_game
global opponent
global my_turn
global game_active
print("RECEIVING DATA")
while connected:
data = client.recv(2048).decode(FORMAT)
print(data)
data = data.split("-")
if data[0] == "2":
if data[1] == "CODE":
print(f"The code for the room is: {data[2]}")
elif data[1] == "OPPONENT":
opponent = data[2]
print(f"You opponent is: {opponent}")
elif data[1] == "START":
print(f"Game has started!")
if int(data[2]) == user_id:
my_turn = True
print(f"You go first")
else:
print(f"{opponent} goes first")
time.sleep(4)
game_active = True
elif data[1] == "MOVE":
board_string = data[2]
#TODO - turn string into 2d array
print(board_string)
if my_turn:
my_turn = False
else:
my_turn = True
else:
pass
# username handle
elif data[1] == "USERNAME":
if data[0] == "1":
print("USERNAME SET SUCCESS")
time.sleep(2)
logged_in = True
else:
print("FAILED TO SET USERNAME :((((")
# create room handle
elif data[1] == "CREATE":
if data[0] == "1":
print("You have sucessfully made a room!")
in_game = True
else:
print("FAILED TO CREATE GAME WTFTFTFT")
# join room handle
elif data[1] == "JOIN":
if data[0] == "1":
print("Joined the game!")
time.sleep(1)
in_game = True
else:
print("Failed to jion game wtftfwtf")
elif data[1] == "MOVE":
if data[0] == "1":
valid_move = True
time.sleep(1)
valid_move = False
else:
print("Not a valid move")
# TODO - check validity
# make move handle
def main():
global logged_in
global in_game
global username
global user_id
global game_active
rec_thread = threading.Thread(target=receive)
rec_thread.start()
while True:
if not username:
username = startup()
user_id = random.randrange(0xFFFFFFFF) # 16 bit random user id
send(f"{username}|{user_id}")
if logged_in:
if not in_game:
get_in_game()
elif not game_active:
print("Waiting for opponent to connect...")
while not game_active:
time.sleep(1)
else:
if my_turn:
print(game_board)
make_move()
else:
time.sleep(1)
main() | 3.453125 | 3 |
src/ToolChainSCDG/procedures/linux/custom_package/getsockname.py | AnonymousSEMA/SEMA-ToolChain | 0 | 12787596 | <reponame>AnonymousSEMA/SEMA-ToolChain<filename>src/ToolChainSCDG/procedures/linux/custom_package/getsockname.py<gh_stars>0
import angr
class getsockname(angr.SimProcedure):
# pylint:disable=arguments-differ
def run(self, sock_fd, addr, length): # pylint:disable=unused-argument
len_addr = self.state.mem[length].int.concrete
for i in range(len_addr):
self.state.memory.store(
addr + i,
self.state.solver.BVS(
"sockname_" + str(i),
8,
key=("api", "getsockname", "sockname_" + str(i)),
),
endness="Iend_LE",
)
# self.state.solver.Unconstrained("unconstrained_ret_%s" % self.display_name, 8*len_addr, key=('api', '?', self.display_name))
return 0
| 2.15625 | 2 |
paper_rock_scissors/_base.py | yehuihe/paper-rock-scissors | 1 | 12787597 | """Base classes for paper rock scissors game
"""
# Author: <NAME> <<EMAIL>>
from abc import ABCMeta, abstractmethod
from enum import Enum, auto
import time
import warnings
class MoveChoice(Enum):
ROCK = auto()
PAPER = auto()
SCISSORS = auto()
class Outcome(Enum):
WIN = auto()
LOSE = auto()
DRAW = auto()
class ListInstanceMixin:
"""Mixin class for all class in paper_rock_scissors."""
def __attrnames(self):
return ''.join('\t%s=%s\n' % (attr, self.__dict__[attr])
for attr in sorted(self.__dict__))
def __repr__(self):
return '<Instance of %s, address %s:\n%s>' % (
self.__class__.__name__,
id(self),
self.__attrnames())
class BaseRole(metaclass=ABCMeta):
"""Base class for roles in paper_rock_scissors.
Warning: This class should not be used directly.
Use derived classes instead.
"""
@abstractmethod
def __init__(self, role, name, score):
self.role = role
self.name = name
self.score = score
@abstractmethod
def _check_params(self):
# name
if not isinstance(self.name, str):
raise ValueError(f"name should be string, "
f"got {self.name} instead.")
# score
if not isinstance(self.score, int) or self.score < 0:
warnings.warn(
"Score must be positive integer or zero; "
f"got {self.score} instead.",
RuntimeWarning,
)
self.score = 0
@abstractmethod
def get_move(self, prompt):
"""Generate current move."""
pass
class GameEnvironment(ListInstanceMixin):
"""GameEnvironment class for paper_rock_scissors.
This class controls the flow of the game.
Parameters
----------
_player : _role.Player
Player role in the GameEnvironment.
_computer : _role.Computer
Computer role in the GameEnvironment.
_target_score : int, default=10
Target score of the game. Anyone reaches this score
will end the game.
_curr_round : int, default=0
Current round of the game.
_max_rounds : int, default=20
Maximum round of the game. Once _curr_round reaches
the max the game ends.
_sleep : int, default=1
Sleep time between each rounds.
_verbose : int, default=0
Verbosity level.
_winner : {_role.Player, _role.Computer}
Winner of the game.
"""
_ROLES_MAPPING = {
MoveChoice.ROCK: [MoveChoice.SCISSORS],
MoveChoice.SCISSORS: [MoveChoice.PAPER],
MoveChoice.PAPER: [MoveChoice.ROCK],
}
def __init__(
self,
player,
computer,
target_score=10,
curr_round=0,
max_rounds=20,
sleep=1,
verbose=0,
winner=None):
self._player = player
self._computer = computer
self._target_score = target_score
self._curr_round = curr_round
self._max_rounds = max_rounds
self._sleep = sleep
self._verbose = verbose
self._winner = winner
@property
def player(self):
return self._player
@property
def computer(self):
return self._computer
@property
def target_score(self):
return self._target_score
@target_score.setter
def target_score(self, value):
self._target_score = value
@property
def curr_round(self):
return self._curr_round
@curr_round.setter
def curr_round(self, value):
self._curr_round = value
@property
def max_rounds(self):
return self._max_rounds
@max_rounds.setter
def max_rounds(self, value):
self._max_rounds = value
@property
def winner(self):
return self._winner
@winner.setter
def winner(self, value):
self._winner = value
@property
def sleep(self):
return self._sleep
@sleep.setter
def sleep(self, value):
self._sleep = value
@property
def verbose(self):
return self._verbose
@verbose.setter
def verbose(self, value):
self._verbose = value
def _check_params(self):
# target_score
if not isinstance(self.target_score, int) or self.target_score <= 0:
warnings.warn(
"target_score must be positive integer; "
f"got {self.target_score} instead.",
RuntimeWarning,
)
# Default target_score set to 10
self.target_score = 10
# max_rounds
if not isinstance(self.max_rounds, int) or \
self.max_rounds < self.target_score:
warnings.warn(
"max_rounds must greater or equal to target_score; "
f"got {self.max_rounds} instead.",
RuntimeWarning,
)
# Default max_rounds set to target_score
self.max_rounds = self.target_score
# sleep
if not isinstance(self._sleep, int) or self._sleep < 0:
warnings.warn(
"sleep must be positive integer;"
f"got {self._sleep} instead.",
RuntimeWarning,
)
# Default sleep set to 1
self._sleep = 1
# verbose
if not isinstance(self._verbose, int) or \
self._verbose < 0 or self._verbose > 3:
warnings.warn(
"verbose must be positive integer between 0 and 3"
f"got {self._verbose} instead.",
RuntimeWarning,
)
# Default verbose set to 1
self._verbose = 1
@staticmethod
def _pprint_rules():
"""Return rules of the current game."""
return "Current winning conditions of the Paper-Rock-Scissors: \n" \
"Paper beats (wraps) rock \n" \
"Rock beats (blunts) scissors \n " \
"Scissors beats (cuts) paper. \n" \
"Game ends while there is a winner " \
"(score reach preset target score) or " \
"total rounds reach the maximum. \n" \
"Press ctrl + C quit the game."
def _pprint_state(self):
"""Return state of the current game."""
return "Current state of the game: \n" \
"%(player_name)s score: %(player_score)d \n" \
"%(computer_name)s score: %(computer_score)d \n" \
"Target Score: %(target_score)d\n" \
"Current Round: %(curr_round)d\n" \
"Maximum Rounds: %(max_rounds)d\n" \
"Sleep: %(sleep)d\nWinner: %(winner)s\n" %\
{'player_name': self.player.name,
'player_score': self.player.score,
'computer_name': self.computer.name,
'computer_score': self.computer.score,
'target_score': self.target_score,
'curr_round': self.curr_round,
'max_rounds': self.max_rounds,
'sleep': self._sleep,
'winner': self.winner}
@staticmethod
def _outcome(player_move, ai_move):
"""Determine the outcome of current round.
Paper beats (wraps) rock.
Rock beats (blunts) scissors.
Scissors beats (cuts) paper.
Parameters
----------
player_move : MoveChoice
Player's move for current round.
ai_move : MoveChoice
AI's move for current round.
Returns
-------
outcome : Outcome
Outcome of the current round.
"""
if player_move is ai_move:
return Outcome.DRAW
elif ai_move in GameEnvironment._ROLES_MAPPING[player_move]:
return Outcome.WIN
else:
return Outcome.LOSE
def play(self):
# Validate roles input parameters
self.player._check_params()
self.computer._check_params()
# Validate input parameters
self._check_params()
# Display game rules
print(self._pprint_rules())
# Game ends while there is a winner or total rounds reach the maximum
while not self.winner and self.curr_round <= self.max_rounds:
# Print current game state
if self.verbose >= 1:
print(self._pprint_state())
# Prompt input from player
# Return MoveChoice
move = self.player.get_move("Choose a move for this round: ")
if self.verbose >= 1:
# Notify player's choice
print("%s's move: %s" % (self.player.name, move.name))
# Computer's turn
print("\n%s is making a decision..." % self.computer.name)
time.sleep(self._sleep)
ai_move = self.computer.get_move("Choose a move for this round: ")
if self.verbose >= 1:
print("%s's move: %s" % (self.computer.name,
ai_move.name))
print("Current round is: %s vs %s" % (move.name,
ai_move.name))
outcome = GameEnvironment._outcome(move, ai_move)
if outcome is Outcome.WIN:
if self.verbose >= 1:
print("Winner of the current round is: %s \n"
% self.player.name)
self.player.score += 1
elif outcome is Outcome.LOSE:
if self.verbose >= 1:
print("Winner of the current round is: %s \n"
% self.computer.name)
self.computer.score += 1
else:
if self.verbose >= 1:
print("It's a draw for this round")
self.curr_round += 1
if self.player.score == self.target_score:
self.winner = self.player
elif self.computer.score == self.target_score:
self.winner = self.computer
# Display final winner of the game
# Whoever has the highest score is the winner
# if there is not winner decided
# If draw then computer wins
if not self.winner:
self.winner = self.player if \
self.player.score > self.computer.score else self.computer
print(f"Winner of the game: {self.winner.name}\n")
print(self._pprint_state())
| 3.5625 | 4 |
app/user/urls.py | opeerator/5G-Testbed-Backend | 0 | 12787598 | <filename>app/user/urls.py
from django.urls import path
from user import views
app_name = 'user'
urlpatterns = [
path('createuser/', views.CreateUserView.as_view(), name='createuser'),
path('createsuperuser/', views.CreateSuperUserView.as_view(), name='createsuperuser'),
path('token/', views.CreateTokenView.as_view(), name='token'),
path('meuser/', views.ManageUserView.as_view(), name='meuser'),
path('mesuperuser/', views.ManageSuperUserView.as_view(), name='mesuperuser'),
]
| 1.984375 | 2 |
yamlapi/demo/tool/read_write_yaml.py | Ironkubi/yamlapi | 19 | 12787599 | <gh_stars>10-100
from ruamel import yaml
from setting.project_config import *
def read_yaml(yaml_absolute_path):
"""
读取yaml文件
:param yaml_absolute_path: 参数为需要读取的yaml文件的绝对路径
:return:
"""
with open(yaml_absolute_path, "r", encoding="utf-8") as f:
data_list = yaml.load(f, Loader=yaml.Loader)
return data_list
# 返回一个数据列表
def write_yaml(yaml_relative, data_list):
"""
写入yaml文件
:param yaml_relative: 第一个参数为需要写入的yaml文件的相对路径
:param data_list: 第二个参数为需要转换的数据
:return:
"""
with open(yaml_path + yaml_relative, "w",
encoding="utf-8") as f:
yaml.dump(data_list, f, Dumper=yaml.RoundTripDumper,
default_flow_style=False, allow_unicode=True, indent=4)
return yaml_relative
# 返回一个yaml文件的相对路径
def merge_yaml():
"""
合并所有yaml文件的方法
:return:
"""
yaml_list = []
for root, dirs, files in os.walk(yaml_path):
# root为当前目录路径
# dirs为当前路径下所有子目录，list格式
# files为当前路径下所有非目录子文件，list格式
for i in files:
if os.path.splitext(i)[1] == '.yaml':
# os.path.splitext()把路径拆分为文件名+扩展名
if i != first_test_case_file:
yaml_list.append(os.path.join(root, i))
else:
the_first_yaml = os.path.join(root, first_test_case_file)
yaml_list.append(the_first_yaml)
# 加入第一个yaml文件
yaml_list.reverse()
# 反转排序
temporary_list = []
for i in yaml_list:
if i:
j = read_yaml(i)
# 调用读取yaml文件的方法
if j:
temporary_list.extend(j)
# 往列表里逐步添加元素
return temporary_list
# 返回一个临时列表
| 2.546875 | 3 |
app/utils/ResponseUtil.py | hustcc/TODO | 7 | 12787600 | <gh_stars>1-10
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Created on 2016年2月19日
@author: hzwangzhiwei
'''
from app.utils.JsonUtil import object_2_json
def standard_response(success, data):
'''
接口的标准json返回值，统一使用同一个方法，便于统一修改
'''
rst = {}
rst['success'] = success
rst['data'] = data
return object_2_json(rst)
| 1.929688 | 2 |
python/language/utilities/unpacking/intro_1.py | futureseadev/hgwxx7 | 6 | 12787601 | <reponame>futureseadev/hgwxx7
# arguments unpacking simplified!!
# *
a, b = range(2)
a
0
b
1
a, b, *rest = range(5)
a
0
b
1
rest
[2,3,4]
# first and last lines
with open('file.name') as f:
first, *_, last = f.readlines()
# Refactor functions
def f(a, b, *args):
print(a, b, *args)
# instead -> *args => a, b, *args
def f(*args):
a, b, *args = args
print(a, b, *args)
| 2.640625 | 3 |
URI/1-Beginner/1041.py | vicenteneto/online-judge-solutions | 0 | 12787602 | <filename>URI/1-Beginner/1041.py
# -*- coding: utf-8 -*-
x, y = [float(x) for x in raw_input().split()]
if x == 0 and y == 0:
print 'Origem'
elif x == 0:
print 'Eixo Y'
elif y == 0:
print 'Eixo X'
elif x > 0:
if y > 0:
print 'Q1'
else:
print 'Q4'
elif x < 0:
if y > 0:
print 'Q2'
else:
print 'Q3'
| 3.59375 | 4 |
ML/learn/algorithm/activation_functions.py | qrsforever/workspace | 2 | 12787603 | <filename>ML/learn/algorithm/activation_functions.py
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# @file activation_functions.py
# @brief
# @author QRS
# @blog qrsforever.github.io
# @version 1.0
# @date 2019-05-29 18:24:39
################################ jupyter-vim #######################################
# https://github.com/qrsforever/vim/blob/master/bundle/.configs/jupyter-vim_conf.vim
# %pylab --no-import-all # noqa
#####################################################################################
import numpy as np
import matplotlib.pyplot as plt
#####################################################################################
# <codecell> global
#####################################################################################
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.rcParams['axes.unicode_minus'] = False
plt.rcParams['figure.figsize'] = (16.0, 8.0)
plt.rcParams['text.usetex'] = True
plt.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}']
xs = np.arange(-6, 6, 0.01)
##
def display(title, yaxis,
func, ftxt = None, xpos=0, xytext=(0,0),
dfunc=None, dftxt=None, dxpos=None, dxytext=None):
plt.title(title, fontsize='xx-large', fontweight='bold', color='b')
plt.ylim(yaxis)
plt.locator_params('both', nbins=8) # 调整x,y坐标系的刻度划分
plt.xticks(fontsize=14) # 设置坐标刻度字体
plt.yticks(fontsize=14)
plt.axhline(lw=1, c='black') # 轴线设置
plt.axvline(lw=1, c='black')
plt.grid(alpha=0.4, ls='-.')
plt.box(on=None)
plt.plot(xs, func(xs), c='r', lw=3)
if ftxt:
plt.annotate(ftxt, xy=(xpos, func(xpos)), xytext=xytext,
textcoords='data', fontsize=16,
arrowprops=dict(width=1, headwidth=6, headlength=8,
facecolor='r', shrink=0.05))
if dfunc:
plt.plot(xs, dfunc(xs), c='g', lw=3)
plt.annotate(dftxt, xy=(dxpos, dfunc(dxpos)), xytext=dxytext,
textcoords='data', fontsize=16,
arrowprops=dict(width=1, headwidth=6, headlength=8,
facecolor='g', shrink=0.05))
#####################################################################################
# <codecell> Binary step (二元阶跃)
#####################################################################################
func = np.vectorize(lambda x: 1 if x > 0 else 0, otypes=[np.float])
dfunc = np.vectorize(lambda x: 0, otypes=[np.float])
text = r"$ f(x) = \left\{\begin{array}{ccc}"
text += r" 0 & for & x\leq 0 \\ "
text += r" 1 & for & x> 0 "
text += r"\end{array}\right. $"
dtext = r"$ f(x)' = \left\{\begin{array}{ccc}"
dtext += r"0 & for & x \neq 0 \\ "
dtext += r"? & for & x = 0 "
dtext += r"\end{array}\right. $"
display('binary step', (-0.1, 1.6),
func, text, 2, (2.2, 0.5),
dfunc, dtext, 0, (-3.5, 0.5)
)
#####################################################################################
# <codecell> Piecewise Linear (线性分段)
#####################################################################################
xmin = -3.5
xmax = 3.5
w = 1/(xmax - xmin)
b = xmax*w
func = np.vectorize(lambda x: 1 if x > xmax else 0 if x < xmin else w*x+b,
otypes=[np.float])
dfunc = np.vectorize(lambda x: 0 if x > xmax or x < xmin else w, otypes=[np.float])
text = r"$ f(x) = \left\{\begin{matrix}"
text += r"0 & for & x < x_{min} \\ "
text += r"wx + b & for & x_{min} \leq x \leq x_{max} \\ "
text += r"1 & for & x > x_{max}"
text += r"\end{matrix}\right. \\ \\ \\ \\"
text += r"w=%.2f; b=%.2f; x_{min}=%.2f; x_{max}=%.2f $" % (w, b, xmin, xmax)
dtext = r"$ f(x)' = \left\{\begin{matrix}"
dtext += r"0 & for & x < x_{min} \\ "
dtext += r"m & for & x_{min} \leq x \leq x_{max} \\ "
dtext += r"0 & for & x > x_{max}"
dtext += r"\end{matrix}\right. $"
display('piecewise linear', (-0.1, 1.6),
func, text, -0.3, (-5.0, 1.0),
dfunc, dtext, 2, (2.2, 0.5))
#####################################################################################
# <codecell> Bipolar
#####################################################################################
func = np.vectorize(lambda x: 1 if x > 0 else -1, otypes=[np.float])
dfunc = np.vectorize(lambda x: 0, otypes=[np.float])
text = r"$ f(x) = \left\{\begin{matrix}"
text += r"-1 & for & x \leq 0 \\ "
text += r"1 & for & x > 0 "
text += r"\end{matrix}\right. $"
dtext = r"$ f(x)' = \left\{\begin{matrix}"
dtext += r"0 & for & x \neq 0 \\ "
dtext += r"? & for & x = 0 "
dtext += r"\end{matrix}\right. $"
display('bipolar', (-1.5, 1.5),
func, text, 1, (2, 0.5),
dfunc, dtext, 0, (-4, 0.5)
)
#####################################################################################
# <codecell> Sigmoid
#####################################################################################
def sigmoid_func(x):
return 1 / (1 + np.exp(-x))
def sigmoid_dfunc(x):
fx = sigmoid_func(x)
return fx * (1 - fx)
text = r"$ f(x) = \dfrac{1}{1 + e^{-x}} $"
dtext = r"$ f(x)' = f(x) * (1-f(x)) $"
display('sigmiod', (-0.2, 1.2),
sigmoid_func, text, 1, (2, 0.5),
sigmoid_dfunc, dtext, -1, (-5, 0.5))
#####################################################################################
# <codecell> Bipolar Sigmoid
#####################################################################################
def bisigmoid_func(x):
return (1 - np.exp(-x)) / (1 + np.exp(-x))
def bisigmoid_dfunc(x):
return 2 * np.exp(x) / ((np.exp(x) + 1) ** 2)
text = r"$ f(x) = \dfrac{1-e^{-x}}{1+e^{-x}} $"
dtext = r"$ f(x)' = \dfrac{2e^x}{(e^x + 1)^2} $"
display('bipolar sigmiod', (-1.2, 1.2),
bisigmoid_func, text, 0.2, (1, -0.5),
bisigmoid_dfunc, dtext, -3, (-4, 0.5))
#####################################################################################
# <codecell> Hyperbolic Tangent, TanH (双曲正切)
#####################################################################################
def tanh_func(x):
return 2 / (1 + np.exp(-2 * x)) - 1
def tanh_dfunc(x):
fx = tanh_func(x)
return 1 - fx ** 2
text = r"$ f(x) = \dfrac{1}{1 + e^{-2x}} - 1 $"
dtext = r"$ f(x)' = 1 - f(x)^2 $"
display('hyperbolic tangent', (-1.2, 1.2),
tanh_func, text, 0.2, (1, -0.5),
tanh_dfunc, dtext, -1.0, (-4, 0.6))
#####################################################################################
# <codecell> Arctangent, Arctan
#####################################################################################
def arctan_func(x):
return np.arctan(x)
def arctan_dfunc(x):
return 1 / (1 + x ** 2)
text = r"$ f(x) = tan^{-1}x $"
dtext = r"$ f(x)' = \dfrac{1}{1+x^2} $"
display('arctangent', (-1.5, 1.5),
arctan_func, text, 0.2, (1, -0.5),
arctan_dfunc, dtext, -1.0, (-4, 1.2))
#####################################################################################
# <codecell> Rectified Linear Units, ReLU
#####################################################################################
relu_func = np.vectorize(lambda x: x if x > 0 else 0, otypes=[np.float])
relu_dfunc = np.vectorize(lambda x: 1 if x > 0 else 0, otypes=[np.float])
text = r"$ f(x) = \left\{\begin{matrix}"
text += r"0 & for & x \leq 0 \\ "
text += r"x & for & x > 0 "
text += r"\end{matrix}\right. $"
dtext = r"$ f(x)' = \left\{\begin{matrix}"
dtext += r"0 & for & x \leq 0 \\ "
dtext += r"1 & for & x > 0 "
dtext += r"\end{matrix}\right. $"
display('relu', (-0.5, 1.5),
relu_func, text, 0.6, (1.8, 0.5),
relu_dfunc, dtext, -2.0, (-3.2, 0.5))
#####################################################################################
# <codecell> Leaky Rectified Linear Units, Leaky ReLU
#####################################################################################
a = 0.1
lrelu_func = np.vectorize(lambda x: x if x > 0 else a*x, otypes=[np.float])
lrelu_dfunc = np.vectorize(lambda x: 1 if x > 0 else a, otypes=[np.float])
text = r"$ f(x) = \left\{\begin{matrix}"
text += r"ax & for & x \leq 0 \\ "
text += r"x & for & x > 0 "
text += r"\end{matrix}\right. \\ \\ \\"
text += r"a=%.2f $" % a
dtext = r"$f(x)' = \left\{\begin{matrix}"
dtext += r"a & for & x \leq 0 \\ "
dtext += r"1 & for & x > 0 "
dtext += r"\end{matrix}\right. $"
display('leaky relu', (-0.5, 1.5),
lrelu_func, text, 0.6, (1.8, 0.5),
lrelu_dfunc, dtext, -2.0, (-3.2, 0.5))
#####################################################################################
# <codecell> Exponential Linear Units, ELU
#####################################################################################
a = 0.5
elu_func = np.vectorize(lambda x: x if x > 0 else a*(np.exp(x)-1), otypes=[np.float])
elu_dfunc = np.vectorize(lambda x: 1 if x > 0 else a*np.exp(x), otypes=[np.float])
text = r"$ f(x) = \left\{\begin{matrix}"
text += r"a(e^x-1) & for & x \leq 0 x \\ "
text += r"x & for & x > 0 "
text += r"\end{matrix}\right. $"
dtext = r"$ f(x)' = \left\{\begin{matrix}"
dtext += r"f(x) + a & for & x \leq 0 \\ "
dtext += r"1 & for & x > 0 "
dtext += r"\end{matrix}\right. \\ \\ \\"
dtext += r"a=%.2f $" % a
display('elu', (-0.5, 1.5),
elu_func, text, 0.6, (1.8, 0.5),
elu_dfunc, dtext, -0.5, (-4.5, 0.5))
#####################################################################################
# <codecell> SoftPlus
#####################################################################################
def softplus_func(x):
return np.log(1+np.exp(x))
def softplus_dfunc(x):
return 1/(1+np.exp(-x))
text = r"$ f(x) = ln(1 + e^x) $"
dtext = r"$ f(x)' = \dfrac{1}{1 + e^{-x}} $"
display('softplus', (-0.5, 1.5),
softplus_func, text, 0.6, (1.8, 0.5),
softplus_dfunc, dtext, -2.0, (-3.2, 0.5))
| 1.765625 | 2 |
examples/paper_benchmarks/GPU/cot_gpu_m12/run_sliced.py | XanaduAI/jet | 29 | 12787604 | import time
import sys
import quimb.tensor as qtn
import cotengra as ctg
import tqdm
from opt_einsum import contract, contract_expression, contract_path, helpers
from opt_einsum.paths import linear_to_ssa, ssa_to_linear
def load_circuit(
n=53,
depth=10,
seed=0 ,
elided=0,
sequence='ABCDCDAB',
swap_trick=False
):
file = f'circuit_n{n}_m{depth}_s{seed}_e{elided}_p{sequence}.qsim'
if swap_trick:
gate_opts={'contract': 'swap-split-gate', 'max_bond': 2}
else:
gate_opts={}
# instantiate the `Circuit` object that
# constructs the initial tensor network:
return qtn.Circuit.from_qasm_file(file, gate_opts=gate_opts)
circ = load_circuit(depth=12, swap_trick=True)
sampler = qtn.MPS_computational_state('0' * (circ.N))
tn = circ.psi & sampler
tn.full_simplify_(output_inds=[])
tn.astype_('complex64')
ctg.hyper._HYPER_SEARCH_SPACE['kahypar']['imbalance']['max'] = 0.1
opt = ctg.HyperOptimizer(
methods=['kahypar'],
max_time=120, # just search for 2 minutes
max_repeats=1000,
progbar=True,
minimize='flops',
slicing_opts={'target_slices': int(sys.argv[1])}
)
info = tn.contract(all, optimize=opt, get='path-info', output_inds=[])
sf = ctg.SliceFinder(info, target_slices=int(sys.argv[1]))
ix_sl, cost_sl = sf.search(temperature=1.0)
ix_sl, cost_sl = sf.search(temperature=0.1)
ix_sl, cost_sl = sf.search(temperature=0.01)
arrays = [t.data for t in tn]
sc = sf.SlicedContractor(arrays)
start = time.time()
c = sc.contract_slice(0, backend="jax")
end = time.time()
print(f"t_0(contract_slice[0])={end-start}")
print(f"res_0(contract_slice[0])={c}")
print("#########################################################")
for i in tqdm.tqdm(range(1, sc.nslices)):
start = time.time()
c = c + sc.contract_slice(i, backend="jax")
end = time.time()
print(f"t_0(contract_slice[{i}])={end-start}")
print(f"res_0(sum to contract_slice[{i}])={c}")
print("#########################################################")
print("#########################################################")
print("#########################################################")
# second run
tn = circ.psi & qtn.MPS_rand_computational_state(circ.N, seed=42)
tn.full_simplify_(output_inds=[]).astype_('complex64')
# update the SlicedContractor's arrays
sc.arrays = tuple(t.data for t in tn)
# perform the contraction
start = time.time()
c = sc.contract_slice(0, backend="jax")
end = time.time()
print(f"t_0(contract_slice[0])={end-start}")
print(f"res_0(contract_slice[0])={c}")
print("#########################################################")
res=0
for i in tqdm.tqdm(range(sc.nslices)):
start = time.time()
res += sc.contract_slice(i, backend="jax")
end = time.time()
print(f"t_1(contract_slice[{i}])={end-start}")
print(f"res_1(contract_slice[{i}])={res}")
# update the SlicedContractor's arrays
sc.arrays = tuple(t.data for t in tn)
print("#########################################################")
# perform the contraction
res=0
for i in tqdm.tqdm(range(sc.nslices)):
start = time.time()
res += sc.contract_slice(i, backend="jax")
end = time.time()
print(f"t_2(contract_slice[{i}])={end-start}")
print(f"res_2(contract_slice[{i}])={res}")
| 1.929688 | 2 |
Leetcode/1000-2000/1858. Longest Word With All Prefixes/1858.py | Next-Gen-UI/Code-Dynamics | 0 | 12787605 | <filename>Leetcode/1000-2000/1858. Longest Word With All Prefixes/1858.py<gh_stars>0
class Solution:
def __init__(self):
self.root = {}
def longestWord(self, words: List[str]) -> str:
ans = ''
for word in words:
self.insert(word)
for word in words:
if not self.allPrefixed(word):
continue
if len(ans) < len(word) or (len(ans) == len(word) and ans > word):
ans = word
return ans
def insert(self, word: str) -> None:
node = self.root
for c in word:
if c not in node:
node[c] = {}
node = node[c]
node['isWord'] = True
def allPrefixed(self, word: str) -> bool:
node = self.root
for c in word:
node = node[c]
if 'isWord' not in node:
return False
return True
| 3.71875 | 4 |
guestbook.py | tgirotto/bigdata-test | 0 | 12787606 | import os
import urllib
import csv
from google.appengine.api import users
from google.appengine.ext import ndb
import jinja2
import webapp2
JINJA_ENVIRONMENT = jinja2.Environment(
loader=jinja2.FileSystemLoader(os.path.dirname(__file__)),
extensions=['jinja2.ext.autoescape'],
autoescape=True)
DEFAULT_GUESTBOOK_NAME = 'default_guestbook'
# We set a parent key on the 'Greetings' to ensure that they are all
# in the same entity group. Queries across the single entity group
# will be consistent. However, the write rate should be limited to
# ~1/second.
def guestbook_key(guestbook_name=DEFAULT_GUESTBOOK_NAME):
"""Constructs a Datastore key for a Guestbook entity.
We use guestbook_name as the key.
"""
return ndb.Key('Guestbook', guestbook_name)
class Author(ndb.Model):
"""Sub model for representing an author."""
identity = ndb.StringProperty(indexed=False)
email = ndb.StringProperty(indexed=False)
class Greeting(ndb.Model):
"""A main model for representing an individual Guestbook entry."""
author = ndb.StructuredProperty(Author)
content = ndb.StringProperty(indexed=False)
date = ndb.DateTimeProperty(auto_now_add=True)
class MainPage(webapp2.RequestHandler):
def get(self):
guestbook_name = self.request.get('guestbook_name',
DEFAULT_GUESTBOOK_NAME)
greetings_query = Greeting.query(
ancestor=guestbook_key(guestbook_name)).order(-Greeting.date)
greetings = greetings_query.fetch(10)
user = users.get_current_user()
if user:
url = users.create_logout_url(self.request.uri)
url_linktext = 'Logout'
else:
url = users.create_login_url(self.request.uri)
url_linktext = 'Login'
template_values = {
'user': user,
'greetings': greetings,
'guestbook_name': urllib.quote_plus(guestbook_name),
'url': url,
'url_linktext': url_linktext,
}
template = JINJA_ENVIRONMENT.get_template('index.html')
self.response.write(template.render(template_values))
class Guestbook(webapp2.RequestHandler):
def post(self):
# We set the same parent key on the 'Greeting' to ensure each
# Greeting is in the same entity group. Queries across the
# single entity group will be consistent. However, the write
# rate to a single entity group should be limited to
# ~1/second.
guestbook_name = self.request.get('guestbook_name',
DEFAULT_GUESTBOOK_NAME)
greeting = Greeting(parent=guestbook_key(guestbook_name))
if users.get_current_user():
greeting.author = Author(
identity=users.get_current_user().user_id(),
email=users.get_current_user().email())
greeting.content = self.request.get('content')
greeting.put()
query_params = {'guestbook_name': guestbook_name}
self.redirect('/?' + urllib.urlencode(query_params))
###############################################################################################
class Order(ndb.Model):
"""A main model for representing an individual Guestbook entry."""
customerId = ndb.StringProperty(indexed=False)
kioskId = ndb.StringProperty(indexed=False)
class JsonFileLoader(webapp2.RequestHandler):
def post(self):
content = self.request.get('content')
type = self.request.get('type')
customerId = '1234'
newOrder = Order(parent=guestbook_key(''))
# print type
# print content
self.response.write(content)
# print self.request.POST['customerId']
# order.customerId = self.request.get('customerId')
#
# order.kioskId = self.request.get('kioskId')
# order.put()
class CsvFileLoader(webapp2.RequestHandler):
def post(self):
print self.request.get('type')
check_values = self.request.POST.getall('file')
array = list(csv.reader(check_values))
for c in array:
print c
self.response.write(self.request.get('file'))
# print self.request.POST['customerId']
# order.customerId = self.request.get('customerId')
#
# order.kioskId = self.request.get('kioskId')
# order.put()
class Key(webapp2.RequestHandler):
def get(self):
print 'hello girotto'
self.response.write(ndb.Key('Guestbook', DEFAULT_GUESTBOOK_NAME))
app = webapp2.WSGIApplication([
# ('/csv', CsvFileLoader),
('/csv/order', CsvFileLoader),
('/csv/kiosk', CsvFileLoader),
], debug=True)
| 2.859375 | 3 |
src/reference/API.py | Limespy/interp-compression | 0 | 12787607 | <reponame>Limespy/interp-compression
import sys
import pathlib
import numpy as np
from collections import namedtuple
from scipy import interpolate
import math
path_package = pathlib.Path(__file__).parent.absolute()
sys.path.insert(1,str(path_package.parent))
import API as compression
#%%═════════════════════════════════════════════════════════════════════
# SETUP
path_data = path_package / 'data'
Reference = namedtuple('Reference',
['raw','atol','cmethod','ostyle','compressed'],
defaults=[1e-5,'interp10','monolith',None])
#%%═════════════════════════════════════════════════════════════════════
# FUNCTIONS
##%%════════════════════════════════════════════════════════════════════
## Auxiliary functions
#%%═════════════════════════════════════════════════════════════════════
# Reference raw data
def raw_poly0(n=1e1):
x = np.linspace(0,1,int(n))
return x, np.zeros(len(x))
#───────────────────────────────────────────────────────────────────────
def raw_poly1(n=1e1):
x = np.linspace(0,1,int(n))
return x, x
#───────────────────────────────────────────────────────────────────────
def raw_poly2(n=1e2):
x = np.linspace(0,1,int(n))
return x, np.array(x**2)
#───────────────────────────────────────────────────────────────────────
def raw_sine(n=1e4):
x = np.linspace(0,1,int(n))
return x, np.array(np.sin(x*2*math.pi))
#───────────────────────────────────────────────────────────────────────
def raw_sine_normal(n=1e4, std=0.1):
rng = np.random.default_rng(12345)
x = np.linspace(0,1,int(n))
return x, np.array(np.sin(x*2*math.pi)) + std*rng.standard_normal(int(n))
#───────────────────────────────────────────────────────────────────────
raw = {'poly0': raw_poly0,
'poly1': raw_poly1,
'poly2': raw_poly2,
'sine': raw_sine}
###═════════════════════════════════════════════════════════════════════
class Data():
'''Data container'''
def __init__(self, function, ytol=1e-2):
self.x, self.y = raw[function]()
self.y_range = np.max(self.y) - np.min(self.y)
self.ytol = ytol
self.xc = None
self.yc = None
#───────────────────────────────────────────────────────────────────
def make_lerp(self):
self.lerp = interpolate.interp1d(self.xc.flatten(), self.yc.flatten(),
assume_sorted=True)
self.residuals = self.lerp(self.x) - self.y
self.residuals_relative = self.residuals / self.ytol
self.residuals_relative_cumulative = np.cumsum(self.residuals_relative)
self.NRMSE = np.std(self.residuals)/self.y_range
self.covariance = np.cov((self.lerp(self.x), self.y))
#───────────────────────────────────────────────────────────────────────
references = [Reference(raw['poly0'],1e-5,'interp10','monolith')]
#───────────────────────────────────────────────────────────────────────
def generate(function, method, ytol=5e-2):
data = Data(function, ytol=ytol)
data.xc, data.yc, _ = compression.compress(data.x, data.y, method=method, ytol=data.ytol)
data.make_lerp()
print(np.amax(np.abs(data.residuals_relative)))
np.savetxt(path_data / (function+'_'+method+'.csv'),
np.concatenate((data.xc, data.yc), axis=1), delimiter=',', header='hmm')
return data
| 2.28125 | 2 |
tests/test_context.py | d-wysocki/flask-resty | 86 | 12787608 | <reponame>d-wysocki/flask-resty<gh_stars>10-100
import pytest
from flask_resty import context
# -----------------------------------------------------------------------------
def test_view_default(app):
with app.test_request_context():
view_1 = object()
view_2 = object()
assert context.get_for_view(view_1, "foo", "missing") == "missing"
context.set_for_view(view_1, "foo", "present")
assert context.get_for_view(view_1, "foo", "missing") == "present"
assert context.get_for_view(view_2, "foo", "missing") == "missing"
def test_get_without_request():
with pytest.raises(RuntimeError, match="outside of request context"):
context.get("foo")
| 2.3125 | 2 |
platypush/message/event/music/snapcast.py | RichardChiang/platypush | 228 | 12787609 | from platypush.message.event import Event
class SnapcastEvent(Event):
""" Base class for Snapcast events """
def __init__(self, host='localhost', *args, **kwargs):
super().__init__(host=host, *args, **kwargs)
class ClientConnectedEvent(SnapcastEvent):
"""
Event fired upon client connection
"""
def __init__(self, client, host='localhost', *args, **kwargs):
super().__init__(client=client, host=host, *args, **kwargs)
class ClientDisconnectedEvent(SnapcastEvent):
"""
Event fired upon client disconnection
"""
def __init__(self, client, host='localhost', *args, **kwargs):
super().__init__(client=client, host=host, *args, **kwargs)
class ClientVolumeChangeEvent(SnapcastEvent):
"""
Event fired upon volume change or mute status change on a client
"""
def __init__(self, client, volume, muted, host='localhost', *args, **kwargs):
super().__init__(client=client, host=host, volume=volume,
muted=muted, *args, **kwargs)
class ClientLatencyChangeEvent(SnapcastEvent):
"""
Event fired upon latency change on a client
"""
def __init__(self, client, latency, host='localhost', *args, **kwargs):
super().__init__(client=client, host=host, latency=latency,
*args, **kwargs)
class ClientNameChangeEvent(SnapcastEvent):
"""
Event fired upon name change of a client
"""
def __init__(self, client, name, host='localhost', *args, **kwargs):
super().__init__(client=client, host=host, name=name,
*args, **kwargs)
class GroupMuteChangeEvent(SnapcastEvent):
"""
Event fired upon mute status change
"""
def __init__(self, group, muted, host='localhost', *args, **kwargs):
super().__init__(group=group, host=host, muted=muted, *args, **kwargs)
class GroupStreamChangeEvent(SnapcastEvent):
"""
Event fired upon group stream change
"""
def __init__(self, group, stream, host='localhost', *args, **kwargs):
super().__init__(group=group, host=host, stream=stream, *args, **kwargs)
class StreamUpdateEvent(SnapcastEvent):
"""
Event fired upon stream update
"""
def __init__(self, stream_id, stream, host='localhost', *args, **kwargs):
super().__init__(stream_id=stream_id, stream=stream, host=host, *args, **kwargs)
class ServerUpdateEvent(SnapcastEvent):
"""
Event fired upon stream update
"""
def __init__(self, server, host='localhost', *args, **kwargs):
super().__init__(server=server, host=host, *args, **kwargs)
# vim:sw=4:ts=4:et:
| 2.4375 | 2 |
class_day02.py | yunjung-lee/class_python_data | 0 | 12787610 | <gh_stars>0
# ## 통계 기반 데이터 분석 (부제: 영상 처리를 통한 데이터 분석 및 통계 처리) ##
#
#
#
#
# 복습퀴즈1. 800x800 화면에서 거북이가 다음 정보를 소유하도록 한다.
#
# [거북이개체, (시작x,y), (끝x,y), (r,g,b), 크기]
#
# 20마리를 생성해서 리스트에 저장한 후에, 각각 이동하도록 한다.
#
#
#
#
# 복습퀴즈2. 구구단을 처리하되, 각 결과의 앞자리~뒷자리까지 합계로 출력한다.
#
# 예) 2x1=3 (값2를 02로 취급해서 0부터 2까지의 합계 : 0+1+2 = 3)
#
# 2x2=10 (값2를 02로 취급해서 0부터 2까지의 합계 : 0+1+2+3+4 = 10)
#
# ...
#
# 2x9=36 (값18을 1부터 8까지 : 1+2+3+4+5+6+7+8=36)
#
# ...
#
# 8x8=15 (값64를 6부터 4까지 : 6+5+4 = 15)
#
#
#
#
# 복습퀴즈3(심화). 행과 열의 크기를 입력받고, 크기만큼 가로 및 세로로 지그재그로
#
# 1부터 출력한다.
#
# 예) 3, 4를 입력하면
#
# 1 2 3 4
#
# 8 7 6 5
#
# 9 10 11 12
#
#
#
#
# 1 6 7 12
#
# 2 5 8 11
#
# 3 4 9 10 | 2.546875 | 3 |
nwb_conversion_tools/utils/createextractors.py | miketrumpis/nwb-conversion-tools | 0 | 12787611 | <filename>nwb_conversion_tools/utils/createextractors.py
from distutils.version import StrictVersion
import numpy as np
try:
from spikeinterface.core.testing_tools import generate_recording, generate_sorting
import spikeinterface as si
if StrictVersion(si.__version__[:4]) >= StrictVersion("0.90"):
HAVE_SI_090 = True
else:
HAVE_SI_090 = False
except ImportError:
HAVE_SI_090 = False
try:
import spikeextractors as se
HAVE_SI013 = True
except ImportError:
HAVE_SI013 = False
def create_si090_example():
assert HAVE_SI_090, "spikeinterface 0.90 not installed"
RX = generate_recording(durations=[10], num_channels=4)
# RX.set_property("prop1", np.arange(RX.get_num_channels()))
# RX.set_property("prop2", np.arange(RX.get_num_channels()) * 2)
SX = generate_sorting(durations=[10])
# SX.set_property("prop1", np.arange(SX.get_num_units()))
# SX.set_property("prop2", np.arange(SX.get_num_units()) * 2)
return RX, SX
def create_si013_example(seed):
assert HAVE_SI013, "spikeextractors not installed"
channel_ids = [0, 1, 2, 3]
num_channels = 4
num_frames = 10000
num_ttls = 30
sampling_frequency = 30000
X = np.random.RandomState(seed=seed).normal(0, 1, (num_channels, num_frames))
geom = np.random.RandomState(seed=seed).normal(0, 1, (num_channels, 2))
X = (X * 100).astype(int)
ttls = np.sort(np.random.permutation(num_frames)[:num_ttls])
RX = se.NumpyRecordingExtractor(timeseries=X, sampling_frequency=sampling_frequency, geom=geom)
RX.set_ttls(ttls)
RX.set_channel_locations([0, 0], channel_ids=0)
RX.add_epoch("epoch1", 0, 10)
RX.add_epoch("epoch2", 10, 20)
for i, channel_id in enumerate(RX.get_channel_ids()):
RX.set_channel_property(channel_id=channel_id, property_name="shared_channel_prop", value=i)
RX2 = se.NumpyRecordingExtractor(timeseries=X, sampling_frequency=sampling_frequency, geom=geom)
RX2.copy_epochs(RX)
times = np.arange(RX.get_num_frames()) / RX.get_sampling_frequency() + 5
RX2.set_times(times)
RX3 = se.NumpyRecordingExtractor(timeseries=X, sampling_frequency=sampling_frequency, geom=geom)
SX = se.NumpySortingExtractor()
SX.set_sampling_frequency(sampling_frequency)
spike_times = [200, 300, 400]
train1 = np.sort(np.rint(np.random.RandomState(seed=seed).uniform(0, num_frames, spike_times[0])).astype(int))
SX.add_unit(unit_id=1, times=train1)
SX.add_unit(unit_id=2, times=np.sort(np.random.RandomState(seed=seed).uniform(0, num_frames, spike_times[1])))
SX.add_unit(unit_id=3, times=np.sort(np.random.RandomState(seed=seed).uniform(0, num_frames, spike_times[2])))
SX.set_unit_property(unit_id=1, property_name="stability", value=80)
SX.add_epoch("epoch1", 0, 10)
SX.add_epoch("epoch2", 10, 20)
SX2 = se.NumpySortingExtractor()
SX2.set_sampling_frequency(sampling_frequency)
spike_times2 = [100, 150, 450]
train2 = np.rint(np.random.RandomState(seed=seed).uniform(0, num_frames, spike_times2[0])).astype(int)
SX2.add_unit(unit_id=3, times=train2)
SX2.add_unit(unit_id=4, times=np.random.RandomState(seed=seed).uniform(0, num_frames, spike_times2[1]))
SX2.add_unit(unit_id=5, times=np.random.RandomState(seed=seed).uniform(0, num_frames, spike_times2[2]))
SX2.set_unit_property(unit_id=4, property_name="stability", value=80)
SX2.set_unit_spike_features(unit_id=3, feature_name="widths", value=np.asarray([3] * spike_times2[0]))
SX2.copy_epochs(SX)
SX2.copy_times(RX2)
for i, unit_id in enumerate(SX2.get_unit_ids()):
SX2.set_unit_property(unit_id=unit_id, property_name="shared_unit_prop", value=i)
SX2.set_unit_spike_features(
unit_id=unit_id, feature_name="shared_unit_feature", value=np.asarray([i] * spike_times2[i])
)
SX3 = se.NumpySortingExtractor()
train3 = np.asarray([1, 20, 21, 35, 38, 45, 46, 47])
SX3.add_unit(unit_id=0, times=train3)
features3 = np.asarray([0, 5, 10, 15, 20, 25, 30, 35])
features4 = np.asarray([0, 10, 20, 30])
feature4_idx = np.asarray([0, 2, 4, 6])
SX3.set_unit_spike_features(unit_id=0, feature_name="dummy", value=features3)
SX3.set_unit_spike_features(unit_id=0, feature_name="dummy2", value=features4, indexes=feature4_idx)
example_info = dict(
channel_ids=channel_ids,
num_channels=num_channels,
num_frames=num_frames,
sampling_frequency=sampling_frequency,
unit_ids=[1, 2, 3],
train1=train1,
train2=train2,
train3=train3,
features3=features3,
unit_prop=80,
channel_prop=(0, 0),
ttls=ttls,
epochs_info=((0, 10), (10, 20)),
geom=geom,
times=times,
)
return RX, RX2, RX3, SX, SX2, SX3, example_info
| 1.851563 | 2 |
brahe/access/access_geometry.py | duncaneddy/brahe | 14 | 12787612 | '''Provide fundamental geometry calculations used by the scheduling.
'''
import math
import numpy as np
import brahe.data_models as bdm
from brahe.utils import fcross
from brahe.constants import RAD2DEG
from brahe.coordinates import sECEFtoENZ, sENZtoAZEL, sECEFtoGEOD, sGEODtoECEF
from brahe.relative_coordinates import rCARTtoRTN
def azelrng(sat_ecef: np.ndarray,
loc_ecef: np.ndarray,
use_degrees: bool = True) -> np.ndarray:
'''Compute satellite azimuth, elevation, and range as viewed from the specified location.
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame
loc_ecef (:obj:`np.ndarray`): Location in ECEF (ITRF) frame.
use_degrees (:obj:`bool`, optional): Return output in degrees. Default: `True`
Returns:
np.ndarray: azimuth elevation and range as array [deg, deg, m]
'''
# Ensure np-ness
sat_ecef = np.asarray(sat_ecef)
loc_ecef = np.asarray(loc_ecef)
# Compute Satellite State in ENZ frame
sat_enz = sECEFtoENZ(loc_ecef[0:3], sat_ecef[0:3], conversion='geodetic')
# Compute Satellite Elevation
azelrng = sENZtoAZEL(sat_enz, use_degrees=use_degrees)[0:3]
return azelrng
def azimuth(sat_ecef: np.ndarray,
loc_ecef: np.ndarray,
use_degrees: bool = True) -> np.ndarray:
'''Compute satellite azimuth as viewed from the specified location.
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame
loc_ecef (:obj:`np.ndarray`): Location in ECEF (ITRF) frame.
use_degrees (:obj:`bool`, optional): Return output in degrees. Default: `True`
Returns:
float: Azimuth [deg]
'''
return azelrng(sat_ecef, loc_ecef, use_degrees=use_degrees)[0]
def elevation(sat_ecef: np.ndarray,
loc_ecef: np.ndarray,
use_degrees: bool = True) -> np.ndarray:
'''Compute satellite elevation as viewed from the specified location.
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame
loc_ecef (:obj:`np.ndarray`): Location in ECEF (ITRF) frame.
use_degrees (:obj:`bool`, optional): Return output in degrees. Default: `True`
Returns:
float: Elevation [deg]
'''
return azelrng(sat_ecef, loc_ecef, use_degrees=use_degrees)[1]
def range(sat_ecef: np.ndarray, loc_ecef: np.ndarray,
use_degrees: bool = True) -> np.ndarray:
'''Compute satellite range from the specified location.
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame.
loc_ecef (:obj:`np.ndarray`): Location in ECEF (ITRF) frame.
use_degrees (:obj:`bool`, optional): Return output in degrees. Default: `True`
Returns:
float: Range [m]
'''
return azelrng(sat_ecef, loc_ecef, use_degrees=use_degrees)[2]
def look_angle(sat_ecef: np.ndarray, loc_ecef: np.ndarray, use_degrees: bool = True) -> np.ndarray:
'''Compute the look angle angle between the satellite and the specific location.
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame.
loc_ecef (:obj:`np.ndarray`): Location in ECEF (ITRF) frame.
use_degrees (:obj:`bool`, optional): Return output in degrees. Default: `True`
Returns:
float: look angle angle [deg]
'''
# Ensure np-ness
sat_ecef = np.asarray(sat_ecef)
loc_ecef = np.asarray(loc_ecef)
# Satellite state
r_sat = sat_ecef[0:3]
# Geodetic sub-satellte point
sat_geod = sECEFtoGEOD(r_sat)
sub_sat_geod = np.array([sat_geod[0], sat_geod[1], 0.0])
sub_sat_ecef = sGEODtoECEF(sub_sat_geod)
# look angle
nadir_dir = (sub_sat_ecef - r_sat) / np.linalg.norm(sub_sat_ecef - r_sat)
target_dir = (loc_ecef - r_sat) / np.linalg.norm(loc_ecef - r_sat)
look_angle = math.acos(np.dot(nadir_dir, target_dir)) * RAD2DEG
return look_angle
def ascdsc(sat_ecef: np.ndarray) -> bdm.AscendingDescending:
'''Compute whether whether satellite is ascending or descending in current
state.
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame.
use_degrees (:obj:`bool`, optional): Return output in degrees. Default: `True`
Returns:
bdm.AscendingDescending: ascending or descending state
'''
# Ensure np-ness
sat_ecef = np.asarray(sat_ecef)
if sat_ecef[5] > 0:
return bdm.AscendingDescending.ascending
elif sat_ecef[5] < 0:
return bdm.AscendingDescending.descending
else:
# Handle unlikely case that satellite is exaclty at 0 Z-velocity
if sat_ecef[2] < 0:
return bdm.AscendingDescending.ascending
else:
return bdm.AscendingDescending.descending
def look_direction(sat_ecef: np.ndarray,
loc_ecef: np.ndarray) -> bdm.LookDirection:
'''Compute the look direction for viewing the startet
Args:
sat_ecef (:obj:`np.ndarray`): Satellite position in the ECEF frame.
loc_ecef (:obj:`np.ndarray`): Location in ECEF (ITRF) frame.\
Returns:
bdm.LookDirection: Look direction. 'left' or 'right'
'''
# Ensure np-ness
sat_ecef = np.asarray(sat_ecef)
loc_ecef = np.asarray(loc_ecef)
# Line of Sight Vector in ECEF Frame
los_ecef = loc_ecef[0:3] - sat_ecef[0:3]
# Apply ECEF to RTN rotation
los_rtn = rCARTtoRTN(sat_ecef) @ los_ecef
# Compute cross product of RTN velocity and RTN LOS
cp = fcross([0, 1, 0], los_rtn)
if np.sign(cp[0]) < 0:
return bdm.LookDirection.right
else:
return bdm.LookDirection.left | 2.515625 | 3 |
Network Simulations/Assignment 3/CSMA/logger.py | neeladripal/bcse-lab | 0 | 12787613 | # function to log the analytics of communication
def storeLogs(senderName, receiverName, effectiveFrames, totalFrames, totalTime, collision, rttStore:list):
# frame size (IEEE 802.3 Ethernet Frame Format)
FRAME_SIZE=72*8
# define bandwidth (in bps)
BANDWIDTH = 1000
logs = "Sender: "+senderName+" ----------------------- "+"Receiver: "+receiverName
logs += "\nTotal frames sent = {}".format(totalFrames)
logs += "\nEffective frames sent = {}".format(effectiveFrames)
logs += "\nTotal time taken = {} seconds".format(int(totalTime))
logs += "\nCollision count = {}".format(int(collision))
# performance metrics
throughput = effectiveFrames / totalFrames
logs += "\nThroughput = {:.5f}".format(throughput)
logs += "\nForwarding Delay = {:.5f} seconds/frame\n\n".format(totalTime/effectiveFrames)
# open the file
file=open("logs.txt",'a')
# append the logs to the file
file.writelines(logs)
# Close the file
file.close()
# Calculate round-trip time from the store
# rtt = 0
# for index in range(0,len(rttStore)):
# rtt += rttStore[index]
# roundTripTime = (rtt/len(rttStore))
# BD = (bandwidth*roundTripTime)
# print('Round trip time = ',roundTripTime)
# file.writelines('Bandwidth-Delay product = {:6d} bits\n'.format(int(BD))) | 2.9375 | 3 |
src/scripts/descriptions/__init__.py | anku94/themis_tritonsort | 11 | 12787614 | from validateDescription import DescriptionValidator
from validateDescription import ValidationFailedError
from descriptionutils import Description
__all__ = ["validateDescription.DescriptionValidator",
"validateDescription.ValidationFailedError",
"descriptionutils.Description"]
| 1.375 | 1 |
tinyq/utils.py | mozillazg/tinyq | 14 | 12787615 | # -*- coding: utf-8 -*-
import importlib
def gen_task_name_via_func(func):
"""生成函数对象对应的 task name"""
return '{name}'.format(name=func.__name__)
def import_object_from_path(path, default_obj_name='app'):
"""从定义的字符串信息中导入对象
:param path: ``task.app``
"""
module_name, obj_name = path.rsplit('.', 1)
if not obj_name:
obj_name = default_obj_name
module = importlib.import_module(module_name)
return getattr(module, obj_name)
| 2.53125 | 3 |
graph/tests/bfs_test.py | RobMcZag/python-algorithms3 | 1 | 12787616 | import unittest
import graph
class BreadthFirstSearchTest(unittest.TestCase):
__runSlowTests = False
def testTinyGraph(self):
g = graph.Graph.from_file('tinyG.txt')
bfs = graph.BreadthFirstSearch(g, 0)
self.assertEqual(7, bfs.count())
self.assertFalse(bfs.connected(7))
self.assertIsNone(bfs.path_to(7))
self.assertFalse(bfs.connected(8))
self.assertIsNone(bfs.path_to(8))
self.assertFalse(bfs.connected(9))
self.assertIsNone(bfs.path_to(9))
self.assertFalse(bfs.connected(12))
self.assertIsNone(bfs.path_to(12))
self.assertEqual([2, 0], bfs.path_to(2))
self.assertEqual(1, bfs.distance(2))
self.assertEqual([3, 5, 0], bfs.path_to(3))
self.assertEqual(2, bfs.distance(3))
self.assertEqual([4, 5, 0], bfs.path_to(4))
self.assertEqual(2, bfs.distance(4))
self.assertEqual([5, 0], bfs.path_to(5))
self.assertEqual(1, bfs.distance(5))
def testMedGraph(self):
g = graph.Graph.from_file('mediumG.txt')
bfs = graph.BreadthFirstSearch(g, 0)
self.assertEqual(250, bfs.count())
self.assertTrue(bfs.connected(123))
self.assertEqual(9, bfs.distance(123))
self.assertEqual([123, 246, 244, 207, 122, 92, 171, 165, 68, 0], bfs.path_to(123))
def testTinyDG(self):
g = graph.Graph.from_file('tinyDG.txt', directed=True)
bfs = graph.BreadthFirstSearch(g, 0)
self.assertEqual(6, bfs.count())
self.assertTrue(bfs.connected(4))
self.assertIsNotNone(bfs.path_to(4))
self.assertFalse(bfs.connected(7))
self.assertIsNone(bfs.path_to(7))
self.assertEqual([2, 4, 5, 0], bfs.path_to(2))
self.assertEqual(3, bfs.distance(2))
def testTinyDAG(self):
g = graph.Graph.from_file('tinyDAG.txt', directed=True)
bfs = graph.BreadthFirstSearch(g, 0)
self.assertEqual(9, bfs.count())
self.assertTrue(bfs.connected(4))
self.assertIsNotNone(bfs.path_to(4))
self.assertFalse(bfs.connected(7))
self.assertIsNone(bfs.path_to(7))
self.assertEqual([12, 9, 6, 0], bfs.path_to(12))
self.assertEqual(3, bfs.distance(12))
if __name__ == '__main__':
unittest.main()
| 3.3125 | 3 |
app/threads.py | candicecz/conp-portal | 0 | 12787617 | <filename>app/threads.py
# -*- coding: utf-8 -*-
"""Threading Module
Module that contains the threaded pipeline searching functions
"""
from boutiques.searcher import Searcher
from boutiques.puller import Puller
import threading
import json
import os
import logging
class UpdatePipelineData(threading.Thread):
"""
Class that handles the threaded updating of the Pipeline
registrty from Zenodo
"""
def __init__(self, path):
super(UpdatePipelineData, self).__init__()
if not os.path.exists('logs'):
os.makedirs('logs')
logging.basicConfig(filename='logs/update_pipeline_thread.log', level=logging.INFO)
self.cache_dir = path
def run(self):
try:
# if cache directory doesn't exist then create it
if not os.path.exists(self.cache_dir):
os.makedirs(self.cache_dir)
# first search for all descriptors
searcher = Searcher(query="", max_results=100, no_trunc=True)
all_descriptors = searcher.search()
# then pull every single descriptor
all_descriptor_ids = list(map(lambda x: x["ID"], all_descriptors))
Puller(all_descriptor_ids).pull()
# fetch every single descriptor into one file
detailed_all_descriptors = [
json.load(open(os.path.join(self.cache_dir,
descriptor["ID"].replace(".", "-") + ".json"),
"r"))
for descriptor in all_descriptors
]
# store data in cache
with open(os.path.join(self.cache_dir,
"all_descriptors.json"),
"w") as f:
json.dump(all_descriptors, f, indent=4)
with open(os.path.join(self.cache_dir,
"detailed_all_descriptors.json"),
"w") as f:
json.dump(detailed_all_descriptors, f, indent=4)
except Exception as e:
logging.exception("An exception occurred in the thread.")
| 2.609375 | 3 |
src/autonmt/configs.py | mehdidou99/autONMT | 0 | 12787618 | import logging
import sys
import yaml
def load_config(filename):
with open(filename, 'r') as stream:
try:
return yaml.safe_load(stream)
except yaml.YAMLError as exc:
print('Invalid configuration')
print(exc)
sys.exit(1)
class LoadAndPreprocessConfig:
def __init__(self, raw) -> None:
expname = raw['name']
self.outputdatadir = raw['data']['output_dir'].rstrip('/')
self.outputdatadir = f'{self.outputdatadir}/{expname}'
self.preprocdir = f'/tmp/{expname}'
self.src_lgs = raw['data']['src_lgs']
self.tgt_lgs = raw['data']['tgt_lgs']
self.corpora = raw['data']['corpora']
self.datadir = raw['data']['dir'].rstrip('/')
self.on_missing_data = raw['data'].get('on_missing_data', [])
self.max_entries_per_corpus = int(raw['data']['max_entries_per_corpus'])
self.preprocessing_steps = raw['preprocessing']['steps']
class StepConfig:
def __init__(this, step_raw) -> None:
this.corpora = step_raw['corpora']
this.script = step_raw['script']
self.step_config = { step: StepConfig(raw[f'preprocessing_{step}']) for step in raw['preprocessing']['steps'] }
self.scriptsdir = raw['preprocessing']['scripts_dir']
self.final_files = raw['preprocessing']['final_files']
class TokenizeConfig:
def __init__(self, raw) -> None:
self.final_files = raw['preprocessing']['final_files']
expname = raw['name']
self.outputdatadir = raw['data']['output_dir'].rstrip('/')
self.outputdatadir = f'{self.outputdatadir}/{expname}'
class BuildVocabConfig:
def __init__(self, raw) -> None:
expname = raw['name']
self.outputdatadir = raw['data']['output_dir'].rstrip('/')
self.outputdatadir = f'{self.outputdatadir}/{expname}'
class VocabConfig:
def __init__(this, vocab_raw) -> None:
this.output = vocab_raw['save_to']
this.files = vocab_raw['files']
self.vocabs = { vocab: VocabConfig(vocab_raw) for vocab, vocab_raw in raw['vocab'].items() }
class SplitConfig:
def __init__(self, raw) -> None:
self.files = raw['splitting']['files']
self.parts = raw['splitting']['parts']
self.remain = raw['splitting']['remain']
self.seed = int(raw['splitting']['seed'])
expname = raw['name']
self.outputdatadir = raw['data']['output_dir'].rstrip('/')
self.outputdatadir = f'{self.outputdatadir}/{expname}'
class TrainConfig:
def __init__(self, raw) -> None:
expname = raw['name']
self.outputdir = raw['train']['output_dir'].rstrip('/')
self.outputdir = f'{self.outputdir}/{expname}'
model_file = raw['model'].get('file')
if model_file is not None:
self.model_file = model_file
self.custom_model = True
else:
self.model_type = raw['model']['type']
self.custom_model = False
self.model_config_file = raw['model']['config']
self.options = raw['train']['options']
class ConfigFactory:
configs = {
'load': LoadAndPreprocessConfig,
'tokenize': TokenizeConfig,
'build_vocab': BuildVocabConfig,
'split': SplitConfig,
'train': TrainConfig
}
def __init__(self, raw) -> None:
self.raw = raw
def build_for(self, step):
if step not in ConfigFactory.configs.keys():
raise NotImplementedError(f'Factory cannot build config for step {step}')
try:
result = ConfigFactory.configs[step](self.raw)
except KeyError as err:
logging.warning(f'Missing config element: {err}')
return None
return result
| 2.40625 | 2 |
utils/lost2.py | xixigaga/GolemQ | 0 | 12787619 | <filename>utils/lost2.py
print(u'分析和展示计算结果...')
ret_codelist = featurs_printable_formatter(ret_codelist)
codelist_saltedfish, ret_codelist_saltedfish, ret_codelist = find_fratcal_saltfish(ret_codelist)
codelist_combo, ret_codelist_combo, ret_codelist = find_fratcal_bootstrap_combo(ret_codelist)
codelist_action, ret_codelist_action, ret_codelist_combo = find_action_long(ret_codelist_combo)
codelist_weak, ret_codelist_weak, ret_codelist = find_fratcal_weakshort(ret_codelist)
codelist_short, ret_codelist_short, ret_codelist_unknown = find_action_sellshort(ret_codelist)
#codelist_hyper_punch, ret_stocklist_hyper_punch, ret_codelist =
#find_action_hyper_punch(ret_codelist)
codelist_unknown = [index[1] for index, symbol in ret_codelist_unknown.iterrows()]
# 将计算的标的分成四类 —— 买入判断'buy'，持有判断'long'，做空判断'short',
# 'slatfish'是垃圾咸鱼票，既没有价格波动性也没有想象空间
if (eval_range in ['etf', 'csindex']):
full_etf_list = perpar_symbol_range('etf')
full_csindex_list = perpar_symbol_range('csindex')
ret_codelist = ret_features_pd.loc[(each_day[-1], slice(None)), :].copy()
symbol_list = ret_codelist.index.get_level_values(level=1).unique()
symbol_list_grouped = [(symbol, 'etf') for symbol in list(set(symbol_list).intersection(set(full_etf_list)))] + \
[(symbol, 'csindex') for symbol in list(set(symbol_list).intersection(set(full_csindex_list)))]
if (eval_range in ['etf']):
symbol_list_grouped = [(symbol, 'etf') for symbol in symbol_list]
elif (eval_range in ['csindex']):
symbol_list_grouped = [(symbol, 'csindex') for symbol in symbol_list]
symbol_list_grouped = list(set(symbol_list_grouped))
else:
symbol_list_grouped = [(symbol, 'buy') for symbol in codelist_action] + \
[(symbol, 'long') for symbol in list(set(codelist_combo).difference(set(codelist_action)))] + \
[(symbol, 'weak') for symbol in codelist_weak] + \
[(symbol, 'short') for symbol in codelist_short] + \
[(symbol, 'saltedfish') for symbol in codelist_saltedfish] + \
[(symbol, 'unknown') for symbol in codelist_unknown] | 2.546875 | 3 |
build/lib/automata_toolkit/nfa_to_dfa.py | b30wulffz/automata-toolkit | 8 | 12787620 | <gh_stars>1-10
from .regex_to_nfa import regex_to_nfa
from .visual_utils import draw_dfa
from .consts import Consts
import itertools
from collections import Counter
def find_permutation(state_list, current_state):
for state in state_list:
if Counter(current_state) == Counter(state):
return state
return current_state
def get_epsilon_closure(nfa, dfa_states, state):
closure_states = []
state_stack = [state]
while len(state_stack) > 0:
current_state = state_stack.pop(0)
closure_states.append(current_state)
alphabet = Consts.EPSILON
if nfa["transition_function"][current_state][alphabet] not in closure_states:
state_stack.extend(nfa["transition_function"][current_state][alphabet])
closure_states = tuple(set(closure_states))
return find_permutation(dfa_states, closure_states)
def nfa_to_dfa(nfa):
dfa = {}
dfa["states"] = ["phi"]
for r in range(1, len(nfa["states"])+1):
dfa["states"].extend(itertools.combinations(nfa["states"], r))
# calculate epsilon closure of all states of nfa
epsilon_closure = {}
for state in nfa["states"]:
epsilon_closure[state] = get_epsilon_closure(nfa, dfa["states"], state)
dfa["initial_state"] = epsilon_closure[nfa["initial_state"]]
dfa["final_states"] = []
for state in dfa["states"]:
if state != "phi":
for nfa_state in state:
if nfa_state in nfa["final_states"]:
dfa["final_states"].append(state)
break
# dfa["alphabets"] = ["a", "b"]
dfa["alphabets"] = list(filter(lambda x:x!=Consts.EPSILON, nfa["alphabets"]))
dfa["transition_function"]= {}
for state in dfa["states"]:
dfa["transition_function"][state] = {}
for alphabet in dfa["alphabets"]:
if state == "phi":
dfa["transition_function"][state][alphabet] = state
else:
transition_states = []
if len(state) == 1:
nfa_state = state[0]
next_nfa_states = nfa["transition_function"][nfa_state][alphabet]
for next_nfa_state in next_nfa_states:
transition_states.extend(epsilon_closure[next_nfa_state])
else:
for nfa_state in state:
nfa_state = tuple([nfa_state])
if dfa["transition_function"][nfa_state][alphabet] != "phi":
transition_states.extend(dfa["transition_function"][nfa_state][alphabet])
transition_states = tuple(set(transition_states))
if len(transition_states) == 0:
dfa["transition_function"][state][alphabet] = "phi"
else:
# find permutation of transition states in states
dfa["transition_function"][state][alphabet] = find_permutation(dfa["states"], transition_states)
state_stack = [dfa["initial_state"]]
dfa["reachable_states"] = []
while len(state_stack) > 0:
current_state = state_stack.pop(0)
if current_state not in dfa["reachable_states"]:
dfa["reachable_states"].append(current_state)
for alphabet in dfa["alphabets"]:
next_state = dfa["transition_function"][current_state][alphabet]
if next_state not in dfa["reachable_states"]:
state_stack.append(next_state)
dfa["final_reachable_states"] = list(set(dfa["final_states"]) & set(dfa["reachable_states"]))
return dfa
if __name__ == "__main__":
reg_exp = "a(a+b)*b"
nfa = regex_to_nfa(reg_exp)
dfa = nfa_to_dfa(nfa)
print()
print(dfa["states"])
print()
print(dfa["initial_state"])
print()
print(dfa["final_states"])
print()
for state in dfa["transition_function"].keys():
print(state,dfa["transition_function"][state])
print()
draw_dfa(dfa, reg_exp) | 2.75 | 3 |
serieswatcher/sqlobject/tests/test_transactions.py | lightcode/SeriesWatcher | 303 | 12787621 | from sqlobject import *
from sqlobject.tests.dbtest import *
########################################
## Transaction test
########################################
class TestSOTrans(SQLObject):
#_cacheValues = False
class sqlmeta:
defaultOrder = 'name'
name = StringCol(length=10, alternateID=True, dbName='name_col')
def test_transaction():
if not supports('transactions'):
return
setupClass(TestSOTrans)
TestSOTrans(name='bob')
TestSOTrans(name='tim')
trans = TestSOTrans._connection.transaction()
try:
TestSOTrans._connection.autoCommit = 'exception'
TestSOTrans(name='joe', connection=trans)
trans.rollback()
trans.begin()
assert ([n.name for n in TestSOTrans.select(connection=trans)]
== ['bob', 'tim'])
b = TestSOTrans.byName('bob', connection=trans)
b.name = 'robert'
trans.commit()
assert b.name == 'robert'
b.name = 'bob'
trans.rollback()
trans.begin()
assert b.name == 'robert'
finally:
TestSOTrans._connection.autoCommit = True
def test_transaction_commit_sync():
if not supports('transactions'):
return
setupClass(TestSOTrans)
trans = TestSOTrans._connection.transaction()
try:
TestSOTrans(name='bob')
bOut = TestSOTrans.byName('bob')
bIn = TestSOTrans.byName('bob', connection=trans)
bIn.name = 'robert'
assert bOut.name == 'bob'
trans.commit()
assert bOut.name == 'robert'
finally:
TestSOTrans._connection.autoCommit = True
def test_transaction_delete(close=False):
if not supports('transactions'):
return
setupClass(TestSOTrans)
trans = TestSOTrans._connection.transaction()
try:
TestSOTrans(name='bob')
bIn = TestSOTrans.byName('bob', connection=trans)
bIn.destroySelf()
bOut = TestSOTrans.select(TestSOTrans.q.name=='bob')
assert bOut.count() == 1
bOutInst = bOut[0]
bOutID = bOutInst.id
trans.commit(close=close)
assert bOut.count() == 0
raises(SQLObjectNotFound, "TestSOTrans.get(bOutID)")
raises(SQLObjectNotFound, "bOutInst.name")
finally:
trans.rollback()
TestSOTrans._connection.autoCommit = True
def test_transaction_delete_with_close():
test_transaction_delete(close=True)
| 2.421875 | 2 |
dlrnapi_client/apis/__init__.py | ssbarnea/dlrnapi_client | 1 | 12787622 | <gh_stars>1-10
from __future__ import absolute_import
# import apis into api package
# from dlrnapi_client.apis.default_api import DefaultApi
| 1.125 | 1 |
analyze/core.py | autocorr/almaimf_nestfit | 0 | 12787623 | <reponame>autocorr/almaimf_nestfit
#!/usr/bin/env python3
"""
====
Core
====
Apply NestFit to test ALMA-IMF N2H+ J=1-0 datasets. This script may be called
from the command line for batch queue processing. Edit the ``__main__`` block.
Post-processing must be parallelized over different sources, so is not
currently effective for these datasets (2).
Authors
-------
<NAME>
Copyright 2021 by <NAME> under the MIT License.
"""
import sys
import shutil
from pathlib import Path
from multiprocessing import Pool
import h5py
import numpy as np
import scipy as sp
import spectral_cube
from astropy import convolution
from astropy import units as u
from astropy import constants as c
from astropy.io import fits
sys.path.append('/lustre/aoc/users/bsvoboda/temp/nestfit')
import nestfit as nf
from nestfit import (
Distribution,
Prior,
ResolvedPlacementPrior,
ConstantPrior,
PriorTransformer,
take_by_components,
)
from . import (PLOT_PATH, DATA_PATH, RUN_PATH, ALL_TARGETS)
FWHM = 2 * np.sqrt(2 * np.log(2))
def fix_cube(filen):
"""
Fix a few things about the cubes so that they don't have to be modified on
every run. The N2H+ model operates in terms brightness temperature units
Kelvin, so the existing units of Jy/beam need to be converted. The data
must also be in ascending frequency order.
"""
cube = spectral_cube.SpectralCube.read(filen, memmap=False)
# Convert cube to K from Jy/beam
cube = cube.to('K')
cube = cube.with_spectral_unit('Hz')
# and into ascending frequency ordering from descending
xarr = cube.spectral_axis
if xarr[1] < xarr[0]:
cube = cube[::-1]
outfilen = f'{Path(filen).stem}_fixed.fits'
cube.write(outfilen, overwrite=True)
def get_cube(field):
target = ALL_TARGETS[field]
cube = spectral_cube.SpectralCube.read(str(target.path), memmap=False)
return cube
def get_cubestack(field):
cube = get_cube(field)
# Cubes are big-endian byteorder, cast to machine little-endian through by
# copying.
cube._data = cube._data.astype(float, copy=False)
rms = cube[:10].std().value
datacube = nf.DataCube(cube, noise_map=rms, trans_id=1)
return nf.CubeStack([datacube])
def get_uniform_priors(field, size=500):
u = np.linspace(0, 1, size)
target = ALL_TARGETS[field]
vsys = target.vsys
# Prior distribution x axes, must be uniform grid spacing.
if field == 'W43-MM1':
x_voff = np.linspace(vsys-7.5, vsys+7.5, size)
x_tex = np.linspace( 2.8, 30.0, size)
x_ltau = np.linspace( -2.0, 2.0, size)
x_sigm = np.linspace( 0.2, 2.0, size)
elif field == 'W51-IRS2':
x_voff = np.linspace( 45.0, 73.0, size)
x_tex = np.linspace( 2.8, 30.0, size)
x_ltau = np.linspace( -2.0, 2.0, size)
x_sigm = np.linspace( 0.2, 2.0, size)
# Prior PDFs values, all uniform
uniform = np.ones_like(u) / size
f_voff = uniform.copy()
f_tex = uniform.copy()
f_ltau = uniform.copy()
f_sigm = uniform.copy()
# Create distribution instances from "x" and "y" values
d_voff = Distribution(x_voff, f_voff)
d_tex = Distribution(x_tex, f_tex)
d_ltau = Distribution(x_ltau, f_ltau)
d_sigm = Distribution(x_sigm, f_sigm)
# Create instances of interpolation objects called in the Nested Sampling.
# The integer indicates the index position of the parameter in the model:
# 0 -> voff, velocity centroid
# 1 -> tex, excitation temperature
# 2 -> ltau, logarithmic tau
# 3 -> sigm, velocity dispersion
# The ordering of the Prior objects in the array is not actually important.
# The `ResolvedPlacementPrior` ties the velocity centroid and dispersion
# together such that components are resolved according to the geometric
# mean separation criteria:
# sep > scale * FWHM * sqrt(sig1 * sig2)
priors = np.array([
ResolvedPlacementPrior(
Prior(d_voff, 0),
Prior(d_sigm, 3),
scale=1.2,
),
Prior(d_tex, 1),
Prior(d_ltau, 2),
])
return PriorTransformer(priors)
def get_empirical_priors(field):
with h5py.File(DATA_PATH/f'{field}.hdf', 'r') as hdf:
bins = hdf['bins'][...]
vals = hdf['vals'][...]
# Make sure that the priors are normalized
vals /= vals.sum(axis=1, keepdims=True)
# Prior distribution x axes, must be uniform grid spacing.
x_voff, x_tex, x_ltau, x_sigm = bins
f_voff, f_tex, f_ltau, f_sigm = vals
# Create distribution instances from "x" and "y" values
d_voff = Distribution(x_voff, f_voff)
d_tex = Distribution(x_tex, f_tex)
d_ltau = Distribution(x_ltau, f_ltau)
d_sigm = Distribution(x_sigm, f_sigm)
# See comment in `get_uniform_priors` on the instantiating the priors.
priors = np.array([
ResolvedPlacementPrior(
Prior(d_voff, 0),
Prior(d_sigm, 3),
scale=1.2,
),
Prior(d_tex, 1),
Prior(d_ltau, 2),
])
return PriorTransformer(priors)
def verify_prior_transformer(utrans, ncomp=1):
"""
The `PriorTransformer` transforms [0, 1] values from the unit cube into the
actual parameter values. Thus it's a good idea to check that 0.0 (minimum),
0.5 (median), and 1.0 (maximum) all give the expected values.
Parameters
----------
utrans : nestfit.PriorTransformer
ncomp : int
Number of velocity components
"""
assert ncomp > 0
utheta = np.zeros(4*ncomp)
for v in (0.0, 0.5, 1.0):
utheta[:] = v
utrans.transform(utheta, ncomp)
print(v, utheta)
def if_store_exists_delete(name):
filen = f'{name}.store'
if Path(filen).exists():
print(f'-- Deleting {filen}')
shutil.rmtree(filen)
def run_nested(field, store_suffix, utrans, nproc=8, ncomp_max=2):
"""
Run the N2H+ line fitting using Nested Sampling. The results will be
written to a ".store" file written to the ``RUN_PATH``.
Parameters
----------
field : str
Target field name
store_suffix : str
Name suffix to append to the field name when creating the HdfStore
file, of the form ``"<RUN_PATH>/{field}_{store_suffix}.store"``.
utrans : nestfit.PriorTransformer
Prior transformer instance, create with e.g., `get_uniform_priors`
nproc : int
Number of processes to use. A value of 1 will use serial execution
without the Python ``multiprocessing`` library.
ncomp_max : int
Maximum number of velocity components to fit in a spectrum. Models will
be fit with an increasing number of components until they are no longer
significant according to the Bayes factors (model i and i-1) or the
maximum number of components is reached.
"""
store_name = RUN_PATH / f'{field}_{store_suffix}'
if_store_exists_delete(store_name)
runner_cls = nf.DiazenyliumRunner
stack = get_cubestack(field)
fitter = nf.CubeFitter(stack, utrans, runner_cls, ncomp_max=ncomp_max,
mn_kwargs={'nlive': 500}, nlive_snr_fact=20)
fitter.fit_cube(store_name=store_name, nproc=nproc)
def get_store(field, store_suffix):
store_path = RUN_PATH / f'{field}_{store_suffix}.store'
assert store_path.exists()
store = nf.HdfStore(str(store_path))
return store
def get_info_kernel(nrad):
hpbw = 0.5 # arcsec
pix_size = 0.11 # arcsec
beam_sigma_pix = hpbw / FWHM / pix_size
k_arr = nf.get_indep_info_kernel(beam_sigma_pix, nrad=nrad)
k_arr = nf.apply_circular_mask(k_arr, radius=nrad//2)
post_kernel = convolution.CustomKernel(k_arr)
return post_kernel
def get_runner(stack, utrans, ncomp=1):
nlon, nlat = stack.spatial_shape
spec_data, has_nans = stack.get_spec_data(nlon//2, nlat//2)
assert not has_nans
runner = nf.DiazenyliumRunner.from_data(spec_data, utrans, ncomp=ncomp)
return runner
def postprocess_run(field, store_suffix, utrans):
print(f':: Post-processing {field}_{store_suffix}')
# Standard deviation in pixels: 1.1 -> FWHM 0.28 as (cf. HPBW / 2 = 0.25 as)
evid_kernel = convolution.Gaussian2DKernel(1.1)
post_kernel = get_info_kernel(6) # 3.5 pixel radius circular window
stack = get_cubestack(field)
runner = get_runner(stack, utrans, ncomp=1)
# begin post-processing steps
with get_store(field, store_suffix) as store:
nf.aggregate_run_attributes(store)
nf.convolve_evidence(store, evid_kernel)
nf.aggregate_run_products(store)
nf.aggregate_run_pdfs(store)
nf.deblend_hf_intensity(store, stack, runner)
nf.generate_predicted_profiles(store, stack, runner)
#nf.convolve_post_pdfs(store, post_kernel, evid_weight=False)
#nf.quantize_conv_marginals(store)
def create_priors_from_posteriors(field, store_suffix):
"""
The summation of the per-pixel posteriors gives a better global prior than
the uniform distributions used initially to test. This also decreases the
run-time because less volume of parameter space is needed to be explored in
order to calculate the evidence.
"""
with get_store(field, store_suffix) as store:
# Run metadata.
pdf_bins = store.hdf['/products/pdf_bins'][...]
# Read in posteriors and MAP values from store file.
# dimensions (b, l)
nbest = store.hdf['/products/conv_nbest'][...]
# dimensions (r, m, p, h, b, l)
post = store.hdf['/products/post_pdfs'][...]
post = take_by_components(post, nbest, incl_zero=False) # -> (m, p, h, b, l)
# Average/summed posterior distibution. Mask positions without
# detections.
apdf = np.nansum(post[...,nbest>=0], axis=(0, 3))
# Normalize the PDFs
apdf /= apdf.sum(axis=1, keepdims=True)
# Write out to HDF5 file
assert pdf_bins.shape == apdf.shape
with h5py.File(DATA_PATH/f'{field}.hdf', 'w') as hdf:
hdf.create_dataset('bins', data=pdf_bins)
hdf.create_dataset('vals', data=apdf)
def export_deblended_to_fits(field, store_suffix):
# FIXME Exporting to FITS is a work-in progress in NestFit, so hard-code
# the header and such here.
with get_store(field, store_suffix) as store:
# dimensions (t, m, S, b, l)
hfdb = store.hdf['/products/hf_deblended'][...]
# dimensions (p, h)
bins = store.hdf['/products/pdf_bins'][...]
header = store.read_header()
restfreq = header['RESTFRQ'] * u.Hz
varr = bins[0] # -> index for vcen
farr = (restfreq - restfreq * (varr * u.km/u.s / c.c)).to('Hz').value
header['CDELT3'] = farr[1] - farr[0]
header['CRPIX3'] = 1
header['CRVAL3'] = farr[0]
header['CTYPE3'] = 'FREQ'
header['CUNIT3'] = 'Hz'
header['NAXIS3'] = farr.shape[0]
# Select 1-0 transition and sum profiles over multiple components
np.nan_to_num(hfdb, copy=False)
cube = hfdb[0,...].sum(axis=0) # -> (S, b, l)
cube = cube.astype('float32')
# Create FITS file
hdu = fits.PrimaryHDU(data=cube, header=header)
hdu.writeto(DATA_PATH/f'{field}_{store_suffix}_hfdb.fits', overwrite=True)
def export_model_to_fits(field, store_suffix):
# FIXME Exporting to FITS is a work-in progress in NestFit, so hard-code
# the header and such here.
dcube = get_cubestack(field).cubes[0]
header = dcube.full_header
with get_store(field, store_suffix) as store:
# dimensions (m, S, b, l)
mcube = store.hdf['/products/model_spec/trans1'][...]
# Select 1-0 transition and sum profiles over multiple components
np.nan_to_num(mcube, copy=False)
sum_mcube = mcube.sum(axis=0) # -> (S, b, l)
sum_mcube = sum_mcube.astype('float32')
# Create FITS file
hdu = fits.PrimaryHDU(data=sum_mcube, header=header)
hdu.writeto(DATA_PATH/f'{field}_{store_suffix}_model.fits', overwrite=True)
if __name__ == '__main__':
args = sys.argv[1:]
assert len(args) > 0
flag = args[0]
assert flag in ('--run-nested', '--post-proc')
# Parameters
field = 'W51-IRS2'
store_suffix = 'test_emp_2comp'
utrans = get_empirical_priors(field)
ncomp_max = 2
if flag == '--run-nested':
run_nested(field, store_suffix, utrans, nproc=16, ncomp_max=ncomp_max)
elif flag == '--post-proc':
postprocess_run(field, store_suffix)
| 2.09375 | 2 |
2018/d02.py | m1el/advent-of-code | 0 | 12787624 | from collections import Counter
from itertools import product
with open('02.txt') as fd:
inp = [l.strip() for l in fd.readlines()]
twos = 0
thre = 0
for row in inp:
c = Counter(row)
if 2 in c.values():
twos += 1
if 3 in c.values():
thre += 1
print(twos*thre)
def diff(sa,sb):
c = 0
for (a, b) in zip(sa,sb):
if a != b: c+= 1
return c
for (a, b) in product(inp, inp):
if diff(a,b) == 1:
print(a)
print(b)
break
| 3.265625 | 3 |
t2t/util.py | wasade/tax2tree | 0 | 12787625 | <reponame>wasade/tax2tree<gh_stars>0
#!/usr/bin/env python
from skbio import parse_fasta
__author__ = "<NAME>"
__copyright__ = "Copyright 2011, The tax2tree project"
__credits__ = ["<NAME>"]
__license__ = "BSD"
__version__ = "1.0"
__maintainer__ = "<NAME>"
__email__ = "<EMAIL>"
__status__ = "Development"
def combine_alignments(fp1, fp2):
"""take two filepointers, combine the files"""
seqs1 = dict(parse_fasta(fp1))
seqs2 = dict(parse_fasta(fp2))
if set(seqs1).intersection(set(seqs2)):
raise ValueError("Conflicting sequence ids in fp1 and fp2")
combined = seqs1
combined.update(seqs2)
return combined
def reroot(tree, tipnames, tmp_nodename="TEMPORARY_ROOT_NODE_NAME"):
"""Returns a tree rerooted based on tipnames"""
node = tree.lowest_common_ancestor(tipnames)
# make a new node that sits inbetween LCA and parent
parent = node.parent
parent.remove(node)
node.parent = None
new_node = parent.__class__()
new_node.name = tmp_nodename
if hasattr(new_node, 'length') and new_node.length:
new_node.length = node.length / 2.0
node.length = node.length / 2.0
else:
new_node.length = 0.0
# add node back to tree and reconnect LCA
parent.append(new_node)
new_node.append(node)
# root at the new node, unset its temporary name
new_tree = tree.root_at(tmp_nodename)
new_root = new_tree.find(tmp_nodename)
new_tree.name = None
new_root.name = None
# collapse single descendents if they exist
new_tree.prune()
return new_tree
def unzip(items):
"""The inverse of zip
Parameters
----------
items : a nested iteratable
Returns
-------
list
The unzipped items
Examples
--------
>>> from t2t.util import unzip
>>> unzip([[1, 2], ['a', 'b']])
[[1, 'a'], [2, 'b']]
"""
if items:
return [list(i) for i in zip(*items)]
else:
return []
| 2.3125 | 2 |
openff/bespokefit/executor/services/coordinator/stages.py | openforcefield/openff-bespokefit | 7 | 12787626 | <reponame>openforcefield/openff-bespokefit<filename>openff/bespokefit/executor/services/coordinator/stages.py
import abc
import json
from collections import defaultdict
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
import httpx
from openff.fragmenter.fragment import Fragment, FragmentationResult
from openff.toolkit.typing.engines.smirnoff import (
AngleHandler,
BondHandler,
ImproperTorsionHandler,
ParameterType,
ProperTorsionHandler,
vdWHandler,
)
from pydantic import Field
from qcelemental.models import AtomicResult, OptimizationResult
from qcelemental.util import serialize
from qcengine.procedures.torsiondrive import TorsionDriveResult
from typing_extensions import Literal
from openff.bespokefit.executor.services import current_settings
from openff.bespokefit.executor.services.coordinator.utils import get_cached_parameters
from openff.bespokefit.executor.services.fragmenter.models import (
FragmenterGETResponse,
FragmenterPOSTBody,
FragmenterPOSTResponse,
)
from openff.bespokefit.executor.services.optimizer.models import (
OptimizerGETResponse,
OptimizerPOSTBody,
OptimizerPOSTResponse,
)
from openff.bespokefit.executor.services.qcgenerator.models import (
QCGeneratorGETPageResponse,
QCGeneratorPOSTBody,
QCGeneratorPOSTResponse,
)
from openff.bespokefit.executor.utilities.redis import (
connect_to_default_redis,
is_redis_available,
)
from openff.bespokefit.executor.utilities.typing import Status
from openff.bespokefit.schema.data import BespokeQCData, LocalQCData
from openff.bespokefit.schema.fitting import BespokeOptimizationSchema
from openff.bespokefit.schema.results import BespokeOptimizationResults
from openff.bespokefit.schema.smirnoff import (
AngleSMIRKS,
BondSMIRKS,
ImproperTorsionSMIRKS,
ProperTorsionSMIRKS,
VdWSMIRKS,
)
from openff.bespokefit.schema.tasks import Torsion1DTask
from openff.bespokefit.utilities.pydantic import BaseModel
from openff.bespokefit.utilities.smirks import (
ForceFieldEditor,
SMIRKSGenerator,
SMIRKSType,
get_cached_torsion_parameters,
)
if TYPE_CHECKING:
from openff.bespokefit.executor.services.coordinator.models import CoordinatorTask
class _Stage(BaseModel, abc.ABC):
type: Literal["base-stage"] = "base-stage"
status: Status = Field("waiting", description="The status of this stage.")
error: Optional[str] = Field(
None, description="The error raised, if any, while running this stage."
)
async def enter(self, task: "CoordinatorTask"):
try:
return await self._enter(task)
except BaseException as e: # lgtm [py/catch-base-exception]
self.status = "errored"
self.error = json.dumps(f"{e.__class__.__name__}: {str(e)}")
async def update(self):
try:
return await self._update()
except BaseException as e: # lgtm [py/catch-base-exception]
self.status = "errored"
self.error = json.dumps(f"{e.__class__.__name__}: {str(e)}")
@abc.abstractmethod
async def _enter(self, task: "CoordinatorTask"):
pass
@abc.abstractmethod
async def _update(self):
pass
class FragmentationStage(_Stage):
type: Literal["fragmentation"] = "fragmentation"
id: Optional[str] = Field(None, description="")
result: Optional[FragmentationResult] = Field(None, description="")
async def _enter(self, task: "CoordinatorTask"):
settings = current_settings()
async with httpx.AsyncClient() as client:
raw_response = await client.post(
f"http://1172.16.17.32:"
f"{settings.BEFLOW_GATEWAY_PORT}"
f"{settings.BEFLOW_API_V1_STR}/"
f"{settings.BEFLOW_FRAGMENTER_PREFIX}",
data=FragmenterPOSTBody(
cmiles=task.input_schema.smiles,
fragmenter=task.input_schema.fragmentation_engine,
target_bond_smarts=task.input_schema.target_torsion_smirks,
).json(),
)
if raw_response.status_code != 200:
self.error = json.dumps(raw_response.text)
self.status = "errored"
return
contents = raw_response.text
post_response = FragmenterPOSTResponse.parse_raw(contents)
self.id = post_response.id
async def _update(self):
if self.status == "errored":
return
settings = current_settings()
async with httpx.AsyncClient() as client:
raw_response = await client.get(
f"http://127.0.0.1:"
f"{settings.BEFLOW_GATEWAY_PORT}"
f"{settings.BEFLOW_API_V1_STR}/"
f"{settings.BEFLOW_FRAGMENTER_PREFIX}/{self.id}"
)
if raw_response.status_code != 200:
self.error = json.dumps(raw_response.text)
self.status = "errored"
return
contents = raw_response.text
get_response = FragmenterGETResponse.parse_raw(contents)
self.result = get_response.result
if (
isinstance(self.result, FragmentationResult)
and len(self.result.fragments) == 0
):
self.error = json.dumps(
"No fragments could be generated for the parent molecule. This likely "
"means that the bespoke parameters that you have generated and are "
"trying to fit are invalid. Please raise an issue on the GitHub issue "
"tracker for further assistance."
)
self.status = "errored"
else:
self.error = get_response.error
self.status = get_response.status
class QCGenerationStage(_Stage):
type: Literal["qc-generation"] = "qc-generation"
ids: Optional[Dict[int, List[str]]] = Field(None, description="")
results: Optional[
Dict[str, Union[AtomicResult, OptimizationResult, TorsionDriveResult]]
] = Field(None, description="")
@staticmethod
def _generate_torsion_parameters(
fragmentation_result: FragmentationResult,
input_schema: BespokeOptimizationSchema,
) -> Tuple[List[ParameterType], List[Fragment]]:
"""
Generate torsion parameters for the fragments using any possible cached parameters.
Args:
fragmentation_result: The result of the fragmentation
input_schema: The input schema detailing the optimisation
Returns:
The list of generated smirks patterns including any cached values, and a list of fragments which require torsiondrives.
"""
settings = current_settings()
cached_torsions = None
if is_redis_available(
host=settings.BEFLOW_REDIS_ADDRESS, port=settings.BEFLOW_REDIS_PORT
):
redis_connection = connect_to_default_redis()
cached_force_field = get_cached_parameters(
fitting_schema=input_schema, redis_connection=redis_connection
)
if cached_force_field is not None:
cached_torsions = cached_force_field["ProperTorsions"].parameters
parent = fragmentation_result.parent_molecule
smirks_gen = SMIRKSGenerator(
initial_force_field=input_schema.initial_force_field,
generate_bespoke_terms=input_schema.smirk_settings.generate_bespoke_terms,
expand_torsion_terms=input_schema.smirk_settings.expand_torsion_terms,
target_smirks=[SMIRKSType.ProperTorsions],
)
new_smirks = []
fragments = []
for fragment_data in fragmentation_result.fragments:
central_bond = fragment_data.bond_indices
fragment_molecule = fragment_data.molecule
bespoke_smirks = smirks_gen.generate_smirks_from_fragment(
parent=parent,
fragment=fragment_molecule,
fragment_map_indices=central_bond,
)
if cached_torsions is not None:
smirks_to_add = []
for smirk in bespoke_smirks:
cached_smirk = get_cached_torsion_parameters(
molecule=fragment_molecule,
bespoke_parameter=smirk,
cached_parameters=cached_torsions,
)
if cached_smirk is not None:
smirks_to_add.append(cached_smirk)
if len(smirks_to_add) == len(bespoke_smirks):
# if we have the same number of parameters we are safe to transfer as they were fit together
new_smirks.extend(smirks_to_add)
else:
# the cached parameter was not fit with the correct number of other parameters
# so use new terms not cached
new_smirks.extend(bespoke_smirks)
fragments.append(fragment_data)
else:
new_smirks.extend(bespoke_smirks)
# only keep track of fragments which require QM calculations as they have no cached values
fragments.append(fragment_data)
return new_smirks, fragments
@staticmethod
async def _generate_parameters(
input_schema: BespokeOptimizationSchema,
fragmentation_result: Optional[FragmentationResult],
) -> List[Fragment]:
"""
Generate a list of parameters which are to be optimised, these are added to the input force field.
The parameters are also added to the parameter list in each stage corresponding to the stage where they will be fit.
"""
initial_force_field = ForceFieldEditor(input_schema.initial_force_field)
new_parameters = []
target_smirks = {*input_schema.target_smirks}
if SMIRKSType.ProperTorsions in target_smirks:
target_smirks.remove(SMIRKSType.ProperTorsions)
(
torsion_parameters,
fragment_jobs,
) = QCGenerationStage._generate_torsion_parameters(
fragmentation_result=fragmentation_result,
input_schema=input_schema,
)
new_parameters.extend(torsion_parameters)
else:
fragment_jobs = []
if len(target_smirks) > 0:
smirks_gen = SMIRKSGenerator(
initial_force_field=input_schema.initial_force_field,
generate_bespoke_terms=input_schema.smirk_settings.generate_bespoke_terms,
target_smirks=[*target_smirks],
smirks_layers=1,
)
parameters = smirks_gen.generate_smirks_from_molecule(
molecule=input_schema.molecule
)
new_parameters.extend(parameters)
# add all new terms to the input force field
initial_force_field.add_parameters(parameters=new_parameters)
parameter_to_type = {
vdWHandler.vdWType: VdWSMIRKS,
BondHandler.BondType: BondSMIRKS,
AngleHandler.AngleType: AngleSMIRKS,
ProperTorsionHandler.ProperTorsionType: ProperTorsionSMIRKS,
ImproperTorsionHandler.ImproperTorsionType: ImproperTorsionSMIRKS,
}
# convert all parameters to bespokefit types
parameters_to_fit = defaultdict(list)
for parameter in new_parameters:
bespoke_parameter = parameter_to_type[parameter.__class__].from_smirnoff(
parameter
)
# We only want to fit if it was not cached
if not bespoke_parameter.cached:
parameters_to_fit[bespoke_parameter.type].append(bespoke_parameter)
# set which parameters should be optimised in each stage
for stage in input_schema.stages:
for hyper_param in stage.parameter_hyperparameters:
stage.parameters.extend(parameters_to_fit[hyper_param.type])
input_schema.initial_force_field = initial_force_field.force_field.to_string()
return fragment_jobs
async def _enter(self, task: "CoordinatorTask"):
settings = current_settings()
fragment_stage = next(
iter(
stage
for stage in task.completed_stages
if stage.type == "fragmentation"
),
None,
)
input_schema = task.input_schema
# TODO: Move these methods onto the celery worker.
try:
fragments = await self._generate_parameters(
fragmentation_result=fragment_stage.result,
input_schema=input_schema,
)
except BaseException as e: # lgtm [py/catch-base-exception]
self.status = "errored"
self.error = json.dumps(
f"Failed to generate SMIRKS patterns that match both the parent and "
f"torsion fragments: {str(e)}"
)
return
target_qc_tasks = defaultdict(list)
targets = [
target for stage in task.input_schema.stages for target in stage.targets
]
for i, target in enumerate(targets):
if not isinstance(target.reference_data, BespokeQCData):
continue
if target.bespoke_task_type() == "torsion1d":
target_qc_tasks[i].extend(
Torsion1DTask(
smiles=fragment.smiles,
central_bond=fragment.bond_indices,
**target.calculation_specification.dict(),
)
for fragment in fragments
)
else:
raise NotImplementedError()
qc_calc_ids = defaultdict(set)
async with httpx.AsyncClient() as client:
for i, qc_tasks in target_qc_tasks.items():
for qc_task in qc_tasks:
raw_response = await client.post(
f"http://127.0.0.1:"
f"{settings.BEFLOW_GATEWAY_PORT}"
f"{settings.BEFLOW_API_V1_STR}/"
f"{settings.BEFLOW_QC_COMPUTE_PREFIX}",
data=QCGeneratorPOSTBody(input_schema=qc_task).json(),
)
if raw_response.status_code != 200:
self.error = json.dumps(raw_response.text)
self.status = "errored"
return
response = QCGeneratorPOSTResponse.parse_raw(raw_response.text)
qc_calc_ids[i].add(response.id)
self.ids = {i: sorted(ids) for i, ids in qc_calc_ids.items()}
async def _update(self):
settings = current_settings()
if self.status == "errored":
return
if (
len([qc_id for target_ids in self.ids.values() for qc_id in target_ids])
== 0
):
# Handle the case were there was no bespoke QC data to generate.
self.status = "success"
self.results = {}
return
async with httpx.AsyncClient() as client:
id_query = "&ids=".join(qc_id for i in self.ids for qc_id in self.ids[i])
raw_response = await client.get(
f"http://127.0.0.1:"
f"{settings.BEFLOW_GATEWAY_PORT}"
f"{settings.BEFLOW_API_V1_STR}/"
f"{settings.BEFLOW_QC_COMPUTE_PREFIX}?ids={id_query}"
)
contents = raw_response.text
if raw_response.status_code != 200:
self.error = json.dumps(raw_response.text)
self.status = "errored"
return
get_responses = QCGeneratorGETPageResponse.parse_raw(contents).contents
statuses = {get_response.status for get_response in get_responses}
errors = [
json.loads(get_response.error)
for get_response in get_responses
if get_response.error is not None
]
self.error = json.dumps(errors)
self.status = "running"
if "errored" in statuses:
self.status = "errored"
elif statuses == {"waiting"}:
self.status = "waiting"
elif statuses == {"success"}:
self.status = "success"
self.results = {
get_response.id: get_response.result for get_response in get_responses
}
class OptimizationStage(_Stage):
type: Literal["optimization"] = "optimization"
id: Optional[str] = Field(
None, description="The id of the optimization associated with this stage."
)
result: Optional[BespokeOptimizationResults] = Field(
None, description="The result of the optimization."
)
@staticmethod
async def _inject_bespoke_qc_data(
qc_generation_stage: QCGenerationStage,
input_schema: BespokeOptimizationSchema,
):
targets = [target for stage in input_schema.stages for target in stage.targets]
for i, target in enumerate(targets):
if not isinstance(target.reference_data, BespokeQCData):
continue
if i not in qc_generation_stage.ids:
continue
local_qc_data = LocalQCData(
qc_records=[
qc_generation_stage.results[result_id]
for result_id in qc_generation_stage.ids[i]
]
)
target.reference_data = local_qc_data
targets_missing_qc_data = [
target
for target in targets
if isinstance(target.reference_data, BespokeQCData)
]
n_targets_missing_qc_data = len(targets_missing_qc_data)
if n_targets_missing_qc_data > 0 and qc_generation_stage.results:
raise RuntimeError(
f"{n_targets_missing_qc_data} targets were missing QC data - this "
f"should likely never happen. Please raise an issue on the GitHub "
f"issue tracker."
)
async def _enter(self, task: "CoordinatorTask"):
settings = current_settings()
completed_stages = {stage.type: stage for stage in task.completed_stages}
input_schema = task.input_schema.copy(deep=True)
# Map the generated QC results into a local QC data class and update the schema
# to target these.
qc_generation_stage: QCGenerationStage = completed_stages["qc-generation"]
try:
await self._inject_bespoke_qc_data(qc_generation_stage, input_schema)
except BaseException as e: # lgtm [py/catch-base-exception]
self.status = "errored"
self.error = json.dumps(
f"Failed to inject the bespoke QC data into the optimization "
f"schema: {str(e)}"
)
return
async with httpx.AsyncClient() as client:
raw_response = await client.post(
f"http://127.0.0.1:"
f"{settings.BEFLOW_GATEWAY_PORT}"
f"{settings.BEFLOW_API_V1_STR}/"
f"{settings.BEFLOW_OPTIMIZER_PREFIX}",
data=serialize(
OptimizerPOSTBody(input_schema=input_schema), encoding="json"
),
)
if raw_response.status_code != 200:
self.error = json.dumps(raw_response.text)
self.status = "errored"
return
response = OptimizerPOSTResponse.parse_raw(raw_response.text)
self.id = response.id
async def _update(self):
settings = current_settings()
if self.status == "errored":
return
async with httpx.AsyncClient() as client:
raw_response = await client.get(
f"http://127.0.0.1:"
f"{settings.BEFLOW_GATEWAY_PORT}"
f"{settings.BEFLOW_API_V1_STR}/"
f"{settings.BEFLOW_OPTIMIZER_PREFIX}/{self.id}"
)
contents = raw_response.text
if raw_response.status_code != 200:
self.error = json.dumps(raw_response.text)
self.status = "errored"
return
get_response = OptimizerGETResponse.parse_raw(contents)
self.result = get_response.result
self.error = get_response.error
self.status = get_response.status
StageType = Union[FragmentationStage, QCGenerationStage, OptimizationStage]
| 1.25 | 1 |
docker-register.py | poporisil/docker-register | 0 | 12787627 | <gh_stars>0
#!/usr/bin/env python
import os
import sys
import argparse
import threading
import subprocess
import time
from urlparse import urlparse
import docker
import etcd
import json
import logging
import logging.handlers
parser = argparse.ArgumentParser(description='Docker Register')
parser.add_argument('-e','--etcd-url', default='http://localhost:4001',
help='etcd url (default: http://localhost:4001)')
parser.add_argument('-d','--docker-url', default='unix://var/run/docker.sock',
help='docker url (default: unix://var/run/docker.sock)')
parser.add_argument('--docker-api-ver', default='1.23',
help='docker api version (default: 1.23)')
parser.add_argument('-t','--ttl', type=int, default=15,
help='register ttl (default: 15)')
parser.add_argument('-l','--log-path', default='.',
help='log path (default: .)')
args = parser.parse_args()
etcdUrl = args.etcd_url
dockerUrl = args.docker_url
dockerApiVer = args.docker_api_ver
ttl = args.ttl
logPath = args.log_path
logger = logging.getLogger('DockerRegister')
handler = logging.handlers.RotatingFileHandler(logPath + '/docker-register.log', backupCount=10)
formatter = logging.Formatter(fmt='%(asctime)s] (%(levelname)s) %(name)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(logging.DEBUG)
logger.info('-- Parameters --------------------------')
logger.info('etcdUrl = %s' % etcdUrl)
logger.info('dockerUrl = %s' % dockerUrl)
logger.info('dockerApiVer = %s' % dockerApiVer)
logger.info('ttl = %d' % ttl)
logger.info('logPath = %s' % logPath)
logger.info('-----------------------------------------')
class DockerRegister(threading.Thread):
def __init__(self):
threading.Thread.__init__(self)
self.dc = docker.Client(base_url=dockerUrl, version=dockerApiVer)
self.ec = etcd.Client(host=urlparse(etcdUrl).hostname, port=urlparse(etcdUrl).port, protocol=urlparse(etcdUrl).scheme)
self.internalIp = subprocess.check_output("ip route get 8.8.8.8 | awk '{print $NF; exit}'", shell=True).strip()
def getContainers(self):
logger.debug('get container list...')
bindedContainers = {}
try:
for container in self.dc.containers():
binded = []
for port in container['Ports']:
if 'PublicPort' in port:
binded.append('%s:%d-%d/%s'%(self.internalIp, port['PublicPort'], port['PrivatePort'], port['Type']))
if binded:
key = container['Image'].split(':')[0] + '/' + container['Id']
bindedContainers[key] = ','.join(binded)
except Exception:
logger.exception('get containers fail')
return None
return bindedContainers
def registerContainers(self, containers):
logger.debug('register containers...')
for key, value in containers.iteritems():
logger.debug('register %s' % key)
try:
self.ec.write('/containers/' + key, value, ttl=ttl)
except Exception:
logger.exception('etcd write fail')
pass
def run(self):
logger.info('start agent!')
while True:
containers = self.getContainers()
if containers:
self.registerContainers(containers)
time.sleep(10)
pass
if __name__ == '__main__':
t = DockerRegister()
t.start()
t.join() | 2.28125 | 2 |
worker/Twitter/service1/coordinatesExtractor.py | LamriAli/remote-extraction-proxy-and-worker | 0 | 12787628 |
class CoordinateExtractor:
@staticmethod
def crawlCoordinates(self,api,fullStructure,uid,graph,userQueue,coordinatesQueue,placeQueue,urlQueue,mediaQueue,tweetQueue):
pass
| 1.78125 | 2 |
src/tools/python/csv_transpose.py | Justintime50/easypost-tools | 1 | 12787629 | <reponame>Justintime50/easypost-tools
import argparse
import csv
# Transpose rows into columns and vice versa for a CSV
# Attribution: [jontsai](https://github.com/jontsai)
# Usage: venv/bin/python csv_transpose.py -f my_file.csv -o output.csv
class CSVTranspose:
def __init__(self):
parser = argparse.ArgumentParser(
description=self.__class__.__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter,
)
parser.add_argument(
'-f',
'--file',
help='Path to the CSV file to modify',
required=True,
)
parser.add_argument(
'-o',
'--outfile',
help='Path to save the modified CSV file',
required=True,
)
parser.parse_args(namespace=self)
def transpose(self):
"""Transpose a CSV by turning rows into columns and columns into rows
Uses zip to transpose a matrix.
In [1]: a = [(1, 2, 3),
...: (4, 5, 6),
...: (7, 8, 9)]
In [2]: list(zip(*a))
Out[2]: [(1, 4, 7), (2, 5, 8), (3, 6, 9)]
See: https://stackoverflow.com/a/4869245/865091
"""
with open(self.file, 'r') as f:
reader = csv.reader(f)
transposed_data = zip(*reader)
with open(self.outfile, 'w') as out:
writer = csv.writer(out)
writer.writerows(transposed_data)
def main():
CSVTranspose().transpose()
if __name__ == '__main__':
main()
| 3.734375 | 4 |
lidar_convenience.py | mrtpk/lidar-buster | 17 | 12787630 | import numpy as np
class LidarTools(object):
'''
Collection of helpers for processing LiDAR point cloud.
'''
def get_bev(self, points, resolution=0.1, pixel_values=None, generate_img=None):
'''
Returns bird's eye view of a LiDAR point cloud for a given resolution.
Optional pixel_values can be used for giving color coded info the point cloud.
Optional generate_img function can be used for creating images.
'''
x = points[:, 0]
y = points[:, 1]
z = points[:, 2]
x_range = -1 * np.ceil(y.max()).astype(np.int), ((y.min()/np.abs(y.min())) * np.floor(y.min())).astype(np.int)
y_range = np.floor(x.min()).astype(np.int), np.ceil(x.max()).astype(np.int)
# Create mapping from a 3D point to a pixel based on resolution
# floor() used to prevent issues with -ve vals rounding upwards causing index out bound error
x_img = (-y / resolution).astype(np.int32) - int(np.floor(x_range[0]/resolution))
y_img = (x / resolution).astype(np.int32) - int(np.floor(y_range[0]/resolution))
img_width = int((x_range[1] - x_range[0])/resolution)
img_height = int((y_range[1] - y_range[0])/resolution)
if pixel_values is None:
pixel_values = (((z - z.min()) / float(z.max() - z.min())) * 255).astype(np.uint8)
if generate_img is None:
img = np.zeros([img_height, img_width], dtype=np.uint8)
img[-y_img, x_img] = pixel_values
return img
return generate_img(img_height, img_width, -y_img, x_img, pixel_values)
def filter_points(self, points, side_range=None, fwd_range=None, \
height_range=None, horizontal_fov=None, vertical_fov=None):
'''
Returns filtered points based on side, forward and height range, and, horizontal and vertical field of view.
'''
x = points[:, 0]
y = points[:, 1]
z = points[:, 2]
r = points[:, 3]
mask = np.full_like(x, True)
if side_range is not None:
side_mask = np.logical_and((y > -side_range[1]), (y < -side_range[0]))
mask = np.logical_and(mask, side_mask)
if fwd_range is not None:
fwd_mask = np.logical_and((x > fwd_range[0]), (x < fwd_range[1]))
mask = np.logical_and(mask, fwd_mask)
if height_range is not None:
height_mask = np.logical_and((z > height_range[0]), (z < height_range[1]))
mask = np.logical_and(mask, height_mask)
if horizontal_fov is not None:
horizontal_fov_mask = np.logical_and(np.arctan2(y, x) > (-horizontal_fov[1] * np.pi / 180), \
np.arctan2(y, x) < (-horizontal_fov[0] * np.pi / 180))
mask = np.logical_and(mask, horizontal_fov_mask)
if vertical_fov is not None:
distance = np.sqrt(x ** 2 + y ** 2 + z ** 2)
vertical_fov_mask = np.logical_and(np.arctan2(z,distance) < (vertical_fov[1] * np.pi / 180), \
np.arctan2(z,distance) > (vertical_fov[0] * np.pi / 180))
mask = np.logical_and(mask, vertical_fov_mask)
indices = np.argwhere(mask).flatten()
return points[indices, :] | 2.765625 | 3 |
practice/bitwise_operations/ex9.py | recursivelycurious/wordnik-repl | 0 | 12787631 | a = 0b10111011
mask = 0b100
desired = a | mask
print bin(desired)
| 2.4375 | 2 |
py/ClientProject/.idea/log/logger.py | Justyer/Secret | 0 | 12787632 | <gh_stars>0
import logging
from logging.handlers import TimedRotatingFileHandler
import sys, os
parent_path = os.path.dirname(os.getcwd())
sys.path.append(parent_path)
#自定义引用
from log.message import *
logpath=os.path.split(os.path.realpath(__file__))[0].split(':')[0] + ':\\gxd\\log'
if not os.path.exists(logpath):
os.makedirs(logpath)
#初始化日志系统
logging.basicConfig(level=logging.INFO, datefmt=None,
format='%(asctime)-24s level-%(levelname)-12s thread-%(thread)-6d %(module)-s.%(funcName)-10s Line:%(lineno)-4d %(message)s',
handlers=[logging.StreamHandler(), TimedRotatingFileHandler(logpath+'\\'+str(os.getpid())+'_client.log', when='D', interval=1, backupCount=15)])
global LOG
LOG=logging.getLogger()
LOG.handlers[0].setLevel(logging.DEBUG)
LOG.handlers[1].setLevel(logging.INFO)
"""
LOG.debug('调试信息')
LOG.info('有用的信息')
LOG.warning('警告信息')
LOG.error('错误信息')
LOG.critical('严重错误信息')""" | 2.1875 | 2 |
kattis/solutions/quickbrownfox.py | yifeng-pan/competitive_programming | 0 | 12787633 | <reponame>yifeng-pan/competitive_programming<gh_stars>0
# https://open.kattis.com/problems/quickbrownfox
cases = int(input())
for i in range(cases):
text = input()
missing = ""
for i in range(26):
c = chr(ord('a') + i)
C = chr(ord('A') + i)
if text.find(c) == -1 & text.find(C) == -1:
missing += c
print("pangram" if len(missing) == 0 else "missing " + missing) | 3.59375 | 4 |
examples/fast_api.py | alex-oleshkevich/aerie | 6 | 12787634 | """
This example requires uvicorn and fastapi.
pip install fastapi uvicorn
Run:
uvicorn examples.fast_api:app
then open http://localhost:8000
Access http://localhost:8000 to list all users.
Access http://localhost:8000/create to create a new user.
"""
import os
import sqlalchemy as sa
import typing as t
from fastapi import Depends, FastAPI
from aerie import Aerie, Base, DbSession
DATABASE_URL = os.environ.get('DATABASE_URL', 'sqlite+aiosqlite:///:memory:')
db = Aerie(DATABASE_URL)
class User(Base):
__tablename__ = 'users'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.String)
def __str__(self) -> str:
return self.name or 'n/a'
app = FastAPI(on_startup=[db.schema.create_tables], on_shutdown=[db.schema.drop_tables])
@app.get("/create")
async def create_user_view(session: DbSession = Depends(db.session)) -> t.Mapping:
count = await session.query(User).count()
user = User(id=count, name=f'User {count}')
session.add(user)
await session.commit()
return {"id": user.id, 'name': user.name}
@app.get("/")
async def list_users_view(session: DbSession = Depends(db.session)) -> t.List:
users = await session.query(User).all()
return [u.name for u in users]
| 3.015625 | 3 |
tools/test_dota.py | qinr/MRDet | 8 | 12787635 | import argparse
import os
import os.path as osp
import pickle
import shutil
import tempfile
import mmcv
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, load_checkpoint
from mmdet.apis import init_dist
from mmdet.core import coco_eval, results2json, wrap_fp16_model, get_classes, tensor2imgs
from mmdet.datasets import build_dataloader, build_dataset
from mmdet.models import build_detector
from DOTA_devkit.ResultMerge_multi_process import mergebypoly_multiprocess
import numpy as np
import os
import cv2
from DOTA_devkit.dota_utils import GetFileFromThisRootDir, custombasename
def draw_bbox(img, bboxes, labels, path, class_names):
img_show = mmcv.image.imread(img)
# bgr
bbox_color = [(0, 255, 0), # green
(255, 0, 0), #深蓝
(255, 255, 0), # 浅蓝，亮
(0, 0, 255), #红
(255, 0, 255), # purple
(255, 128, 0), #天蓝（比浅蓝深一点）
(0, 255, 255), #黄
(207, 203, 211), #white
(128, 255, 0), # 青色
(128, 0, 255), #玫红
(255, 0, 128), # 紫
(0, 128, 255), # 橘色
(0, 255, 128), #草绿
(0, 0, 128), #深红
(128, 0, 0)] #藏蓝
text_color = (255, 0, 0) # green
for bbox, label in zip(bboxes, labels):
bbox_int = bbox.astype(np.int32)
pts = np.array([[bbox_int[0], bbox_int[1]],
[bbox_int[2], bbox_int[3]],
[bbox_int[4], bbox_int[5]],
[bbox_int[6], bbox_int[7]]], dtype=np.int32)
cv2.polylines(img_show, [pts], True, bbox_color[label], thickness=2)
# cv2.polylines(img_show, [pts], True, text_color, thickness=2)
label_text = class_names[
label] if class_names is not None else 'cls {}'.format(label)
font = 0.5
# cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] - 2),
# cv2.FONT_HERSHEY_COMPLEX, font, text_color)
cv2.imwrite(path, img)
def draw_result(data, result, outdir, class_names, score_thr=0.001):
bbox_result = result
img_metas = data['img_meta'][0].data[0]
if not os.path.exists(outdir):
os.makedirs(outdir)
for img_meta in img_metas:
h, w, _ = img_meta['ori_shape']
filename = img_meta['filename']
img = mmcv.imread(filename)
img_show = img[:h, :w, :]
path = os.path.basename(os.path.splitext(img_meta['filename'])[0])
path = os.path.join(outdir, path + '.jpg')
bboxes = np.vstack(bbox_result)
labels = [
np.full(bbox.shape[0], i, dtype=np.int32)
for i, bbox in enumerate(bbox_result)
]
labels = np.concatenate(labels)
if score_thr > 0:
assert bboxes.shape[1] == 9
scores = bboxes[:, -1]
inds = scores > score_thr
bboxes = bboxes[inds, :]
labels = labels[inds]
draw_bbox(img_show, bboxes, labels, path, class_names)
def single_gpu_test(model, data_loader, outdir, show=False):
model.eval()
# model.eval()，让model变成测试模式，对dropout和batch normalization的操作在训练和测试的时候是不一样的
results = []
dataset = data_loader.dataset
prog_bar = mmcv.ProgressBar(len(dataset))
for i, data in enumerate(data_loader):
with torch.no_grad():
result = model(return_loss=False, rescale=not show, **data)
results.append(result)
if show:
draw_result(data, result, osp.join(outdir, 'images'), dataset.CLASSES, score_thr=0.001)
batch_size = data['img'][0].size(0)
for _ in range(batch_size):
prog_bar.update()
return results
def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
"""Test model with multiple gpus.
This method tests model with multiple gpus and collects the results
under two different modes: gpu and cpu modes. By setting 'gpu_collect=True'
it encodes results to gpu tensors and use gpu communication for results
collection. On cpu mode it saves the results on different gpus to 'tmpdir'
and collects them by the rank 0 worker.
Args:
model (nn.Module): Model to be tested.
data_loader (nn.Dataloader): Pytorch data loader.
tmpdir (str): Path of directory to save the temporary results from
different gpus under cpu mode.
gpu_collect (bool): Option to use either gpu or cpu to collect results.
Returns:
list: The prediction results.
"""
model.eval()
results = []
dataset = data_loader.dataset
rank, world_size = get_dist_info()
if rank == 0:
prog_bar = mmcv.ProgressBar(len(dataset))
for i, data in enumerate(data_loader):
with torch.no_grad():
result = model(return_loss=False, rescale=True, **data)
results.append(result)
if rank == 0:
batch_size = data['img'][0].size(0)
for _ in range(batch_size * world_size):
prog_bar.update()
# collect results from all ranks
if gpu_collect:
results = collect_results_gpu(results, len(dataset))
else:
results = collect_results_cpu(results, len(dataset), tmpdir)
return results
def collect_results_cpu(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
def collect_results_gpu(result_part, size):
rank, world_size = get_dist_info()
# dump result part to tensor with pickle
part_tensor = torch.tensor(
bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
# gather all result part tensor shape
shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
shape_list = [shape_tensor.clone() for _ in range(world_size)]
dist.all_gather(shape_list, shape_tensor)
# padding result part tensor to max length
shape_max = torch.tensor(shape_list).max()
part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda')
part_send[:shape_tensor[0]] = part_tensor
part_recv_list = [
part_tensor.new_zeros(shape_max) for _ in range(world_size)
]
# gather all result part
dist.all_gather(part_recv_list, part_send)
if rank == 0:
part_list = []
for recv, shape in zip(part_recv_list, shape_list):
part_list.append(
pickle.loads(recv[:shape[0]].cpu().numpy().tobytes()))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
return ordered_results
def parse_args():
parser = argparse.ArgumentParser(description='MMDet test detector')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument('--outdir', help='output dir')
parser.add_argument('--out', help='output result file') # .pkl文件
parser.add_argument(
'--gpu_collect',
action='store_true',
help='whether to use gpu to collect results')
parser.add_argument('--show', action='store_true', help='show results')
parser.add_argument('--tmpdir', help='tmp dir for writing some results')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
return args
def write_dota_results(path, boxes, dataset, threshold=0.001):
'''
:param path: output dir path
:param boxes: list(list(ndarray))
:param threshold: 置信度下限，小于此置信度的bbox不输出
:return:
'''
classes = dataset.CLASSES
img_infos = dataset.img_infos
assert len(boxes) == len(img_infos)
print("write no merge results\n")
for i, img_info in enumerate(img_infos):
# print("img {}: {}".format(i, img_info['id']))
img_id = img_info['id']
for j, cls in enumerate(classes):
txt_path = osp.join(path, 'Task1_' + cls + '.txt')
with open(txt_path, 'a') as f:
box = boxes[i][j] # (n, 9)
inds = box[:, 8] > threshold
box = box[inds]
for k in range(box.shape[0]):
# print(cls)
# print('{} {} {} {} {} {} {} {} {} {}\n'.format(
# img_id, box[k, 8],
# int(box[k, 0]), int(box[k, 1]),
# int(box[k, 2]), int(box[k, 3]),
# int(box[k, 4]), int(box[k, 5]),
# int(box[k, 6]), int(box[k, 7])))
f.write('{} {} {} {} {} {} {} {} {} {}\n'.format(
img_id, box[k, 8],
int(box[k, 0]), int(box[k, 1]),
int(box[k, 2]), int(box[k, 3]),
int(box[k, 4]), int(box[k, 5]),
int(box[k, 6]), int(box[k, 7])))
def main():
args = parse_args()
assert args.out , \
('Please specify at least one operation (save or show the results) '
'with the argument "--out"')
if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
raise ValueError('The output file must be a pkl file.')
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
imgs_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
wrap_fp16_model(model)
checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
# old versions did not save class info in checkpoints, this walkaround is
# for backward compatibility
if 'CLASSES' in checkpoint['meta']:
model.CLASSES = checkpoint['meta']['CLASSES']
else:
model.CLASSES = dataset.CLASSES
if not distributed:
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model, data_loader, args.outdir, args.show)
# outputs:list(list(ndarray)),外层list:图片，内层list:类别
else:
model = MMDistributedDataParallel(model.cuda())
outputs = multi_gpu_test(model, data_loader, args.tmpdir,
args.gpu_collect)
rank, _ = get_dist_info()
# 将结果保存到.pkl文件中
if args.out and rank == 0:
print('\nwriting results to {}'.format(args.out))
mmcv.dump(outputs, osp.join(args.outdir, args.out))
if __name__ == '__main__':
# os.environ['CUDA_VISIBLE_DEVICES'] = '0'
main()
| 1.8125 | 2 |
CybORG/CybORG/Agents/Branching_Agents/utils.py | tranhoangkhuongvn/cage-challenge-1 | 0 | 12787636 | import numpy as np
import gym
import torch
import random
from argparse import ArgumentParser
import os
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
from scipy.ndimage.filters import gaussian_filter1d
class Stats():
def __init__(self, num_episodes=20000, num_states = 6, log_dir='./', continuous=False):
self.episode_rewards = np.zeros(num_episodes)
self.episode_lengths = np.zeros(num_episodes)
if not continuous:
self.visitation_count = np.zeros((num_states, num_episodes))
self.target_count = np.zeros((num_states, num_episodes))
self.log_dir = log_dir
def log_data(self, file_name):
save_name = self.log_dir + file_name
np.savez(save_name, reward=self.episode_rewards, step=self.episode_lengths)
def plot_rewards(ax, episodes_ydata, smoothing_window = 100, label="",c='b', alpha=0.5):
#smoothing_window = 100
overall_stats_q_learning = []
for trialdata in episodes_ydata:
overall_stats_q_learning.append(pd.Series(trialdata.episode_rewards).rolling(smoothing_window, min_periods=smoothing_window).mean())
#overall_stats_q_learning.append(pd.Series(trialdata.episode_rewards).rolling(smoothing_window, min_periods=smoothing_window).mean().data)
m_stats_q_learning = np.mean(overall_stats_q_learning, axis=0)
std_stats_q_learning = np.std(overall_stats_q_learning, axis=0)
ax.plot(range(len(m_stats_q_learning)), m_stats_q_learning, label=label, c=c)
ax.fill_between(range(len(std_stats_q_learning)), m_stats_q_learning - std_stats_q_learning, m_stats_q_learning + std_stats_q_learning, alpha=alpha, edgecolor=c, facecolor=c)
#ax.set_ylabel('Score')
#ax.set_xlabel('Episode #')
#ax.grid()
def plot_steps(ax, episodes_ydata, smoothing_window = 100, label="",c='g',alpha=0.5):
#smoothing_window = 100
overall_stats_q_learning = []
for trialdata in episodes_ydata:
overall_stats_q_learning.append(pd.Series(trialdata.episode_lengths).rolling(smoothing_window, min_periods=smoothing_window).mean())
#overall_stats_q_learning.append(pd.Series(trialdata.episode_lengths).rolling(smoothing_window, min_periods=smoothing_window).mean().data)
m_stats_q_learning = np.mean(overall_stats_q_learning, axis=0)
std_stats_q_learning = np.std(overall_stats_q_learning, axis=0)
ax.plot(range(len(m_stats_q_learning)), m_stats_q_learning, label=label, c=c)
ax.fill_between(range(len(std_stats_q_learning)), m_stats_q_learning - std_stats_q_learning, m_stats_q_learning + std_stats_q_learning, alpha=alpha, edgecolor=c, facecolor=c)
#ax.set_ylabel('Steps')
#ax.set_xlabel('Episode #')
#ax.grid()
def plot_visitation_counts(episodes_ydata, smoothing_window = 1000, c=['b', 'g', 'r', 'y', 'k', 'c'], num_states = None):
if not num_states:
num_states = len(episodes_ydata[0].visitation_count)
overall_stats_q_learning = [[] for i in range(num_states)]
for trialdata in episodes_ydata:
for state in range(num_states):
overall_stats_q_learning[state].append(pd.Series(trialdata.visitation_count[state]).rolling(smoothing_window, min_periods=smoothing_window).mean().data)
for state in range(num_states):
m_stats_q_learning = np.mean(overall_stats_q_learning[state], axis=0)
std_stats_q_learning = np.std(overall_stats_q_learning[state], axis=0)
plt.plot(range(len(m_stats_q_learning)), m_stats_q_learning, c=c[state])
plt.fill_between(range(len(std_stats_q_learning)), m_stats_q_learning - std_stats_q_learning, m_stats_q_learning + std_stats_q_learning, alpha=0.5, edgecolor=c[state], facecolor=c[state])
def plot_target_counts(episodes_ydata, smoothing_window = 1000, c=['b', 'g', 'r', 'y', 'k', 'c']):
num_states = len(episodes_ydata[0].target_count)
overall_stats_q_learning = [[] for i in range(num_states)]
for trialdata in episodes_ydata:
for state in range(num_states):
overall_stats_q_learning[state].append(pd.Series(trialdata.target_count[state]).rolling(smoothing_window, min_periods=smoothing_window).mean().data)
for state in range(num_states):
m_stats_q_learning = np.mean(overall_stats_q_learning[state], axis=0)
std_stats_q_learning = np.std(overall_stats_q_learning[state], axis=0)
plt.plot(range(len(m_stats_q_learning)), m_stats_q_learning, c=c[state])
plt.fill_between(range(len(std_stats_q_learning)), m_stats_q_learning - std_stats_q_learning, m_stats_q_learning + std_stats_q_learning, alpha=0.5, edgecolor=c[state], facecolor=c[state])
def plot_q_values(model, observation_space, action_space):
res = 100
test_observations = np.linspace(observation_space.low, observation_space.high, res)
print((action_space.n, res))
q_values = np.zeros((action_space.n, res))
for action in range(action_space.n):
for obs in range(res):
q_values[action, obs] = model.predict(test_observations[obs])[0, action]
plt.plot(test_observations, q_values[action])
def arguments():
parser = ArgumentParser()
parser.add_argument('--env', default = 'BipedalWalker-v3')
return parser.parse_args()
def save(agent, rewards, args):
path = './runs/{}/'.format(args.env)
try:
os.makedirs(path)
except:
pass
torch.save(agent.q.state_dict(), os.path.join(path, 'model_state_dict'))
plt.cla()
plt.plot(rewards, c = 'r', alpha = 0.3)
plt.plot(gaussian_filter1d(rewards, sigma = 5), c = 'r', label = 'Rewards')
plt.xlabel('Episodes')
plt.ylabel('Cumulative reward')
plt.title('Branching DDQN: {}'.format(args.env))
plt.savefig(os.path.join(path, 'reward.png'))
pd.DataFrame(rewards, columns = ['Reward']).to_csv(os.path.join(path, 'rewards.csv'), index = False)
class AgentConfig:
def __init__(self,
epsilon_start = 1.,
epsilon_final = 0.01,
epsilon_decay = 8000,
gamma = 0.99,
lr = 1e-4,
target_net_update_freq = 1000,
memory_size = 100000,
batch_size = 128,
learning_starts = 5000,
max_frames = 10000000):
self.epsilon_start = epsilon_start
self.epsilon_final = epsilon_final
self.epsilon_decay = epsilon_decay
self.epsilon_by_frame = lambda i: self.epsilon_final + (self.epsilon_start - self.epsilon_final) * np.exp(-1. * i / self.epsilon_decay)
self.gamma =gamma
self.lr =lr
self.target_net_update_freq =target_net_update_freq
self.memory_size =memory_size
self.batch_size =batch_size
self.learning_starts = learning_starts
self.max_frames = max_frames
class ExperienceReplayMemory:
def __init__(self, capacity):
self.capacity = capacity
self.memory = []
def push(self, transition):
self.memory.append(transition)
if len(self.memory) > self.capacity:
del self.memory[0]
def sample(self, batch_size):
batch = random.sample(self.memory, batch_size)
states = []
actions = []
rewards = []
next_states = []
dones = []
for b in batch:
states.append(b[0])
actions.append(b[1])
rewards.append(b[2])
next_states.append(b[3])
dones.append(b[4])
return states, actions, rewards, next_states, dones
def __len__(self):
return len(self.memory)
import torch
import collections
import random
class ReplayBuffer():
def __init__(self,buffer_limit,action_space,device):
self.buffer = collections.deque(maxlen=buffer_limit)
self.action_space = action_space
self.device = device
def put(self, transition):
self.buffer.append(transition)
def sample(self, n):
mini_batch = random.sample(self.buffer, n)
state_lst, reward_lst, next_state_lst, done_mask_lst = [], [], [], []
actions_lst = [[] for i in range(self.action_space)]
for transition in mini_batch:
state, actions,reward, next_state, done_mask = transition
state_lst.append(state)
for idx in range(self.action_space):
actions_lst[idx].append(actions[idx])
reward_lst.append([reward])
next_state_lst.append(next_state)
done_mask_lst.append([done_mask])
actions_lst = [torch.tensor(x,dtype= torch.float).to(self.device) for x in actions_lst]
return torch.tensor(state_lst, dtype=torch.float).to(self.device),\
actions_lst ,torch.tensor(reward_lst).to(self.device),\
torch.tensor(next_state_lst, dtype=torch.float).to(self.device),\
torch.tensor(done_mask_lst).to(self.device)
def size(self):
return len(self.buffer)
class TensorEnv(gym.Wrapper):
def __init__(self, env_name):
super().__init__(gym.make(env_name))
def process(self, x):
return torch.tensor(x).reshape(1,-1).float()
def reset(self):
return self.process(super().reset())
def step(self, a):
ns, r, done, infos = super().step(a)
return self.process(ns), r, done, infos
class BranchingTensorEnv(TensorEnv):
def __init__(self, env_name, n):
super().__init__(env_name)
self.n = n
self.discretized = np.linspace(-1.,1., self.n)
def step(self, a):
action = np.array([self.discretized[aa] for aa in a])
return super().step(action)
| 2.46875 | 2 |
C-101/test.py | shabd07/C-101 | 0 | 12787637 | a = input("write your name")
print(a) | 2.84375 | 3 |
machine-learning-box/sales-prediction/py_scripts/data.py | akito19/treasure-boxes | 0 | 12787638 | import os
import pandas as pd
os.system(f"{sys.executable} -m pip install -U pytd==0.8.0 td-client")
import pytd
from tdclient.errors import NotFoundError
def database_exists(database, client):
try:
client.api_client.database(database)
return True
except NotFoundError:
pass
return False
def create_database_if_not_exists(database, client):
if database_exists(database, client):
print(f"DB {database} already exists")
return False
else:
client.api_client.create_database(database)
print(f"Created DB: {database}")
return True
def table_exists(database, table, client):
try:
client.api_client.table(database, table)
return True
except NotFoundError:
pass
return False
def upload_dataset(database, table):
apikey = os.environ["TD_API_KEY"]
apiserver = os.environ["TD_API_SERVER"]
client = pytd.Client(apikey=apikey, endpoint=apiserver)
if database_exists(database, client) and table_exists(database, table, client):
print("Target database and table exist. Skip")
return True
target_url = "https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv"
df = pd.read_csv(target_url)
create_database_if_not_exists(database, client)
client.load_table_from_dataframe(df, f"{database}.{table}", if_exists="overwrite")
return True
| 2.59375 | 3 |
magic_8_ball.py | kruschk/automate-the-boring-stuff | 0 | 12787639 | <gh_stars>0
#!/usr/bin/python3
import random
def get_answer(answer_number):
if answer_number == 1:
return "It is certain"
elif answer_number == 2:
return "It is decidedly so"
elif answer_number == 3:
return "Yes"
elif answer_number == 4:
return "Reply lazy try again"
elif answer_number == 5:
return "Ask again later"
elif answer_number == 6:
return "Concentrate and ask again"
elif answer_number == 7:
return "My reply is no"
elif answer_number == 8:
return "Outlook not so good"
elif answer_number == 9:
return "Very doubtful"
#r = random.randint(1, 9)
#fortune = get_answer(r)
#print(fortune)
# following is equivalent to above three lines
print(get_answer(random.randint(1, 9)))
| 3.96875 | 4 |
Python/Django/assert/task_1.py | Mikhail-Kushnerev/YA_Study | 0 | 12787640 | <filename>Python/Django/assert/task_1.py
def series_sum(incoming):
# Конкатенирует все элементы списка, приводя их к строкам."
result = ''
for i in incoming:
result += str(i)
return result
# Первое тестирование: проверьте, корректно ли сработает функция series_sum(),
# если ей на вход передать список из целых и дробных чисел.
mixed_numbers = [1, 2, 3.0, 4.0] # Список из int и float
result_numbers = '123.04.0' # Ожидаемый результат, который должна вернуть series_sum()
print(series_sum(mixed_numbers))
# Вместо многоточия напишите утверждение, которое должно быть проверено
assert series_sum(mixed_numbers) == result_numbers, (
'Функция series_sum() не работает со списком чисел')
# Второе тестирование: проверьте, корректно ли сработает функция series_sum(),
# если ей на вход передать список из чисел и строк.
mixed_numbers_strings = [1, 2, 'string'] # Cписок из чисел и строк
result_numbers_strings = '12string' # Ожидаемый результат, который должна вернуть series_sum()
print(series_sum(mixed_numbers_strings))
# Вместо многоточия напишите утверждение, которое должно быть проверено
assert series_sum(mixed_numbers_strings) == result_numbers_strings, (
'Функция series_sum() не работает со смешанным списком')
# Третье тестирование: проверьте, корректно ли сработает функция series_sum(),
# если ей на вход передать пустой список.
empty = [] # Пустой список
result_empty = '' # Ожидаемый результат, который должна вернуть series_sum()
# Вместо многоточия напишите утверждение, которое должно быть проверено
assert series_sum(empty) == result_empty, (
'Функция series_sum() не работает с пустым списком') | 3.03125 | 3 |
prob/dice.py | ccorbell/gametheory | 0 | 12787641 | <gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 18 23:51:10 2022
@author: <NAME>
"""
import random
class Dice:
D6 = [n for n in range(1, 7)]
D20 = [n for n in range(1, 21)]
def d6():
return random.choice(Dice.D6)
def d20():
return random.choice(Dice.D20)
def nd6(n:int):
rolls = []
for i in range(0, n):
rolls.append(Dice.d6())
return rolls
def nd6sum(n:int):
return sum(Dice.nd6(n))
def nd20(n:int):
rolls = []
for i in range(0, n):
rolls.append(Dice.d20())
return rolls
def nd20sum(n:int):
return sum(Dice.nd20(n))
| 3.703125 | 4 |
project/backend/migrations/0005_alter_item_category.py | ryan-lam/hackathonx2021 | 1 | 12787642 | # Generated by Django 3.2 on 2021-06-26 00:30
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('backend', '0004_alter_item_description'),
]
operations = [
migrations.AlterField(
model_name='item',
name='category',
field=models.CharField(default='Dinosaurs', max_length=200),
),
]
| 1.570313 | 2 |
shot.py | RanlyX/web_screenshot | 0 | 12787643 | <reponame>RanlyX/web_screenshot
# !usr/bin/python
# -*- coding:utf-8 -*-
import re
import sys
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# Text color define
T_LIGHT_RED = "\033[1;31m"
NORMAL = "\033[m"
# Some phish url will be report "hxxp" or "hxxps"
def refinedURL(url):
refined = re.sub("^hxxp", "http", url)
return refined
# Setting browser arguments
def initBrowser():
chrome_options = Options()
chrome_options.add_argument("--headless")
chrome_options.add_argument("--disable-gpu")
chrome_options.add_argument('window-size=1200x600')
return chrome_options
# It will print red text
def printRedStr(str_):
sys.stdout.write(T_LIGHT_RED)
print(str_)
sys.stdout.write(NORMAL)
# Some phish url will be report "hxxp" or "hxxps"
def shot(browser, url, name):
if(browser):
browser.get(url)
try:
print browser.switch_to.alert.text
browser.switch_to.alert.accept()
except:
print("no alert")
browser.get_screenshot_as_file(name+'.png')
else:
printStr("Browser isn't set!")
def main():
# Set browser
chrome_options = initBrowser()
browser = webdriver.Chrome(executable_path=r"./chromedriver", chrome_options=chrome_options)
# If need time out
# browser.set_page_load_timeout(600)
# browser.set_script_timeout(600)
url = sys.argv[1]
name = sys.argv[2]
print(url, name)
try:
shot(browser, url, name)
except:
print("Shot fail!")
browser.quit()
if __name__ == '__main__':
main()
| 2.703125 | 3 |
pypi_librarian/class_user.py | jayvdb/pypi_librarian | 3 | 12787644 | # coding=utf-8
"""
Data and actions for user
"""
from typing import List
import pypi_xmlrpc
from pypi_librarian.class_package import Package
class User(object):
"""
Properties and methods
"""
def __init__(self, name: str) -> None:
"""
Initialize values
:param name:
"""
self.name = name
def get_packages_name(self) -> List[str]:
"""
xmlprc call to get user info, but just names
:return:
"""
packages = pypi_xmlrpc.user_packages(self.name)
return packages
def get_packages(self, name: str, version: str) -> List[Package]:
"""
Load all packages for user, entire package objects
:param name:
:param version:
:return:
"""
raise NotImplementedError()
| 2.8125 | 3 |
lib/tests/python/old/test_bug.py | re-Isearch/re-Isearch | 2 | 12787645 | #!/opt/BSN/bin/python
#-------------------------------------------------
#ident "%Z%%Y%%M% %I% %G% %U% BSN"
import sys
import string
from IB import *
#############################################################################
##
##
#############################################################################
query = sys.argv[1:] and sys.argv[1] or 'chain test OR'
junk="/tmp/DOC";
pdb = VIDB(junk);
print "This is PyIB version %s/%s" % (string.split(sys.version)[0], pdb.GetVersionID());
print "Copyright (c) 1999 Basis Systeme netzwerk/Munich";
if not pdb.IsDbCompatible():
raise ValueError, "The specified database '%s' is not compatible with this version. Re-index!" % `junk`
print "Database ", junk, " has ", pdb.GetTotalRecords(), " elements";
fields=pdb.GetFields();
print fields;
| 2.3125 | 2 |
omtdrspub/elastic/exe_elastic_resourcelist.py | openminted/omtd-rspub-elastic | 0 | 12787646 | <filename>omtdrspub/elastic/exe_elastic_resourcelist.py
import os
from os.path import basename
from urllib.parse import urljoin
import logging
from resync import Resource
from resync import ResourceList
from resync.list_base_with_index import ListBaseWithIndex
from rspub.core.executors import Executor, SitemapData, ExecutorEvent
from rspub.core.rs_enum import Capability
from rspub.util import defaults
from omtdrspub.elastic.elastic_query_manager import ElasticQueryManager
from omtdrspub.elastic.elastic_rs_paras import ElasticRsParameters
from omtdrspub.elastic.model.resource_doc import ResourceDoc
MAX_RESULT_WINDOW = 10000
LOG = logging.getLogger(__name__)
class ElasticResourceListExecutor(Executor):
def __init__(self, rs_parameters: ElasticRsParameters):
super(ElasticResourceListExecutor, self).__init__(rs_parameters)
self.query_manager = ElasticQueryManager(self.para.elastic_host, self.para.elastic_port)
def execute(self, filenames=None):
# filenames is not necessary, we use it only to match the method signature
self.date_start_processing = defaults.w3c_now()
self.observers_inform(self, ExecutorEvent.execution_start, date_start_processing=self.date_start_processing)
if not os.path.exists(self.para.abs_metadata_dir()):
os.makedirs(self.para.abs_metadata_dir())
self.prepare_metadata_dir()
sitemap_data_iter = self.generate_rs_documents()
self.post_process_documents(sitemap_data_iter)
self.date_end_processing = defaults.w3c_now()
self.create_index(sitemap_data_iter)
capabilitylist_data = self.create_capabilitylist()
self.update_resource_sync(capabilitylist_data)
self.observers_inform(self, ExecutorEvent.execution_end, date_end_processing=self.date_end_processing,
new_sitemaps=sitemap_data_iter)
return sitemap_data_iter
def prepare_metadata_dir(self):
if self.para.is_saving_sitemaps:
self.clear_metadata_dir()
def generate_rs_documents(self, filenames: iter = None) -> [SitemapData]:
self.query_manager.refresh_index(self.para.elastic_index)
# filenames is not necessary, we use it only to match the method signature
sitemap_data_iter = []
generator = self.resourcelist_generator()
for sitemap_data, sitemap in generator():
sitemap_data_iter.append(sitemap_data)
return sitemap_data_iter
def create_index(self, sitemap_data_iter: iter):
if len(sitemap_data_iter) > 1:
resourcelist_index = ResourceList()
resourcelist_index.sitemapindex = True
resourcelist_index.md_at = self.date_start_processing
resourcelist_index.md_completed = self.date_end_processing
index_path = self.para.abs_metadata_path("resourcelist-index.xml")
rel_index_path = os.path.relpath(index_path, self.para.resource_dir)
index_url = urljoin(self.para.url_prefix, defaults.sanitize_url_path(rel_index_path))
resourcelist_index.link_set(rel="up", href=self.para.capabilitylist_url())
for sitemap_data in sitemap_data_iter:
resourcelist_index.add(Resource(uri=sitemap_data.uri, md_at=sitemap_data.doc_start,
md_completed=sitemap_data.doc_end))
if sitemap_data.document_saved:
LOG.info("Updating document: " + basename(sitemap_data.path))
self.update_rel_index(index_url, sitemap_data.path)
self.finish_sitemap(-1, resourcelist_index)
def save_sitemap(self, sitemap, path):
sitemap.pretty_xml = self.para.is_saving_pretty_xml
# writing the string sitemap.as_xml() to disk results in encoding=ASCII on some systems.
# due to https://docs.python.org/3.4/library/xml.etree.elementtree.html#write
#sitemap.write(path)
if sitemap.sitemapindex:
self.write_index(sitemap, path)
else:
sitemap.write(path)
@staticmethod
def write_index(sitemap: ListBaseWithIndex, path):
"""Return XML serialization of this list taken to be sitemapindex entries
"""
sitemap.default_capability()
s = sitemap.new_sitemap()
return s.resources_as_xml(sitemap, sitemapindex=True, fh=path)
def resourcelist_generator(self) -> iter:
def generator() -> [SitemapData, ResourceList]:
resourcelist = None
ordinal = self.find_ordinal(Capability.resourcelist.name)
resource_count = 0
doc_start = None
resource_generator = self.resource_generator()
for resource_count, resource in resource_generator():
# stuff resource into resourcelist
if resourcelist is None:
resourcelist = ResourceList()
doc_start = defaults.w3c_now()
resourcelist.md_at = doc_start
resourcelist.add(resource)
# under conditions: yield the current resourcelist
if resource_count % self.para.max_items_in_list == 0:
ordinal += 1
doc_end = defaults.w3c_now()
resourcelist.md_completed = doc_end
LOG.info("Generating resourcelist #:" + str(ordinal) + "...")
sitemap_data = self.finish_sitemap(ordinal, resourcelist, doc_start=doc_start, doc_end=doc_end)
LOG.info("Resource list # " + str(ordinal) + " successfully generated")
yield sitemap_data, resourcelist
resourcelist = None
# under conditions: yield the current and last resourcelist
if resourcelist:
ordinal += 1
doc_end = defaults.w3c_now()
resourcelist.md_completed = doc_end
# if ordinal == 0:
# if we have a single doc, set ordinal to -1 so that the finish_sitemap will not append the
# ordinal to the filename
# ordinal = -1
# print("Generating resourcelist")
# else:
LOG.info("Generating resourcelist #:" + str(ordinal) + "...")
sitemap_data = self.finish_sitemap(ordinal, resourcelist, doc_start=doc_start, doc_end=doc_end)
LOG.info("Resource list # " + str(ordinal) + " successfully generated")
yield sitemap_data, resourcelist
return generator
def resource_generator(self) -> iter:
def generator(count=0) -> [int, Resource]:
elastic_page_generator = self.elastic_page_generator()
erased_changes = False
for e_page in elastic_page_generator():
if not erased_changes:
# this will happen at the first scroll
self.erase_changes()
LOG.info("Changes erased")
erased_changes = True
for e_hit in e_page:
e_source = e_hit['_source']
e_doc = ResourceDoc.as_resource_doc(e_source)
count += 1
uri = e_doc.location.uri_from_path(para_url_prefix=self.para.url_prefix,
para_res_root_dir=self.para.res_root_dir)
ln = []
if e_doc.ln:
for link in e_doc.ln:
link_uri = link.href.uri_from_path(para_url_prefix=self.para.url_prefix,
para_res_root_dir=self.para.res_root_dir)
ln.append({'href': link_uri, 'rel': link.rel, 'mime': link.mime})
resource = Resource(uri=uri, length=e_doc.length,
lastmod=e_doc.lastmod,
md5=e_doc.md5,
mime_type=e_doc.mime,
ln=ln)
yield count, resource
self.observers_inform(self, ExecutorEvent.created_resource, resource=resource,
count=count)
return generator
def elastic_page_generator(self) -> iter:
def generator() -> iter:
query = {
"query": {
"bool": {
"must": [
{
"term": {"resource_set": self.para.resource_set}
}
]
}
}
}
return self.query_manager.scan_and_scroll(index=self.para.elastic_index,
doc_type=self.para.elastic_resource_doc_type,
query=query,
max_items_in_list=self.para.max_items_in_list,
max_result_window=MAX_RESULT_WINDOW)
return generator
def erase_changes(self):
self.query_manager.delete_all_index_set_type_docs(index=self.para.elastic_index,
doc_type=self.para.elastic_change_doc_type,
resource_set=self.para.resource_set)
| 1.859375 | 2 |
jobs/types/job_run_result.py | soham4abc/oppia | 0 | 12787647 | <gh_stars>0
# coding: utf-8
#
# Copyright 2021 The Oppia Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Error classes for model audits."""
from __future__ import absolute_import # pylint: disable=import-only-modules
from __future__ import unicode_literals # pylint: disable=import-only-modules
import python_utils
class JobRunResult(python_utils.OBJECT):
"""Encapsulates the result of a job run.
The stdout and stderr are string values analogous to a program's stdout and
stderr pipes (reserved for standard output and errors, respectively).
"""
__slots__ = ('_stdout', '_stderr')
def __init__(self, stdout='', stderr=''):
"""Initializes a new JobRunResult instance.
Args:
stdout: str. The standard output from a job run.
stderr: str. The error output from a job run.
"""
self._stdout, self._stderr = stdout, stderr
@property
def stdout(self):
"""Returns the standard output of the job result.
Returns:
str. The standard output of the job result.
"""
return self._stdout
@property
def stderr(self):
"""Returns the error output of the job result.
Returns:
str. The error output of the job result.
"""
return self._stderr
def __getstate__(self):
"""Called by pickle to get the value that uniquely defines self."""
return (self._stdout, self._stderr)
def __setstate__(self, state):
"""Called by pickle to build an instance from __getstate__'s value."""
self._stdout, self._stderr = state
def __eq__(self, other):
return (
(self._stdout, self._stderr) == (other._stdout, other._stderr) # pylint: disable=protected-access
if self.__class__ is other.__class__ else NotImplemented)
def __ne__(self, other):
return (
not (self == other)
if self.__class__ is other.__class__ else NotImplemented)
def __hash__(self):
return hash((self._stdout, self._stderr))
def __repr__(self):
return 'JobRunResult(stdout=%r, stderr=%r)' % (
self._stdout, self._stderr)
| 2.265625 | 2 |
Script update background test/Script_normal_update_win_zip.py | dacicas/Update_script | 0 | 12787648 | <filename>Script update background test/Script_normal_update_win_zip.py<gh_stars>0
#!/usr/bin/env python3
import os, fnmatch
import subprocess
import time
import gspread
from oauth2client.service_account import ServiceAccountCredentials
from pprint import pprint
import time
import random
import wget
import shutil
import pyautogui
from stat import S_IREAD, S_IRGRP, S_IROTH
import pyperclip
import threading
scope = ["https://spreadsheets.google.com/feeds",'https://www.googleapis.com/auth/spreadsheets',"https://www.googleapis.com/auth/drive.file","https://www.googleapis.com/auth/drive"]
credentials = ServiceAccountCredentials.from_json_keyfile_name("TestUpdateBeta-dbbd302b4f47.json", scope)
client = gspread.authorize(credentials)
sheet = client.open("IncercareBetaUpdates").sheet1
data = sheet.get_all_records()
def updateareNumeProfil(numar):
file = open("resources/profilename.txt","w")
value = "Primut"+str(numar)
file.write(value)
file.close()
return value
def schimbatNume(propertyName):
number = int(propertyName[6:len(propertyName)])+1
return updateareNumeProfil(number)
file = open("resources/profilename.txt")
my_env = file.readline()
print(my_env)
file.close()
for statusFisier in os.listdir("C:\\Users\\dani\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles\\"):
x = "Primut" in statusFisier
if (x):
my_env = schimbatNume(my_env)
print("Nume profil: " + my_env)
break
def create_user_js(new_path_to_my_env):
os.chdir(new_path_to_my_env)
user_js = open("User.js", "w")
user_js.write('user_pref("app.update.enabled", false);\n'
'user_pref("app.update.auto", false);\n'
'user_pref("app.update.log", true);\n'
'user_pref("app.update.log.file", true);\n'
'user_pref("app.update.BITS.enabled", true);\n'
'user_pref("app.update.doorhanger", false);\n'
'user_pref("browser.shell.checkDefaultBrowser", false);\n'
'user_pref("browser.tabs.warnOnClose", false);\n'
'user_pref("browser.aboutwelcome.enabled", false);\n'
'user_pref("browser.tabs.warnOnCloseOtherTabs", false);\n')
user_js.close()
os.chmod("User.js", S_IREAD)
def get_folder_name(my_string_env):
list_of_profiles = os.listdir("C:\\Users\\dani\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles")
pattern = my_string_env
for x in list_of_profiles:
if fnmatch.fnmatch(x, pattern):
print(x)
return str(x)
def copy_AUS_SVC_Clipboard():
time.sleep(60)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.press("h")
time.sleep(1)
pyautogui.press("a")
time.sleep(1)
pyautogui.press("enter")
time.sleep(1)
pyautogui.click(x=1106, y=489)
time.sleep(1)
pyautogui.hotkey('ctrl', 'shift', 'j')
time.sleep(5)
pyautogui.click(button='right')
time.sleep(5)
pyautogui.click(x=1154, y=588)
time.sleep(5)
pyautogui.click(x=1358, y=587)
time.sleep(30)
pyautogui.click()
time.sleep(5)
paste_in_new_File()
time.sleep(5)
BITS_check()
pyautogui.click(x=291, y=460 )
pyautogui.hotkey('ctrl', 'shift', 'q')
print("Done running copy_AUS_SVC_Clipboard")
def copy_AUS_SVC_Clipboard_AR():
time.sleep(60)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=1035, y=17)#Help
time.sleep(1)
pyautogui.click(x=1008, y=228)#About Fx
time.sleep(1)
pyautogui.press(x=618, y=407)#Update Fx
time.sleep(1)
pyautogui.click(x=1106, y=489)
time.sleep(1)
pyautogui.hotkey('ctrl', 'shift', 'j')
time.sleep(5)
pyautogui.click(button='right')
time.sleep(5)
pyautogui.click(x=1232, y=679)#Copy to clipboard
time.sleep(5)
pyautogui.click(x=1172, y=673)#Clipboard
time.sleep(30)
pyautogui.click()
time.sleep(5)
paste_in_new_File()
time.sleep(5)
BITS_check()
pyautogui.click(x=291, y=460)
pyautogui.hotkey('ctrl', 'shift', 'q')
print("Done running copy_AUS_SVC_Clipboard_AR")
def copy_AUS_SVC_Clipboard_RU():
time.sleep(60)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=368, y=14)#Help
time.sleep(1)
pyautogui.click(x=461, y=173)#About Fx
time.sleep(1)
pyautogui.press("enter")
time.sleep(1)
pyautogui.click(x=1106, y=489)
time.sleep(1)
pyautogui.hotkey('ctrl', 'shift', 'j')
time.sleep(5)
pyautogui.click(button='right')
time.sleep(5)
pyautogui.click(x=1227, y=595)#Copy to clipboard
time.sleep(5)
pyautogui.click(x=1443, y=598)#Clipboard
time.sleep(30)
time.sleep(5)
paste_in_new_File()
time.sleep(5)
BITS_check()
time.sleep(2)
pyautogui.click(x=174, y=460)
pyautogui.hotkey('ctrl', 'shift', 'q')
print("Done running copy_AUS_SVC_Clipboard_RU")
def copy_AUS_SVC_Clipboard_zh_CH():
time.sleep(60)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=334, y=12)#Help
time.sleep(1)
pyautogui.click(x=382, y=230)#About Fx
time.sleep(1)
pyautogui.press("enter")
time.sleep(1)
pyautogui.click(x=1106, y=489)
time.sleep(1)
pyautogui.hotkey('ctrl', 'shift', 'j')
time.sleep(5)
pyautogui.click(button='right')
time.sleep(5)
pyautogui.click(x=1200, y=631)#Copy to clipboard
time.sleep(5)
pyautogui.click(x=1291, y=631)#Clipboard
time.sleep(30)
pyautogui.click()
time.sleep(5)
paste_in_new_File()
time.sleep(5)
BITS_check()
pyautogui.click(x=291, y=460)
pyautogui.hotkey('ctrl', 'shift', 'q')
print("Done running copy_AUS_SVC_Clipboard_zh-CH")
def copy_AUS_SVC_Clipboard_es_ES():
time.sleep(60)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=363, y=14)#Help
time.sleep(1)
pyautogui.click(x=473, y=224)#About Fx
time.sleep(1)
pyautogui.press("enter")
time.sleep(1)
pyautogui.click(x=1106, y=489)
time.sleep(1)
pyautogui.hotkey('ctrl', 'shift', 'j')
time.sleep(5)
pyautogui.click(button='right')
time.sleep(5)
pyautogui.click(x=1209, y=631)#Copy to clipboard
time.sleep(5)
pyautogui.click(x=1364, y=629)#Clipboard
time.sleep(30)
pyautogui.click()
time.sleep(5)
paste_in_new_File()
time.sleep(5)
BITS_check()
pyautogui.click(x=291, y=460)
pyautogui.hotkey('ctrl', 'shift', 'q')
print("Done running copy_AUS_SVC_Clipboard_es-ES")
def copy_AUS_SVC_Clipboard_JA():
time.sleep(60)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=422, y=14)#Help
time.sleep(1)
pyautogui.click(x=459, y=231)#About Fx
time.sleep(1)
pyautogui.press("enter")
time.sleep(1)
pyautogui.click(x=1106, y=489)
time.sleep(1)
pyautogui.hotkey('ctrl', 'shift', 'j')
time.sleep(5)
pyautogui.click(button='right')
time.sleep(5)
pyautogui.click(x=1215, y=630)#Copy to clipboard
time.sleep(5)
pyautogui.click(x=1408, y=628)#Clipboard
time.sleep(30)
pyautogui.click()
time.sleep(5)
paste_in_new_File()
time.sleep(5)
BITS_check()
pyautogui.click(x=291, y=460)
pyautogui.hotkey('ctrl', 'shift', 'q')
print("Done running copy_AUS_SVC_Clipboard_JA")
def copy_AUS_SVC_Clipboard_DE():
time.sleep(60)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=355, y=13)#Help
time.sleep(1)
pyautogui.click(x=447, y=221)#About Fx
time.sleep(1)
pyautogui.press("enter")
time.sleep(1)
pyautogui.click(x=1106, y=489)
time.sleep(1)
pyautogui.hotkey('ctrl', 'shift', 'j')
time.sleep(5)
pyautogui.click(button='right')
time.sleep(5)
pyautogui.click(x=1228, y=633)#Copy to clipboard
time.sleep(5)
pyautogui.click(x=1431, y=624)#Clipboard
time.sleep(30)
pyautogui.click()
time.sleep(5)
paste_in_new_File()
time.sleep(5)
BITS_check()
pyautogui.click(x=291, y=460)
pyautogui.hotkey('ctrl', 'shift', 'q')
print("Done running copy_AUS_SVC_Clipboard_DE")
def copy_AUS_SVC_Clipboard_FR():
time.sleep(60)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=384, y=11)#Help
time.sleep(1)
pyautogui.click(x=465, y=227)#About Fx
time.sleep(1)
pyautogui.press("enter")
time.sleep(1)
pyautogui.click(x=1106, y=489)
time.sleep(1)
pyautogui.hotkey('ctrl', 'shift', 'j')
time.sleep(5)
pyautogui.click(button='right')
time.sleep(5)
pyautogui.click(x=1215, y=631)#Copy to clipboard
time.sleep(5)
pyautogui.click(x=1410, y=630)#Clipboard
time.sleep(30)
pyautogui.click()
time.sleep(5)
paste_in_new_File()
time.sleep(5)
BITS_check()
pyautogui.click(x=291, y=460)
pyautogui.hotkey('ctrl', 'shift', 'q')
print("Done running copy_AUS_SVC_Clipboard_FR")
def copy_AUS_SVC_Clipboard_KO():
time.sleep(60)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=353, y=12)#Help
time.sleep(1)
pyautogui.click(x=405, y=225)#About Fx
time.sleep(1)
pyautogui.press("enter")
time.sleep(1)
pyautogui.click(x=1106, y=489)
time.sleep(1)
pyautogui.hotkey('ctrl', 'shift', 'j')
time.sleep(5)
pyautogui.click(button='right')
time.sleep(5)
pyautogui.click(x=1196, y=634)#Copy to clipboard
time.sleep(5)
pyautogui.click(x=1348, y=624)#Clipboard
time.sleep(30)
pyautogui.click()
time.sleep(5)
paste_in_new_File()
time.sleep(5)
BITS_check()
pyautogui.click(x=291, y=460)
pyautogui.hotkey('ctrl', 'shift', 'q')
print("Done running copy_AUS_SVC_Clipboard_KO")
def copy_AUS_SVC_Clipboard_PT():
time.sleep(60)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=361, y=12)#Help
time.sleep(1)
pyautogui.click(x=436, y=228)#About Fx
time.sleep(1)
pyautogui.press("enter")
time.sleep(1)
pyautogui.click(x=1106, y=489)
time.sleep(1)
pyautogui.hotkey('ctrl', 'shift', 'j')
time.sleep(5)
pyautogui.click(button='right')
time.sleep(5)
pyautogui.click(x=1220, y=631)#Copy to clipboard
time.sleep(5)
pyautogui.click(x=1419, y=630)#Clipboard
time.sleep(30)
pyautogui.click()
time.sleep(5)
paste_in_new_File()
time.sleep(5)
BITS_check()
pyautogui.click(x=291, y=460)
pyautogui.hotkey('ctrl', 'shift', 'q')
print("Done running copy_AUS_SVC_Clipboard_PT")
def copy_AUS_SVC_Clipboard_PL():
time.sleep(60)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=329, y=14)#Help
time.sleep(1)
pyautogui.click(x=420, y=231)#About Fx
time.sleep(1)
pyautogui.press("enter")
time.sleep(1)
pyautogui.click(x=1106, y=489)
time.sleep(1)
pyautogui.hotkey('ctrl', 'shift', 'j')
time.sleep(5)
pyautogui.click(button='right')
time.sleep(5)
pyautogui.click(x=1220, y=629)#Copy to clipboard
time.sleep(5)
pyautogui.click(x=1396, y=630)#Clipboard
time.sleep(30)
pyautogui.click()
time.sleep(5)
paste_in_new_File()
time.sleep(5)
BITS_check()
pyautogui.click(x=291, y=460)
pyautogui.hotkey('ctrl', 'shift', 'q')
print("Done running copy_AUS_SVC_Clipboard_PL")
def copy_AUS_SVC_Clipboard_VI():
time.sleep(60)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=466, y=12)#Help
time.sleep(1)
pyautogui.click(x=511, y=226)#About Fx
time.sleep(1)
pyautogui.press("enter")
time.sleep(1)
pyautogui.click(x=1106, y=489)
time.sleep(1)
pyautogui.hotkey('ctrl', 'shift', 'j')
time.sleep(5)
pyautogui.click(button='right')
time.sleep(5)
pyautogui.click(x=1212, y=630)#Copy to clipboard
time.sleep(5)
pyautogui.click(x=1400, y=630)#Clipboard
time.sleep(30)
pyautogui.click()
time.sleep(5)
paste_in_new_File()
time.sleep(5)
BITS_check()
pyautogui.click(x=291, y=460)
pyautogui.hotkey('ctrl', 'shift', 'q')
print("Done running copy_AUS_SVC_Clipboard_VI")
def copy_AUS_SVC_Clipboard_TR():
time.sleep(60)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=347, y=17)#Help
time.sleep(1)
pyautogui.click(x=399, y=226)#About Fx
time.sleep(1)
pyautogui.press("enter")
time.sleep(1)
pyautogui.click(x=1106, y=489)
time.sleep(1)
pyautogui.hotkey('ctrl', 'shift', 'j')
time.sleep(5)
pyautogui.click(button='right')
time.sleep(5)
pyautogui.click(x=1198, y=630)#Copy to clipboard
time.sleep(5)
pyautogui.click(x=1353, y=627)#Clipboard
time.sleep(30)
pyautogui.click()
time.sleep(5)
paste_in_new_File()
time.sleep(5)
BITS_check()
pyautogui.click(x=291, y=460)
pyautogui.hotkey('ctrl', 'shift', 'q')
print("Done running copy_AUS_SVC_Clipboard_TR")
def paste_in_new_File():
os.chdir(new_path)
BITS_testing = open('BITS_testing.txt', "w", encoding='utf-8')
BITS_testing.write(pyperclip.paste())
BITS_testing.close()
os.chmod("BITS_Testing.txt", S_IREAD)
def BITS_check():
os.chdir(new_path)
with open("BITS_testing.txt", encoding="UTF-8") as bits:
if "Starting BITS download with url" in bits.read():
print("BITS WAS USED")
else:
print("BITS was not used/ERROR")
def Screenshot():
print("Running screenshot...")
time.sleep(20)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.press("h")
time.sleep(1)
pyautogui.press("a")
time.sleep(4)
Screenshot = pyautogui.screenshot()
Screenshot.save(r'{0}\\dovada_upd.png'.format(last_path))
print("Done running screenshot.")
def Screenshot_AR():
print("Running screenshot...")
time.sleep(20)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=1035, y=17)
time.sleep(1)
pyautogui.click(x=1008, y=228)
time.sleep(4)
Screenshot = pyautogui.screenshot()
Screenshot.save(r'{0}\\dovada_upd.png'.format(last_path))
print("Done running screenshot.")
def Screenshot_RU():
print("Running screenshot...")
time.sleep(20)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=368, y=14)
time.sleep(1)
pyautogui.click(x=461, y=234)
time.sleep(4)
Screenshot = pyautogui.screenshot()
Screenshot.save(r'{0}\\dovada_upd.png'.format(last_path))
print("Done running screenshot.")
def Screenshot_zh_CH():
print("Running screenshot...")
time.sleep(20)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=334, y=12)
time.sleep(1)
pyautogui.click(x=382, y=230)
time.sleep(4)
Screenshot = pyautogui.screenshot()
Screenshot.save(r'{0}\\dovada_upd.png'.format(last_path))
print("Done running screenshot.")
def Screenshot_es_ES():
print("Running screenshot...")
time.sleep(20)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=363, y=14)
time.sleep(1)
pyautogui.click(x=473, y=224)
time.sleep(4)
Screenshot = pyautogui.screenshot()
Screenshot.save(r'{0}\\dovada_upd.png'.format(last_path))
print("Done running screenshot.")
def Screenshot_ja():
print("Running screenshot...")
time.sleep(20)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=422, y=14)
time.sleep(1)
pyautogui.click(x=459, y=231)
time.sleep(4)
Screenshot = pyautogui.screenshot()
Screenshot.save(r'{0}\\dovada_upd.png'.format(last_path))
print("Done running screenshot.")
def Screenshot_DE():
print("Running screenshot...")
time.sleep(20)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=355, y=13)
time.sleep(1)
pyautogui.click(x=447, y=221)
time.sleep(4)
Screenshot = pyautogui.screenshot()
Screenshot.save(r'{0}\\dovada_upd.png'.format(last_path))
print("Done running screenshot.")
def Screenshot_FR():
print("Running screenshot...")
time.sleep(20)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=384, y=11)
time.sleep(1)
pyautogui.click(x=465, y=227)
time.sleep(4)
Screenshot = pyautogui.screenshot()
Screenshot.save(r'{0}\\dovada_upd.png'.format(last_path))
print("Done running screenshot.")
def Screenshot_KO():
print("Running screenshot...")
time.sleep(20)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=353, y=12)
time.sleep(1)
pyautogui.click(x=405, y=225)
time.sleep(4)
Screenshot = pyautogui.screenshot()
Screenshot.save(r'{0}\\dovada_upd.png'.format(last_path))
print("Done running screenshot.")
def Screenshot_PT():
print("Running screenshot...")
time.sleep(20)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=361, y=12)
time.sleep(1)
pyautogui.click(x=436, y=228)
time.sleep(4)
Screenshot = pyautogui.screenshot()
Screenshot.save(r'{0}\\dovada_upd.png'.format(last_path))
print("Done running screenshot.")
def Screenshot_PL():
print("Running screenshot...")
time.sleep(20)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=329, y=14)
time.sleep(1)
pyautogui.click(x=420, y=231)
time.sleep(4)
Screenshot = pyautogui.screenshot()
Screenshot.save(r'{0}\\dovada_upd.png'.format(last_path))
print("Done running screenshot.")
def Screenshot_VI():
print("Running screenshot...")
time.sleep(20)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=466, y=12)
time.sleep(1)
pyautogui.click(x=511, y=226)
time.sleep(4)
Screenshot = pyautogui.screenshot()
Screenshot.save(r'{0}\\dovada_upd.png'.format(last_path))
print("Done running screenshot.")
def Screenshot_TR():
print("Running screenshot...")
time.sleep(20)
pyautogui.click(x=1286, y=59)
pyautogui.press("altleft")
time.sleep(1)
pyautogui.click(x=347, y=17)
time.sleep(1)
pyautogui.click(x=399, y=226)
time.sleep(4)
Screenshot = pyautogui.screenshot()
Screenshot.save(r'{0}\\dovada_upd.png'.format(last_path))
print("Done running screenshot.")
def Choose_screenshot():
if locale == "en-US":
Screenshot()
elif locale == "zh-CN":
Screenshot_zh_CH()
elif locale == "es-ES":
Screenshot_es_ES()
elif locale == "ja":
Screenshot_ja()
elif locale == "de":
Screenshot_DE()
elif locale == "fr":
Screenshot_FR()
elif locale == "ru":
Screenshot_RU()
elif locale == "ar":
Screenshot_AR()
elif locale == "ko":
Screenshot_KO()
elif locale == "pt-PT":
Screenshot_PT()
elif locale == "vi":
Screenshot_VI()
elif locale == "tr":
Screenshot_TR()
elif locale == "pl":
Screenshot_PL()
def Choose_lang():
if locale == "en-US":
copy_AUS_SVC_Clipboard()
elif locale == "zh-CN":
copy_AUS_SVC_Clipboard_zh_CH()
elif locale == "es-ES":
copy_AUS_SVC_Clipboard_es_ES()
elif locale == "ja":
copy_AUS_SVC_Clipboard_JA()
elif locale == "de":
copy_AUS_SVC_Clipboard_DE()
elif locale == "fr":
copy_AUS_SVC_Clipboard_FR()
elif locale == "ru":
copy_AUS_SVC_Clipboard_RU()
elif locale == "ar":
copy_AUS_SVC_Clipboard_AR()
elif locale == "ko":
copy_AUS_SVC_Clipboard_KO()
elif locale == "pt-PT":
copy_AUS_SVC_Clipboard_PT()
elif locale == "vi":
copy_AUS_SVC_Clipboard_VI()
elif locale == "tr":
copy_AUS_SVC_Clipboard_TR()
elif locale == "pl":
copy_AUS_SVC_Clipboard_PL()
print(os.getcwd())
first_test_starting_version = sheet.cell(34, 5).value
first_test_starting_version_string = str(first_test_starting_version)
locale = sheet.cell(34, 4).value
parent_dir = "D:\Test_Firefox"
path = os.path.join(parent_dir, first_test_starting_version_string)
os.mkdir(path)
os.chdir(path)
print(os.getcwd())
URL = "https://archive.mozilla.org/pub/firefox/candidates/"+first_test_starting_version_string+"-candidates/build1/win64/{0}/".format(locale)
print(URL)
zip = ".zip"
firefox_exe_msi_prefix = "firefox-"
filename = firefox_exe_msi_prefix+first_test_starting_version_string+zip
print(filename)
var_wgt = URL + filename
print(var_wgt)
print(wget.download(var_wgt, filename))
subprocess.call(["tar", "-xf", "D:/Test_Firefox/87.0b4/{0}".format(filename)])
firefox = "firefox"
new_path = os.path.join(parent_dir, first_test_starting_version_string, firefox)
print(new_path)
os.chdir(new_path)
print(os.getcwd())
print(subprocess.run(['firefox', "-version", "|", "more"], stdout=subprocess.PIPE).stdout.decode('utf-8'))
profile_path = str("C:\\Users\\dani\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles")
subprocess.call(["firefox", "-CreateProfile", my_env])
time.sleep(10)
os.chdir(profile_path)
my_string_env = "*."+my_env
full_profile_folder = get_folder_name(my_string_env)
print(type(full_profile_folder))
print(type(profile_path))
full_profile_path = os.path.join(profile_path, full_profile_folder)
os.chdir(full_profile_path)
create_user_js(full_profile_path)
print(os.getcwd())
##change working directorie to where firefox is!!! fixed ?
last_path = path + "\\firefox"
os.chdir(last_path)
print(os.getcwd())
thread1 = threading.Thread(target=Choose_lang)
thread2 = threading.Thread(target=Choose_screenshot)
thread1.start()
print(subprocess.run(['firefox', "-version", "|", "more"], stdout=subprocess.PIPE).stdout.decode('utf-8'))
subprocess.call(["firefox", "-P", "{0}".format(my_env), "-jsconsole"])
print(subprocess.run(['firefox', "-version", "|", "more"], stdout=subprocess.PIPE).stdout.decode('utf-8'))
#asta e ca sa vad ca s-a facut update-ul ca sa nu trebuiasca sa aleg profilul
#FYI daca dai restart atunci ma pune sa aleg profilul
time.sleep(10)
thread2.start()
print(subprocess.run(['firefox', "-version", "|", "more"], stdout=subprocess.PIPE).stdout.decode('utf-8'))
subprocess.call(["firefox", "-P", "{0}".format(my_env), "-jsconsole"])
print(subprocess.run(['firefox', "-version", "|", "more"], stdout=subprocess.PIPE).stdout.decode('utf-8')) | 2.28125 | 2 |
experiments/archived/20210119/bag_model/__init__.py | fxnnxc/text_summarization | 5 | 12787649 | from . import text_summarization_annealing # noqa
from .models import model, hub_interface # noqa
from .criterions import kld_loss
from .modules import pgn
#from .modules import # noqa | 0.949219 | 1 |
preprocess.py | akoksal/Turkish-Word2Vec | 175 | 12787650 | <filename>preprocess.py
from __future__ import print_function
import os.path
import sys
from gensim.corpora import WikiCorpus
import xml.etree.ElementTree as etree
import warnings
import logging
import string
from gensim import utils
def tokenize_tr(content,token_min_len=2,token_max_len=50,lower=True):
if lower:
lowerMap = {ord(u'A'): u'a',ord(u'A'): u'a',ord(u'B'): u'b',ord(u'C'): u'c',ord(u'Ç'): u'ç',ord(u'D'): u'd',ord(u'E'): u'e',ord(u'F'): u'f',ord(u'G'): u'g',ord(u'Ğ'): u'ğ',ord(u'H'): u'h',ord(u'I'): u'ı',ord(u'İ'): u'i',ord(u'J'): u'j',ord(u'K'): u'k',ord(u'L'): u'l',ord(u'M'): u'm',ord(u'N'): u'n',ord(u'O'): u'o',ord(u'Ö'): u'ö',ord(u'P'): u'p',ord(u'R'): u'r',ord(u'S'): u's',ord(u'Ş'): u'ş',ord(u'T'): u't',ord(u'U'): u'u',ord(u'Ü'): u'ü',ord(u'V'): u'v',ord(u'Y'): u'y',ord(u'Z'): u'z'}
content = content.translate(lowerMap)
return [
utils.to_unicode(token) for token in utils.tokenize(content, lower=False, errors='ignore')
if token_min_len <= len(token) <= token_max_len and not token.startswith('_')
]
if __name__ == '__main__':
if len(sys.argv) < 3:
print("Please provide two arguments, first one is path to the wikipedia dump, second one is path to the output file")
print("Example command: python3 preprocess.py trwiki-20180101-pages-articles.xml.bz2 wiki.tr.txt")
sys.exit()
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
inputFile = sys.argv[1]
outputFile = sys.argv[2]
wiki = WikiCorpus(inputFile, lemmatize=False,tokenizer_func = tokenize_tr)
logging.info("Wikipedia dump is opened.")
output = open(outputFile,"w",encoding="utf-8")
logging.info("Output file is created.")
i = 0
for text in wiki.get_texts():
output.write(" ".join(text)+"\n")
i+=1
if (i % 10000 == 0):
logging.info("Saved " +str(i) + " articles.")
output.close()
| 2.484375 | 2 |
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