fumiyau/python_comments_free_500000 · Datasets at Hugging Face

f73629463525a2647b03c609ca5952eb27adc3d4

7,419

py

Python

Scripts/Plotting/Posteriors_cosmo_model1/Posteriors_cosmo_model1_alternative_dust.py

LBJ-Wade/GALLUMI_public

dbef3ff1ae6934c9551a44cbbe0270e2f17f5527

[ "MIT" ]

1

2021-12-15T00:17:15.000Z

Scripts/Plotting/Posteriors_cosmo_model1/Posteriors_cosmo_model1_alternative_dust.py

NNSSA/GALLUMI_public

4529ab32ccfc281e5976f482fe556b672b8f464f

[ "MIT" ]

null

Scripts/Plotting/Posteriors_cosmo_model1/Posteriors_cosmo_model1_alternative_dust.py

NNSSA/GALLUMI_public

4529ab32ccfc281e5976f482fe556b672b8f464f

[ "MIT" ]

null

import numpy as np from matplotlib import pyplot as plt import glob from matplotlib import patches as mpatches import scipy.ndimage from scipy.interpolate import PchipInterpolator plt.style.use("../template.mplstyle") # purple - green - darkgoldenrod - blue - red colors = ['purple', '#306B37', 'darkgoldenrod', '#3F7BB6', '#BF4145'] linestyles = [(0, (1,1.05)), (0, (3, 1, 1, 1)), (0, (1,3)), (0, (3,3.65)), (0, (3,2.772)), (0, (3, 1, 1, 1, 1, 1))] ######################################################################################### def ctr_level2d(histogram2d, lvl, infinite=False): hist = histogram2d.flatten()*1. hist.sort() cum_hist = np.cumsum(hist[::-1]) cum_hist /= cum_hist[-1] alvl = np.searchsorted(cum_hist, lvl)[::-1] clist = [0]+[hist[-i] for i in alvl]+[hist.max()] if not infinite: return clist[1:] return clist def get_hist2d(datax, datay, num_bins=40, weights=[None]): if not any(weights): weights = np.ones(len(datax)) hist, bin_edgesx, bin_edgesy = np.histogram2d(datax, datay, bins=num_bins, weights=weights) bin_centresx = 0.5*(bin_edgesx[1:]+bin_edgesx[:-1]) bin_centresy = 0.5*(bin_edgesy[1:]+bin_edgesy[:-1]) return hist, bin_edgesx, bin_edgesy, bin_centresx, bin_centresy def adjust_lightness(color, amount=0.5): import matplotlib.colors as mc import colorsys try: c = mc.cnames[color] except: c = color c = colorsys.rgb_to_hls(*mc.to_rgb(c)) return colorsys.hls_to_rgb(c[0], max(0, min(1, amount * c[1])), c[2]) def plot_hist2d(datax, datay, ax, num_bins=30, weights=[None], color=None, zorder=0): if not any(weights): weights = np.ones(len(datax)) if color == None: color="black" hist, bin_edgesx, bin_edgesy, bin_centresx, bin_centresy = get_hist2d(datax, datay, num_bins=num_bins, weights=weights) interpolation_smoothing = 3. gaussian_smoothing = 0.5 sigma = interpolation_smoothing * gaussian_smoothing interp_y_centers = scipy.ndimage.zoom(bin_centresy, interpolation_smoothing, mode='reflect') interp_x_centers = scipy.ndimage.zoom(bin_centresx,interpolation_smoothing, mode='reflect') interp_hist = scipy.ndimage.zoom(hist, interpolation_smoothing, mode='reflect') interp_smoothed_hist = scipy.ndimage.filters.gaussian_filter(interp_hist, [sigma,sigma], mode='reflect') ax.contourf(interp_x_centers, interp_y_centers, np.transpose(interp_smoothed_hist), colors=[adjust_lightness(color,1.4), adjust_lightness(color,0.8)], levels=ctr_level2d(interp_smoothed_hist.copy(), [0.68, 0.95]), zorder=zorder, alpha=0.45) ax.contour(interp_x_centers, interp_y_centers, np.transpose(interp_smoothed_hist), colors=[color, adjust_lightness(color,0.8)], linewidths=2., levels=ctr_level2d(interp_smoothed_hist.copy(), [0.68, 0.95]), zorder=zorder) ################################################################################################## UVLF_Overzier = [] UVLF_Bouwens = [] UVLF_Casey = [] for filepath in glob.iglob('../../Data/UVLF_HST_ST_model1/*__*.txt'): data = np.loadtxt(filepath) UVLF_Overzier.append(data) for filepath in glob.iglob('../../Data/UVLF_HST_ST_model1_Bouwens2016/*__*.txt'): data = np.loadtxt(filepath) UVLF_Bouwens.append(data) for filepath in glob.iglob('../../Data/UVLF_HST_ST_model1_Casey2014/*__*.txt'): data = np.loadtxt(filepath) UVLF_Casey.append(data) UVLF_Overzier = np.vstack(np.array(UVLF_Overzier)) UVLF_Bouwens = np.vstack(np.array(UVLF_Bouwens)) UVLF_Casey = np.vstack(np.array(UVLF_Casey)) betadata = np.loadtxt("Beta_parameters.txt", unpack=True) betainterp = PchipInterpolator(betadata[0], betadata[1]) dbetadMUVinterp = PchipInterpolator(betadata[0], betadata[2]) def betaAverage(z, MUV): if MUV < -19.5: return dbetadMUVinterp(z) * (MUV + 19.5) + betainterp(z) return (betainterp(z) + 2.33) * np.exp((dbetadMUVinterp(z) * (MUV + 19.5)) / (betainterp(z) + 2.33)) - 2.33 @np.vectorize def AUV(z, MUV, index): if z < 2.5 or z > 8: return 0. sigmabeta = 0.34 if index==0: return max(0., 4.54 + 0.2 * np.log(10) * (2.07**2) * (sigmabeta**2) + 2.07 * betaAverage(z, MUV)) # Overzier 2011 if index==1: return max(0., 3.36 + 0.2 * np.log(10) * (2.04**2) * (sigmabeta**2) + 2.04 * betaAverage(z, MUV)) # Casey 2014 if index==2: return max(0., 2.45 + 0.2 * np.log(10) * (1.1**2) * (sigmabeta**2) + 1.1 * betaAverage(z, MUV)) # Bouwens 2016 plt.figure(figsize=(24.,6.)) ax1 = plt.subplot(131) ax2 = plt.subplot(132) ax3 = plt.subplot(133) ax1.tick_params(axis='x', which='major', pad=6) ax2.tick_params(axis='x', which='major', pad=6) ax3.tick_params(axis='x', which='major', pad=6) ax1.tick_params(axis='both', which='major', labelsize=26) ax1.tick_params(axis='both', which='minor', labelsize=26) ax2.tick_params(axis='both', which='major', labelsize=26) ax2.tick_params(axis='both', which='minor', labelsize=26) ax3.tick_params(axis='both', which='major', labelsize=26) ax3.tick_params(axis='both', which='minor', labelsize=26) for axis in ['top','bottom','left','right']: ax1.spines[axis].set_linewidth(2.2) ax2.spines[axis].set_linewidth(2.2) ax3.spines[axis].set_linewidth(2.2) ############### ax1.plot(MUV:=np.linspace(-23,-16, 100), AUV(6., MUV, 0), color=colors[3], lw=2.5) ax1.plot(MUV:=np.linspace(-23,-16, 100), AUV(6., MUV, 1), linestyle=linestyles[2], color=colors[1], lw=3.) ax1.plot(MUV:=np.linspace(-23,-16, 100), AUV(6., MUV, 2), linestyle=linestyles[3], color=colors[-1], lw=2.5) ax1.set_xlabel(r'$M_\mathrm{UV}$', labelpad=10, fontsize=30) ax1.set_ylabel(r'$A_\mathrm{UV}$', labelpad=12, fontsize=30) ax1.set_xlim(-23, -16) ax1.set_ylim(0., 1.3) patch_blue = mpatches.Patch(color=colors[3], lw=1.5, label=r"$\mathrm{Overzier\ 2011}$") patch_green = mpatches.Patch(color=colors[1], lw=1.5, label=r"$\mathrm{Casey\ 2014}$") patch_yellow = mpatches.Patch(color=colors[-1], lw=1.5, label=r"$\mathrm{Bouwens\ 2016}$") leg = ax1.legend(handles=[patch_blue, patch_green,patch_yellow], loc="upper right", frameon=False, markerfirst=False, prop={'size': 21}, handlelength=1.9, handletextpad=0.5) ############### plot_hist2d(datax=UVLF_Overzier[:,-7], datay=UVLF_Overzier[:,2], ax=ax2, num_bins=20, weights=UVLF_Overzier[:,0], color=colors[3], zorder=3) plot_hist2d(datax=UVLF_Bouwens[:,-7], datay=UVLF_Bouwens[:,2], ax=ax2, num_bins=20, weights=UVLF_Bouwens[:,0], color=colors[-1], zorder=2) plot_hist2d(datax=UVLF_Casey[:,-7], datay=UVLF_Casey[:,2], ax=ax2, num_bins=20, weights=UVLF_Casey[:,0], color=colors[1], zorder=1) ax2.set_xlabel(r'$\Omega_\mathrm{m}$', labelpad=10, fontsize=30) ax2.set_ylabel(r'$\sigma_8$', labelpad=8, fontsize=30) ax2.set_xlim(0.2, 0.4) ax2.set_ylim(0.3, 1.3) ############### plot_hist2d(datax=UVLF_Overzier[:,5], datay=UVLF_Overzier[:,2], ax=ax3, num_bins=20, weights=UVLF_Overzier[:,0], color=colors[3], zorder=3) plot_hist2d(datax=UVLF_Bouwens[:,5], datay=UVLF_Bouwens[:,2], ax=ax3, num_bins=20, weights=UVLF_Bouwens[:,0], color=colors[-1], zorder=2) plot_hist2d(datax=UVLF_Casey[:,5], datay=UVLF_Casey[:,2], ax=ax3, num_bins=20, weights=UVLF_Casey[:,0], color=colors[1], zorder=1) ax3.set_ylabel(r'$\sigma_8$', labelpad=8, fontsize=30) ax3.set_xlabel(r'$n_\mathrm{s}$', labelpad=10, fontsize=30) ax3.set_xlim(0.7, 1.3) ax3.set_ylim(0.3, 1.3) plt.savefig("Posteriors_cosmo_model1_alternative_dust.pdf")

45.515337

244

0.669632

import numpy as np from matplotlib import pyplot as plt import glob from matplotlib import patches as mpatches import scipy.ndimage from scipy.interpolate import PchipInterpolator plt.style.use("../template.mplstyle") colors = ['purple', '#306B37', 'darkgoldenrod', '#3F7BB6', '#BF4145'] linestyles = [(0, (1,1.05)), (0, (3, 1, 1, 1)), (0, (1,3)), (0, (3,3.65)), (0, (3,2.772)), (0, (3, 1, 1, 1, 1, 1))]

true

f7362a6ca3d104844807c89068693be7e4cdaf8b

346

py

Python

sdk/compute/azure-mgmt-compute/azure/mgmt/compute/_version.py

jalauzon-msft/azure-sdk-for-python

15967f5c6d3376f2334a382486ba86339786e028

[ "MIT" ]

1

2022-02-01T18:50:12.000Z

sdk/compute/azure-mgmt-compute/azure/mgmt/compute/_version.py

ellhe-blaster/azure-sdk-for-python

82193ba5e81cc5e5e5a5239bba58abe62e86f469

[ "MIT" ]

null

sdk/compute/azure-mgmt-compute/azure/mgmt/compute/_version.py

ellhe-blaster/azure-sdk-for-python

82193ba5e81cc5e5e5a5239bba58abe62e86f469

[ "MIT" ]

null

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- VERSION = "27.1.0"

38.444444

76

0.416185

VERSION = "27.1.0"

true

f7362b15d038c90e57d13b0b966653c28ef74792

3,507

py

Python

train.py

Yugeeth/chat-bot

3198fb160f743c7be1f377d2febb889423da8c06

[ "MIT" ]

null

train.py

Yugeeth/chat-bot

3198fb160f743c7be1f377d2febb889423da8c06

[ "MIT" ]

null

train.py

Yugeeth/chat-bot

3198fb160f743c7be1f377d2febb889423da8c06

[ "MIT" ]

null

import numpy as np import random import json import torch import torch.nn as nn from torch.utils.data import Dataset, DataLoader from nltk_utils import bag_of_words, tokenize, stem from model import NeuralNet with open('intents.json', 'r') as f: intents = json.load(f) all_words = [] tags = [] xy = [] # loop through each sentence in our intents patterns for intent in intents['intents']: tag = intent['tag'] # add to tag list tags.append(tag) for pattern in intent['patterns']: # tokenize each word in the sentence w = tokenize(pattern) # add to our words list all_words.extend(w) # add to xy pair xy.append((w, tag)) # stem and lower each word ignore_words = ['?', '.', '!'] all_words = [stem(w) for w in all_words if w not in ignore_words] # remove duplicates and sort all_words = sorted(set(all_words)) tags = sorted(set(tags)) print(len(xy), "patterns") print(len(tags), "tags:", tags) print(len(all_words), "unique stemmed words:", all_words) # create training data X_train = [] y_train = [] for (pattern_sentence, tag) in xy: # X: bag of words for each pattern_sentence bag = bag_of_words(pattern_sentence, all_words) X_train.append(bag) # y: PyTorch CrossEntropyLoss needs only class labels, not one-hot label = tags.index(tag) y_train.append(label) X_train = np.array(X_train) y_train = np.array(y_train) # Hyper-parameters num_epochs = 1000 batch_size = 8 learning_rate = 0.001 input_size = len(X_train[0]) hidden_size = 8 output_size = len(tags) print(input_size, output_size) class ChatDataset(Dataset): def __init__(self): self.n_samples = len(X_train) self.x_data = X_train self.y_data = y_train # support indexing such that dataset[i] can be used to get i-th sample def __getitem__(self, index): return self.x_data[index], self.y_data[index] # we can call len(dataset) to return the size def __len__(self): return self.n_samples dataset = ChatDataset() train_loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, num_workers=0) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = NeuralNet(input_size, hidden_size, output_size).to(device) # Loss and optimizer criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) # Train the model for epoch in range(num_epochs): for (words, labels) in train_loader: words = words.to(device) labels = labels.to(dtype=torch.long).to(device) # Forward pass outputs = model(words) # if y would be one-hot, we must apply # labels = torch.max(labels, 1)[1] loss = criterion(outputs, labels) # Backward and optimize optimizer.zero_grad() loss.backward() optimizer.step() if (epoch+1) % 100 == 0: print (f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}') print(f'final loss: {loss.item():.4f}') data = { "model_state": model.state_dict(), "input_size": input_size, "hidden_size": hidden_size, "output_size": output_size, "all_words": all_words, "tags": tags } FILE = "data.pth" torch.save(data, FILE) print(f'training complete. file saved to {FILE}')

27.186047

75

0.633875

import numpy as np import random import json import torch import torch.nn as nn from torch.utils.data import Dataset, DataLoader from nltk_utils import bag_of_words, tokenize, stem from model import NeuralNet with open('intents.json', 'r') as f: intents = json.load(f) all_words = [] tags = [] xy = [] for intent in intents['intents']: tag = intent['tag'] tags.append(tag) for pattern in intent['patterns']: w = tokenize(pattern) all_words.extend(w) xy.append((w, tag)) ignore_words = ['?', '.', '!'] all_words = [stem(w) for w in all_words if w not in ignore_words] all_words = sorted(set(all_words)) tags = sorted(set(tags)) print(len(xy), "patterns") print(len(tags), "tags:", tags) print(len(all_words), "unique stemmed words:", all_words) X_train = [] y_train = [] for (pattern_sentence, tag) in xy: bag = bag_of_words(pattern_sentence, all_words) X_train.append(bag) label = tags.index(tag) y_train.append(label) X_train = np.array(X_train) y_train = np.array(y_train) num_epochs = 1000 batch_size = 8 learning_rate = 0.001 input_size = len(X_train[0]) hidden_size = 8 output_size = len(tags) print(input_size, output_size) class ChatDataset(Dataset): def __init__(self): self.n_samples = len(X_train) self.x_data = X_train self.y_data = y_train def __getitem__(self, index): return self.x_data[index], self.y_data[index] def __len__(self): return self.n_samples dataset = ChatDataset() train_loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, num_workers=0) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = NeuralNet(input_size, hidden_size, output_size).to(device) criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) for epoch in range(num_epochs): for (words, labels) in train_loader: words = words.to(device) labels = labels.to(dtype=torch.long).to(device) outputs = model(words) loss = criterion(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step() if (epoch+1) % 100 == 0: print (f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}') print(f'final loss: {loss.item():.4f}') data = { "model_state": model.state_dict(), "input_size": input_size, "hidden_size": hidden_size, "output_size": output_size, "all_words": all_words, "tags": tags } FILE = "data.pth" torch.save(data, FILE) print(f'training complete. file saved to {FILE}')

true

f7362b1d3b7934ae1c2a4ac2d3bcb7a2674d48d6

1,310

py

Python

para_averaging.py

TurkuNLP/paraphrase-classification

625f0cf5223ecff9d25c2a4f558ca39fa5ecc794

[ "Apache-2.0" ]

null

para_averaging.py

TurkuNLP/paraphrase-classification

625f0cf5223ecff9d25c2a4f558ca39fa5ecc794

[ "Apache-2.0" ]

null

para_averaging.py

TurkuNLP/paraphrase-classification

625f0cf5223ecff9d25c2a4f558ca39fa5ecc794

[ "Apache-2.0" ]

null

import torch.nn.functional as F import torch import para_model class ParaAvgModel(para_model.PARAModel): def __init__(self, **args): super().__init__(**args) # self.drop_layer=torch.nn.Dropout(p=0.2) self.cls_layer=torch.nn.Linear(self.bert.config.hidden_size*5, args['num_classes']) def forward(self, batch): input_ids = batch['input_ids'] token_type_ids = batch['token_type_ids'] attention_mask = batch['attention_mask'] cls_mask = batch['cls_mask'] sep1_mask = batch['sep1_mask'] sep2_mask = batch['sep2_mask'] left_mask = batch['left_mask'] right_mask = batch['right_mask'] enc = self.bert(input_ids=input_ids,attention_mask=attention_mask,token_type_ids=token_type_ids)[0] #BxS_LENxSIZE; BxSIZE cls = (enc*cls_mask.unsqueeze(-1)).sum(1) # enc.pooler_output sep1 = (enc*sep1_mask.unsqueeze(-1)).sum(1) sep2 = (enc*sep2_mask.unsqueeze(-1)).sum(1) left = (enc*left_mask.unsqueeze(-1)).sum(1) / left_mask.sum(-1).unsqueeze(-1) right = (enc*right_mask.unsqueeze(-1)).sum(1) / right_mask.sum(-1).unsqueeze(-1) catenated = torch.cat((cls, sep1, sep2, left, right), -1) # dropped = self.drop_layer(catenated) return self.cls_layer(catenated)

43.666667

129

0.655725

import torch.nn.functional as F import torch import para_model class ParaAvgModel(para_model.PARAModel): def __init__(self, **args): super().__init__(**args) self.cls_layer=torch.nn.Linear(self.bert.config.hidden_size*5, args['num_classes']) def forward(self, batch): input_ids = batch['input_ids'] token_type_ids = batch['token_type_ids'] attention_mask = batch['attention_mask'] cls_mask = batch['cls_mask'] sep1_mask = batch['sep1_mask'] sep2_mask = batch['sep2_mask'] left_mask = batch['left_mask'] right_mask = batch['right_mask'] enc = self.bert(input_ids=input_ids,attention_mask=attention_mask,token_type_ids=token_type_ids)[0] cls = (enc*cls_mask.unsqueeze(-1)).sum(1) sep1 = (enc*sep1_mask.unsqueeze(-1)).sum(1) sep2 = (enc*sep2_mask.unsqueeze(-1)).sum(1) left = (enc*left_mask.unsqueeze(-1)).sum(1) / left_mask.sum(-1).unsqueeze(-1) right = (enc*right_mask.unsqueeze(-1)).sum(1) / right_mask.sum(-1).unsqueeze(-1) catenated = torch.cat((cls, sep1, sep2, left, right), -1) return self.cls_layer(catenated)

true

f7362b4bfc9f1a5b48252b4883e71f75146af047

15,417

py

Python

mne/viz/circle.py

joewalter/mne-python

b0629bea7f5e8e94d9e2e889f45a35f9657e6dbc

[ "BSD-3-Clause" ]

null

mne/viz/circle.py

joewalter/mne-python

b0629bea7f5e8e94d9e2e889f45a35f9657e6dbc

[ "BSD-3-Clause" ]

null

mne/viz/circle.py

joewalter/mne-python

b0629bea7f5e8e94d9e2e889f45a35f9657e6dbc

[ "BSD-3-Clause" ]

null

"""Functions to plot on circle as for connectivity """ from __future__ import print_function # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr> # Denis Engemann <denis.engemann@gmail.com> # Martin Luessi <mluessi@nmr.mgh.harvard.edu> # # License: Simplified BSD from itertools import cycle from functools import partial import numpy as np from .utils import plt_show from ..externals.six import string_types def circular_layout(node_names, node_order, start_pos=90, start_between=True, group_boundaries=None, group_sep=10): """Create layout arranging nodes on a circle. Parameters ---------- node_names : list of str Node names. node_order : list of str List with node names defining the order in which the nodes are arranged. Must have the elements as node_names but the order can be different. The nodes are arranged clockwise starting at "start_pos" degrees. start_pos : float Angle in degrees that defines where the first node is plotted. start_between : bool If True, the layout starts with the position between the nodes. This is the same as adding "180. / len(node_names)" to start_pos. group_boundaries : None | array-like List of of boundaries between groups at which point a "group_sep" will be inserted. E.g. "[0, len(node_names) / 2]" will create two groups. group_sep : float Group separation angle in degrees. See "group_boundaries". Returns ------- node_angles : array, shape=(len(node_names,)) Node angles in degrees. """ n_nodes = len(node_names) if len(node_order) != n_nodes: raise ValueError('node_order has to be the same length as node_names') if group_boundaries is not None: boundaries = np.array(group_boundaries, dtype=np.int) if np.any(boundaries >= n_nodes) or np.any(boundaries < 0): raise ValueError('"group_boundaries" has to be between 0 and ' 'n_nodes - 1.') if len(boundaries) > 1 and np.any(np.diff(boundaries) <= 0): raise ValueError('"group_boundaries" must have non-decreasing ' 'values.') n_group_sep = len(group_boundaries) else: n_group_sep = 0 boundaries = None # convert it to a list with indices node_order = [node_order.index(name) for name in node_names] node_order = np.array(node_order) if len(np.unique(node_order)) != n_nodes: raise ValueError('node_order has repeated entries') node_sep = (360. - n_group_sep * group_sep) / n_nodes if start_between: start_pos += node_sep / 2 if boundaries is not None and boundaries[0] == 0: # special case when a group separator is at the start start_pos += group_sep / 2 boundaries = boundaries[1:] if n_group_sep > 1 else None node_angles = np.ones(n_nodes, dtype=np.float) * node_sep node_angles[0] = start_pos if boundaries is not None: node_angles[boundaries] += group_sep node_angles = np.cumsum(node_angles)[node_order] return node_angles def _plot_connectivity_circle_onpick(event, fig=None, axes=None, indices=None, n_nodes=0, node_angles=None, ylim=[9, 10]): """Isolates connections around a single node when user left clicks a node. On right click, resets all connections.""" if event.inaxes != axes: return if event.button == 1: # left click # click must be near node radius if not ylim[0] <= event.ydata <= ylim[1]: return # all angles in range [0, 2*pi] node_angles = node_angles % (np.pi * 2) node = np.argmin(np.abs(event.xdata - node_angles)) patches = event.inaxes.patches for ii, (x, y) in enumerate(zip(indices[0], indices[1])): patches[ii].set_visible(node in [x, y]) fig.canvas.draw() elif event.button == 3: # right click patches = event.inaxes.patches for ii in range(np.size(indices, axis=1)): patches[ii].set_visible(True) fig.canvas.draw() def plot_connectivity_circle(con, node_names, indices=None, n_lines=None, node_angles=None, node_width=None, node_colors=None, facecolor='black', textcolor='white', node_edgecolor='black', linewidth=1.5, colormap='hot', vmin=None, vmax=None, colorbar=True, title=None, colorbar_size=0.2, colorbar_pos=(-0.3, 0.1), fontsize_title=12, fontsize_names=8, fontsize_colorbar=8, padding=6., fig=None, subplot=111, interactive=True, node_linewidth=2., show=True): """Visualize connectivity as a circular graph. Note: This code is based on the circle graph example by Nicolas P. Rougier http://www.labri.fr/perso/nrougier/coding/. Parameters ---------- con : array Connectivity scores. Can be a square matrix, or a 1D array. If a 1D array is provided, "indices" has to be used to define the connection indices. node_names : list of str Node names. The order corresponds to the order in con. indices : tuple of arrays | None Two arrays with indices of connections for which the connections strenghts are defined in con. Only needed if con is a 1D array. n_lines : int | None If not None, only the n_lines strongest connections (strength=abs(con)) are drawn. node_angles : array, shape=(len(node_names,)) | None Array with node positions in degrees. If None, the nodes are equally spaced on the circle. See mne.viz.circular_layout. node_width : float | None Width of each node in degrees. If None, the minimum angle between any two nodes is used as the width. node_colors : list of tuples | list of str List with the color to use for each node. If fewer colors than nodes are provided, the colors will be repeated. Any color supported by matplotlib can be used, e.g., RGBA tuples, named colors. facecolor : str Color to use for background. See matplotlib.colors. textcolor : str Color to use for text. See matplotlib.colors. node_edgecolor : str Color to use for lines around nodes. See matplotlib.colors. linewidth : float Line width to use for connections. colormap : str Colormap to use for coloring the connections. vmin : float | None Minimum value for colormap. If None, it is determined automatically. vmax : float | None Maximum value for colormap. If None, it is determined automatically. colorbar : bool Display a colorbar or not. title : str The figure title. colorbar_size : float Size of the colorbar. colorbar_pos : 2-tuple Position of the colorbar. fontsize_title : int Font size to use for title. fontsize_names : int Font size to use for node names. fontsize_colorbar : int Font size to use for colorbar. padding : float Space to add around figure to accommodate long labels. fig : None | instance of matplotlib.pyplot.Figure The figure to use. If None, a new figure with the specified background color will be created. subplot : int | 3-tuple Location of the subplot when creating figures with multiple plots. E.g. 121 or (1, 2, 1) for 1 row, 2 columns, plot 1. See matplotlib.pyplot.subplot. interactive : bool When enabled, left-click on a node to show only connections to that node. Right-click shows all connections. node_linewidth : float Line with for nodes. show : bool Show figure if True. Returns ------- fig : instance of matplotlib.pyplot.Figure The figure handle. axes : instance of matplotlib.axes.PolarAxesSubplot The subplot handle. """ import matplotlib.pyplot as plt import matplotlib.path as m_path import matplotlib.patches as m_patches n_nodes = len(node_names) if node_angles is not None: if len(node_angles) != n_nodes: raise ValueError('node_angles has to be the same length ' 'as node_names') # convert it to radians node_angles = node_angles * np.pi / 180 else: # uniform layout on unit circle node_angles = np.linspace(0, 2 * np.pi, n_nodes, endpoint=False) if node_width is None: # widths correspond to the minimum angle between two nodes dist_mat = node_angles[None, :] - node_angles[:, None] dist_mat[np.diag_indices(n_nodes)] = 1e9 node_width = np.min(np.abs(dist_mat)) else: node_width = node_width * np.pi / 180 if node_colors is not None: if len(node_colors) < n_nodes: node_colors = cycle(node_colors) else: # assign colors using colormap node_colors = [plt.cm.spectral(i / float(n_nodes)) for i in range(n_nodes)] # handle 1D and 2D connectivity information if con.ndim == 1: if indices is None: raise ValueError('indices has to be provided if con.ndim == 1') elif con.ndim == 2: if con.shape[0] != n_nodes or con.shape[1] != n_nodes: raise ValueError('con has to be 1D or a square matrix') # we use the lower-triangular part indices = np.tril_indices(n_nodes, -1) con = con[indices] else: raise ValueError('con has to be 1D or a square matrix') # get the colormap if isinstance(colormap, string_types): colormap = plt.get_cmap(colormap) # Make figure background the same colors as axes if fig is None: fig = plt.figure(figsize=(8, 8), facecolor=facecolor) # Use a polar axes if not isinstance(subplot, tuple): subplot = (subplot,) axes = plt.subplot(*subplot, polar=True, axisbg=facecolor) # No ticks, we'll put our own plt.xticks([]) plt.yticks([]) # Set y axes limit, add additional space if requested plt.ylim(0, 10 + padding) # Remove the black axes border which may obscure the labels axes.spines['polar'].set_visible(False) # Draw lines between connected nodes, only draw the strongest connections if n_lines is not None and len(con) > n_lines: con_thresh = np.sort(np.abs(con).ravel())[-n_lines] else: con_thresh = 0. # get the connections which we are drawing and sort by connection strength # this will allow us to draw the strongest connections first con_abs = np.abs(con) con_draw_idx = np.where(con_abs >= con_thresh)[0] con = con[con_draw_idx] con_abs = con_abs[con_draw_idx] indices = [ind[con_draw_idx] for ind in indices] # now sort them sort_idx = np.argsort(con_abs) con_abs = con_abs[sort_idx] con = con[sort_idx] indices = [ind[sort_idx] for ind in indices] # Get vmin vmax for color scaling if vmin is None: vmin = np.min(con[np.abs(con) >= con_thresh]) if vmax is None: vmax = np.max(con) vrange = vmax - vmin # We want to add some "noise" to the start and end position of the # edges: We modulate the noise with the number of connections of the # node and the connection strength, such that the strongest connections # are closer to the node center nodes_n_con = np.zeros((n_nodes), dtype=np.int) for i, j in zip(indices[0], indices[1]): nodes_n_con[i] += 1 nodes_n_con[j] += 1 # initialize random number generator so plot is reproducible rng = np.random.mtrand.RandomState(seed=0) n_con = len(indices[0]) noise_max = 0.25 * node_width start_noise = rng.uniform(-noise_max, noise_max, n_con) end_noise = rng.uniform(-noise_max, noise_max, n_con) nodes_n_con_seen = np.zeros_like(nodes_n_con) for i, (start, end) in enumerate(zip(indices[0], indices[1])): nodes_n_con_seen[start] += 1 nodes_n_con_seen[end] += 1 start_noise[i] *= ((nodes_n_con[start] - nodes_n_con_seen[start]) / float(nodes_n_con[start])) end_noise[i] *= ((nodes_n_con[end] - nodes_n_con_seen[end]) / float(nodes_n_con[end])) # scale connectivity for colormap (vmin<=>0, vmax<=>1) con_val_scaled = (con - vmin) / vrange # Finally, we draw the connections for pos, (i, j) in enumerate(zip(indices[0], indices[1])): # Start point t0, r0 = node_angles[i], 10 # End point t1, r1 = node_angles[j], 10 # Some noise in start and end point t0 += start_noise[pos] t1 += end_noise[pos] verts = [(t0, r0), (t0, 5), (t1, 5), (t1, r1)] codes = [m_path.Path.MOVETO, m_path.Path.CURVE4, m_path.Path.CURVE4, m_path.Path.LINETO] path = m_path.Path(verts, codes) color = colormap(con_val_scaled[pos]) # Actual line patch = m_patches.PathPatch(path, fill=False, edgecolor=color, linewidth=linewidth, alpha=1.) axes.add_patch(patch) # Draw ring with colored nodes height = np.ones(n_nodes) * 1.0 bars = axes.bar(node_angles, height, width=node_width, bottom=9, edgecolor=node_edgecolor, lw=node_linewidth, facecolor='.9', align='center') for bar, color in zip(bars, node_colors): bar.set_facecolor(color) # Draw node labels angles_deg = 180 * node_angles / np.pi for name, angle_rad, angle_deg in zip(node_names, node_angles, angles_deg): if angle_deg >= 270: ha = 'left' else: # Flip the label, so text is always upright angle_deg += 180 ha = 'right' axes.text(angle_rad, 10.4, name, size=fontsize_names, rotation=angle_deg, rotation_mode='anchor', horizontalalignment=ha, verticalalignment='center', color=textcolor) if title is not None: plt.title(title, color=textcolor, fontsize=fontsize_title, axes=axes) if colorbar: sm = plt.cm.ScalarMappable(cmap=colormap, norm=plt.Normalize(vmin, vmax)) sm.set_array(np.linspace(vmin, vmax)) cb = plt.colorbar(sm, ax=axes, use_gridspec=False, shrink=colorbar_size, anchor=colorbar_pos) cb_yticks = plt.getp(cb.ax.axes, 'yticklabels') cb.ax.tick_params(labelsize=fontsize_colorbar) plt.setp(cb_yticks, color=textcolor) # Add callback for interaction if interactive: callback = partial(_plot_connectivity_circle_onpick, fig=fig, axes=axes, indices=indices, n_nodes=n_nodes, node_angles=node_angles) fig.canvas.mpl_connect('button_press_event', callback) plt_show(show) return fig, axes

37.23913

79

0.621846

from __future__ import print_function from itertools import cycle from functools import partial import numpy as np from .utils import plt_show from ..externals.six import string_types def circular_layout(node_names, node_order, start_pos=90, start_between=True, group_boundaries=None, group_sep=10): n_nodes = len(node_names) if len(node_order) != n_nodes: raise ValueError('node_order has to be the same length as node_names') if group_boundaries is not None: boundaries = np.array(group_boundaries, dtype=np.int) if np.any(boundaries >= n_nodes) or np.any(boundaries < 0): raise ValueError('"group_boundaries" has to be between 0 and ' 'n_nodes - 1.') if len(boundaries) > 1 and np.any(np.diff(boundaries) <= 0): raise ValueError('"group_boundaries" must have non-decreasing ' 'values.') n_group_sep = len(group_boundaries) else: n_group_sep = 0 boundaries = None node_order = [node_order.index(name) for name in node_names] node_order = np.array(node_order) if len(np.unique(node_order)) != n_nodes: raise ValueError('node_order has repeated entries') node_sep = (360. - n_group_sep * group_sep) / n_nodes if start_between: start_pos += node_sep / 2 if boundaries is not None and boundaries[0] == 0: start_pos += group_sep / 2 boundaries = boundaries[1:] if n_group_sep > 1 else None node_angles = np.ones(n_nodes, dtype=np.float) * node_sep node_angles[0] = start_pos if boundaries is not None: node_angles[boundaries] += group_sep node_angles = np.cumsum(node_angles)[node_order] return node_angles def _plot_connectivity_circle_onpick(event, fig=None, axes=None, indices=None, n_nodes=0, node_angles=None, ylim=[9, 10]): if event.inaxes != axes: return if event.button == 1: if not ylim[0] <= event.ydata <= ylim[1]: return node_angles = node_angles % (np.pi * 2) node = np.argmin(np.abs(event.xdata - node_angles)) patches = event.inaxes.patches for ii, (x, y) in enumerate(zip(indices[0], indices[1])): patches[ii].set_visible(node in [x, y]) fig.canvas.draw() elif event.button == 3: patches = event.inaxes.patches for ii in range(np.size(indices, axis=1)): patches[ii].set_visible(True) fig.canvas.draw() def plot_connectivity_circle(con, node_names, indices=None, n_lines=None, node_angles=None, node_width=None, node_colors=None, facecolor='black', textcolor='white', node_edgecolor='black', linewidth=1.5, colormap='hot', vmin=None, vmax=None, colorbar=True, title=None, colorbar_size=0.2, colorbar_pos=(-0.3, 0.1), fontsize_title=12, fontsize_names=8, fontsize_colorbar=8, padding=6., fig=None, subplot=111, interactive=True, node_linewidth=2., show=True): import matplotlib.pyplot as plt import matplotlib.path as m_path import matplotlib.patches as m_patches n_nodes = len(node_names) if node_angles is not None: if len(node_angles) != n_nodes: raise ValueError('node_angles has to be the same length ' 'as node_names') node_angles = node_angles * np.pi / 180 else: node_angles = np.linspace(0, 2 * np.pi, n_nodes, endpoint=False) if node_width is None: dist_mat = node_angles[None, :] - node_angles[:, None] dist_mat[np.diag_indices(n_nodes)] = 1e9 node_width = np.min(np.abs(dist_mat)) else: node_width = node_width * np.pi / 180 if node_colors is not None: if len(node_colors) < n_nodes: node_colors = cycle(node_colors) else: node_colors = [plt.cm.spectral(i / float(n_nodes)) for i in range(n_nodes)] if con.ndim == 1: if indices is None: raise ValueError('indices has to be provided if con.ndim == 1') elif con.ndim == 2: if con.shape[0] != n_nodes or con.shape[1] != n_nodes: raise ValueError('con has to be 1D or a square matrix') indices = np.tril_indices(n_nodes, -1) con = con[indices] else: raise ValueError('con has to be 1D or a square matrix') if isinstance(colormap, string_types): colormap = plt.get_cmap(colormap) if fig is None: fig = plt.figure(figsize=(8, 8), facecolor=facecolor) if not isinstance(subplot, tuple): subplot = (subplot,) axes = plt.subplot(*subplot, polar=True, axisbg=facecolor) plt.xticks([]) plt.yticks([]) # Set y axes limit, add additional space if requested plt.ylim(0, 10 + padding) # Remove the black axes border which may obscure the labels axes.spines['polar'].set_visible(False) # Draw lines between connected nodes, only draw the strongest connections if n_lines is not None and len(con) > n_lines: con_thresh = np.sort(np.abs(con).ravel())[-n_lines] else: con_thresh = 0. # get the connections which we are drawing and sort by connection strength # this will allow us to draw the strongest connections first con_abs = np.abs(con) con_draw_idx = np.where(con_abs >= con_thresh)[0] con = con[con_draw_idx] con_abs = con_abs[con_draw_idx] indices = [ind[con_draw_idx] for ind in indices] # now sort them sort_idx = np.argsort(con_abs) con_abs = con_abs[sort_idx] con = con[sort_idx] indices = [ind[sort_idx] for ind in indices] # Get vmin vmax for color scaling if vmin is None: vmin = np.min(con[np.abs(con) >= con_thresh]) if vmax is None: vmax = np.max(con) vrange = vmax - vmin # We want to add some "noise" to the start and end position of the # edges: We modulate the noise with the number of connections of the # node and the connection strength, such that the strongest connections # are closer to the node center nodes_n_con = np.zeros((n_nodes), dtype=np.int) for i, j in zip(indices[0], indices[1]): nodes_n_con[i] += 1 nodes_n_con[j] += 1 # initialize random number generator so plot is reproducible rng = np.random.mtrand.RandomState(seed=0) n_con = len(indices[0]) noise_max = 0.25 * node_width start_noise = rng.uniform(-noise_max, noise_max, n_con) end_noise = rng.uniform(-noise_max, noise_max, n_con) nodes_n_con_seen = np.zeros_like(nodes_n_con) for i, (start, end) in enumerate(zip(indices[0], indices[1])): nodes_n_con_seen[start] += 1 nodes_n_con_seen[end] += 1 start_noise[i] *= ((nodes_n_con[start] - nodes_n_con_seen[start]) / float(nodes_n_con[start])) end_noise[i] *= ((nodes_n_con[end] - nodes_n_con_seen[end]) / float(nodes_n_con[end])) # scale connectivity for colormap (vmin<=>0, vmax<=>1) con_val_scaled = (con - vmin) / vrange # Finally, we draw the connections for pos, (i, j) in enumerate(zip(indices[0], indices[1])): # Start point t0, r0 = node_angles[i], 10 # End point t1, r1 = node_angles[j], 10 # Some noise in start and end point t0 += start_noise[pos] t1 += end_noise[pos] verts = [(t0, r0), (t0, 5), (t1, 5), (t1, r1)] codes = [m_path.Path.MOVETO, m_path.Path.CURVE4, m_path.Path.CURVE4, m_path.Path.LINETO] path = m_path.Path(verts, codes) color = colormap(con_val_scaled[pos]) # Actual line patch = m_patches.PathPatch(path, fill=False, edgecolor=color, linewidth=linewidth, alpha=1.) axes.add_patch(patch) # Draw ring with colored nodes height = np.ones(n_nodes) * 1.0 bars = axes.bar(node_angles, height, width=node_width, bottom=9, edgecolor=node_edgecolor, lw=node_linewidth, facecolor='.9', align='center') for bar, color in zip(bars, node_colors): bar.set_facecolor(color) # Draw node labels angles_deg = 180 * node_angles / np.pi for name, angle_rad, angle_deg in zip(node_names, node_angles, angles_deg): if angle_deg >= 270: ha = 'left' else: # Flip the label, so text is always upright angle_deg += 180 ha = 'right' axes.text(angle_rad, 10.4, name, size=fontsize_names, rotation=angle_deg, rotation_mode='anchor', horizontalalignment=ha, verticalalignment='center', color=textcolor) if title is not None: plt.title(title, color=textcolor, fontsize=fontsize_title, axes=axes) if colorbar: sm = plt.cm.ScalarMappable(cmap=colormap, norm=plt.Normalize(vmin, vmax)) sm.set_array(np.linspace(vmin, vmax)) cb = plt.colorbar(sm, ax=axes, use_gridspec=False, shrink=colorbar_size, anchor=colorbar_pos) cb_yticks = plt.getp(cb.ax.axes, 'yticklabels') cb.ax.tick_params(labelsize=fontsize_colorbar) plt.setp(cb_yticks, color=textcolor) # Add callback for interaction if interactive: callback = partial(_plot_connectivity_circle_onpick, fig=fig, axes=axes, indices=indices, n_nodes=n_nodes, node_angles=node_angles) fig.canvas.mpl_connect('button_press_event', callback) plt_show(show) return fig, axes

true

f7362b4e7693e7a3993c8f04eaae12595182ff80

34

py

Python

turkishsuffix/__init__.py

ugur-zongur/turkishsuffix

f013e34f3094b5357244b1fd426ce9594c9c36ab

[ "MIT" ]

8

2018-09-12T03:45:49.000Z

2021-03-05T15:55:11.000Z

turkishsuffix/__init__.py

ugur-zongur/turkishsuffix

f013e34f3094b5357244b1fd426ce9594c9c36ab

[ "MIT" ]

1

2020-04-07T16:06:12.000Z

turkishsuffix/__init__.py

ugur-zongur/turkishsuffix

f013e34f3094b5357244b1fd426ce9594c9c36ab

[ "MIT" ]

2

2018-03-02T11:08:33.000Z

2020-03-12T22:10:00.000Z

from .suffix import turkishSuffix

17

33

0.852941

from .suffix import turkishSuffix

true

f7362c07e55a424db3a7dda53b5b90643a1ec703

9,664

py

Python

lib/airflow/airflow/operators/subdag.py

SteNicholas/ai-flow

2c70547981f1516f0e37bbe6936a1b7cccd31822

[ "Apache-2.0" ]

79

2021-10-15T07:32:27.000Z

2022-03-28T04:10:19.000Z

lib/airflow/airflow/operators/subdag.py

SteNicholas/ai-flow

2c70547981f1516f0e37bbe6936a1b7cccd31822

[ "Apache-2.0" ]

153

2021-10-15T05:23:46.000Z

2022-02-23T06:07:10.000Z

lib/airflow/airflow/operators/subdag.py

SteNicholas/ai-flow

2c70547981f1516f0e37bbe6936a1b7cccd31822

[ "Apache-2.0" ]

23

2021-10-15T02:36:37.000Z

2022-03-17T02:59:27.000Z

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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. """The module which provides a way to nest your DAGs and so your levels of complexity.""" from enum import Enum from typing import Dict, Optional from sqlalchemy.orm.session import Session from airflow.api.common.experimental.get_task_instance import get_task_instance from airflow.exceptions import AirflowException, TaskInstanceNotFound from airflow.models import DagRun from airflow.models.dag import DAG, DagContext from airflow.models.pool import Pool from airflow.models.taskinstance import TaskInstance from airflow.sensors.base import BaseSensorOperator from airflow.utils.decorators import apply_defaults from airflow.utils.session import create_session, provide_session from airflow.utils.state import State from airflow.utils.types import DagRunType class SkippedStatePropagationOptions(Enum): """Available options for skipped state propagation of subdag's tasks to parent dag tasks.""" ALL_LEAVES = 'all_leaves' ANY_LEAF = 'any_leaf' class SubDagOperator(BaseSensorOperator): """ This runs a sub dag. By convention, a sub dag's dag_id should be prefixed by its parent and a dot. As in `parent.child`. Although SubDagOperator can occupy a pool/concurrency slot, user can specify the mode=reschedule so that the slot will be released periodically to avoid potential deadlock. :param subdag: the DAG object to run as a subdag of the current DAG. :param session: sqlalchemy session :param conf: Configuration for the subdag :type conf: dict :param propagate_skipped_state: by setting this argument you can define whether the skipped state of leaf task(s) should be propagated to the parent dag's downstream task. """ ui_color = '#555' ui_fgcolor = '#fff' @provide_session @apply_defaults def __init__( self, *, subdag: DAG, session: Optional[Session] = None, conf: Optional[Dict] = None, propagate_skipped_state: Optional[SkippedStatePropagationOptions] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.subdag = subdag self.conf = conf self.propagate_skipped_state = propagate_skipped_state self._validate_dag(kwargs) self._validate_pool(session) def _validate_dag(self, kwargs): dag = kwargs.get('dag') or DagContext.get_current_dag() if not dag: raise AirflowException('Please pass in the `dag` param or call within a DAG context manager') if dag.dag_id + '.' + kwargs['task_id'] != self.subdag.dag_id: raise AirflowException( "The subdag's dag_id should have the form '{{parent_dag_id}}.{{this_task_id}}'. " "Expected '{d}.{t}'; received '{rcvd}'.".format( d=dag.dag_id, t=kwargs['task_id'], rcvd=self.subdag.dag_id ) ) def _validate_pool(self, session): if self.pool: conflicts = [t for t in self.subdag.tasks if t.pool == self.pool] if conflicts: # only query for pool conflicts if one may exist pool = session.query(Pool).filter(Pool.slots == 1).filter(Pool.pool == self.pool).first() if pool and any(t.pool == self.pool for t in self.subdag.tasks): raise AirflowException( 'SubDagOperator {sd} and subdag task{plural} {t} both ' 'use pool {p}, but the pool only has 1 slot. The ' 'subdag tasks will never run.'.format( sd=self.task_id, plural=len(conflicts) > 1, t=', '.join(t.task_id for t in conflicts), p=self.pool, ) ) def _get_dagrun(self, execution_date): dag_runs = DagRun.find( dag_id=self.subdag.dag_id, execution_date=execution_date, ) return dag_runs[0] if dag_runs else None def _reset_dag_run_and_task_instances(self, dag_run, execution_date): """ Set the DagRun state to RUNNING and set the failed TaskInstances to None state for scheduler to pick up. :param dag_run: DAG run :param execution_date: Execution date :return: None """ with create_session() as session: dag_run.state = State.RUNNING session.merge(dag_run) failed_task_instances = ( session.query(TaskInstance) .filter(TaskInstance.dag_id == self.subdag.dag_id) .filter(TaskInstance.execution_date == execution_date) .filter(TaskInstance.state.in_([State.FAILED, State.UPSTREAM_FAILED])) ) for task_instance in failed_task_instances: task_instance.state = State.NONE session.merge(task_instance) session.commit() def pre_execute(self, context): execution_date = context['execution_date'] dag_run = self._get_dagrun(execution_date) if dag_run is None: dag_run = self.subdag.create_dagrun( run_type=DagRunType.SCHEDULED, execution_date=execution_date, state=State.RUNNING, conf=self.conf, external_trigger=True, ) self.log.info("Created DagRun: %s", dag_run.run_id) if 'notification_server_uri' in context and context['notification_server_uri']: from airflow.events.scheduler_events import DagRunCreatedEvent from notification_service.client import NotificationClient dag_run_created_event = DagRunCreatedEvent( dag_id=self.subdag.dag_id, execution_date=dag_run.execution_date ).to_event() try: client = NotificationClient(server_uri=context['notification_server_uri'], default_namespace=dag_run_created_event.namespace, sender=dag_run_created_event.sender) self.log.info("SubDagOperator sending event: {}".format(dag_run_created_event)) client.send_event(dag_run_created_event) finally: client.close() else: self.log.info("Found existing DagRun: %s", dag_run.run_id) if dag_run.state == State.FAILED: self._reset_dag_run_and_task_instances(dag_run, execution_date) def poke(self, context): execution_date = context['execution_date'] dag_run = self._get_dagrun(execution_date=execution_date) return dag_run.state != State.RUNNING def post_execute(self, context, result=None): execution_date = context['execution_date'] dag_run = self._get_dagrun(execution_date=execution_date) self.log.info("Execution finished. State is %s", dag_run.state) if dag_run.state != State.SUCCESS: raise AirflowException(f"Expected state: SUCCESS. Actual state: {dag_run.state}") if self.propagate_skipped_state and self._check_skipped_states(context): self._skip_downstream_tasks(context) def _check_skipped_states(self, context): leaves_tis = self._get_leaves_tis(context['execution_date']) if self.propagate_skipped_state == SkippedStatePropagationOptions.ANY_LEAF: return any(ti.state == State.SKIPPED for ti in leaves_tis) if self.propagate_skipped_state == SkippedStatePropagationOptions.ALL_LEAVES: return all(ti.state == State.SKIPPED for ti in leaves_tis) raise AirflowException( f'Unimplemented SkippedStatePropagationOptions {self.propagate_skipped_state} used.' ) def _get_leaves_tis(self, execution_date): leaves_tis = [] for leaf in self.subdag.leaves: try: ti = get_task_instance( dag_id=self.subdag.dag_id, task_id=leaf.task_id, execution_date=execution_date ) leaves_tis.append(ti) except TaskInstanceNotFound: continue return leaves_tis def _skip_downstream_tasks(self, context): self.log.info( 'Skipping downstream tasks because propagate_skipped_state is set to %s ' 'and skipped task(s) were found.', self.propagate_skipped_state, ) downstream_tasks = context['task'].downstream_list self.log.debug('Downstream task_ids %s', downstream_tasks) if downstream_tasks: self.skip(context['dag_run'], context['execution_date'], downstream_tasks) self.log.info('Done.')

41.835498

105

0.640625

from enum import Enum from typing import Dict, Optional from sqlalchemy.orm.session import Session from airflow.api.common.experimental.get_task_instance import get_task_instance from airflow.exceptions import AirflowException, TaskInstanceNotFound from airflow.models import DagRun from airflow.models.dag import DAG, DagContext from airflow.models.pool import Pool from airflow.models.taskinstance import TaskInstance from airflow.sensors.base import BaseSensorOperator from airflow.utils.decorators import apply_defaults from airflow.utils.session import create_session, provide_session from airflow.utils.state import State from airflow.utils.types import DagRunType class SkippedStatePropagationOptions(Enum): ALL_LEAVES = 'all_leaves' ANY_LEAF = 'any_leaf' class SubDagOperator(BaseSensorOperator): ui_color = '#555' ui_fgcolor = '#fff' @provide_session @apply_defaults def __init__( self, *, subdag: DAG, session: Optional[Session] = None, conf: Optional[Dict] = None, propagate_skipped_state: Optional[SkippedStatePropagationOptions] = None, **kwargs, ) -> None: super().__init__(**kwargs) self.subdag = subdag self.conf = conf self.propagate_skipped_state = propagate_skipped_state self._validate_dag(kwargs) self._validate_pool(session) def _validate_dag(self, kwargs): dag = kwargs.get('dag') or DagContext.get_current_dag() if not dag: raise AirflowException('Please pass in the `dag` param or call within a DAG context manager') if dag.dag_id + '.' + kwargs['task_id'] != self.subdag.dag_id: raise AirflowException( "The subdag's dag_id should have the form '{{parent_dag_id}}.{{this_task_id}}'. " "Expected '{d}.{t}'; received '{rcvd}'.".format( d=dag.dag_id, t=kwargs['task_id'], rcvd=self.subdag.dag_id ) ) def _validate_pool(self, session): if self.pool: conflicts = [t for t in self.subdag.tasks if t.pool == self.pool] if conflicts: # only query for pool conflicts if one may exist pool = session.query(Pool).filter(Pool.slots == 1).filter(Pool.pool == self.pool).first() if pool and any(t.pool == self.pool for t in self.subdag.tasks): raise AirflowException( 'SubDagOperator {sd} and subdag task{plural} {t} both ' 'use pool {p}, but the pool only has 1 slot. The ' 'subdag tasks will never run.'.format( sd=self.task_id, plural=len(conflicts) > 1, t=', '.join(t.task_id for t in conflicts), p=self.pool, ) ) def _get_dagrun(self, execution_date): dag_runs = DagRun.find( dag_id=self.subdag.dag_id, execution_date=execution_date, ) return dag_runs[0] if dag_runs else None def _reset_dag_run_and_task_instances(self, dag_run, execution_date): with create_session() as session: dag_run.state = State.RUNNING session.merge(dag_run) failed_task_instances = ( session.query(TaskInstance) .filter(TaskInstance.dag_id == self.subdag.dag_id) .filter(TaskInstance.execution_date == execution_date) .filter(TaskInstance.state.in_([State.FAILED, State.UPSTREAM_FAILED])) ) for task_instance in failed_task_instances: task_instance.state = State.NONE session.merge(task_instance) session.commit() def pre_execute(self, context): execution_date = context['execution_date'] dag_run = self._get_dagrun(execution_date) if dag_run is None: dag_run = self.subdag.create_dagrun( run_type=DagRunType.SCHEDULED, execution_date=execution_date, state=State.RUNNING, conf=self.conf, external_trigger=True, ) self.log.info("Created DagRun: %s", dag_run.run_id) if 'notification_server_uri' in context and context['notification_server_uri']: from airflow.events.scheduler_events import DagRunCreatedEvent from notification_service.client import NotificationClient dag_run_created_event = DagRunCreatedEvent( dag_id=self.subdag.dag_id, execution_date=dag_run.execution_date ).to_event() try: client = NotificationClient(server_uri=context['notification_server_uri'], default_namespace=dag_run_created_event.namespace, sender=dag_run_created_event.sender) self.log.info("SubDagOperator sending event: {}".format(dag_run_created_event)) client.send_event(dag_run_created_event) finally: client.close() else: self.log.info("Found existing DagRun: %s", dag_run.run_id) if dag_run.state == State.FAILED: self._reset_dag_run_and_task_instances(dag_run, execution_date) def poke(self, context): execution_date = context['execution_date'] dag_run = self._get_dagrun(execution_date=execution_date) return dag_run.state != State.RUNNING def post_execute(self, context, result=None): execution_date = context['execution_date'] dag_run = self._get_dagrun(execution_date=execution_date) self.log.info("Execution finished. State is %s", dag_run.state) if dag_run.state != State.SUCCESS: raise AirflowException(f"Expected state: SUCCESS. Actual state: {dag_run.state}") if self.propagate_skipped_state and self._check_skipped_states(context): self._skip_downstream_tasks(context) def _check_skipped_states(self, context): leaves_tis = self._get_leaves_tis(context['execution_date']) if self.propagate_skipped_state == SkippedStatePropagationOptions.ANY_LEAF: return any(ti.state == State.SKIPPED for ti in leaves_tis) if self.propagate_skipped_state == SkippedStatePropagationOptions.ALL_LEAVES: return all(ti.state == State.SKIPPED for ti in leaves_tis) raise AirflowException( f'Unimplemented SkippedStatePropagationOptions {self.propagate_skipped_state} used.' ) def _get_leaves_tis(self, execution_date): leaves_tis = [] for leaf in self.subdag.leaves: try: ti = get_task_instance( dag_id=self.subdag.dag_id, task_id=leaf.task_id, execution_date=execution_date ) leaves_tis.append(ti) except TaskInstanceNotFound: continue return leaves_tis def _skip_downstream_tasks(self, context): self.log.info( 'Skipping downstream tasks because propagate_skipped_state is set to %s ' 'and skipped task(s) were found.', self.propagate_skipped_state, ) downstream_tasks = context['task'].downstream_list self.log.debug('Downstream task_ids %s', downstream_tasks) if downstream_tasks: self.skip(context['dag_run'], context['execution_date'], downstream_tasks) self.log.info('Done.')

true

f7362d25cfa413edc1cc0f2e3479ec9ea1277b43

367

py

Python

1_The_Basics/1_1_Welcome/main.py

Aurora-College-SDD-2022/Python4SDD

068b74bafd0c5fda2d9a99dc694911419d67b28a

[ "CC0-1.0" ]

null

1_The_Basics/1_1_Welcome/main.py

Aurora-College-SDD-2022/Python4SDD

068b74bafd0c5fda2d9a99dc694911419d67b28a

[ "CC0-1.0" ]

3

2021-01-03T11:02:03.000Z

2021-01-03T11:03:15.000Z

1_The_Basics/1_1_Welcome/main.py

Aurora-College-SDD-2022/Python4SDD

068b74bafd0c5fda2d9a99dc694911419d67b28a

[ "CC0-1.0" ]

1

2021-02-18T22:05:16.000Z

""" your first python program. Replace the words 'look at me I am coding' with your own message to let the world know you are now an apprentice code monkey. """ def print_message(): """prints a simple message.""" print("look at me I am coding") #Ignore everything below this line...we will talk about it later if __name__ == "__main__": print_message()

28.230769

64

0.708447

def print_message(): print("look at me I am coding") if __name__ == "__main__": print_message()

true

f7362d59a01649e753aafc6708a779ef76437b4c

8,296

py

Python

python/GafferSceneUI/OutputsUI.py

ddesmond/gaffer

4f25df88103b7893df75865ea919fb035f92bac0

[ "BSD-3-Clause" ]

561

2016-10-18T04:30:48.000Z

2022-03-30T06:52:04.000Z

python/GafferSceneUI/OutputsUI.py

ddesmond/gaffer

4f25df88103b7893df75865ea919fb035f92bac0

[ "BSD-3-Clause" ]

1,828

2016-10-14T19:01:46.000Z

2022-03-30T16:07:19.000Z

python/GafferSceneUI/OutputsUI.py

ddesmond/gaffer

4f25df88103b7893df75865ea919fb035f92bac0

[ "BSD-3-Clause" ]

120

2016-10-18T15:19:13.000Z

2021-12-20T16:28:23.000Z

########################################################################## # # Copyright (c) 2012, John Haddon. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above # copyright notice, this list of conditions and the following # disclaimer. # # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided with # the distribution. # # * Neither the name of John Haddon nor the names of # any other contributors to this software may be used to endorse or # promote products derived from this software without specific prior # written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS # IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ########################################################################## import functools import imath import IECore import IECoreScene import Gaffer import GafferUI import GafferScene import GafferSceneUI ########################################################################## # Metadata ########################################################################## Gaffer.Metadata.registerNode( GafferScene.Outputs, "description", """ Defines the image outputs to be created by the renderer. Arbitrary outputs can be defined within the UI and also via the `Outputs::addOutput()` API. Commonly used outputs may also be predefined at startup via a config file - see $GAFFER_ROOT/startup/gui/outputs.py for an example. """, plugs = { "outputs" : [ "description", """ The outputs defined by this node. """, "plugValueWidget:type", "GafferSceneUI.OutputsUI.OutputsPlugValueWidget", ], "outputs.*" : [ "plugValueWidget:type", "GafferSceneUI.OutputsUI.ChildPlugValueWidget", ], "outputs.*.parameters.quantize.value" : [ "description", """ The bit depth of the image. """, "preset:8 bit", IECore.IntVectorData( [ 0, 255, 0, 255 ] ), "preset:16 bit", IECore.IntVectorData( [ 0, 65535, 0, 65535 ] ), "preset:Float", IECore.IntVectorData( [ 0, 0, 0, 0 ] ), "plugValueWidget:type", "GafferUI.PresetsPlugValueWidget", ], "outputs.*.fileName" : [ "plugValueWidget:type", "GafferUI.FileSystemPathPlugValueWidget", "path:bookmarks", "image", "path:leaf", True, ], "outputs.*.active" : [ "boolPlugValueWidget:displayMode", "switch", ], } ) ########################################################################## # Custom PlugValueWidgets for listing outputs ########################################################################## class OutputsPlugValueWidget( GafferUI.PlugValueWidget ) : def __init__( self, plug ) : column = GafferUI.ListContainer( spacing = 6 ) GafferUI.PlugValueWidget.__init__( self, column, plug ) with column : # this will take care of laying out our list of outputs, as # each output is represented as a child plug of the main plug. GafferUI.PlugLayout( plug ) # now we just need a little footer with a button for adding new outputs with GafferUI.ListContainer( GafferUI.ListContainer.Orientation.Horizontal, spacing = 4 ) : GafferUI.MenuButton( image="plus.png", hasFrame=False, menu = GafferUI.Menu( Gaffer.WeakMethod( self.__addMenuDefinition ) ) ) GafferUI.Spacer( imath.V2i( 1 ), maximumSize = imath.V2i( 100000, 1 ), parenting = { "expand" : True } ) def hasLabel( self ) : return True def _updateFromPlug( self ) : pass def __addMenuDefinition( self ) : node = self.getPlug().node() currentNames = set( [ output["name"].getValue() for output in node["outputs"].children() ] ) m = IECore.MenuDefinition() registeredOutputs = node.registeredOutputs() for name in registeredOutputs : menuPath = name if not menuPath.startswith( "/" ) : menuPath = "/" + menuPath m.append( menuPath, { "command" : functools.partial( node.addOutput, name ), "active" : name not in currentNames } ) if len( registeredOutputs ) : m.append( "/BlankDivider", { "divider" : True } ) m.append( "/Blank", { "command" : functools.partial( node.addOutput, "", IECoreScene.Output( "", "", "" ) ) } ) return m # A widget for representing an individual output. class ChildPlugValueWidget( GafferUI.PlugValueWidget ) : def __init__( self, childPlug ) : column = GafferUI.ListContainer( GafferUI.ListContainer.Orientation.Vertical, spacing=4 ) GafferUI.PlugValueWidget.__init__( self, column, childPlug ) with column : with GafferUI.ListContainer( GafferUI.ListContainer.Orientation.Horizontal, spacing=4 ) as header : collapseButton = GafferUI.Button( image = "collapsibleArrowRight.png", hasFrame=False ) collapseButton.clickedSignal().connect( Gaffer.WeakMethod( self.__collapseButtonClicked ), scoped = False ) GafferUI.PlugValueWidget.create( childPlug["active"] ) self.__label = GafferUI.Label( self.__namePlug().getValue() ) GafferUI.Spacer( imath.V2i( 1 ), maximumSize = imath.V2i( 100000, 1 ), parenting = { "expand" : True } ) self.__deleteButton = GafferUI.Button( image = "delete.png", hasFrame=False ) self.__deleteButton.clickedSignal().connect( Gaffer.WeakMethod( self.__deleteButtonClicked ), scoped = False ) self.__deleteButton.setVisible( False ) self.__detailsColumn = GafferUI.ListContainer( GafferUI.ListContainer.Orientation.Vertical, spacing = 4 ) self.__detailsColumn.setVisible( False ) header.enterSignal().connect( Gaffer.WeakMethod( self.__enter ), scoped = False ) header.leaveSignal().connect( Gaffer.WeakMethod( self.__leave ), scoped = False ) def hasLabel( self ) : return True def _updateFromPlug( self ) : with self.getContext() : enabled = self.getPlug()["active"].getValue() self.__label.setEnabled( enabled ) self.__detailsColumn.setEnabled( enabled ) self.__label.setText( self.__namePlug().getValue() ) def __namePlug( self ) : plug = self.getPlug() # backwards compatibility with old plug layout return plug.getChild( "label" ) or plug.getChild( "name" ) def __fileNamePlug( self ) : plug = self.getPlug() # backwards compatibility with old plug layout return plug.getChild( "fileName" ) or plug.getChild( "name" ) def __enter( self, widget ) : self.__deleteButton.setVisible( True ) def __leave( self, widget ) : self.__deleteButton.setVisible( False ) def __collapseButtonClicked( self, button ) : visible = not self.__detailsColumn.getVisible() if visible and not len( self.__detailsColumn ) : # Build details section the first time it is shown, # to avoid excessive overhead in the initial UI build. with self.__detailsColumn : GafferUI.PlugWidget( self.__namePlug() ) GafferUI.PlugWidget( self.__fileNamePlug() ) GafferUI.PlugWidget( self.getPlug()["type"] ) GafferUI.PlugWidget( self.getPlug()["data"] ) GafferUI.CompoundDataPlugValueWidget( self.getPlug()["parameters"] ) GafferUI.Divider( GafferUI.Divider.Orientation.Horizontal ) self.__detailsColumn.setVisible( visible ) button.setImage( "collapsibleArrowDown.png" if visible else "collapsibleArrowRight.png" ) def __deleteButtonClicked( self, button ) : with Gaffer.UndoScope( self.getPlug().ancestor( Gaffer.ScriptNode ) ) : self.getPlug().parent().removeChild( self.getPlug() )

31.30566

114

0.674301

true

f7362d686b031b37aec567847d3c0a6b7d8027e9

1,864

py

Python

tradebook/users/models.py

isuryanarayanan/tradebook-backend

86c9e3c5adccb13b95945604c3d1e5c43066532c

[ "MIT" ]

null

tradebook/users/models.py

isuryanarayanan/tradebook-backend

86c9e3c5adccb13b95945604c3d1e5c43066532c

[ "MIT" ]

null

tradebook/users/models.py

isuryanarayanan/tradebook-backend

86c9e3c5adccb13b95945604c3d1e5c43066532c

[ "MIT" ]

null

""" User account models """ from django.db import models from django.contrib.auth.models import PermissionsMixin from django.contrib.auth.models import AbstractUser from django.contrib.auth.base_user import BaseUserManager class UserManager(BaseUserManager): use_in_migrations = True def _create_user(self, username, email, password, **extra_fields): """ Create and save a user with the given username, email, and password. """ if not username: raise ValueError('The given username must be set') email = self.normalize_email(email) username = self.model.normalize_username(username) user = self.model(username=username, email=email, **extra_fields) user.set_password(password) user.save(using=self._db) return user def create_user(self, username, email=None, password=None, **extra_fields): extra_fields.setdefault('is_staff', False) extra_fields.setdefault('is_superuser', False) return self._create_user(username, email, password, **extra_fields) def create_superuser(self, username, email=None, password=None, **extra_fields): extra_fields.setdefault('is_staff', True) extra_fields.setdefault('is_superuser', True) if extra_fields.get('is_staff') is not True: raise ValueError('Superuser must have is_staff=True.') if extra_fields.get('is_superuser') is not True: raise ValueError('Superuser must have is_superuser=True.') return self._create_user(username, email, password, **extra_fields) class User(AbstractUser): email = models.EmailField(unique=True) objects = UserManager() USERNAME_FIELD = 'email' REQUIRED_FIELDS = [] class Meta: verbose_name = "user" verbose_name_plural = "users" app_label = "users"

35.846154

84

0.688841

from django.db import models from django.contrib.auth.models import PermissionsMixin from django.contrib.auth.models import AbstractUser from django.contrib.auth.base_user import BaseUserManager class UserManager(BaseUserManager): use_in_migrations = True def _create_user(self, username, email, password, **extra_fields): if not username: raise ValueError('The given username must be set') email = self.normalize_email(email) username = self.model.normalize_username(username) user = self.model(username=username, email=email, **extra_fields) user.set_password(password) user.save(using=self._db) return user def create_user(self, username, email=None, password=None, **extra_fields): extra_fields.setdefault('is_staff', False) extra_fields.setdefault('is_superuser', False) return self._create_user(username, email, password, **extra_fields) def create_superuser(self, username, email=None, password=None, **extra_fields): extra_fields.setdefault('is_staff', True) extra_fields.setdefault('is_superuser', True) if extra_fields.get('is_staff') is not True: raise ValueError('Superuser must have is_staff=True.') if extra_fields.get('is_superuser') is not True: raise ValueError('Superuser must have is_superuser=True.') return self._create_user(username, email, password, **extra_fields) class User(AbstractUser): email = models.EmailField(unique=True) objects = UserManager() USERNAME_FIELD = 'email' REQUIRED_FIELDS = [] class Meta: verbose_name = "user" verbose_name_plural = "users" app_label = "users"

true

f7362de671c7775a19073b3d25d77d55bee0b8d2

1,602

py

Python

test/test_instructions/test_invocations.py

ronyhe/pyjvm

bb15bed8719335c09779f95d4bc16947b7bb7b98

[ "MIT" ]

15

2018-07-31T11:25:18.000Z

2021-07-28T09:13:21.000Z

test/test_instructions/test_invocations.py

ronyhe/pyjvm

bb15bed8719335c09779f95d4bc16947b7bb7b98

[ "MIT" ]

null

test/test_instructions/test_invocations.py

ronyhe/pyjvm

bb15bed8719335c09779f95d4bc16947b7bb7b98

[ "MIT" ]

1

2021-07-22T07:36:23.000Z

from jawa.constants import ConstantPool from jawa.util.bytecode import Instruction from pyjvm.core.actions import Pop, Invoke from pyjvm.core.class_loaders import FixedClassLoader from pyjvm.core.jvm_class import JvmClass, BytecodeMethod, MethodKey from pyjvm.core.jvm_types import Integer, RootObjectType from test.utils import constant_instruction, assert_instruction, SOME_INT def test_invoke_v(): method_name = 'method_name' class_name = 'class_name' consts = ConstantPool() descriptor = '(II)V' key = MethodKey(method_name, descriptor) no_op = Instruction.create('nop') method = BytecodeMethod( name='method_name', descriptor='(II)V', max_locals=5, max_stack=5, instructions=[no_op, no_op], args=[Integer, Integer], ) jvm_class = JvmClass( class_name, RootObjectType.refers_to, consts, methods={ key: method } ) method_ref = consts.create_method_ref(class_name, method_name, descriptor) instruction = constant_instruction('invokevirtual', method_ref) loader = FixedClassLoader({ class_name: jvm_class }) instance = loader.default_instance(class_name) arg_value = SOME_INT arguments = [instance, arg_value, arg_value] reversed_arguments = list(reversed(arguments)) assert_instruction( constants=consts, loader=loader, instruction=instruction, op_stack=reversed_arguments, expected=[ Pop(3), Invoke(class_name, key, arguments) ] )

27.62069

78

0.673533

from jawa.constants import ConstantPool from jawa.util.bytecode import Instruction from pyjvm.core.actions import Pop, Invoke from pyjvm.core.class_loaders import FixedClassLoader from pyjvm.core.jvm_class import JvmClass, BytecodeMethod, MethodKey from pyjvm.core.jvm_types import Integer, RootObjectType from test.utils import constant_instruction, assert_instruction, SOME_INT def test_invoke_v(): method_name = 'method_name' class_name = 'class_name' consts = ConstantPool() descriptor = '(II)V' key = MethodKey(method_name, descriptor) no_op = Instruction.create('nop') method = BytecodeMethod( name='method_name', descriptor='(II)V', max_locals=5, max_stack=5, instructions=[no_op, no_op], args=[Integer, Integer], ) jvm_class = JvmClass( class_name, RootObjectType.refers_to, consts, methods={ key: method } ) method_ref = consts.create_method_ref(class_name, method_name, descriptor) instruction = constant_instruction('invokevirtual', method_ref) loader = FixedClassLoader({ class_name: jvm_class }) instance = loader.default_instance(class_name) arg_value = SOME_INT arguments = [instance, arg_value, arg_value] reversed_arguments = list(reversed(arguments)) assert_instruction( constants=consts, loader=loader, instruction=instruction, op_stack=reversed_arguments, expected=[ Pop(3), Invoke(class_name, key, arguments) ] )

true

f7362deaf881659f32749a483965edb06eda5a8f

33,921

py

Python

src/qtt/instrument_drivers/virtual_awg.py

jsaez8/qtt

fa6497ace86a255f33a2192ba01d063d07d6895e

[ "MIT" ]

null

src/qtt/instrument_drivers/virtual_awg.py

jsaez8/qtt

fa6497ace86a255f33a2192ba01d063d07d6895e

[ "MIT" ]

null

src/qtt/instrument_drivers/virtual_awg.py

jsaez8/qtt

fa6497ace86a255f33a2192ba01d063d07d6895e

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Wed Aug 31 13:04:09 2016 @author: diepencjv """ # %% import numpy as np import scipy.signal import logging import warnings import qcodes from qcodes import Instrument from qcodes.plots.pyqtgraph import QtPlot from qcodes.data.data_array import DataArray import qtt import qtt.utilities.tools logger = logging.getLogger(__name__) # %% class virtual_awg(Instrument): """ Attributes: _awgs (list): handles to instruments awg_map (dict) hardware (Instrument): contains AWG to plunger values corr (float): unknown delay_FPGA (float): time delay of signals going through fridge """ def __init__(self, name, instruments=[], awg_map=None, hardware=None, verbose=1, **kwargs): super().__init__(name, **kwargs) logger.info('initialize virtual_awg %s' % name) self._awgs = instruments self.awg_map = awg_map self.hardware = hardware self.verbose = verbose self.delay_FPGA = 2.0e-6 # should depend on filterboxes self.corr = .0 # legacy code, specific for FPGA board not used any more self.maxdatapts = 16e6 # This used to be set to the fpga maximum, but that maximum should not be handled here self.awg_seq = None if len(self._awgs) == 0 and self.verbose: print('no physical AWGs connected') elif len(self._awgs) == 1: self.awg_cont = self._awgs[0] self.awg_cont.set('run_mode', 'CONT') elif len(self._awgs) == 2 and 'awg_mk' in self.awg_map: self.awg_cont = self._awgs[self.awg_map['awg_mk'][0]] self.awg_cont.set('run_mode', 'CONT') self.awg_seq = self._awgs[(self.awg_map['awg_mk'][0] + 1) % 2] self._set_seq_mode(self.awg_seq) self.delay_AWG = self.hardware.parameters['delay_AWG'].get() else: raise Exception( 'Configuration of AWGs not supported by virtual_awg instrument') self.AWG_clock = 1e8 self.ch_amp = 4.0 for awg in self._awgs: awg.set('clock_freq', self.AWG_clock) awg.delete_all_waveforms_from_list() for i in range(1, 5): awg.set('ch%s_amp' % i, self.ch_amp) def _set_seq_mode(self, a): a.set('run_mode', 'SEQ') a.sequence_length.set(1) a.set_sqel_trigger_wait(1, 0) def get_idn(self): ''' Overrule because the default VISA command does not work ''' IDN = {'vendor': 'QuTech', 'model': 'virtual_awg', 'serial': None, 'firmware': None} return IDN def awg_gate(self, gate): """ Return true of the gate can be controlled by the awg Args: gate () """ if gate is None: return False if isinstance(gate, dict): # vector scan, assume we can do it fast if all components are fast return np.all([self.awg_gate(g) for g in gate]) if self.awg_map is None: return False if gate in self.awg_map: return True else: return False def stop(self, verbose=0): ''' Stops all AWGs and turns of all channels ''' for awg in self._awgs: awg.stop() for i in range(1, 5): awg.set('ch%d_state' % i, 0) if verbose: print('Stopped AWGs') def sweep_init(self, waveforms, period=1e-3, delete=True, samp_freq=None): ''' Send waveform(s) to gate(s) Arguments: waveforms (dict): the waveforms with the gates as keys period (float): period of the waveform in seconds Returns: sweep_info (dict): the keys are tuples of the awgs and channels to activate Example: -------- >> sweep_info = sweep_init(waveforms) ''' sweepgates = [g for g in waveforms] if delete: for awg in self._awgs: awg.delete_all_waveforms_from_list() awgs = [self._awgs[self.awg_map[g][0]] for g in sweepgates] if 'fpga_mk' in self.awg_map: marker_info = self.awg_map['fpga_mk'] marker_delay = self.delay_FPGA marker_name = 'fpga_mk' elif 'm4i_mk' in self.awg_map: marker_info = self.awg_map['m4i_mk'] if samp_freq is not None: pretrigger_period = 16 / samp_freq else: pretrigger_period = 0 marker_delay = self.delay_FPGA + pretrigger_period marker_name = 'm4i_mk' awgs.append(self._awgs[marker_info[0]]) sweep_info = dict() wave_len = len(waveforms[sweepgates[0]]['wave']) for g in sweepgates: sweep_info[self.awg_map[g]] = dict() sweep_info[self.awg_map[g]]['waveform'] = waveforms[g]['wave'] sweep_info[self.awg_map[g]]['marker1'] = np.zeros(wave_len) sweep_info[self.awg_map[g]]['marker2'] = np.zeros(wave_len) if 'name' in waveforms[g]: sweep_info[self.awg_map[g]]['name'] = waveforms[g]['name'] else: sweep_info[self.awg_map[g]]['name'] = 'waveform_%s' % g if marker_info[:2] == self.awg_map[g]: sweep_info[marker_info[:2]]['delay'] = marker_delay # marker points marker_points = np.zeros(wave_len) marker_points[int(marker_delay * self.AWG_clock):(int(marker_delay * self.AWG_clock) + wave_len // 20)] = 1.0 if marker_info[:2] not in sweep_info: sweep_info[marker_info[:2]] = dict() sweep_info[marker_info[:2]]['waveform'] = np.zeros(wave_len) sweep_info[marker_info[:2]]['marker1'] = np.zeros(wave_len) sweep_info[marker_info[:2]]['marker2'] = np.zeros(wave_len) for g in sweepgates: marker_name += '_%s' % g sweep_info[marker_info[:2]]['name'] = marker_name sweep_info[marker_info[:2]]['delay'] = marker_delay sweep_info[marker_info[:2]]['marker%d' % marker_info[2]] = marker_points self._awgs[marker_info[0]].set( 'ch%i_m%i_low' % (marker_info[1], marker_info[2]), 0) self._awgs[marker_info[0]].set( 'ch%i_m%i_high' % (marker_info[1], marker_info[2]), 2.6) # awg marker if getattr(self, 'awg_seq', None) is not None: awg_info = self.awg_map['awg_mk'] if awg_info[:2] not in sweep_info: awgs.append(self._awgs[awg_info[0]]) sweep_info[awg_info[:2]] = dict() sweep_info[awg_info[:2]]['waveform'] = np.zeros(wave_len) sweep_info[awg_info[:2]]['marker1'] = np.zeros(wave_len) sweep_info[awg_info[:2]]['marker2'] = np.zeros(wave_len) sweep_info[awg_info[:2]]['name'] = 'awg_mk' awg_marker = np.zeros(wave_len) awg_marker[0:wave_len // 20] = 1 awg_marker = np.roll( awg_marker, wave_len - int(self.delay_AWG * self.AWG_clock)) sweep_info[awg_info[:2]]['marker%d' % self.awg_map['awg_mk'][2]] = awg_marker self._awgs[awg_info[0]].set( 'ch%i_m%i_low' % (awg_info[1], awg_info[2]), 0) self._awgs[awg_info[0]].set( 'ch%i_m%i_high' % (awg_info[1], awg_info[2]), 2.6) # send waveforms if delete: for sweep in sweep_info: try: self._awgs[sweep[0]].send_waveform_to_list(sweep_info[sweep]['waveform'], sweep_info[ sweep]['marker1'], sweep_info[sweep]['marker2'], sweep_info[sweep]['name']) except Exception as ex: print(ex) print('sweep_info[sweep][waveform] %s' % (sweep_info[sweep]['waveform'].shape,)) print('sweep_info[sweep][marker1] %s' % (sweep_info[sweep]['marker1'].shape,)) print('sweep_info[sweep][marker2] %s' % (sweep_info[sweep]['marker2'].shape,)) return sweep_info def sweep_run(self, sweep_info): ''' Activate AWG(s) and channel(s) for the sweep(s). Arguments: sweep_info (dict): the keys are tuples of the awgs and channels to activate ''' for sweep in sweep_info: if hasattr(self, 'awg_seq') and self._awgs[sweep[0]] == self.awg_seq: self._awgs[sweep[0]].set_sqel_waveform( sweep_info[sweep]['name'], sweep[1], 1) self._awgs[sweep[0]].set_sqel_loopcnt_to_inf(1) self._awgs[sweep[0]].set_sqel_event_jump_target_index( sweep[1], 1) self._awgs[sweep[0]].set_sqel_event_jump_type(1, 'IND') else: self._awgs[sweep[0]].set( 'ch%i_waveform' % sweep[1], sweep_info[sweep]['name']) for sweep in sweep_info: self._awgs[sweep[0]].set('ch%i_state' % sweep[1], 1) awgnrs = set([sweep[0] for sweep in sweep_info]) for nr in awgnrs: self._awgs[nr].run() def make_sawtooth(self, sweeprange, period, width=.95, repetitionnr=1, start_zero=False): '''Make a sawtooth with a decline width determined by width. Not yet scaled with awg_to_plunger value. Arguments: sweeprange (float): the range of voltages to sweep over period (float): the period of the triangular signal Returns: wave_raw (array): raw data which represents the waveform ''' samplerate = 1. / self.AWG_clock tt = np.arange(0, period * repetitionnr + samplerate, samplerate) v_wave = float(sweeprange / ((self.ch_amp / 2.0))) wave_raw = (v_wave / 2) * scipy.signal.sawtooth(2 * np.pi * tt / period, width=width) # idx_zero = np.argmin(np.abs(wave_raw)) # wave_raw = np.roll(wave_raw, wave_raw.size-idx_zero) if start_zero: o = int((wave_raw.size) * (1 - width) / 2) wave_raw = np.roll(wave_raw, o) return wave_raw def make_pulses(self, voltages, waittimes, reps=1, filtercutoff=None, mvrange=None): """Make a pulse sequence with custom voltage levels and wait times at each level. Arguments: voltages (list of floats): voltage levels to be applied in the sequence waittimes (list of floats): duration of each pulse in the sequence reps (int): number of times to repeat the pulse sequence in the waveform filtercutoff (float): cutoff frequency of a 1st order butterworth filter to make the pulse steps smoother Returns: wave_raw (array): raw data which represents the waveform """ if len(waittimes) != len(voltages): raise Exception('Number of voltage levels must be equal to the number of wait times') samples = [int(x * self.AWG_clock) for x in waittimes] if mvrange is None: mvrange = [max(voltages), min(voltages)] v_wave = float((mvrange[0] - mvrange[1]) / self.ch_amp) v_prop = [2 * ((x - mvrange[1]) / (mvrange[0] - mvrange[1])) - 1 for x in voltages] wave_raw = np.concatenate([x * v_wave * np.ones(y) for x, y in zip(v_prop, samples)]) if filtercutoff is not None: b, a = scipy.signal.butter(1, 0.5 * filtercutoff / self.AWG_clock, btype='low', analog=False, output='ba') wave_raw = scipy.signal.filtfilt(b, a, wave_raw) wave_raw = np.tile(wave_raw, reps) return wave_raw def check_frequency_waveform(self, period, width): """ Check whether a sawtooth waveform with specified period can be generated """ old_sr = self.AWG_clock new_sr = 5 / (period * (1 - width)) if (new_sr) > old_sr: warnings.warn('awg sampling frequency %.1f MHz is too low for signal requested (sr %.1f [MHz], period %.1f [ms])' % ( old_sr / 1e6, new_sr / 1e6, 1e3 * period), UserWarning) return new_sr def sweep_gate(self, gate, sweeprange, period, width=.95, wave_name=None, delete=True): ''' Send a sawtooth signal with the AWG to a gate to sweep. Also send a marker to the measurement instrument. Args: gate (string): the name of the gate to sweep sweeprange (float): the range of voltages to sweep over period (float): the period of the triangular signal Returns: waveform (dict): The waveform being send with the AWG. sweep_info (dict): the keys are tuples of the awgs and channels to activate Example: >>> waveform, sweep_info = sweep_gate('P1',sweeprange=60,period=1e-3) ''' self.check_frequency_waveform(period, width) self.check_amplitude(gate, sweeprange) start_zero = True waveform = dict() wave_raw = self.make_sawtooth(sweeprange, period, width, start_zero=start_zero) awg_to_plunger = self.hardware.parameters['awg_to_%s' % gate].get() wave = wave_raw / awg_to_plunger waveform[gate] = dict() waveform[gate]['wave'] = wave if wave_name is None: waveform[gate]['name'] = 'sweep_%s' % gate else: waveform[gate]['name'] = wave_name sweep_info = self.sweep_init(waveform, period, delete) self.sweep_run(sweep_info) waveform['width'] = width waveform['start_zero'] = start_zero waveform['sweeprange'] = sweeprange waveform['samplerate'] = 1 / self.AWG_clock waveform['period'] = period for channels in sweep_info: if 'delay' in sweep_info[channels]: waveform['markerdelay'] = sweep_info[channels]['delay'] return waveform, sweep_info def sweep_gate_virt(self, gate_comb, sweeprange, period, width=.95, delete=True): ''' Send a sawtooth signal with the AWG to a linear combination of gates to sweep. Also send a marker to the measurement instrument. Arguments: gate_comb (dict): the gates to sweep and the coefficients as values sweeprange (float): the range of voltages to sweep over period (float): the period of the triangular signal Returns: waveform (dict): The waveform being send with the AWG. sweep_info (dict): the keys are tuples of the awgs and channels to activate ''' self.check_frequency_waveform(period, width) waveform = dict() for g in gate_comb: self.check_amplitude(g, gate_comb[g] * sweeprange) for g in gate_comb: wave_raw = self.make_sawtooth(sweeprange, period, width) awg_to_plunger = self.hardware.parameters['awg_to_%s' % g].get() wave = wave_raw * gate_comb[g] / awg_to_plunger waveform[g] = dict() waveform[g]['wave'] = wave waveform[g]['name'] = 'sweep_%s' % g sweep_info = self.sweep_init(waveform, period, delete) self.sweep_run(sweep_info) waveform['width'] = width waveform['sweeprange'] = sweeprange waveform['samplerate'] = 1 / self.AWG_clock waveform['period'] = period for channels in sweep_info: if 'delay' in sweep_info[channels]: waveform['markerdelay'] = sweep_info[channels]['delay'] return waveform, sweep_info def sweepandpulse_gate(self, sweepdata, pulsedata, wave_name=None, delete=True, shift_zero=True): ''' Makes and outputs a waveform which overlays a sawtooth signal to sweep a gate, with a pulse sequence. A marker is sent to the measurement instrument at the start of the waveform. IMPORTANT: The function offsets the voltages values so that the last point is 0 V on all gates (i.e. it centers the pulse sequence on the last point) Args: sweepdata (dict): inputs for the sawtooth (gate, sweeprange, period, width). See sweep_gate for more info. pulsedata (dict): inputs for the pulse sequence (gate_voltages, waittimes). See pulse_gates for more info. Returns: waveform (dict): The waveform being sent with the AWG. sweep_info (dict): the keys are tuples of the awgs and channels to activate ''' sweepgate = sweepdata['gate'] sweeprange = sweepdata['sweeprange'] period = sweepdata['period'] width = sweepdata.get('width', 0.95) gate_voltages = pulsedata['gate_voltages'].copy() if shift_zero: for g in gate_voltages: gate_voltages[g] = [x - gate_voltages[g][-1] for x in gate_voltages[g]] waittimes = pulsedata['waittimes'] filtercutoff = pulsedata.get('filtercutoff', None) pulsesamp = [int(round(x * self.AWG_clock)) for x in waittimes] sawsamp = int(round(period * width * self.AWG_clock)) pulsereps = int(np.ceil(self.AWG_clock * period * width / sum(pulsesamp))) allvoltages = np.concatenate([v for v in gate_voltages.values()]) mvrange = [max(allvoltages), min(allvoltages)] self.check_frequency_waveform(period, width) waveform = dict() wave_sweep = self.make_sawtooth(sweeprange, period, width) for g in gate_voltages: self.check_amplitude(g, sweeprange + (mvrange[0] - mvrange[1])) for g in gate_voltages: wave_raw = self.make_pulses(gate_voltages[g], waittimes, reps=pulsereps, filtercutoff=filtercutoff, mvrange=mvrange) wave_raw = wave_raw[:sawsamp] wave_raw = np.pad(wave_raw, (0, len(wave_sweep) - len(wave_raw)), 'edge') if sweepgate == g: wave_raw += wave_sweep awg_to_plunger = self.hardware.parameters['awg_to_%s' % g].get() wave = wave_raw / awg_to_plunger waveform[g] = dict() waveform[g]['wave'] = wave if wave_name is None: waveform[g]['name'] = 'sweepandpulse_%s' % g else: waveform[g]['name'] = wave_name sweep_info = self.sweep_init(waveform, period, delete) self.sweep_run(sweep_info) waveform['width'] = width waveform['sweeprange'] = sweeprange waveform['samplerate'] = 1 / self.AWG_clock waveform['period'] = period waveform['pulse_voltages'] = gate_voltages waveform['pulse_waittimes'] = waittimes for channels in sweep_info: if 'delay' in sweep_info[channels]: waveform['markerdelay'] = sweep_info[channels]['delay'] return waveform, sweep_info def sweep_process(self, data, waveform, Naverage=1, direction='forwards', start_offset=1): """ Process the data returned by reading out based on the shape of the sawtooth send with the AWG. Args: data (list or Nxk array): the data (N is the number of samples) waveform (dict): contains the wave and the sawtooth width Naverage (int): number of times the signal was averaged direction (string): option to use backwards signal i.o. forwards Returns: data_processed (array): The data after dropping part of it. Example: >> data_processed = sweep_process(data, waveform, 25) """ width = waveform['width'] if isinstance(data, list): data = np.array(data) if direction == 'forwards': end = int(np.floor(width * data.shape[0] - 1)) data_processed = data[start_offset:end] elif direction == 'backwards': begin = int(np.ceil(width * data.shape[0] + 1)) data_processed = data[begin:] data_processed = data_processed[::-1] data_processed = np.array(data_processed) / Naverage return data_processed def sweep_2D(self, samp_freq, sweepgates, sweepranges, resolution, width=.95, comp=None, delete=True): ''' Send sawtooth signals to the sweepgates which effectively do a 2D scan. The first sweepgate is the fast changing gate (on the horizontal axis). Arguments: samp_freq (float): sampling frequency of the measurement instrument in Hertz. sweepgates (list): two strings with names of gates to sweep sweepranges (list): two floats for sweepranges in milliVolts Returns: waveform (dict): The waveforms being send with the AWG. sweep_info (dict): the keys are tuples of the awgs and channels to activate ''' # JP: I think FPGA exceptions should not be handled by awg # if resolution[0] * resolution[1] > self.maxdatapts: # raise Exception('resolution is set higher than FPGA memory allows') if self.corr != 0: raise Exception('please do not use the .corr setting any more') error_corr = resolution[0] * self.corr period_horz = resolution[0] / samp_freq + error_corr period_vert = resolution[1] * period_horz self.check_frequency_waveform(period_horz, width) for g, r in zip(sweepgates, sweepranges): self.check_amplitude(g, r) waveform = dict() # horizontal waveform wave_horz_raw = self.make_sawtooth( sweepranges[0], period_horz, repetitionnr=resolution[1]) awg_to_plunger_horz = self.hardware.parameters[ 'awg_to_%s' % sweepgates[0]].get() wave_horz = wave_horz_raw / awg_to_plunger_horz waveform[sweepgates[0]] = dict() waveform[sweepgates[0]]['wave'] = wave_horz waveform[sweepgates[0]]['name'] = 'sweep_2D_horz_%s' % sweepgates[0] # vertical waveform wave_vert_raw = self.make_sawtooth(sweepranges[1], period_vert) awg_to_plunger_vert = self.hardware.parameters[ 'awg_to_%s' % sweepgates[1]].get() wave_vert = wave_vert_raw / awg_to_plunger_vert waveform[sweepgates[1]] = dict() waveform[sweepgates[1]]['wave'] = wave_vert waveform[sweepgates[1]]['name'] = 'sweep_2D_vert_%s' % sweepgates[1] if comp is not None: for g in comp: if g not in sweepgates: waveform[g] = dict() waveform[g]['wave'] = comp[g]['vert'] * \ wave_vert + comp[g]['horz'] * wave_horz waveform[g]['name'] = 'sweep_2D_comp_%s' % g else: raise Exception('Can not compensate a sweepgate') sweep_info = self.sweep_init(waveform, period=period_vert, delete=delete, samp_freq=samp_freq) self.sweep_run(sweep_info) waveform['width_horz'] = width waveform['sweeprange_horz'] = sweepranges[0] waveform['width_vert'] = width waveform['sweeprange_vert'] = sweepranges[1] waveform['resolution'] = resolution waveform['samplerate'] = 1 / self.AWG_clock waveform['period'] = period_vert waveform['period_horz'] = period_horz for channels in sweep_info: if 'delay' in sweep_info[channels]: waveform['markerdelay'] = sweep_info[channels]['delay'] return waveform, sweep_info def sweep_2D_virt(self, samp_freq, gates_horz, gates_vert, sweepranges, resolution, width=.95, delete=True): ''' Send sawtooth signals to the linear combinations of gates set by gates_horz and gates_vert which effectively do a 2D scan of two virtual gates. The horizontal direction is the direction where the AWG signal is changing fastest. It is the first element in the resolution and sweepranges. Arguments: samp_freq (float): sampling frequency of the measurement instrument in Hertz. gates_horz (dict): the gates for the horizontal direction and their coefficients gates_vert (dict): the gates for the vertical direction and their coefficients sweepranges (list): two floats for sweepranges in milliVolts resolution (list): two ints for numbers of pixels Returns: waveform (dict): The waveforms being send with the AWG. sweep_info (dict): the keys are tuples of the awgs and channels to activate ''' # JP: I think FPGA exceptions should not be handled by awg # if resolution[0] * resolution[1] > self.maxdatapts: # raise Exception('resolution is set higher than memory allows') error_corr = resolution[0] * self.corr period_horz = resolution[0] / samp_freq + error_corr period_vert = resolution[1] * period_horz new_sr = self.check_frequency_waveform(period_horz, width) # self.reset_AWG(new_sr) waveform = dict() # horizontal virtual gate for g in gates_horz: self.check_amplitude(g, sweepranges[0] * gates_horz[g]) for g in gates_horz: wave_raw = self.make_sawtooth(sweepranges[0], period_horz, repetitionnr=resolution[1]) awg_to_plunger = self.hardware.parameters['awg_to_%s' % g].get() wave = wave_raw * gates_horz[g] / awg_to_plunger waveform[g] = dict() waveform[g]['wave'] = wave waveform[g]['name'] = 'sweep_2D_virt_%s' % g # vertical virtual gate for g in gates_vert: self.check_amplitude(g, sweepranges[1] * gates_vert[g]) for g in gates_vert: wave_raw = self.make_sawtooth(sweepranges[1], period_vert) awg_to_plunger = self.hardware.parameters['awg_to_%s' % g].get() wave = wave_raw * gates_vert[g] / awg_to_plunger if g in waveform: waveform[g]['wave'] = waveform[g]['wave'] + wave else: waveform[g] = dict() waveform[g]['wave'] = wave waveform[g]['name'] = 'sweep_2D_virt_%s' % g # TODO: Implement compensation of sensing dot plunger sweep_info = self.sweep_init(waveform, period=period_vert, delete=delete, samp_freq=samp_freq) self.sweep_run(sweep_info) waveform['width_horz'] = width waveform['sweeprange_horz'] = sweepranges[0] waveform['width_vert'] = width waveform['sweeprange_vert'] = sweepranges[1] waveform['resolution'] = resolution waveform['samplerate'] = 1 / self.AWG_clock waveform['period'] = period_vert waveform['period_horz'] = period_horz for channels in sweep_info: if 'delay' in sweep_info[channels]: waveform['markerdelay'] = sweep_info[channels]['delay'] return waveform, sweep_info def sweep_2D_process(self, data, waveform, diff_dir=None): ''' Process data from sweep_2D Arguments: data (list): the raw measured data waveform (dict): The waveforms that was sent with the AWG. Returns: data_processed (list): the processed data ''' width_horz = waveform['width_horz'] width_vert = waveform['width_vert'] resolution = waveform['resolution'] # split up the fpga data in chunks of horizontal sweeps chunks_ch1 = [data[x:x + resolution[0]] for x in range(0, len(data), resolution[0])] chunks_ch1 = [chunks_ch1[i][1:int(width_horz * len(chunks_ch1[i]))] for i in range(0, len(chunks_ch1))] data_processed = chunks_ch1[:int(width_vert * len(chunks_ch1))] if diff_dir is not None: data_processed = qtt.utilities.tools.diffImageSmooth(data_processed, dy=diff_dir, sigma=1) return data_processed def pulse_gates(self, gate_voltages, waittimes, reps=1, filtercutoff=None, reset_to_zero=False, delete=True): ''' Send a pulse sequence with the AWG that can span over any gate space. Sends a marker to measurement instrument at the start of the sequence. Only works with physical gates. Arguments: gate_voltages (dict): keys are gates to apply the sequence to, and values are arrays with the voltage levels to be applied in the sequence waittimes (list of floats): duration of each pulse in the sequence reset_to_zero (bool): if True, the function offsets the voltages values so that the last point is 0V on all gates (i.e. it centers the pulse sequence on the last point). Returns: waveform (dict): The waveform being send with the AWG. sweep_info (dict): the keys are tuples of the awgs and channels to activate ''' period = sum(waittimes) if reset_to_zero: for g in gate_voltages: gate_voltages[g] = [x - gate_voltages[g][-1] for x in gate_voltages[g]] allvoltages = np.concatenate([v for v in gate_voltages.values()]) mvrange = [max(allvoltages), min(allvoltages)] waveform = dict() for g in gate_voltages: wave_raw = self.make_pulses(gate_voltages[g], waittimes, reps=reps, filtercutoff=filtercutoff, mvrange=mvrange) awg_to_plunger = self.hardware.parameters['awg_to_%s' % g].get() wave = wave_raw / awg_to_plunger waveform[g] = dict() waveform[g]['wave'] = wave waveform[g]['name'] = 'pulses_%s' % g sweep_info = self.sweep_init(waveform, period, delete) self.sweep_run(sweep_info) waveform['voltages'] = gate_voltages waveform['samplerate'] = 1 / self.AWG_clock waveform['waittimes'] = waittimes for channels in sweep_info: if 'delay' in sweep_info[channels]: waveform['markerdelay'] = sweep_info[channels]['delay'] return waveform, sweep_info def reset_AWG(self, clock=1e8): """ Reset AWG to videomode and scanfast """ self.AWG_clock = clock for a in self._awgs: a.clock_freq.set(clock) a.trigger_mode.set('CONT') a.trigger_source.set('INT') for ii in range(1, 5): f = getattr(a, 'ch%d_amp' % ii) val = f() if val != 4.0: warnings.warn('AWG channel %d output not at 4.0 V' % ii) if self.awg_seq is not None: self._set_seq_mode(self.awg_seq) def set_amplitude(self, amplitude): """ Set the AWG peak-to-peak amplitude for all channels Args: amplitude (float): peak-to-peak amplitude (V) """ if amplitude < 0.02: warnings.warn('Trying to set AWG amplitude too low, setting it to minimum (20mV)') amplitude = 0.02 elif amplitude > 4.5: warnings.warn('Trying to set AWG amplitude too high, setting it to maximum (4.5V)') amplitude = 4.5 # tektronics 5014 has precision of 1mV self.ch_amp = round(amplitude, 3) for awg in self._awgs: for i in range(1, 5): awg.set('ch%s_amp' % i, self.ch_amp) def check_amplitude(self, gate, mvrange): """ Calculates the lowest allowable AWG peak-to-peak amplitude based on the ranges to be applied to the gates. If the AWG amplitude is too low, it gives a warning and increases the amplitude. Args: gate (str): name of the gate to check mvrange (float): voltage range, in mV, that the gate needs to reach """ min_amp = mvrange / self.hardware.parameters['awg_to_%s' % gate].get() if min_amp > 4: raise(Exception('Sweep range of gate %s is larger than maximum allowed by the AWG' % gate)) if self.ch_amp < min_amp: min_amp = np.ceil(min_amp * 10) / 10 self.set_amplitude(min_amp) warnings.warn('AWG amplitude too low for this range, setting to %.1f' % min_amp) # %% def plot_wave_raw(wave_raw, samplerate=None, station=None): ''' Plot the raw wave Arguments: wave_raw (array): raw data which represents the waveform Returns: plot (QtPlot): the plot showing the data ''' if samplerate is None: if station is None: raise Exception('There is no station') samplerate = 1 / station.awg.getattr('AWG_clock') else: samplerate = samplerate horz_var = np.arange(0, len(wave_raw) * samplerate, samplerate) x = DataArray(name='time(s)', label='time (s)', preset_data=horz_var, is_setpoint=True) y = DataArray( label='sweep value (mV)', preset_data=wave_raw, set_arrays=(x,)) plot = QtPlot(x, y) return plot def sweep_2D_process(data, waveform, diff_dir=None): ''' Process data from sweep_2D Arguments: data (list): the raw measured data waveform (dict): The waveforms that was sent with the AWG. Returns: data_processed (list): the processed data ''' width_horz = waveform['width_horz'] width_vert = waveform['width_vert'] resolution = waveform['resolution'] # split up the fpga data in chunks of horizontal sweeps chunks_ch1 = [data[x:x + resolution[0]] for x in range(0, len(data), resolution[0])] chunks_ch1 = [chunks_ch1[i][1:int(width_horz * len(chunks_ch1[i]))] for i in range(0, len(chunks_ch1))] data_processed = chunks_ch1[:int(width_vert * len(chunks_ch1))] if diff_dir is not None: data_processed = qtt.utilities.tools.diffImageSmooth(data_processed, dy=diff_dir, sigma=1) return data_processed

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import numpy as np import scipy.signal import logging import warnings import qcodes from qcodes import Instrument from qcodes.plots.pyqtgraph import QtPlot from qcodes.data.data_array import DataArray import qtt import qtt.utilities.tools logger = logging.getLogger(__name__) class virtual_awg(Instrument): def __init__(self, name, instruments=[], awg_map=None, hardware=None, verbose=1, **kwargs): super().__init__(name, **kwargs) logger.info('initialize virtual_awg %s' % name) self._awgs = instruments self.awg_map = awg_map self.hardware = hardware self.verbose = verbose self.delay_FPGA = 2.0e-6 self.corr = .0 self.maxdatapts = 16e6 self.awg_seq = None if len(self._awgs) == 0 and self.verbose: print('no physical AWGs connected') elif len(self._awgs) == 1: self.awg_cont = self._awgs[0] self.awg_cont.set('run_mode', 'CONT') elif len(self._awgs) == 2 and 'awg_mk' in self.awg_map: self.awg_cont = self._awgs[self.awg_map['awg_mk'][0]] self.awg_cont.set('run_mode', 'CONT') self.awg_seq = self._awgs[(self.awg_map['awg_mk'][0] + 1) % 2] self._set_seq_mode(self.awg_seq) self.delay_AWG = self.hardware.parameters['delay_AWG'].get() else: raise Exception( 'Configuration of AWGs not supported by virtual_awg instrument') self.AWG_clock = 1e8 self.ch_amp = 4.0 for awg in self._awgs: awg.set('clock_freq', self.AWG_clock) awg.delete_all_waveforms_from_list() for i in range(1, 5): awg.set('ch%s_amp' % i, self.ch_amp) def _set_seq_mode(self, a): a.set('run_mode', 'SEQ') a.sequence_length.set(1) a.set_sqel_trigger_wait(1, 0) def get_idn(self): IDN = {'vendor': 'QuTech', 'model': 'virtual_awg', 'serial': None, 'firmware': None} return IDN def awg_gate(self, gate): if gate is None: return False if isinstance(gate, dict): return np.all([self.awg_gate(g) for g in gate]) if self.awg_map is None: return False if gate in self.awg_map: return True else: return False def stop(self, verbose=0): for awg in self._awgs: awg.stop() for i in range(1, 5): awg.set('ch%d_state' % i, 0) if verbose: print('Stopped AWGs') def sweep_init(self, waveforms, period=1e-3, delete=True, samp_freq=None): sweepgates = [g for g in waveforms] if delete: for awg in self._awgs: awg.delete_all_waveforms_from_list() awgs = [self._awgs[self.awg_map[g][0]] for g in sweepgates] if 'fpga_mk' in self.awg_map: marker_info = self.awg_map['fpga_mk'] marker_delay = self.delay_FPGA marker_name = 'fpga_mk' elif 'm4i_mk' in self.awg_map: marker_info = self.awg_map['m4i_mk'] if samp_freq is not None: pretrigger_period = 16 / samp_freq else: pretrigger_period = 0 marker_delay = self.delay_FPGA + pretrigger_period marker_name = 'm4i_mk' awgs.append(self._awgs[marker_info[0]]) sweep_info = dict() wave_len = len(waveforms[sweepgates[0]]['wave']) for g in sweepgates: sweep_info[self.awg_map[g]] = dict() sweep_info[self.awg_map[g]]['waveform'] = waveforms[g]['wave'] sweep_info[self.awg_map[g]]['marker1'] = np.zeros(wave_len) sweep_info[self.awg_map[g]]['marker2'] = np.zeros(wave_len) if 'name' in waveforms[g]: sweep_info[self.awg_map[g]]['name'] = waveforms[g]['name'] else: sweep_info[self.awg_map[g]]['name'] = 'waveform_%s' % g if marker_info[:2] == self.awg_map[g]: sweep_info[marker_info[:2]]['delay'] = marker_delay marker_points = np.zeros(wave_len) marker_points[int(marker_delay * self.AWG_clock):(int(marker_delay * self.AWG_clock) + wave_len // 20)] = 1.0 if marker_info[:2] not in sweep_info: sweep_info[marker_info[:2]] = dict() sweep_info[marker_info[:2]]['waveform'] = np.zeros(wave_len) sweep_info[marker_info[:2]]['marker1'] = np.zeros(wave_len) sweep_info[marker_info[:2]]['marker2'] = np.zeros(wave_len) for g in sweepgates: marker_name += '_%s' % g sweep_info[marker_info[:2]]['name'] = marker_name sweep_info[marker_info[:2]]['delay'] = marker_delay sweep_info[marker_info[:2]]['marker%d' % marker_info[2]] = marker_points self._awgs[marker_info[0]].set( 'ch%i_m%i_low' % (marker_info[1], marker_info[2]), 0) self._awgs[marker_info[0]].set( 'ch%i_m%i_high' % (marker_info[1], marker_info[2]), 2.6) if getattr(self, 'awg_seq', None) is not None: awg_info = self.awg_map['awg_mk'] if awg_info[:2] not in sweep_info: awgs.append(self._awgs[awg_info[0]]) sweep_info[awg_info[:2]] = dict() sweep_info[awg_info[:2]]['waveform'] = np.zeros(wave_len) sweep_info[awg_info[:2]]['marker1'] = np.zeros(wave_len) sweep_info[awg_info[:2]]['marker2'] = np.zeros(wave_len) sweep_info[awg_info[:2]]['name'] = 'awg_mk' awg_marker = np.zeros(wave_len) awg_marker[0:wave_len // 20] = 1 awg_marker = np.roll( awg_marker, wave_len - int(self.delay_AWG * self.AWG_clock)) sweep_info[awg_info[:2]]['marker%d' % self.awg_map['awg_mk'][2]] = awg_marker self._awgs[awg_info[0]].set( 'ch%i_m%i_low' % (awg_info[1], awg_info[2]), 0) self._awgs[awg_info[0]].set( 'ch%i_m%i_high' % (awg_info[1], awg_info[2]), 2.6) if delete: for sweep in sweep_info: try: self._awgs[sweep[0]].send_waveform_to_list(sweep_info[sweep]['waveform'], sweep_info[ sweep]['marker1'], sweep_info[sweep]['marker2'], sweep_info[sweep]['name']) except Exception as ex: print(ex) print('sweep_info[sweep][waveform] %s' % (sweep_info[sweep]['waveform'].shape,)) print('sweep_info[sweep][marker1] %s' % (sweep_info[sweep]['marker1'].shape,)) print('sweep_info[sweep][marker2] %s' % (sweep_info[sweep]['marker2'].shape,)) return sweep_info def sweep_run(self, sweep_info): for sweep in sweep_info: if hasattr(self, 'awg_seq') and self._awgs[sweep[0]] == self.awg_seq: self._awgs[sweep[0]].set_sqel_waveform( sweep_info[sweep]['name'], sweep[1], 1) self._awgs[sweep[0]].set_sqel_loopcnt_to_inf(1) self._awgs[sweep[0]].set_sqel_event_jump_target_index( sweep[1], 1) self._awgs[sweep[0]].set_sqel_event_jump_type(1, 'IND') else: self._awgs[sweep[0]].set( 'ch%i_waveform' % sweep[1], sweep_info[sweep]['name']) for sweep in sweep_info: self._awgs[sweep[0]].set('ch%i_state' % sweep[1], 1) awgnrs = set([sweep[0] for sweep in sweep_info]) for nr in awgnrs: self._awgs[nr].run() def make_sawtooth(self, sweeprange, period, width=.95, repetitionnr=1, start_zero=False): samplerate = 1. / self.AWG_clock tt = np.arange(0, period * repetitionnr + samplerate, samplerate) v_wave = float(sweeprange / ((self.ch_amp / 2.0))) wave_raw = (v_wave / 2) * scipy.signal.sawtooth(2 * np.pi * tt / period, width=width) if start_zero: o = int((wave_raw.size) * (1 - width) / 2) wave_raw = np.roll(wave_raw, o) return wave_raw def make_pulses(self, voltages, waittimes, reps=1, filtercutoff=None, mvrange=None): if len(waittimes) != len(voltages): raise Exception('Number of voltage levels must be equal to the number of wait times') samples = [int(x * self.AWG_clock) for x in waittimes] if mvrange is None: mvrange = [max(voltages), min(voltages)] v_wave = float((mvrange[0] - mvrange[1]) / self.ch_amp) v_prop = [2 * ((x - mvrange[1]) / (mvrange[0] - mvrange[1])) - 1 for x in voltages] wave_raw = np.concatenate([x * v_wave * np.ones(y) for x, y in zip(v_prop, samples)]) if filtercutoff is not None: b, a = scipy.signal.butter(1, 0.5 * filtercutoff / self.AWG_clock, btype='low', analog=False, output='ba') wave_raw = scipy.signal.filtfilt(b, a, wave_raw) wave_raw = np.tile(wave_raw, reps) return wave_raw def check_frequency_waveform(self, period, width): old_sr = self.AWG_clock new_sr = 5 / (period * (1 - width)) if (new_sr) > old_sr: warnings.warn('awg sampling frequency %.1f MHz is too low for signal requested (sr %.1f [MHz], period %.1f [ms])' % ( old_sr / 1e6, new_sr / 1e6, 1e3 * period), UserWarning) return new_sr def sweep_gate(self, gate, sweeprange, period, width=.95, wave_name=None, delete=True): self.check_frequency_waveform(period, width) self.check_amplitude(gate, sweeprange) start_zero = True waveform = dict() wave_raw = self.make_sawtooth(sweeprange, period, width, start_zero=start_zero) awg_to_plunger = self.hardware.parameters['awg_to_%s' % gate].get() wave = wave_raw / awg_to_plunger waveform[gate] = dict() waveform[gate]['wave'] = wave if wave_name is None: waveform[gate]['name'] = 'sweep_%s' % gate else: waveform[gate]['name'] = wave_name sweep_info = self.sweep_init(waveform, period, delete) self.sweep_run(sweep_info) waveform['width'] = width waveform['start_zero'] = start_zero waveform['sweeprange'] = sweeprange waveform['samplerate'] = 1 / self.AWG_clock waveform['period'] = period for channels in sweep_info: if 'delay' in sweep_info[channels]: waveform['markerdelay'] = sweep_info[channels]['delay'] return waveform, sweep_info def sweep_gate_virt(self, gate_comb, sweeprange, period, width=.95, delete=True): self.check_frequency_waveform(period, width) waveform = dict() for g in gate_comb: self.check_amplitude(g, gate_comb[g] * sweeprange) for g in gate_comb: wave_raw = self.make_sawtooth(sweeprange, period, width) awg_to_plunger = self.hardware.parameters['awg_to_%s' % g].get() wave = wave_raw * gate_comb[g] / awg_to_plunger waveform[g] = dict() waveform[g]['wave'] = wave waveform[g]['name'] = 'sweep_%s' % g sweep_info = self.sweep_init(waveform, period, delete) self.sweep_run(sweep_info) waveform['width'] = width waveform['sweeprange'] = sweeprange waveform['samplerate'] = 1 / self.AWG_clock waveform['period'] = period for channels in sweep_info: if 'delay' in sweep_info[channels]: waveform['markerdelay'] = sweep_info[channels]['delay'] return waveform, sweep_info def sweepandpulse_gate(self, sweepdata, pulsedata, wave_name=None, delete=True, shift_zero=True): sweepgate = sweepdata['gate'] sweeprange = sweepdata['sweeprange'] period = sweepdata['period'] width = sweepdata.get('width', 0.95) gate_voltages = pulsedata['gate_voltages'].copy() if shift_zero: for g in gate_voltages: gate_voltages[g] = [x - gate_voltages[g][-1] for x in gate_voltages[g]] waittimes = pulsedata['waittimes'] filtercutoff = pulsedata.get('filtercutoff', None) pulsesamp = [int(round(x * self.AWG_clock)) for x in waittimes] sawsamp = int(round(period * width * self.AWG_clock)) pulsereps = int(np.ceil(self.AWG_clock * period * width / sum(pulsesamp))) allvoltages = np.concatenate([v for v in gate_voltages.values()]) mvrange = [max(allvoltages), min(allvoltages)] self.check_frequency_waveform(period, width) waveform = dict() wave_sweep = self.make_sawtooth(sweeprange, period, width) for g in gate_voltages: self.check_amplitude(g, sweeprange + (mvrange[0] - mvrange[1])) for g in gate_voltages: wave_raw = self.make_pulses(gate_voltages[g], waittimes, reps=pulsereps, filtercutoff=filtercutoff, mvrange=mvrange) wave_raw = wave_raw[:sawsamp] wave_raw = np.pad(wave_raw, (0, len(wave_sweep) - len(wave_raw)), 'edge') if sweepgate == g: wave_raw += wave_sweep awg_to_plunger = self.hardware.parameters['awg_to_%s' % g].get() wave = wave_raw / awg_to_plunger waveform[g] = dict() waveform[g]['wave'] = wave if wave_name is None: waveform[g]['name'] = 'sweepandpulse_%s' % g else: waveform[g]['name'] = wave_name sweep_info = self.sweep_init(waveform, period, delete) self.sweep_run(sweep_info) waveform['width'] = width waveform['sweeprange'] = sweeprange waveform['samplerate'] = 1 / self.AWG_clock waveform['period'] = period waveform['pulse_voltages'] = gate_voltages waveform['pulse_waittimes'] = waittimes for channels in sweep_info: if 'delay' in sweep_info[channels]: waveform['markerdelay'] = sweep_info[channels]['delay'] return waveform, sweep_info def sweep_process(self, data, waveform, Naverage=1, direction='forwards', start_offset=1): width = waveform['width'] if isinstance(data, list): data = np.array(data) if direction == 'forwards': end = int(np.floor(width * data.shape[0] - 1)) data_processed = data[start_offset:end] elif direction == 'backwards': begin = int(np.ceil(width * data.shape[0] + 1)) data_processed = data[begin:] data_processed = data_processed[::-1] data_processed = np.array(data_processed) / Naverage return data_processed def sweep_2D(self, samp_freq, sweepgates, sweepranges, resolution, width=.95, comp=None, delete=True): if self.corr != 0: raise Exception('please do not use the .corr setting any more') error_corr = resolution[0] * self.corr period_horz = resolution[0] / samp_freq + error_corr period_vert = resolution[1] * period_horz self.check_frequency_waveform(period_horz, width) for g, r in zip(sweepgates, sweepranges): self.check_amplitude(g, r) waveform = dict() wave_horz_raw = self.make_sawtooth( sweepranges[0], period_horz, repetitionnr=resolution[1]) awg_to_plunger_horz = self.hardware.parameters[ 'awg_to_%s' % sweepgates[0]].get() wave_horz = wave_horz_raw / awg_to_plunger_horz waveform[sweepgates[0]] = dict() waveform[sweepgates[0]]['wave'] = wave_horz waveform[sweepgates[0]]['name'] = 'sweep_2D_horz_%s' % sweepgates[0] wave_vert_raw = self.make_sawtooth(sweepranges[1], period_vert) awg_to_plunger_vert = self.hardware.parameters[ 'awg_to_%s' % sweepgates[1]].get() wave_vert = wave_vert_raw / awg_to_plunger_vert waveform[sweepgates[1]] = dict() waveform[sweepgates[1]]['wave'] = wave_vert waveform[sweepgates[1]]['name'] = 'sweep_2D_vert_%s' % sweepgates[1] if comp is not None: for g in comp: if g not in sweepgates: waveform[g] = dict() waveform[g]['wave'] = comp[g]['vert'] * \ wave_vert + comp[g]['horz'] * wave_horz waveform[g]['name'] = 'sweep_2D_comp_%s' % g else: raise Exception('Can not compensate a sweepgate') sweep_info = self.sweep_init(waveform, period=period_vert, delete=delete, samp_freq=samp_freq) self.sweep_run(sweep_info) waveform['width_horz'] = width waveform['sweeprange_horz'] = sweepranges[0] waveform['width_vert'] = width waveform['sweeprange_vert'] = sweepranges[1] waveform['resolution'] = resolution waveform['samplerate'] = 1 / self.AWG_clock waveform['period'] = period_vert waveform['period_horz'] = period_horz for channels in sweep_info: if 'delay' in sweep_info[channels]: waveform['markerdelay'] = sweep_info[channels]['delay'] return waveform, sweep_info def sweep_2D_virt(self, samp_freq, gates_horz, gates_vert, sweepranges, resolution, width=.95, delete=True): error_corr = resolution[0] * self.corr period_horz = resolution[0] / samp_freq + error_corr period_vert = resolution[1] * period_horz new_sr = self.check_frequency_waveform(period_horz, width) waveform = dict() for g in gates_horz: self.check_amplitude(g, sweepranges[0] * gates_horz[g]) for g in gates_horz: wave_raw = self.make_sawtooth(sweepranges[0], period_horz, repetitionnr=resolution[1]) awg_to_plunger = self.hardware.parameters['awg_to_%s' % g].get() wave = wave_raw * gates_horz[g] / awg_to_plunger waveform[g] = dict() waveform[g]['wave'] = wave waveform[g]['name'] = 'sweep_2D_virt_%s' % g for g in gates_vert: self.check_amplitude(g, sweepranges[1] * gates_vert[g]) for g in gates_vert: wave_raw = self.make_sawtooth(sweepranges[1], period_vert) awg_to_plunger = self.hardware.parameters['awg_to_%s' % g].get() wave = wave_raw * gates_vert[g] / awg_to_plunger if g in waveform: waveform[g]['wave'] = waveform[g]['wave'] + wave else: waveform[g] = dict() waveform[g]['wave'] = wave waveform[g]['name'] = 'sweep_2D_virt_%s' % g sweep_info = self.sweep_init(waveform, period=period_vert, delete=delete, samp_freq=samp_freq) self.sweep_run(sweep_info) waveform['width_horz'] = width waveform['sweeprange_horz'] = sweepranges[0] waveform['width_vert'] = width waveform['sweeprange_vert'] = sweepranges[1] waveform['resolution'] = resolution waveform['samplerate'] = 1 / self.AWG_clock waveform['period'] = period_vert waveform['period_horz'] = period_horz for channels in sweep_info: if 'delay' in sweep_info[channels]: waveform['markerdelay'] = sweep_info[channels]['delay'] return waveform, sweep_info def sweep_2D_process(self, data, waveform, diff_dir=None): width_horz = waveform['width_horz'] width_vert = waveform['width_vert'] resolution = waveform['resolution'] chunks_ch1 = [data[x:x + resolution[0]] for x in range(0, len(data), resolution[0])] chunks_ch1 = [chunks_ch1[i][1:int(width_horz * len(chunks_ch1[i]))] for i in range(0, len(chunks_ch1))] data_processed = chunks_ch1[:int(width_vert * len(chunks_ch1))] if diff_dir is not None: data_processed = qtt.utilities.tools.diffImageSmooth(data_processed, dy=diff_dir, sigma=1) return data_processed def pulse_gates(self, gate_voltages, waittimes, reps=1, filtercutoff=None, reset_to_zero=False, delete=True): period = sum(waittimes) if reset_to_zero: for g in gate_voltages: gate_voltages[g] = [x - gate_voltages[g][-1] for x in gate_voltages[g]] allvoltages = np.concatenate([v for v in gate_voltages.values()]) mvrange = [max(allvoltages), min(allvoltages)] waveform = dict() for g in gate_voltages: wave_raw = self.make_pulses(gate_voltages[g], waittimes, reps=reps, filtercutoff=filtercutoff, mvrange=mvrange) awg_to_plunger = self.hardware.parameters['awg_to_%s' % g].get() wave = wave_raw / awg_to_plunger waveform[g] = dict() waveform[g]['wave'] = wave waveform[g]['name'] = 'pulses_%s' % g sweep_info = self.sweep_init(waveform, period, delete) self.sweep_run(sweep_info) waveform['voltages'] = gate_voltages waveform['samplerate'] = 1 / self.AWG_clock waveform['waittimes'] = waittimes for channels in sweep_info: if 'delay' in sweep_info[channels]: waveform['markerdelay'] = sweep_info[channels]['delay'] return waveform, sweep_info def reset_AWG(self, clock=1e8): self.AWG_clock = clock for a in self._awgs: a.clock_freq.set(clock) a.trigger_mode.set('CONT') a.trigger_source.set('INT') for ii in range(1, 5): f = getattr(a, 'ch%d_amp' % ii) val = f() if val != 4.0: warnings.warn('AWG channel %d output not at 4.0 V' % ii) if self.awg_seq is not None: self._set_seq_mode(self.awg_seq) def set_amplitude(self, amplitude): if amplitude < 0.02: warnings.warn('Trying to set AWG amplitude too low, setting it to minimum (20mV)') amplitude = 0.02 elif amplitude > 4.5: warnings.warn('Trying to set AWG amplitude too high, setting it to maximum (4.5V)') amplitude = 4.5 self.ch_amp = round(amplitude, 3) for awg in self._awgs: for i in range(1, 5): awg.set('ch%s_amp' % i, self.ch_amp) def check_amplitude(self, gate, mvrange): min_amp = mvrange / self.hardware.parameters['awg_to_%s' % gate].get() if min_amp > 4: raise(Exception('Sweep range of gate %s is larger than maximum allowed by the AWG' % gate)) if self.ch_amp < min_amp: min_amp = np.ceil(min_amp * 10) / 10 self.set_amplitude(min_amp) warnings.warn('AWG amplitude too low for this range, setting to %.1f' % min_amp) def plot_wave_raw(wave_raw, samplerate=None, station=None): if samplerate is None: if station is None: raise Exception('There is no station') samplerate = 1 / station.awg.getattr('AWG_clock') else: samplerate = samplerate horz_var = np.arange(0, len(wave_raw) * samplerate, samplerate) x = DataArray(name='time(s)', label='time (s)', preset_data=horz_var, is_setpoint=True) y = DataArray( label='sweep value (mV)', preset_data=wave_raw, set_arrays=(x,)) plot = QtPlot(x, y) return plot def sweep_2D_process(data, waveform, diff_dir=None): width_horz = waveform['width_horz'] width_vert = waveform['width_vert'] resolution = waveform['resolution'] chunks_ch1 = [data[x:x + resolution[0]] for x in range(0, len(data), resolution[0])] chunks_ch1 = [chunks_ch1[i][1:int(width_horz * len(chunks_ch1[i]))] for i in range(0, len(chunks_ch1))] data_processed = chunks_ch1[:int(width_vert * len(chunks_ch1))] if diff_dir is not None: data_processed = qtt.utilities.tools.diffImageSmooth(data_processed, dy=diff_dir, sigma=1) return data_processed

true

f7362e1dc422d449f05e6af959692365df74ce49

5,374

py

Python

artic/deprecated/plot_amplicon_depth.py

MarkusHaak/fieldbioinformatics

3d291477a3d84968816c8e57e6078fc80135f422

[ "MIT" ]

72

2018-12-21T22:48:50.000Z

2022-02-24T17:04:53.000Z

artic/deprecated/plot_amplicon_depth.py

MarkusHaak/fieldbioinformatics

3d291477a3d84968816c8e57e6078fc80135f422

[ "MIT" ]

70

2020-02-05T13:39:09.000Z

2022-03-29T01:47:19.000Z

artic/deprecated/plot_amplicon_depth.py

MarkusHaak/fieldbioinformatics

3d291477a3d84968816c8e57e6078fc80135f422

[ "MIT" ]

54

2019-03-11T13:33:21.000Z

2022-03-21T09:27:50.000Z

#!/usr/bin/env python3 """ Plot the mean read depth per amplicon. This has been written for use in the ARTIC pipeline so there are no file checks - it assumes the following: * the primer scheme is in ARTIC format * the input depth files are in the format: `chrom\treadgroup\tposition\tdepth * readgroup equates to primer pool * the primer pairs in the scheme are sorted by amplicon number (i.e. readgroups are interleaved) * depth values are provided for all positions (see output of make_depth_mask.py for expected format) """ from .vcftagprimersites import read_bed_file import sys import pandas as pd import numpy as np import argparse import os os.environ['QT_QPA_PLATFORM'] = 'offscreen' import seaborn as sns import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt def go(args): # get the primer scheme primerScheme = read_bed_file(args.primerScheme) # number the amplicons in the scheme and link them to primer start site ampliconCounter = 1 # store the amplicon number and starts by read group dict rgAmplicons = {} rgStarts = {} # process the primers by readgroup for primer in primerScheme: poolName = primer['PoolName'] if poolName not in rgAmplicons: rgAmplicons[poolName] = [] rgStarts[poolName] = [] if primer['direction'] == '+': rgAmplicons[poolName].append(ampliconCounter) rgStarts[poolName].append(primer['start']) ampliconCounter += 1 # for pandas cut func to create bins, we need to add an extra value to the starts (just use inf) for startList in rgStarts.values(): startList.append(np.inf) # process the depth files dfs = {} for depthFile in args.depthFiles: # read in the depth file df = pd.read_csv(depthFile, sep='\t', header=None, names=['refName', 'readGroup', 'position', 'depth'], dtype={'refName': str, 'readGroup': str, 'position': int, 'depth': int}, usecols=(0, 1, 2, 3),) # check that there aren't too many positions in the depth data for plotting # assert len(df.index) < 30000, "error: too many data points to plot" # check all ref positions have a depth value startPos = df["position"][0] endPos = df["position"][df.index[-1]] assert len(df.index) == ((endPos - startPos) + 1), "error: depth needs to be reported at all positions" # check the primer scheme contains the readgroup rgList = df.readGroup.unique() assert len(rgList) == 1, "error: depth file has %d readgroups, need 1 (%s)" % ( len(rgList), depthFile) rg = rgList[0] assert rg in rgAmplicons, "error: readgroup not found in provided primer scheme (%s)" % ( rg) # get the amplicon starts for this readgroup amplicons = sorted(rgAmplicons[rg]) starts = sorted(rgStarts[rg]) # bin read depths by amplicon for this readgroup df['amplicon'] = pd.cut( x=df['position'], bins=starts, labels=amplicons) # store the mean of each bin bins = (df.groupby(['amplicon'])[ 'depth'].mean()).rename(depthFile.name) # add to the pile assert rg not in dfs, "error: readgroup present in multiple files (%s)" % ( rg) dfs[rg] = bins # combine the series data from each input file newDF = pd.concat(dfs, axis=1) newDF.sort_index(axis=0, inplace=True) newDF.reset_index(inplace=True) # melt the DF for seaborn newDF = newDF.melt("amplicon", var_name="read group", value_name="mean amplicon read depth") newDF = newDF.dropna() # plot the bar g = sns.catplot(data=newDF, x="amplicon", y="mean amplicon read depth", hue="read group", height=4, aspect=3, kind="bar", dodge=False, legend=False) g.set(yscale="log") g.fig.suptitle(args.sampleID) plt.legend(loc='upper right') plt.xticks(rotation=45, size=6) plt.savefig(args.outFilePrefix + "-barplot.png") plt.close() # plot the box g = sns.catplot(data=newDF, x="read group", y="mean amplicon read depth", kind="box") g.fig.suptitle(args.sampleID) plt.savefig(args.outFilePrefix + "-boxplot.png") plt.close() def main(): parser = argparse.ArgumentParser() parser.add_argument('--primerScheme', required=True, help='the ARTIC primer scheme') parser.add_argument('--sampleID', required=True, help='the sample ID for the provided depth files') parser.add_argument('--outFilePrefix', default="./amplicon-depth", help='the prefix to give the output plot file') parser.add_argument( "depthFiles", type=argparse.FileType('r'), nargs='+', help='the depth files produced by make_depth_mask.py') args = parser.parse_args() go(args) if __name__ == "__main__": main()

34.896104

116

0.592668

from .vcftagprimersites import read_bed_file import sys import pandas as pd import numpy as np import argparse import os os.environ['QT_QPA_PLATFORM'] = 'offscreen' import seaborn as sns import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt def go(args): primerScheme = read_bed_file(args.primerScheme) ampliconCounter = 1 rgAmplicons = {} rgStarts = {} for primer in primerScheme: poolName = primer['PoolName'] if poolName not in rgAmplicons: rgAmplicons[poolName] = [] rgStarts[poolName] = [] if primer['direction'] == '+': rgAmplicons[poolName].append(ampliconCounter) rgStarts[poolName].append(primer['start']) ampliconCounter += 1 for startList in rgStarts.values(): startList.append(np.inf) dfs = {} for depthFile in args.depthFiles: df = pd.read_csv(depthFile, sep='\t', header=None, names=['refName', 'readGroup', 'position', 'depth'], dtype={'refName': str, 'readGroup': str, 'position': int, 'depth': int}, usecols=(0, 1, 2, 3),) # assert len(df.index) < 30000, "error: too many data points to plot" # check all ref positions have a depth value startPos = df["position"][0] endPos = df["position"][df.index[-1]] assert len(df.index) == ((endPos - startPos) + 1), "error: depth needs to be reported at all positions" # check the primer scheme contains the readgroup rgList = df.readGroup.unique() assert len(rgList) == 1, "error: depth file has %d readgroups, need 1 (%s)" % ( len(rgList), depthFile) rg = rgList[0] assert rg in rgAmplicons, "error: readgroup not found in provided primer scheme (%s)" % ( rg) # get the amplicon starts for this readgroup amplicons = sorted(rgAmplicons[rg]) starts = sorted(rgStarts[rg]) # bin read depths by amplicon for this readgroup df['amplicon'] = pd.cut( x=df['position'], bins=starts, labels=amplicons) # store the mean of each bin bins = (df.groupby(['amplicon'])[ 'depth'].mean()).rename(depthFile.name) # add to the pile assert rg not in dfs, "error: readgroup present in multiple files (%s)" % ( rg) dfs[rg] = bins # combine the series data from each input file newDF = pd.concat(dfs, axis=1) newDF.sort_index(axis=0, inplace=True) newDF.reset_index(inplace=True) # melt the DF for seaborn newDF = newDF.melt("amplicon", var_name="read group", value_name="mean amplicon read depth") newDF = newDF.dropna() # plot the bar g = sns.catplot(data=newDF, x="amplicon", y="mean amplicon read depth", hue="read group", height=4, aspect=3, kind="bar", dodge=False, legend=False) g.set(yscale="log") g.fig.suptitle(args.sampleID) plt.legend(loc='upper right') plt.xticks(rotation=45, size=6) plt.savefig(args.outFilePrefix + "-barplot.png") plt.close() # plot the box g = sns.catplot(data=newDF, x="read group", y="mean amplicon read depth", kind="box") g.fig.suptitle(args.sampleID) plt.savefig(args.outFilePrefix + "-boxplot.png") plt.close() def main(): parser = argparse.ArgumentParser() parser.add_argument('--primerScheme', required=True, help='the ARTIC primer scheme') parser.add_argument('--sampleID', required=True, help='the sample ID for the provided depth files') parser.add_argument('--outFilePrefix', default="./amplicon-depth", help='the prefix to give the output plot file') parser.add_argument( "depthFiles", type=argparse.FileType('r'), nargs='+', help='the depth files produced by make_depth_mask.py') args = parser.parse_args() go(args) if __name__ == "__main__": main()

true

f736303816e77fc8b6b0357f24619105dad9aa31

1,043

py

Python

2_Scraper-Scripts/RunThis.py

kshahnazari1998/SociaGrow-Public

54042634ad8d16ea044bfd1fe265d68e1d74102b

[ "MIT" ]

null

2_Scraper-Scripts/RunThis.py

kshahnazari1998/SociaGrow-Public

54042634ad8d16ea044bfd1fe265d68e1d74102b

[ "MIT" ]

null

2_Scraper-Scripts/RunThis.py

kshahnazari1998/SociaGrow-Public

54042634ad8d16ea044bfd1fe265d68e1d74102b

[ "MIT" ]

null

import subprocess import time from VpnConnect import VpnConnects vpc = VpnConnects() while True: time.sleep(10) processf2 = subprocess.Popen(["python", "Targetaccountstatus.py"]) time.sleep(3600) processf2.kill() # Scrape User processmain = subprocess.Popen(["python", "ManagerAdmin.py"]) time.sleep(45) print("Opened The SubProcesses") process1 = subprocess.Popen(["python", "Manager1.py"]) process2 = subprocess.Popen(["python", "Manager2.py"]) process3 = subprocess.Popen(["python", "Manager3.py"]) process4 = subprocess.Popen(["python", "Manager4.py"]) process5 = subprocess.Popen(["python", "Manager5.py"]) time.sleep(6000) processmain.kill() process1.kill() process2.kill() process3.kill() process4.kill() process5.kill() print("Killed the Managers") time.sleep(10) subprocess.call("TASKKILL /f /IM CHROME.EXE") subprocess.call("TASKKILL /f /IM CHROMEDRIVER.EXE") time.sleep(45)

24.255814

71

0.634708

import subprocess import time from VpnConnect import VpnConnects vpc = VpnConnects() while True: time.sleep(10) processf2 = subprocess.Popen(["python", "Targetaccountstatus.py"]) time.sleep(3600) processf2.kill() processmain = subprocess.Popen(["python", "ManagerAdmin.py"]) time.sleep(45) print("Opened The SubProcesses") process1 = subprocess.Popen(["python", "Manager1.py"]) process2 = subprocess.Popen(["python", "Manager2.py"]) process3 = subprocess.Popen(["python", "Manager3.py"]) process4 = subprocess.Popen(["python", "Manager4.py"]) process5 = subprocess.Popen(["python", "Manager5.py"]) time.sleep(6000) processmain.kill() process1.kill() process2.kill() process3.kill() process4.kill() process5.kill() print("Killed the Managers") time.sleep(10) subprocess.call("TASKKILL /f /IM CHROME.EXE") subprocess.call("TASKKILL /f /IM CHROMEDRIVER.EXE") time.sleep(45)

true

f73632ee2a6251e65ea96df144d8af26abd3cbc3

1,818

py

Python

python/heliosUpdateTargetSecretKey/heliosUpdateTargetSecretKey.py

ped998/scripts

0dcaaf47f9676210e1c972a5d59d8d0de82a1d93

[ "Apache-2.0" ]

null

python/heliosUpdateTargetSecretKey/heliosUpdateTargetSecretKey.py

ped998/scripts

0dcaaf47f9676210e1c972a5d59d8d0de82a1d93

[ "Apache-2.0" ]

null

python/heliosUpdateTargetSecretKey/heliosUpdateTargetSecretKey.py

ped998/scripts

0dcaaf47f9676210e1c972a5d59d8d0de82a1d93

[ "Apache-2.0" ]

null

#!/usr/bin/env python """Helios Update Secret Key for External Targets""" from pyhesity import * from datetime import datetime import getpass ### command line arguments import argparse parser = argparse.ArgumentParser() parser.add_argument('-u', '--username', type=str, default='helios') parser.add_argument('-pwd', '--password', type=str) parser.add_argument('-a', '--accesskey', type=str, required=True) parser.add_argument('-s', '--secretkey', type=str, default='') args = parser.parse_args() username = args.username password = args.password accesskey = args.accesskey secretkey = args.secretkey while secretkey is None or len(secretkey) < 2: secretkey = getpass.getpass("Please enter the secretkey: ") ### authenticate apiauth(vip='helios.cohesity.com', username=username, domain='local', password=password) now = datetime.now() dateString = now.strftime("%Y-%m-%d") f = open('vaults-updated-%s.txt' % dateString, 'w') for hcluster in heliosClusters(): heliosCluster(hcluster['name']) cluster = api('get', 'cluster') if cluster: print('%s' % hcluster['name']) f.write('%s\n' % hcluster['name']) vaults = api('get', 'vaults') if len(vaults) > 0: vaults = [v for v in vaults if 'amazon' in v['config'] and v['config']['amazon']['accessKeyId'] == accesskey] for vault in vaults: print(' updating key for target: %s...' % vault['name']) f.write(' updating key for target: %s...\n' % vault['name']) vault['config']['amazon']['secretAccessKey'] = secretkey result = api('put', 'vaults/%s' % vault['id'], vault) else: print('%s (trouble accessing cluster)' % hcluster['name']) f.write('%s (trouble accessing cluster)\n' % hcluster['name']) f.close()

35.647059

121

0.636964

from pyhesity import * from datetime import datetime import getpass () parser.add_argument('-u', '--username', type=str, default='helios') parser.add_argument('-pwd', '--password', type=str) parser.add_argument('-a', '--accesskey', type=str, required=True) parser.add_argument('-s', '--secretkey', type=str, default='') args = parser.parse_args() username = args.username password = args.password accesskey = args.accesskey secretkey = args.secretkey while secretkey is None or len(secretkey) < 2: secretkey = getpass.getpass("Please enter the secretkey: ") .com', username=username, domain='local', password=password) now = datetime.now() dateString = now.strftime("%Y-%m-%d") f = open('vaults-updated-%s.txt' % dateString, 'w') for hcluster in heliosClusters(): heliosCluster(hcluster['name']) cluster = api('get', 'cluster') if cluster: print('%s' % hcluster['name']) f.write('%s\n' % hcluster['name']) vaults = api('get', 'vaults') if len(vaults) > 0: vaults = [v for v in vaults if 'amazon' in v['config'] and v['config']['amazon']['accessKeyId'] == accesskey] for vault in vaults: print(' updating key for target: %s...' % vault['name']) f.write(' updating key for target: %s...\n' % vault['name']) vault['config']['amazon']['secretAccessKey'] = secretkey result = api('put', 'vaults/%s' % vault['id'], vault) else: print('%s (trouble accessing cluster)' % hcluster['name']) f.write('%s (trouble accessing cluster)\n' % hcluster['name']) f.close()

true

f73632faf3e8e654e58ba098197bb5b21ddef5a6

6,133

py

Python

managesf/controllers/api/v2/base.py

softwarefactory-project/managesf

7018d041291f50b90e782ca31d0cfc67abd10170

[ "Apache-2.0" ]

1

2018-08-02T23:30:03.000Z

managesf/controllers/api/v2/base.py

softwarefactory-project/managesf

7018d041291f50b90e782ca31d0cfc67abd10170

[ "Apache-2.0" ]

1

2021-12-13T18:24:10.000Z

2021-12-13T20:10:39.000Z

managesf/controllers/api/v2/base.py

softwarefactory-project/managesf

7018d041291f50b90e782ca31d0cfc67abd10170

[ "Apache-2.0" ]

null

# # Copyright (C) 2017 Red Hat # # 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. import logging import os.path import re from pecan import conf from pecan import request, response, abort, expose from pecan.rest import RestController from managesf.model.yamlbkd.engine import SFResourceBackendEngine from managesf import policy # TODO do it with v2 from managesf.model import SFUserCRUD from git.exc import GitCommandError logger = logging.getLogger(__name__) # TODO move to managesf.api once users API is started def get_user_groups(username): user_email = SFUserCRUD().get(username=username).get('email') logger.info('Found email %s for username %s' % (user_email, username)) resources_engine = SFResourceBackendEngine( os.path.join(conf.resources['workdir'], 'read'), conf.resources['subdir']) try: resources = resources_engine.get( conf.resources['master_repo'], 'master') except GitCommandError: logger.info("Unable to read groups from the resources engine.") logger.info("It is probably because we are boostrapping SF.") return [] groups = resources['resources'].get('groups', {}) return [g for g in groups if user_email in groups[g]['members']] def authorize(rule_name, target): if not request.remote_user: request.remote_user = request.headers.get('X-Remote-User') credentials = {'username': request.remote_user, 'groups': []} if request.remote_user: credentials['groups'] = get_user_groups(request.remote_user) return policy.authorize(rule_name, target, credentials) class APIv2RestController(RestController): def __init__(self, *args, **kwargs): super(APIv2RestController, self).__init__(*args, **kwargs) self._logger = logging.getLogger( 'managesf.v2.controllers.%s' % self.__class__.__name__) class APIv2RestProxyController(APIv2RestController): manager = None policies_map = {'get .+/path/to/(?P<x>.+)/command': 'managesf.policy.name'} def _find_policy(self, lookup): """Find policy according to REST path.""" for expr in self.policies_map: regex = re.compile(expr) if regex.search(lookup): target_elements = regex.search(lookup).groupdict() return {'policy': self.policies_map[expr], 'target_elements': target_elements} return {} def _policy_target(self, verb, target_elements, *args, **kwargs): # override me target = {} return target # This makes the assumption that backend services always return JSON. # This is true for all of them except gerrit, which will not be covered # this way. def _do(self, verb): def action(*args, **kwargs): if not self.manager: return abort(404, detail='This service is not configured.') path = request.path lookup = ("%s %s" % (verb, path)) pol_scan = self._find_policy(lookup) pol, target_elements = None, {} if pol_scan: pol = pol_scan['policy'] target_elements = pol_scan['target_elements'] if not kwargs and request.content_length: if 'json' in request.content_type: kwargs = request.json else: kwargs = request.params target = self._policy_target(verb, target_elements, *args, **kwargs) if not pol: # Unknown endpoint, default behavior is to forbid access pol = 'rule:none' if not authorize(pol, target=target): return abort(401, detail='Failure to comply with policy %s' % pol) # HACK The RestController's routing method seems to discard # extensions on the last remainder. This is a dirty fix full_path = request.path_url last_arg = full_path.split('/')[-1] if last_arg and args[-1] != last_arg: args = args[:-1] + (last_arg, ) if request.content_length and 'json' in request.content_type: proxied_response = getattr(self.manager, verb)( *args, json=kwargs) self._logger.debug( "calling passthrough manager with " "args: '%s', json: '%s'" % (args, kwargs)) elif kwargs: proxied_response = getattr(self.manager, verb)( *args, params=kwargs) self._logger.debug( "calling passthrough manager with " "args: '%s', params: '%s'" % (args, kwargs)) else: proxied_response = getattr(self.manager, verb)(*args) response.status = proxied_response.status_code if int(proxied_response.status_code) > 399: response.text = proxied_response.text return abort(proxied_response.status_code) else: return proxied_response.json() return action @expose('json') def get(self, *args, **kwargs): return self._do('get')(*args, **kwargs) @expose('json') def post(self, *args, **kwargs): return self._do('post')(*args, **kwargs) @expose('json') def put(self, *args, **kwargs): return self._do('put')(*args, **kwargs) @expose('json') def delete(self, *args, **kwargs): return self._do('delete')(*args, **kwargs)

38.093168

79

0.608022

import logging import os.path import re from pecan import conf from pecan import request, response, abort, expose from pecan.rest import RestController from managesf.model.yamlbkd.engine import SFResourceBackendEngine from managesf import policy from managesf.model import SFUserCRUD from git.exc import GitCommandError logger = logging.getLogger(__name__) def get_user_groups(username): user_email = SFUserCRUD().get(username=username).get('email') logger.info('Found email %s for username %s' % (user_email, username)) resources_engine = SFResourceBackendEngine( os.path.join(conf.resources['workdir'], 'read'), conf.resources['subdir']) try: resources = resources_engine.get( conf.resources['master_repo'], 'master') except GitCommandError: logger.info("Unable to read groups from the resources engine.") logger.info("It is probably because we are boostrapping SF.") return [] groups = resources['resources'].get('groups', {}) return [g for g in groups if user_email in groups[g]['members']] def authorize(rule_name, target): if not request.remote_user: request.remote_user = request.headers.get('X-Remote-User') credentials = {'username': request.remote_user, 'groups': []} if request.remote_user: credentials['groups'] = get_user_groups(request.remote_user) return policy.authorize(rule_name, target, credentials) class APIv2RestController(RestController): def __init__(self, *args, **kwargs): super(APIv2RestController, self).__init__(*args, **kwargs) self._logger = logging.getLogger( 'managesf.v2.controllers.%s' % self.__class__.__name__) class APIv2RestProxyController(APIv2RestController): manager = None policies_map = {'get .+/path/to/(?P<x>.+)/command': 'managesf.policy.name'} def _find_policy(self, lookup): for expr in self.policies_map: regex = re.compile(expr) if regex.search(lookup): target_elements = regex.search(lookup).groupdict() return {'policy': self.policies_map[expr], 'target_elements': target_elements} return {} def _policy_target(self, verb, target_elements, *args, **kwargs): target = {} return target def _do(self, verb): def action(*args, **kwargs): if not self.manager: return abort(404, detail='This service is not configured.') path = request.path lookup = ("%s %s" % (verb, path)) pol_scan = self._find_policy(lookup) pol, target_elements = None, {} if pol_scan: pol = pol_scan['policy'] target_elements = pol_scan['target_elements'] if not kwargs and request.content_length: if 'json' in request.content_type: kwargs = request.json else: kwargs = request.params target = self._policy_target(verb, target_elements, *args, **kwargs) if not pol: pol = 'rule:none' if not authorize(pol, target=target): return abort(401, detail='Failure to comply with policy %s' % pol) # extensions on the last remainder. This is a dirty fix full_path = request.path_url last_arg = full_path.split('/')[-1] if last_arg and args[-1] != last_arg: args = args[:-1] + (last_arg, ) if request.content_length and 'json' in request.content_type: proxied_response = getattr(self.manager, verb)( *args, json=kwargs) self._logger.debug( "calling passthrough manager with " "args: '%s', json: '%s'" % (args, kwargs)) elif kwargs: proxied_response = getattr(self.manager, verb)( *args, params=kwargs) self._logger.debug( "calling passthrough manager with " "args: '%s', params: '%s'" % (args, kwargs)) else: proxied_response = getattr(self.manager, verb)(*args) response.status = proxied_response.status_code if int(proxied_response.status_code) > 399: response.text = proxied_response.text return abort(proxied_response.status_code) else: return proxied_response.json() return action @expose('json') def get(self, *args, **kwargs): return self._do('get')(*args, **kwargs) @expose('json') def post(self, *args, **kwargs): return self._do('post')(*args, **kwargs) @expose('json') def put(self, *args, **kwargs): return self._do('put')(*args, **kwargs) @expose('json') def delete(self, *args, **kwargs): return self._do('delete')(*args, **kwargs)

true

f73633465de3445622fe64746dfb0a3abd0dc8d9

4,477

py

Python

sponge-kb/sponge-kb-mpd-mpc/src/main/resources/sponge/mpd-mpc/mpd_mpc_player.py

mnpas/sponge

7190f23ae888bbef49d0fbb85157444d6ea48bcd

[ "Apache-2.0" ]

9

2017-12-16T21:48:57.000Z

2022-01-06T12:22:24.000Z

sponge-kb/sponge-kb-mpd-mpc/src/main/resources/sponge/mpd-mpc/mpd_mpc_player.py

mnpas/sponge

7190f23ae888bbef49d0fbb85157444d6ea48bcd

[ "Apache-2.0" ]

3

2020-12-18T11:56:46.000Z

2022-03-31T18:37:10.000Z

sponge-kb/sponge-kb-mpd-mpc/src/main/resources/sponge/mpd-mpc/mpd_mpc_player.py

mnpas/sponge

7190f23ae888bbef49d0fbb85157444d6ea48bcd

[ "Apache-2.0" ]

2

2019-12-29T16:08:32.000Z

2020-06-15T14:05:34.000Z

""" Sponge Knowledge Base MPD player. """ class MpdPlayer(Action): def onConfigure(self): self.withLabel("Player").withDescription("The MPD player.") self.withArgs([ # The song info arguments. StringType("song").withLabel("Song").withNullable().withReadOnly() .withProvided(ProvidedMeta().withValue()), StringType("album").withLabel("Album").withNullable().withReadOnly() .withProvided(ProvidedMeta().withValue()), StringType("date").withLabel("Date").withNullable().withReadOnly() .withProvided(ProvidedMeta().withValue()), # The position arguments. IntegerType("position").withLabel("Position").withNullable().withAnnotated() .withMinValue(0).withMaxValue(100) .withFeatures({"widget":"slider", "group":"position"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable()), StringType("time").withLabel("Time").withNullable().withReadOnly() .withFeatures({"group":"position"}).withProvided(ProvidedMeta().withValue()), # The navigation arguments. VoidType("prev").withLabel("Previous").withAnnotated() .withFeatures({"icon":IconInfo().withName("skip-previous").withSize(30), "group":"navigation", "align":"center"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable()), BooleanType("play").withLabel("Play").withAnnotated().withFeatures({"group":"navigation"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable().withLazyUpdate()), VoidType("next").withLabel("Next").withAnnotated() .withFeatures({"icon":IconInfo().withName("skip-next").withSize(30), "group":"navigation"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable()), # The volume argument. IntegerType("volume").withLabel("Volume").withAnnotated().withMinValue(0).withMaxValue(100) .withFeatures({"widget":"slider"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable().withLazyUpdate()), # The mode arguments. BooleanType("repeat").withLabel("Repeat").withAnnotated() .withFeatures({"group":"mode", "widget":"toggleButton", "icon":"repeat", "align":"right"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable().withLazyUpdate()), BooleanType("single").withLabel("Single").withAnnotated() .withFeatures({"group":"mode", "widget":"toggleButton", "icon":"numeric-1"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable().withLazyUpdate()), BooleanType("random").withLabel("Random").withAnnotated() .withFeatures({"group":"mode", "widget":"toggleButton", "icon":"shuffle"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable().withLazyUpdate()), BooleanType("consume").withLabel("Consume").withAnnotated() .withFeatures({"group":"mode", "widget":"toggleButton", "icon":"pac-man"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable().withLazyUpdate()) ]).withNonCallable().withActivatable() self.withFeatures({"refreshEvents":["statusPolling", "mpdNotification_.*"], "icon":"music", "contextActions":[ SubAction("MpdPlaylist"), SubAction("MpdFindAndAddToPlaylist"), SubAction("ViewSongInfo"), SubAction("ViewSongLyrics"), SubAction("MpdLibrary"), SubAction("ViewMpdStatus"), ]}) def onIsActive(self, context): return sponge.getVariable("mpc").isConnected() def onProvideArgs(self, context): """This callback method: a) Modifies the MPD state by using the argument values submitted by the user. The names are specified in the context.submit set, the values are stored in the context.current map. b) Sets the values of arguments that are to be provided to the client. The names are specified in the context.provide set, the values are to be stored in the context.provided map. """ MpdPlayerProvideArgsRuntime(context).run()

58.142857

118

0.624972

class MpdPlayer(Action): def onConfigure(self): self.withLabel("Player").withDescription("The MPD player.") self.withArgs([ StringType("song").withLabel("Song").withNullable().withReadOnly() .withProvided(ProvidedMeta().withValue()), StringType("album").withLabel("Album").withNullable().withReadOnly() .withProvided(ProvidedMeta().withValue()), StringType("date").withLabel("Date").withNullable().withReadOnly() .withProvided(ProvidedMeta().withValue()), IntegerType("position").withLabel("Position").withNullable().withAnnotated() .withMinValue(0).withMaxValue(100) .withFeatures({"widget":"slider", "group":"position"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable()), StringType("time").withLabel("Time").withNullable().withReadOnly() .withFeatures({"group":"position"}).withProvided(ProvidedMeta().withValue()), VoidType("prev").withLabel("Previous").withAnnotated() .withFeatures({"icon":IconInfo().withName("skip-previous").withSize(30), "group":"navigation", "align":"center"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable()), BooleanType("play").withLabel("Play").withAnnotated().withFeatures({"group":"navigation"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable().withLazyUpdate()), VoidType("next").withLabel("Next").withAnnotated() .withFeatures({"icon":IconInfo().withName("skip-next").withSize(30), "group":"navigation"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable()), IntegerType("volume").withLabel("Volume").withAnnotated().withMinValue(0).withMaxValue(100) .withFeatures({"widget":"slider"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable().withLazyUpdate()), BooleanType("repeat").withLabel("Repeat").withAnnotated() .withFeatures({"group":"mode", "widget":"toggleButton", "icon":"repeat", "align":"right"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable().withLazyUpdate()), BooleanType("single").withLabel("Single").withAnnotated() .withFeatures({"group":"mode", "widget":"toggleButton", "icon":"numeric-1"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable().withLazyUpdate()), BooleanType("random").withLabel("Random").withAnnotated() .withFeatures({"group":"mode", "widget":"toggleButton", "icon":"shuffle"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable().withLazyUpdate()), BooleanType("consume").withLabel("Consume").withAnnotated() .withFeatures({"group":"mode", "widget":"toggleButton", "icon":"pac-man"}) .withProvided(ProvidedMeta().withValue().withOverwrite().withSubmittable().withLazyUpdate()) ]).withNonCallable().withActivatable() self.withFeatures({"refreshEvents":["statusPolling", "mpdNotification_.*"], "icon":"music", "contextActions":[ SubAction("MpdPlaylist"), SubAction("MpdFindAndAddToPlaylist"), SubAction("ViewSongInfo"), SubAction("ViewSongLyrics"), SubAction("MpdLibrary"), SubAction("ViewMpdStatus"), ]}) def onIsActive(self, context): return sponge.getVariable("mpc").isConnected() def onProvideArgs(self, context): MpdPlayerProvideArgsRuntime(context).run()

true

f7363416c8ea9009adc45b2b90d259e589d247e0

14,015

py

Python

gsom.py

cperales/pygsom

ac4d4818f441d862cb5183e1d2ea814e3f805759

[ "MIT" ]

null

gsom.py

cperales/pygsom

ac4d4818f441d862cb5183e1d2ea814e3f805759

[ "MIT" ]

null

gsom.py

cperales/pygsom

ac4d4818f441d862cb5183e1d2ea814e3f805759

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ The MIT License (MIT) Copyright (c) 2015 Philipp Ludwig <git@philippludwig.net> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ """@package GSOM This is an implementation of the growing self-organizing map. Different possible approaches for the GSOM have been presented in the past by various researchers. To make things clear, this implementation is based on the one described in the work of: Alahakoon, Damminda, S. Halgamuge, and Bala Srinivasan: "Dynamic self-organizing maps with controlled growth for knowledge discovery." Neural Networks, IEEE Transactions on 11.3 (2000): 601-614. Sadly, this article is not as comprehensive as desirable. Therefore this implementation should not be taken as a reference, but as a best-effort version. Some details of the algorithm have been assembled based on the work of Mengxue Cao et. al, who described their approach within their work: "Growing Self-Organizing Map Approach for Semantic Acquisition Modeling way within their work" Refer to both papers for further details. Additionally, this algorithm picks up some of the aspects proposed in the work of: Andreas Nürnberger and Marcin Detyniecki: "Externally growing self-organizing maps and its application to e-mail database visualization and exploration" """ from math import log, exp import itertools import math import random import scipy class GSOMNode: """ Represents one node in a growing SOM. """ R = random.Random() def __init__(self, dim, x, y, data): """ Initialize this node. """ # Create a weight vector of the given dimension: # Initialize the weight vector with random values between 0 and 1. self.weights = scipy.array([self.R.random() for _ in range(dim)]) # Remember the error occuring at this particular node self.error = 0.0 # Holds the number of the iteration during the node has been inserted. self.it = 0 # Holds the number of the last iteration where the node has won. self.last_it = 0 # Holds the best-matching data. self.data = data self.last_changed = 0 # This node has no neighbours yet. self.right = None self.left = None self.up = None self.down = None # Copy the given coordinates. self.x, self.y = x, y def adjust_weights(self, target, learn_rate): """ Adjust the weights of this node. """ for w in range(0, len(target)): self.weights[w] += learn_rate * (target[w] - self.weights[w]) def is_boundary(self): """ Check if this node is at the boundary of the map. """ if not self.right: return True if not self.left: return True if not self.up: return True if not self.down: return True return False class GSOM: """ Represents a growing self-organizing map. """ @staticmethod def _distance(v1, v2): """ Calculate the euclidean distance between two scipy arrays.""" dist = 0.0 for v, w in zip(v1, v2): dist += pow(v - w, 2) return dist def _find_bmu(self, vec): """ Find the best matching unit within the map for the given input_ vector. """ dist=float("inf") winner = False for node in self.nodes: d = self._distance(vec, node.weights) if d < dist: dist = d winner = node return winner def _find_similar_boundary(self, node): """ Find the most similar boundary node to the given node. """ dist = float("inf") winner = False for boundary in self.nodes: if not boundary.is_boundary(): continue if boundary == node: continue d = self._distance(node.weights, boundary.weights) if d < dist: dist = d winner = node return winner def __init__(self, X, y, spread_factor=0.5): """ Initializes this GSOM using the given data. """ # Assign the data self.data = [] for fn, t in zip(X, y): arr = scipy.array([t]) self.data.append([fn, arr]) # Determine the dimension of the data. self.dim = len(self.data[0][0]) # Calculate the growing threshold: self._GT = -self.dim * math.log(spread_factor, 2) # Create the 4 starting Nodes. self.nodes = [] n00 = GSOMNode(dim=self.dim, x=0, y=0, data=self.data) n01 = GSOMNode(self.dim, 0, 1, self.data) n10 = GSOMNode(self.dim, 1, 0, self.data) n11 = GSOMNode(self.dim, 1, 1, self.data) self.nodes.extend([n00, n01, n10, n11]) # Create starting topology n00.right = n10 n00.up = n01 n01.right = n11 n01.down = n00 n10.up = n11 n10.left = n00 n11.left = n01 n11.down = n10 # Set properties self.it = 0 # Current iteration self.max_it = len(self.data) self.num_it = 1000 # Total iterations self.init_lr = 0.1 # Initial value of the learning rate self.alpha = 0.1 self.output = open("gsom.csv", "w") def train(self): # Select the next input_. input_ = random.choice(self.data)[1] input_ = random.choice(self.data)[0] # Calculate the learn rate. # Note that the learning rate, according to the original paper, # is reseated for every new input_. learn_rate = self.init_lr * self.alpha * (1 - 1.5/len(self.nodes)) # We now present the input_ several times to the network. # It is unclear what's a good number here, since no publication # took the effort to name a value. However, the implementation # provided by Arkadi Kagan presents the input_ 20 times, so we # will copy that here. recalc_nodes = [] for _ in range(20): # Find the best matching unit BMU = self._find_bmu(input_) BMU.last_it = self.it # Adapt the weights of the direct topological neighbours neighbours = [] neighbours.append(BMU) if BMU.left: neighbours.append(BMU.left) if BMU.right: neighbours.append(BMU.right) if BMU.up: neighbours.append(BMU.up) if BMU.down: neighbours.append(BMU.down) if BMU not in recalc_nodes: recalc_nodes.append(BMU) for node in neighbours: node.adjust_weights(input_, learn_rate) if node not in recalc_nodes: recalc_nodes.append(node) # Calculate the error. err = self._distance(BMU.weights, input_) # Add the error to the node. growing, nodes = self._node_add_error(BMU, err) if growing: recalc_nodes.extend(nodes) # Count the iteration self.it += 1 # Re-Calc representative data elements for changed nodes. used_data = [] for node in self.nodes: used_data.append(node.data) for node in recalc_nodes: dist = float("inf") winner = False winner_fn = False for fn, point in self.data: # if fn in used_data: continue d = self._distance(point, node.weights) if(d < dist): dist = d winner = point winner_fn = fn if node.data != winner_fn: node.data = winner_fn node.last_changed = self.it self.output.write(str(node.x) + "," + str(node.y)\ + ",change\n") used_data.append(winner_fn) # Remove unused nodes. self._remove_unused_nodes() def _node_add_error(self, node, error): """ Add the given error to the error value of the given node. This will also take care of growing the map (if necessary) and distributing the error along the neighbours (if necessary) """ node.error += error # Consider growing if node.error > self._GT: if not node.is_boundary(): # Find the boundary node which is most similar to this node. node = self._find_similar_boundary(node) if not node: print("GSOM: Error: No free boundary node found!") """ Old method: # Distribute the error along the neighbours. # Since this is not a boundary node, this node must have # 4 neighbours. node.error = 0.5 * self._GT node.left.error += 0.25 * node.left.error node.right.error += 0.25 * node.right.error node.up.error += 0.25 * node.up.error node.down.error += 0.25 * node.down.error """ nodes = self._grow(node) return True, nodes return False, 0 def _grow(self, node): """ Grow this GSOM. """ # We grow this GSOM at every possible direction. nodes = [] if node.left is None: nn = self._insert(node.x - 1, node.y, node) nodes.append(nn) print("Growing left at: (" + str(node.x) + "," + str(node.y)\ + ") -> (" + str(nn.x) + ", " + str(nn.y) + ")") if node.right is None: nn = self._insert(node.x + 1, node.y, node) nodes.append(nn) print("Growing right at: (" + str(node.x) + "," + str(node.y)\ + ") -> (" + str(nn.x) + ", " + str(nn.y) + ")") if node.up is None: nn = self._insert(node.x, node.y + 1, node) nodes.append(nn) print("Growing up at: (" + str(node.x) + "," + str(node.y) +\ ") -> (" + str(nn.x) + ", " + str(nn.y) + ")") if node.down is None: nn = self._insert(node.x, node.y - 1, node) nodes.append(nn) print("Growing down at: (" + str(node.x) + "," + str(node.y) +\ ") -> (" + str(nn.x) + ", " + str(nn.y) + ")") return nodes def _insert(self, x, y, init_node): # Create new node new_node = GSOMNode(self.dim, x, y, self.data) self.nodes.append(new_node) # Save the number of the current iteration. We need this to prune # this node later (if neccessary). new_node.it = new_node.last_it = self.it # Create the connections to possible neighbouring nodes. for node in self.nodes: # Left, Right, Up, Down if node.x == x - 1 and node.y == y: new_node.left = node node.right = new_node if node.x == x + 1 and node.y == y: new_node.right = node node.left = new_node if node.x == x and node.y == y + 1: new_node.up = node node.down = new_node if node.x == x and node.y == y - 1: new_node.down = node node.up = new_node # Calculate new weights, look for a neighbour. neigh = new_node.left if neigh is None: neigh = new_node.right if neigh is None: neigh = new_node.up if neigh is None: neigh = new_node.down if neigh is None: print("_insert: No neighbour found!") for i in range(0, len(new_node.weights)): new_node.weights[i] = 2 * init_node.weights[i] - neigh.weights[i] return new_node def _remove_unused_nodes(self): """ Remove all nodes from the GSOM that have not been used. """ to_remove = [] # Iterate over all nodes. for node in self.nodes: # Different rules for nodes that have been used or not. iterations_not_won = self.it - node.last_it # If we have 50 nodes, every node is allowed not to win 50 times # in a row. This means every node must be picked at least once. if iterations_not_won < len(self.nodes) * 4.0 * (1 + self.it/len(self.data)) : continue # First, remove the connections to the neighbouring nodes. if node.left: node.left.right = None if node.up: node.up.down = None if node.down: node.down.up = None if node.right: node.right.left = None # Save this node for removing. to_remove.append(node) # Now remove all marked nodes. for node in to_remove: print("Removing node @ " + str(node.x) + ", " + str(node.y) + \ " - Current it: " + str(self.it) + " - Last time won: " +\ str(node.last_it)) if node.data: self.output.write(node.data + "," + str(node.x)+","+str(node.y)\ + ",remove\n") self.nodes.remove(node)

36.028278

99

0.577595

from math import log, exp import itertools import math import random import scipy class GSOMNode: R = random.Random() def __init__(self, dim, x, y, data): self.weights = scipy.array([self.R.random() for _ in range(dim)]) self.error = 0.0 self.it = 0 self.last_it = 0 self.data = data self.last_changed = 0 self.right = None self.left = None self.up = None self.down = None self.x, self.y = x, y def adjust_weights(self, target, learn_rate): for w in range(0, len(target)): self.weights[w] += learn_rate * (target[w] - self.weights[w]) def is_boundary(self): if not self.right: return True if not self.left: return True if not self.up: return True if not self.down: return True return False class GSOM: @staticmethod def _distance(v1, v2): dist = 0.0 for v, w in zip(v1, v2): dist += pow(v - w, 2) return dist def _find_bmu(self, vec): dist=float("inf") winner = False for node in self.nodes: d = self._distance(vec, node.weights) if d < dist: dist = d winner = node return winner def _find_similar_boundary(self, node): dist = float("inf") winner = False for boundary in self.nodes: if not boundary.is_boundary(): continue if boundary == node: continue d = self._distance(node.weights, boundary.weights) if d < dist: dist = d winner = node return winner def __init__(self, X, y, spread_factor=0.5): self.data = [] for fn, t in zip(X, y): arr = scipy.array([t]) self.data.append([fn, arr]) self.dim = len(self.data[0][0]) self._GT = -self.dim * math.log(spread_factor, 2) self.nodes = [] n00 = GSOMNode(dim=self.dim, x=0, y=0, data=self.data) n01 = GSOMNode(self.dim, 0, 1, self.data) n10 = GSOMNode(self.dim, 1, 0, self.data) n11 = GSOMNode(self.dim, 1, 1, self.data) self.nodes.extend([n00, n01, n10, n11]) n00.right = n10 n00.up = n01 n01.right = n11 n01.down = n00 n10.up = n11 n10.left = n00 n11.left = n01 n11.down = n10 self.it = 0 self.max_it = len(self.data) self.num_it = 1000 self.init_lr = 0.1 self.alpha = 0.1 self.output = open("gsom.csv", "w") def train(self): input_ = random.choice(self.data)[1] input_ = random.choice(self.data)[0] learn_rate = self.init_lr * self.alpha * (1 - 1.5/len(self.nodes)) # took the effort to name a value. However, the implementation # provided by Arkadi Kagan presents the input_ 20 times, so we # will copy that here. recalc_nodes = [] for _ in range(20): # Find the best matching unit BMU = self._find_bmu(input_) BMU.last_it = self.it # Adapt the weights of the direct topological neighbours neighbours = [] neighbours.append(BMU) if BMU.left: neighbours.append(BMU.left) if BMU.right: neighbours.append(BMU.right) if BMU.up: neighbours.append(BMU.up) if BMU.down: neighbours.append(BMU.down) if BMU not in recalc_nodes: recalc_nodes.append(BMU) for node in neighbours: node.adjust_weights(input_, learn_rate) if node not in recalc_nodes: recalc_nodes.append(node) # Calculate the error. err = self._distance(BMU.weights, input_) # Add the error to the node. growing, nodes = self._node_add_error(BMU, err) if growing: recalc_nodes.extend(nodes) # Count the iteration self.it += 1 # Re-Calc representative data elements for changed nodes. used_data = [] for node in self.nodes: used_data.append(node.data) for node in recalc_nodes: dist = float("inf") winner = False winner_fn = False for fn, point in self.data: # if fn in used_data: continue d = self._distance(point, node.weights) if(d < dist): dist = d winner = point winner_fn = fn if node.data != winner_fn: node.data = winner_fn node.last_changed = self.it self.output.write(str(node.x) + "," + str(node.y)\ + ",change\n") used_data.append(winner_fn) # Remove unused nodes. self._remove_unused_nodes() def _node_add_error(self, node, error): node.error += error # Consider growing if node.error > self._GT: if not node.is_boundary(): # Find the boundary node which is most similar to this node. node = self._find_similar_boundary(node) if not node: print("GSOM: Error: No free boundary node found!") nodes = self._grow(node) return True, nodes return False, 0 def _grow(self, node): # We grow this GSOM at every possible direction. nodes = [] if node.left is None: nn = self._insert(node.x - 1, node.y, node) nodes.append(nn) print("Growing left at: (" + str(node.x) + "," + str(node.y)\ + ") -> (" + str(nn.x) + ", " + str(nn.y) + ")") if node.right is None: nn = self._insert(node.x + 1, node.y, node) nodes.append(nn) print("Growing right at: (" + str(node.x) + "," + str(node.y)\ + ") -> (" + str(nn.x) + ", " + str(nn.y) + ")") if node.up is None: nn = self._insert(node.x, node.y + 1, node) nodes.append(nn) print("Growing up at: (" + str(node.x) + "," + str(node.y) +\ ") -> (" + str(nn.x) + ", " + str(nn.y) + ")") if node.down is None: nn = self._insert(node.x, node.y - 1, node) nodes.append(nn) print("Growing down at: (" + str(node.x) + "," + str(node.y) +\ ") -> (" + str(nn.x) + ", " + str(nn.y) + ")") return nodes def _insert(self, x, y, init_node): # Create new node new_node = GSOMNode(self.dim, x, y, self.data) self.nodes.append(new_node) # Save the number of the current iteration. We need this to prune # this node later (if neccessary). new_node.it = new_node.last_it = self.it # Create the connections to possible neighbouring nodes. for node in self.nodes: # Left, Right, Up, Down if node.x == x - 1 and node.y == y: new_node.left = node node.right = new_node if node.x == x + 1 and node.y == y: new_node.right = node node.left = new_node if node.x == x and node.y == y + 1: new_node.up = node node.down = new_node if node.x == x and node.y == y - 1: new_node.down = node node.up = new_node # Calculate new weights, look for a neighbour. neigh = new_node.left if neigh is None: neigh = new_node.right if neigh is None: neigh = new_node.up if neigh is None: neigh = new_node.down if neigh is None: print("_insert: No neighbour found!") for i in range(0, len(new_node.weights)): new_node.weights[i] = 2 * init_node.weights[i] - neigh.weights[i] return new_node def _remove_unused_nodes(self): to_remove = [] # Iterate over all nodes. for node in self.nodes: # Different rules for nodes that have been used or not. iterations_not_won = self.it - node.last_it # If we have 50 nodes, every node is allowed not to win 50 times # in a row. This means every node must be picked at least once. if iterations_not_won < len(self.nodes) * 4.0 * (1 + self.it/len(self.data)) : continue # First, remove the connections to the neighbouring nodes. if node.left: node.left.right = None if node.up: node.up.down = None if node.down: node.down.up = None if node.right: node.right.left = None # Save this node for removing. to_remove.append(node) # Now remove all marked nodes. for node in to_remove: print("Removing node @ " + str(node.x) + ", " + str(node.y) + \ " - Current it: " + str(self.it) + " - Last time won: " +\ str(node.last_it)) if node.data: self.output.write(node.data + "," + str(node.x)+","+str(node.y)\ + ",remove\n") self.nodes.remove(node)

true

f736343fd5da597b2fb04dc9aa414c2b0598f993

4,724

py

Python

elmerbot/commands/search.py

scott-hand/elmerbot

cb356da60dcf7d6b6d3bef22341765c043e48f87

[ "MIT" ]

null

elmerbot/commands/search.py

scott-hand/elmerbot

cb356da60dcf7d6b6d3bef22341765c043e48f87

[ "MIT" ]

null

elmerbot/commands/search.py

scott-hand/elmerbot

cb356da60dcf7d6b6d3bef22341765c043e48f87

[ "MIT" ]

null

import discord import string from elmerbot.commands import ElmerCommand __all__ = ["SearchCommand", "InfoCommand"] class SearchCommand(ElmerCommand): command = "search" description = ( "Search for a whisky by name. Optionally put a number of results to limit it to in front of " "your query.\n" "Examples: `!search stagg 2014` or `!search 10 stagg`" ) async def handle(self, client, message, args): self._logger.info("Got search command") if client.data.stale: await message.channel.send("One moment, reloading review data...") await message.channel.trigger_typing() first, _, rest = args.partition(" ") pattern = args choices = 5 if first.isnumeric(): choices = int(first) pattern = rest # Whitelist characters to eliminate Markdown injection whitelist = string.ascii_letters + string.digits + "'()-., " pattern = "".join([c for c in pattern if c in whitelist]) results = client.data.search(pattern, choices) # Stop now if there's nothing to show if not results: em = discord.Embed(title='No results found for "{}".'.format(pattern), colour=0xDD0000) await message.channel.send(embed=em) return # Compile results, stopping when there's a large drop in confidence. output = [] last_conf = results[0][2] for token, whisky_id, conf in results: if last_conf - conf > 3: break last_conf = conf output.append("**{}** [#{}]".format(token, whisky_id)) hits = len(output) output += [ "", "Use **!info <id>** to get review information. The <id> is the number in brackets \ from search results.", ] em = discord.Embed( title='{} Results for "{}":'.format(hits, pattern), description="\n".join(output), colour=0x00DD00 ) await message.channel.send(embed=em) class InfoCommand(ElmerCommand): command = "info" description = ( "Search for a whisky by name. Optionally put a number of results to limit it to in front of your " "query.\n" "Examples: `!search stagg 2014` or `!search 10 stagg`" ) async def handle(self, client, message, args): self._logger.info("Got info command") pending_msg = None if client.data.stale: pending_msg = await message.channel.send("One moment, reloading review data...") await message.channel.trigger_typing() if args.isnumeric(): whisky_id = int(args) else: # Whitelist characters to eliminate Markdown injection whitelist = string.ascii_letters + string.digits + "'()-., " pattern = "".join([c for c in args if c in whitelist]) result = client.data.search(pattern, 1) if not result: em = discord.Embed( title='Could not find "{}"'.format(pattern), description="Try using **!search** first.", colour=0xDD0000, ) await message.channel.send(embed=em) return whisky_id = result[0][1] data = client.data.find(whisky_id) if pending_msg: await pending_msg.delete() output = [] # Generate stats ratings = [row["rating"] for row in data if row["rating"]] if ratings: avg_rating = round(sum(ratings) / float(len(ratings)), 2) delta = round(avg_rating - client.data.avg, 2) delta = "+" + str(delta) if delta >= 0.0 else str(delta) output.append("**Average rating:** {} based on {} reviews with scores.".format(avg_rating, len(ratings))) output.append( "It is {} from the global average \ of {} with standard deviation {}".format( delta, client.data.avg, client.data.stddev ) ) output.append("**Most recent reviews:**") for idx, review in enumerate(client.data.most_recent(whisky_id=whisky_id)): output.append( "{}. {} gave it {}. [Link]({})".format( idx + 1, review["username"], review["rating"] or "no rating", review["link"] ) ) else: output.append("**Average rating:** No reviews with scores.") em = discord.Embed(title="{}".format(data[0]["name"]), description="\n".join(output), colour=0x00DD00) await message.channel.send(embed=em)

40.033898

117

0.556308

import discord import string from elmerbot.commands import ElmerCommand __all__ = ["SearchCommand", "InfoCommand"] class SearchCommand(ElmerCommand): command = "search" description = ( "Search for a whisky by name. Optionally put a number of results to limit it to in front of " "your query.\n" "Examples: `!search stagg 2014` or `!search 10 stagg`" ) async def handle(self, client, message, args): self._logger.info("Got search command") if client.data.stale: await message.channel.send("One moment, reloading review data...") await message.channel.trigger_typing() first, _, rest = args.partition(" ") pattern = args choices = 5 if first.isnumeric(): choices = int(first) pattern = rest whitelist = string.ascii_letters + string.digits + "'()-., " pattern = "".join([c for c in pattern if c in whitelist]) results = client.data.search(pattern, choices) # Stop now if there's nothing to show if not results: em = discord.Embed(title='No results found for "{}".'.format(pattern), colour=0xDD0000) await message.channel.send(embed=em) return output = [] last_conf = results[0][2] for token, whisky_id, conf in results: if last_conf - conf > 3: break last_conf = conf output.append("**{}** [#{}]".format(token, whisky_id)) hits = len(output) output += [ "", "Use **!info <id>** to get review information. The <id> is the number in brackets \ from search results.", ] em = discord.Embed( title='{} Results for "{}":'.format(hits, pattern), description="\n".join(output), colour=0x00DD00 ) await message.channel.send(embed=em) class InfoCommand(ElmerCommand): command = "info" description = ( "Search for a whisky by name. Optionally put a number of results to limit it to in front of your " "query.\n" "Examples: `!search stagg 2014` or `!search 10 stagg`" ) async def handle(self, client, message, args): self._logger.info("Got info command") pending_msg = None if client.data.stale: pending_msg = await message.channel.send("One moment, reloading review data...") await message.channel.trigger_typing() if args.isnumeric(): whisky_id = int(args) else: # Whitelist characters to eliminate Markdown injection whitelist = string.ascii_letters + string.digits + "'()-., " pattern = "".join([c for c in args if c in whitelist]) result = client.data.search(pattern, 1) if not result: em = discord.Embed( title='Could not find "{}"'.format(pattern), description="Try using **!search** first.", colour=0xDD0000, ) await message.channel.send(embed=em) return whisky_id = result[0][1] data = client.data.find(whisky_id) if pending_msg: await pending_msg.delete() output = [] ratings = [row["rating"] for row in data if row["rating"]] if ratings: avg_rating = round(sum(ratings) / float(len(ratings)), 2) delta = round(avg_rating - client.data.avg, 2) delta = "+" + str(delta) if delta >= 0.0 else str(delta) output.append("**Average rating:** {} based on {} reviews with scores.".format(avg_rating, len(ratings))) output.append( "It is {} from the global average \ of {} with standard deviation {}".format( delta, client.data.avg, client.data.stddev ) ) output.append("**Most recent reviews:**") for idx, review in enumerate(client.data.most_recent(whisky_id=whisky_id)): output.append( "{}. {} gave it {}. [Link]({})".format( idx + 1, review["username"], review["rating"] or "no rating", review["link"] ) ) else: output.append("**Average rating:** No reviews with scores.") em = discord.Embed(title="{}".format(data[0]["name"]), description="\n".join(output), colour=0x00DD00) await message.channel.send(embed=em)

true

f736354e5a0f20eea35eceff7914e22c35491058

7,259

py

Python

tests/test_grid.py

eEcoLiDAR/lcMacroPipeline

91709f93ef53a3e453f0ce967e1094688688f684

[ "Apache-2.0" ]

2

2021-02-17T14:41:50.000Z

2021-04-07T12:06:21.000Z

tests/test_grid.py

eEcoLiDAR/lcMacroPipeline

91709f93ef53a3e453f0ce967e1094688688f684

[ "Apache-2.0" ]

16

2020-03-11T08:39:46.000Z

2020-05-20T10:42:29.000Z

tests/test_grid.py

eEcoLiDAR/Laserfarm

91709f93ef53a3e453f0ce967e1094688688f684

[ "Apache-2.0" ]

2

2020-07-06T08:18:56.000Z

2020-08-19T12:00:26.000Z

from pathlib import Path import unittest import numpy as np import pylas from laserfarm.grid import Grid try: import matplotlib matplotlib_available = True except ModuleNotFoundError: matplotlib_available = False if matplotlib_available: matplotlib.use('Agg') import matplotlib.pyplot as plt class TestValidGridSetup(unittest.TestCase): def setUp(self): self.grid = Grid() self.grid.setup(0., 0., 20., 20., 5) def test_gridMins(self): np.testing.assert_allclose(self.grid.grid_mins, [0., 0.]) def test_gridMaxs(self): np.testing.assert_allclose(self.grid.grid_maxs, [20., 20.]) def test_gridWidth(self): np.testing.assert_allclose(self.grid.grid_width, 20.) def test_tileWidth(self): np.testing.assert_allclose(self.grid.tile_width, 4.) def test_tileIndexForPoint(self): np.testing.assert_array_equal(self.grid.get_tile_index(0.1, 0.2), (0, 0)) def test_tileIndexForArray(self): np.testing.assert_array_equal(self.grid.get_tile_index((0.1, 19.9), (0.2, 19.8)), ((0, 0), (4, 4))) def test_tileBoundsForPoint(self): np.testing.assert_array_equal(self.grid.get_tile_bounds(0, 0), ((0., 0.), (4., 4.))) def test_tileBoundsForArray(self): np.testing.assert_array_equal(self.grid.get_tile_bounds((0, 0), (0, 1)), (((0., 0.), (0., 4.)), ((4., 4.), (4., 8.)))) class TestInvalidGridSetup(unittest.TestCase): def test_fractionalNumberOfTilesGrid(self): with self.assertRaises(ValueError): grid = Grid() grid.setup(0., 0., 20., 20., 0.1) def test_zeroNumberOfTilesGrid(self): with self.assertRaises(ValueError): grid = Grid() grid.setup(0., 0., 20., 20., 0) def test_zeroWidthGrid(self): with self.assertRaises(ValueError): grid = Grid() grid.setup(0., 0., 0., 20., 5) def test_rectangularGrid(self): with self.assertRaises(ValueError): grid = Grid() grid.setup(0., 0., 10., 20., 5) class TestRealGridValid(unittest.TestCase): _test_dir = 'test_tmp_dir' _test_data_dir = 'testdata' _test_tile_idx = [101, 101] _test_file_name = 'C_43FN1_1_1.LAZ' _min_x = -113107.8100 _min_y = 214783.8700 _max_x = 398892.1900 _max_y = 726783.87 _n_tiles_sides = 256 plot = False def setUp(self): self.grid = Grid() self.grid.setup(min_x=self._min_x, min_y=self._min_y, max_x=self._max_x, max_y=self._max_y, n_tiles_side=self._n_tiles_sides) self._test_data_path = Path(self._test_data_dir).joinpath(self._test_file_name) self.points = _read_points_from_file(str(self._test_data_path)) def test_isPointInTile(self): x_pts, y_pts = self.points.T mask_valid_points = self.grid.is_point_in_tile(x_pts, y_pts, *self._test_tile_idx) self.assertTrue(np.all(mask_valid_points)) class TestRealGridLowPrecision(TestRealGridValid): """ The following tile has been obtained by using large scale parameters (0.1) in the PDAL LAS writer. Some points thus fall outside the tile boundary when read from the file. """ _test_file_name = 'C_43FN1_1.LAZ' def test_isPointInTile(self): x_pts, y_pts = self.points.T mask_valid_points = self.grid.is_point_in_tile(x_pts, y_pts, *self._test_tile_idx) if self.plot and matplotlib_available: _plot_points_and_tile(self.grid, self.points[~mask_valid_points], self._test_tile_idx, self._test_data_path.with_suffix('.png').name) self.assertFalse(np.all(mask_valid_points)) def test_isPointInTileWithPrecision(self): x_pts, y_pts = self.points.T precision = np.abs(np.rint(self._max_y) - self._max_y) mask_valid_points = self.grid.is_point_in_tile(x_pts, y_pts, *self._test_tile_idx, precision=precision) self.assertTrue(np.all(mask_valid_points)) class TestRealGridLowPrecisionRoundedOrigin(TestRealGridValid): """ The following tile has been obtained by rounding off the coordinates of the origin and by using the default scale parameters (0.01) in the PDAL LAS writer. """ _test_file_name = 'C_43FN1_1.LAZ' _test_tile_idx = [101, 101] _min_x = -113108.00 _min_y = 214784.00 _max_x = 398892.00 _max_y = 726784.00 def test_isPointInTile(self): x_pts, y_pts = self.points.T mask_valid_points = self.grid.is_point_in_tile(x_pts, y_pts, *self._test_tile_idx) if self.plot and matplotlib_available: _plot_points_and_tile(self.grid, self.points[~mask_valid_points], self._test_tile_idx, self._test_data_path.with_suffix('.png').name) self.assertFalse(np.all(mask_valid_points)) def test_isPointInTileWithPrecision(self): x_pts, y_pts = self.points.T mask_valid_points = self.grid.is_point_in_tile(x_pts, y_pts, *self._test_tile_idx, precision=0.01) self.assertTrue(np.all(mask_valid_points)) def _read_points_from_file(filename): file = pylas.read(filename) return np.column_stack((file.x, file.y)) def _plot_points_and_tile(grid, points, tile_indices, filename=None): """ Plot points :param grid: grid object :param points: (Nx2) array containing X,Y coordinates of the points :param tile_indices: [N_x, N_y], where N_i is the integer tile index along dimension i :param filename: optional, path where to save plot """ # plot points x_pts, y_pts = points.T plt.scatter(x_pts, y_pts, color='r') # plot tile tile_mins, tile_maxs = grid.get_tile_bounds(*tile_indices) line = np.array((tile_mins, [tile_mins[0], tile_maxs[1]], tile_maxs, [tile_maxs[0], tile_mins[1]], tile_mins)) x, y = line.T plt.plot(x, y, color='k') # add tile label x_cntr, y_cntr = (tile_mins + tile_maxs) / 2. plt.text(x_cntr, y_cntr, '({}, {})'.format(*tile_indices), horizontalalignment='center', verticalalignment='center') if filename is not None: plt.savefig(filename, dpi=300) else: plt.show() plt.close(plt.figure())

35.067633

87

0.573908

from pathlib import Path import unittest import numpy as np import pylas from laserfarm.grid import Grid try: import matplotlib matplotlib_available = True except ModuleNotFoundError: matplotlib_available = False if matplotlib_available: matplotlib.use('Agg') import matplotlib.pyplot as plt class TestValidGridSetup(unittest.TestCase): def setUp(self): self.grid = Grid() self.grid.setup(0., 0., 20., 20., 5) def test_gridMins(self): np.testing.assert_allclose(self.grid.grid_mins, [0., 0.]) def test_gridMaxs(self): np.testing.assert_allclose(self.grid.grid_maxs, [20., 20.]) def test_gridWidth(self): np.testing.assert_allclose(self.grid.grid_width, 20.) def test_tileWidth(self): np.testing.assert_allclose(self.grid.tile_width, 4.) def test_tileIndexForPoint(self): np.testing.assert_array_equal(self.grid.get_tile_index(0.1, 0.2), (0, 0)) def test_tileIndexForArray(self): np.testing.assert_array_equal(self.grid.get_tile_index((0.1, 19.9), (0.2, 19.8)), ((0, 0), (4, 4))) def test_tileBoundsForPoint(self): np.testing.assert_array_equal(self.grid.get_tile_bounds(0, 0), ((0., 0.), (4., 4.))) def test_tileBoundsForArray(self): np.testing.assert_array_equal(self.grid.get_tile_bounds((0, 0), (0, 1)), (((0., 0.), (0., 4.)), ((4., 4.), (4., 8.)))) class TestInvalidGridSetup(unittest.TestCase): def test_fractionalNumberOfTilesGrid(self): with self.assertRaises(ValueError): grid = Grid() grid.setup(0., 0., 20., 20., 0.1) def test_zeroNumberOfTilesGrid(self): with self.assertRaises(ValueError): grid = Grid() grid.setup(0., 0., 20., 20., 0) def test_zeroWidthGrid(self): with self.assertRaises(ValueError): grid = Grid() grid.setup(0., 0., 0., 20., 5) def test_rectangularGrid(self): with self.assertRaises(ValueError): grid = Grid() grid.setup(0., 0., 10., 20., 5) class TestRealGridValid(unittest.TestCase): _test_dir = 'test_tmp_dir' _test_data_dir = 'testdata' _test_tile_idx = [101, 101] _test_file_name = 'C_43FN1_1_1.LAZ' _min_x = -113107.8100 _min_y = 214783.8700 _max_x = 398892.1900 _max_y = 726783.87 _n_tiles_sides = 256 plot = False def setUp(self): self.grid = Grid() self.grid.setup(min_x=self._min_x, min_y=self._min_y, max_x=self._max_x, max_y=self._max_y, n_tiles_side=self._n_tiles_sides) self._test_data_path = Path(self._test_data_dir).joinpath(self._test_file_name) self.points = _read_points_from_file(str(self._test_data_path)) def test_isPointInTile(self): x_pts, y_pts = self.points.T mask_valid_points = self.grid.is_point_in_tile(x_pts, y_pts, *self._test_tile_idx) self.assertTrue(np.all(mask_valid_points)) class TestRealGridLowPrecision(TestRealGridValid): _test_file_name = 'C_43FN1_1.LAZ' def test_isPointInTile(self): x_pts, y_pts = self.points.T mask_valid_points = self.grid.is_point_in_tile(x_pts, y_pts, *self._test_tile_idx) if self.plot and matplotlib_available: _plot_points_and_tile(self.grid, self.points[~mask_valid_points], self._test_tile_idx, self._test_data_path.with_suffix('.png').name) self.assertFalse(np.all(mask_valid_points)) def test_isPointInTileWithPrecision(self): x_pts, y_pts = self.points.T precision = np.abs(np.rint(self._max_y) - self._max_y) mask_valid_points = self.grid.is_point_in_tile(x_pts, y_pts, *self._test_tile_idx, precision=precision) self.assertTrue(np.all(mask_valid_points)) class TestRealGridLowPrecisionRoundedOrigin(TestRealGridValid): _test_file_name = 'C_43FN1_1.LAZ' _test_tile_idx = [101, 101] _min_x = -113108.00 _min_y = 214784.00 _max_x = 398892.00 _max_y = 726784.00 def test_isPointInTile(self): x_pts, y_pts = self.points.T mask_valid_points = self.grid.is_point_in_tile(x_pts, y_pts, *self._test_tile_idx) if self.plot and matplotlib_available: _plot_points_and_tile(self.grid, self.points[~mask_valid_points], self._test_tile_idx, self._test_data_path.with_suffix('.png').name) self.assertFalse(np.all(mask_valid_points)) def test_isPointInTileWithPrecision(self): x_pts, y_pts = self.points.T mask_valid_points = self.grid.is_point_in_tile(x_pts, y_pts, *self._test_tile_idx, precision=0.01) self.assertTrue(np.all(mask_valid_points)) def _read_points_from_file(filename): file = pylas.read(filename) return np.column_stack((file.x, file.y)) def _plot_points_and_tile(grid, points, tile_indices, filename=None): x_pts, y_pts = points.T plt.scatter(x_pts, y_pts, color='r') tile_mins, tile_maxs = grid.get_tile_bounds(*tile_indices) line = np.array((tile_mins, [tile_mins[0], tile_maxs[1]], tile_maxs, [tile_maxs[0], tile_mins[1]], tile_mins)) x, y = line.T plt.plot(x, y, color='k') x_cntr, y_cntr = (tile_mins + tile_maxs) / 2. plt.text(x_cntr, y_cntr, '({}, {})'.format(*tile_indices), horizontalalignment='center', verticalalignment='center') if filename is not None: plt.savefig(filename, dpi=300) else: plt.show() plt.close(plt.figure())

true

f73635595a57c8cdfe5c9fda02943fccf1922333

2,431

py

Python

examples/crud_rest_api/config.py

NeolithEra/Flask-AppBuilder

d9561bf89d925a2f5d74c226318884733b52718f

[ "BSD-3-Clause" ]

3,862

2015-01-01T11:59:24.000Z

2022-03-31T19:23:55.000Z

examples/crud_rest_api/config.py

NeolithEra/Flask-AppBuilder

d9561bf89d925a2f5d74c226318884733b52718f

[ "BSD-3-Clause" ]

1,566

2015-01-01T23:26:11.000Z

2022-03-31T16:23:08.000Z

examples/crud_rest_api/config.py

NeolithEra/Flask-AppBuilder

d9561bf89d925a2f5d74c226318884733b52718f

[ "BSD-3-Clause" ]

1,352

2015-01-02T10:45:59.000Z

2022-03-26T20:56:48.000Z

import os basedir = os.path.abspath(os.path.dirname(__file__)) CSRF_ENABLED = True SECRET_KEY = "\2\1thisismyscretkey\1\2\e\y\y\h" OPENID_PROVIDERS = [ {"name": "Google", "url": "https://www.google.com/accounts/o8/id"}, {"name": "Yahoo", "url": "https://me.yahoo.com"}, {"name": "AOL", "url": "http://openid.aol.com/<username>"}, {"name": "Flickr", "url": "http://www.flickr.com/<username>"}, {"name": "MyOpenID", "url": "https://www.myopenid.com"}, ] SQLALCHEMY_DATABASE_URI = "sqlite:///" + os.path.join(basedir, "app.db") # SQLALCHEMY_DATABASE_URI = 'mysql://username:password@mysqlserver.local/quickhowto' # SQLALCHEMY_DATABASE_URI = 'postgresql://scott:tiger@localhost:5432/myapp' # SQLALCHEMY_ECHO = True SQLALCHEMY_POOL_RECYCLE = 3 BABEL_DEFAULT_LOCALE = "en" BABEL_DEFAULT_FOLDER = "translations" LANGUAGES = { "en": {"flag": "gb", "name": "English"}, "pt": {"flag": "pt", "name": "Portuguese"}, "pt_BR": {"flag": "br", "name": "Pt Brazil"}, "es": {"flag": "es", "name": "Spanish"}, "fr": {"flag": "fr", "name": "French"}, "de": {"flag": "de", "name": "German"}, "zh": {"flag": "cn", "name": "Chinese"}, "ru": {"flag": "ru", "name": "Russian"}, "pl": {"flag": "pl", "name": "Polish"}, "el": {"flag": "gr", "name": "Greek"}, "ja_JP": {"flag": "jp", "name": "Japanese"}, } # ------------------------------ # GLOBALS FOR GENERAL APP's # ------------------------------ FAB_API_SWAGGER_UI = True UPLOAD_FOLDER = basedir + "/app/static/uploads/" IMG_UPLOAD_FOLDER = basedir + "/app/static/uploads/" IMG_UPLOAD_URL = "/static/uploads/" AUTH_TYPE = 1 # AUTH_LDAP_SERVER = "ldap://dc.domain.net" AUTH_ROLE_ADMIN = "Admin" AUTH_ROLE_PUBLIC = "Public" APP_NAME = "F.A.B. Example" APP_THEME = "" # default # APP_THEME = "cerulean.css" # COOL # APP_THEME = "amelia.css" # APP_THEME = "cosmo.css" # APP_THEME = "cyborg.css" # COOL # APP_THEME = "flatly.css" # APP_THEME = "journal.css" # APP_THEME = "readable.css" # APP_THEME = "simplex.css" # APP_THEME = "slate.css" # COOL # APP_THEME = "spacelab.css" # NICE # APP_THEME = "united.css" # APP_THEME = "darkly.css" # APP_THEME = "lumen.css" # APP_THEME = "paper.css" # APP_THEME = "sandstone.css" # APP_THEME = "solar.css" # APP_THEME = "superhero.css" #FAB_ROLES = { # "ReadOnly": [ # [".*", "can_list"], # [".*", "can_show"], # [".*", "menu_access"] # ] #}

30.772152

84

0.588235

import os basedir = os.path.abspath(os.path.dirname(__file__)) CSRF_ENABLED = True SECRET_KEY = "\2\1thisismyscretkey\1\2\e\y\y\h" OPENID_PROVIDERS = [ {"name": "Google", "url": "https://www.google.com/accounts/o8/id"}, {"name": "Yahoo", "url": "https://me.yahoo.com"}, {"name": "AOL", "url": "http://openid.aol.com/<username>"}, {"name": "Flickr", "url": "http://www.flickr.com/<username>"}, {"name": "MyOpenID", "url": "https://www.myopenid.com"}, ] SQLALCHEMY_DATABASE_URI = "sqlite:///" + os.path.join(basedir, "app.db") SQLALCHEMY_POOL_RECYCLE = 3 BABEL_DEFAULT_LOCALE = "en" BABEL_DEFAULT_FOLDER = "translations" LANGUAGES = { "en": {"flag": "gb", "name": "English"}, "pt": {"flag": "pt", "name": "Portuguese"}, "pt_BR": {"flag": "br", "name": "Pt Brazil"}, "es": {"flag": "es", "name": "Spanish"}, "fr": {"flag": "fr", "name": "French"}, "de": {"flag": "de", "name": "German"}, "zh": {"flag": "cn", "name": "Chinese"}, "ru": {"flag": "ru", "name": "Russian"}, "pl": {"flag": "pl", "name": "Polish"}, "el": {"flag": "gr", "name": "Greek"}, "ja_JP": {"flag": "jp", "name": "Japanese"}, } # ------------------------------ FAB_API_SWAGGER_UI = True UPLOAD_FOLDER = basedir + "/app/static/uploads/" IMG_UPLOAD_FOLDER = basedir + "/app/static/uploads/" IMG_UPLOAD_URL = "/static/uploads/" AUTH_TYPE = 1 # AUTH_LDAP_SERVER = "ldap://dc.domain.net" AUTH_ROLE_ADMIN = "Admin" AUTH_ROLE_PUBLIC = "Public" APP_NAME = "F.A.B. Example" APP_THEME = "" # default # APP_THEME = "cerulean.css" # COOL # APP_THEME = "amelia.css" # APP_THEME = "cosmo.css" # APP_THEME = "cyborg.css" # COOL # APP_THEME = "flatly.css" # APP_THEME = "journal.css" # APP_THEME = "readable.css" # APP_THEME = "simplex.css" # APP_THEME = "slate.css" # COOL # APP_THEME = "spacelab.css" # NICE # APP_THEME = "united.css" # APP_THEME = "darkly.css" # APP_THEME = "lumen.css" # APP_THEME = "paper.css" # APP_THEME = "sandstone.css" # APP_THEME = "solar.css" # APP_THEME = "superhero.css" #FAB_ROLES = { # "ReadOnly": [ # [".*", "can_list"], # [".*", "can_show"], # [".*", "menu_access"] # ] #}

true

f73635be840dcaa8541d5a931f7dd2184a0e9aca

2,888

py

Python

test/test_custom.py

lyon916/pyecharts

40efd119ec7bccb4bc3d5dab985ec7aca2936911

[ "MIT" ]

null

test/test_custom.py

lyon916/pyecharts

40efd119ec7bccb4bc3d5dab985ec7aca2936911

[ "MIT" ]

null

test/test_custom.py

lyon916/pyecharts

40efd119ec7bccb4bc3d5dab985ec7aca2936911

[ "MIT" ]

null

#!/usr/bin/env python #coding=utf-8 from pyecharts import Bar, Line, Scatter, EffectScatter, Kline def test_custom(): # custom_0 attr = ['A', 'B', 'C', 'D', 'E', 'F'] v1 = [10, 20, 30, 40, 50, 60] v2 = [15, 25, 35, 45, 55, 65] v3 = [38, 28, 58, 48, 78, 68] bar = Bar("Line - Bar 示例") bar.add("bar", attr, v1) line = Line() line.add("line", v2, v3) bar.custom(line.get_series()) bar.show_config() bar.render() # custom_1 v1 = [10, 20, 30, 40, 50, 60] v2 = [30, 30, 30, 30, 30, 30] v3 = [50, 50, 50, 50, 50, 50] v4 = [10, 10, 10, 10, 10, 10] es = EffectScatter("Scatter - EffectScatter 示例") es.add("es", v1, v2) scatter = Scatter() scatter.add("scatter", v1, v3) es.custom(scatter.get_series()) es_1 = EffectScatter() es_1.add("es_1", v1, v4, symbol='pin', effect_scale=5) es.custom(es_1.get_series()) es.show_config() es.render() # custom_2 import random v1 = [[2320.26, 2320.26, 2287.3, 2362.94], [2300, 2291.3, 2288.26, 2308.38], [2295.35, 2346.5, 2295.35, 2345.92], [2347.22, 2358.98, 2337.35, 2363.8], [2360.75, 2382.48, 2347.89, 2383.76], [2383.43, 2385.42, 2371.23, 2391.82], [2377.41, 2419.02, 2369.57, 2421.15], [2425.92, 2428.15, 2417.58, 2440.38], [2411, 2433.13, 2403.3, 2437.42], [2432.68, 2334.48, 2427.7, 2441.73], [2430.69, 2418.53, 2394.22, 2433.89], [2416.62, 2432.4, 2414.4, 2443.03], [2441.91, 2421.56, 2418.43, 2444.8], [2420.26, 2382.91, 2373.53, 2427.07], [2383.49, 2397.18, 2370.61, 2397.94], [2378.82, 2325.95, 2309.17, 2378.82], [2322.94, 2314.16, 2308.76, 2330.88], [2320.62, 2325.82, 2315.01, 2338.78], [2313.74, 2293.34, 2289.89, 2340.71], [2297.77, 2313.22, 2292.03, 2324.63], [2322.32, 2365.59, 2308.92, 2366.16], [2364.54, 2359.51, 2330.86, 2369.65], [2332.08, 2273.4, 2259.25, 2333.54], [2274.81, 2326.31, 2270.1, 2328.14], [2333.61, 2347.18, 2321.6, 2351.44], [2340.44, 2324.29, 2304.27, 2352.02], [2326.42, 2318.61, 2314.59, 2333.67], [2314.68, 2310.59, 2296.58, 2320.96], [2309.16, 2286.6, 2264.83, 2333.29], [2282.17, 2263.97, 2253.25, 2286.33], [2255.77, 2270.28, 2253.31, 2276.22]] attr = ["2017/7/{}".format(i + 1) for i in range(31)] kline = Kline("Kline - Line 示例") kline.add("日K", attr, v1) line_1 = Line() line_1.add("line-1", attr, [random.randint(2400, 2500) for _ in range(31)]) line_2 = Line() line_2.add("line-2", attr, [random.randint(2400, 2500) for _ in range(31)]) kline.custom(line_1.get_series()) kline.custom(line_2.get_series()) kline.show_config() kline.render()

35.654321

79

0.532895

from pyecharts import Bar, Line, Scatter, EffectScatter, Kline def test_custom(): attr = ['A', 'B', 'C', 'D', 'E', 'F'] v1 = [10, 20, 30, 40, 50, 60] v2 = [15, 25, 35, 45, 55, 65] v3 = [38, 28, 58, 48, 78, 68] bar = Bar("Line - Bar 示例") bar.add("bar", attr, v1) line = Line() line.add("line", v2, v3) bar.custom(line.get_series()) bar.show_config() bar.render() v1 = [10, 20, 30, 40, 50, 60] v2 = [30, 30, 30, 30, 30, 30] v3 = [50, 50, 50, 50, 50, 50] v4 = [10, 10, 10, 10, 10, 10] es = EffectScatter("Scatter - EffectScatter 示例") es.add("es", v1, v2) scatter = Scatter() scatter.add("scatter", v1, v3) es.custom(scatter.get_series()) es_1 = EffectScatter() es_1.add("es_1", v1, v4, symbol='pin', effect_scale=5) es.custom(es_1.get_series()) es.show_config() es.render() import random v1 = [[2320.26, 2320.26, 2287.3, 2362.94], [2300, 2291.3, 2288.26, 2308.38], [2295.35, 2346.5, 2295.35, 2345.92], [2347.22, 2358.98, 2337.35, 2363.8], [2360.75, 2382.48, 2347.89, 2383.76], [2383.43, 2385.42, 2371.23, 2391.82], [2377.41, 2419.02, 2369.57, 2421.15], [2425.92, 2428.15, 2417.58, 2440.38], [2411, 2433.13, 2403.3, 2437.42], [2432.68, 2334.48, 2427.7, 2441.73], [2430.69, 2418.53, 2394.22, 2433.89], [2416.62, 2432.4, 2414.4, 2443.03], [2441.91, 2421.56, 2418.43, 2444.8], [2420.26, 2382.91, 2373.53, 2427.07], [2383.49, 2397.18, 2370.61, 2397.94], [2378.82, 2325.95, 2309.17, 2378.82], [2322.94, 2314.16, 2308.76, 2330.88], [2320.62, 2325.82, 2315.01, 2338.78], [2313.74, 2293.34, 2289.89, 2340.71], [2297.77, 2313.22, 2292.03, 2324.63], [2322.32, 2365.59, 2308.92, 2366.16], [2364.54, 2359.51, 2330.86, 2369.65], [2332.08, 2273.4, 2259.25, 2333.54], [2274.81, 2326.31, 2270.1, 2328.14], [2333.61, 2347.18, 2321.6, 2351.44], [2340.44, 2324.29, 2304.27, 2352.02], [2326.42, 2318.61, 2314.59, 2333.67], [2314.68, 2310.59, 2296.58, 2320.96], [2309.16, 2286.6, 2264.83, 2333.29], [2282.17, 2263.97, 2253.25, 2286.33], [2255.77, 2270.28, 2253.31, 2276.22]] attr = ["2017/7/{}".format(i + 1) for i in range(31)] kline = Kline("Kline - Line 示例") kline.add("日K", attr, v1) line_1 = Line() line_1.add("line-1", attr, [random.randint(2400, 2500) for _ in range(31)]) line_2 = Line() line_2.add("line-2", attr, [random.randint(2400, 2500) for _ in range(31)]) kline.custom(line_1.get_series()) kline.custom(line_2.get_series()) kline.show_config() kline.render()

true

f73636427e005194ae2caceac942ddd1063e52d3

1,078

py

Python

code/src/plan2scene/texture_gen/custom_ops/noise.py

madhawav/plan2scene

cc3481f503fc096d1a50ea4fbcc668b2a3b75fb5

[ "MIT" ]

305

2021-06-09T23:30:34.000Z

2022-03-30T02:49:45.000Z

code/src/plan2scene/texture_gen/custom_ops/noise.py

madhawav/plan2scene

cc3481f503fc096d1a50ea4fbcc668b2a3b75fb5

[ "MIT" ]

8

2021-06-11T01:59:26.000Z

2022-03-24T21:32:21.000Z

code/src/plan2scene/texture_gen/custom_ops/noise.py

madhawav/plan2scene

cc3481f503fc096d1a50ea4fbcc668b2a3b75fb5

[ "MIT" ]

32

2021-06-09T23:19:23.000Z

2022-03-05T14:03:15.000Z

# Code adapted from https://github.com/henzler/neuraltexture/blob/master/code/custom_ops/noise/noise.py from torch import nn from torch.autograd import Function import plan2scene.texture_gen.utils.neural_texture_helper as utils_nt import noise_cuda import torch import numpy as np from torch.autograd import gradcheck class NoiseFunction(Function): @staticmethod def forward(ctx, position, seed): ctx.save_for_backward(position, seed) noise = noise_cuda.forward(position, seed) return noise @staticmethod def backward(ctx, grad_noise): position, seed = ctx.saved_tensors d_position_bilinear = noise_cuda.backward(position, seed) d_position = torch.stack([torch.zeros_like(d_position_bilinear), d_position_bilinear], dim=0) return grad_noise.unsqueeze(2) * d_position, None class Noise(nn.Module): def __init__(self): super(Noise, self).__init__() def forward(self, position, seed): noise = NoiseFunction.apply(position.contiguous(), seed.contiguous()) return noise

29.944444

103

0.730056

from torch import nn from torch.autograd import Function import plan2scene.texture_gen.utils.neural_texture_helper as utils_nt import noise_cuda import torch import numpy as np from torch.autograd import gradcheck class NoiseFunction(Function): @staticmethod def forward(ctx, position, seed): ctx.save_for_backward(position, seed) noise = noise_cuda.forward(position, seed) return noise @staticmethod def backward(ctx, grad_noise): position, seed = ctx.saved_tensors d_position_bilinear = noise_cuda.backward(position, seed) d_position = torch.stack([torch.zeros_like(d_position_bilinear), d_position_bilinear], dim=0) return grad_noise.unsqueeze(2) * d_position, None class Noise(nn.Module): def __init__(self): super(Noise, self).__init__() def forward(self, position, seed): noise = NoiseFunction.apply(position.contiguous(), seed.contiguous()) return noise

true

f736365887c9571c3e54f907948e54ccd5725e4e

6,414

py

Python

src/rdb/models/environment.py

juliangruendner/ketos_brain_api

6ec7e01a0996abb03dba090d832a5e1020df4180

[ "MIT" ]

null

src/rdb/models/environment.py

juliangruendner/ketos_brain_api

6ec7e01a0996abb03dba090d832a5e1020df4180

[ "MIT" ]

null

src/rdb/models/environment.py

juliangruendner/ketos_brain_api

6ec7e01a0996abb03dba090d832a5e1020df4180

[ "MIT" ]

null

from rdb.rdb import db, LowerCaseText from enum import Enum import datetime from resources.adminAccess import is_admin_user from flask import g import rdb.models.image as Image from dockerUtil.dockerClient import dockerClient, wait_for_it import config import requests import uuid from flask_restful import abort import rdb.models.user as User class Environment(db.Model): """Environment Class""" __tablename__ = "environment" id = db.Column(db.Integer, autoincrement=True, primary_key=True) name = db.Column(db.Text, nullable=False) container_id = db.Column(db.Text, nullable=False) container_name = db.Column(db.Text, nullable=False) status = db.Column(LowerCaseText, nullable=False) jupyter_port = db.Column(db.Text, nullable=False) jupyter_token = db.Column(db.Text, nullable=False) jupyter_url = None description = db.Column(db.Text) creator_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False) # authorized_users = db.relationship('User', lazy='subquery', secondary='user_environment_access') image_id = db.Column(db.Integer, db.ForeignKey('image.id'), nullable=False) ml_models = db.relationship('MLModel', lazy='select', cascade='delete, delete-orphan', backref='environment') created_at = db.Column(db.DateTime(timezone=True), server_default=db.func.now()) updated_at = db.Column(db.DateTime(timezone=True), server_default=db.func.now(), onupdate=datetime.datetime.now) def __init__(self): super(Environment, self).__init__() def __repr__(self): """Display when printing a environment object""" return "<ID: {}, Name: {}, description: {}>".format(self.id, self.name, self.description) def as_dict(self): """Convert object to dictionary""" return {c.name: getattr(self, c.name) for c in self.__table__.columns} def handle_jupyter_data(self): if is_admin_user() or g.user.id == self.creator_id: self.set_jupyter_url() else: self.hide_jupyter_data() def start_jupyter(self): # wait for container api to be up and running wait_for_it(self.container_name, 5000) # start jupyter notebook and get jupyter token resp = requests.post('http://' + self.container_name + ':5000/jupyter').json() self.jupyter_token = str(resp['jupyter_token']) self.status = Environment.Status.running.value def set_jupyter_url(self): # TODO: read host address from os - for now use config host = config.KETOS_HOST self.jupyter_url = host + ':' + self.jupyter_port + '/?token=' + self.jupyter_token def hide_jupyter_data(self): self.jupyter_port = None self.jupyter_token = None self.jupyter_url = None def get_data_directory(self): return config.KETOS_DATA_FOLDER + '/environments_data/' + self.container_name class Status(Enum): running = 'running' stopped = 'stopped' def get_open_port(): import socket s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.bind(("", 0)) s.listen(1) port = s.getsockname()[1] s.close() return port def create(name, desc, image_id, raise_abort=True): e = Environment() e.name = name e.description = desc i = Image.get(image_id, raise_abort=raise_abort) e.image_id = i.id e.creator_id = g.user.id image_name = config.DOCKER_REGISTRY_DOMAIN + "/" + i.name e.jupyter_port = get_open_port() e.container_name = str(uuid.uuid4().hex) container = dockerClient.containers.run(image_name, name=e.container_name, detach=True, network=config.PROJECT_NAME+"_environment", ports={"8000/tcp": e.jupyter_port}, volumes={e.get_data_directory(): {'bind': '/mlenvironment/models', 'mode': 'rw'}, config.KETOS_DATA_FOLDER+'/auth': {'bind': '/root/src/auth', 'mode': 'ro'}} ) e.container_id = container.id e.start_jupyter() db.session.add(e) db.session.commit() e.set_jupyter_url() return e def abort_if_environment_doesnt_exist(env_id): abort(404, message="environment {} doesn't exist".format(env_id)) def get(env_id, raise_abort=True): e = Environment.query.get(env_id) if raise_abort and not e: abort_if_environment_doesnt_exist(env_id) e.handle_jupyter_data() return e def get_all(): envs = Environment.query.all() for e in envs: e.handle_jupyter_data() return envs def get_all_for_user(user_id): envs = Environment.query.filter_by(creator_id=user_id).all() for e in envs: e.handle_jupyter_data() return envs def get_by_image_id(image_id): envs = Environment.query.filter_by(image_id=image_id).all() for e in envs: e.handle_jupyter_data() return envs def update(env_id, status=None, name=None, desc=None, raise_abort=True): e = get(env_id, raise_abort=raise_abort) User.check_request_for_logged_in_user(e.creator_id) if status and not e.status == status: if status == Environment.Status.running.value: dockerClient.containers.get(e.container_id).start() e.start_jupyter() elif status == Environment.Status.stopped.value: dockerClient.containers.get(e.container_id).stop() else: if raise_abort: abort(400, message="status {} is not allowed".format(status)) else: return None e.status = status if name: e.name = name if desc: e.description = desc db.session.commit() return e def delete(env_id, raise_abort=True): e = get(env_id, raise_abort=raise_abort) User.check_request_for_logged_in_user(e.creator_id) if not e.status == 'stopped': if raise_abort: abort(405, message="environment must be stopped before it can be deleted") else: return None container = dockerClient.containers.get(e.container_id) container.remove(force=True) db.session.delete(e) db.session.commit() return env_id

30.112676

128

0.63985

from rdb.rdb import db, LowerCaseText from enum import Enum import datetime from resources.adminAccess import is_admin_user from flask import g import rdb.models.image as Image from dockerUtil.dockerClient import dockerClient, wait_for_it import config import requests import uuid from flask_restful import abort import rdb.models.user as User class Environment(db.Model): __tablename__ = "environment" id = db.Column(db.Integer, autoincrement=True, primary_key=True) name = db.Column(db.Text, nullable=False) container_id = db.Column(db.Text, nullable=False) container_name = db.Column(db.Text, nullable=False) status = db.Column(LowerCaseText, nullable=False) jupyter_port = db.Column(db.Text, nullable=False) jupyter_token = db.Column(db.Text, nullable=False) jupyter_url = None description = db.Column(db.Text) creator_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False) image_id = db.Column(db.Integer, db.ForeignKey('image.id'), nullable=False) ml_models = db.relationship('MLModel', lazy='select', cascade='delete, delete-orphan', backref='environment') created_at = db.Column(db.DateTime(timezone=True), server_default=db.func.now()) updated_at = db.Column(db.DateTime(timezone=True), server_default=db.func.now(), onupdate=datetime.datetime.now) def __init__(self): super(Environment, self).__init__() def __repr__(self): return "<ID: {}, Name: {}, description: {}>".format(self.id, self.name, self.description) def as_dict(self): return {c.name: getattr(self, c.name) for c in self.__table__.columns} def handle_jupyter_data(self): if is_admin_user() or g.user.id == self.creator_id: self.set_jupyter_url() else: self.hide_jupyter_data() def start_jupyter(self): wait_for_it(self.container_name, 5000) resp = requests.post('http://' + self.container_name + ':5000/jupyter').json() self.jupyter_token = str(resp['jupyter_token']) self.status = Environment.Status.running.value def set_jupyter_url(self): host = config.KETOS_HOST self.jupyter_url = host + ':' + self.jupyter_port + '/?token=' + self.jupyter_token def hide_jupyter_data(self): self.jupyter_port = None self.jupyter_token = None self.jupyter_url = None def get_data_directory(self): return config.KETOS_DATA_FOLDER + '/environments_data/' + self.container_name class Status(Enum): running = 'running' stopped = 'stopped' def get_open_port(): import socket s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.bind(("", 0)) s.listen(1) port = s.getsockname()[1] s.close() return port def create(name, desc, image_id, raise_abort=True): e = Environment() e.name = name e.description = desc i = Image.get(image_id, raise_abort=raise_abort) e.image_id = i.id e.creator_id = g.user.id image_name = config.DOCKER_REGISTRY_DOMAIN + "/" + i.name e.jupyter_port = get_open_port() e.container_name = str(uuid.uuid4().hex) container = dockerClient.containers.run(image_name, name=e.container_name, detach=True, network=config.PROJECT_NAME+"_environment", ports={"8000/tcp": e.jupyter_port}, volumes={e.get_data_directory(): {'bind': '/mlenvironment/models', 'mode': 'rw'}, config.KETOS_DATA_FOLDER+'/auth': {'bind': '/root/src/auth', 'mode': 'ro'}} ) e.container_id = container.id e.start_jupyter() db.session.add(e) db.session.commit() e.set_jupyter_url() return e def abort_if_environment_doesnt_exist(env_id): abort(404, message="environment {} doesn't exist".format(env_id)) def get(env_id, raise_abort=True): e = Environment.query.get(env_id) if raise_abort and not e: abort_if_environment_doesnt_exist(env_id) e.handle_jupyter_data() return e def get_all(): envs = Environment.query.all() for e in envs: e.handle_jupyter_data() return envs def get_all_for_user(user_id): envs = Environment.query.filter_by(creator_id=user_id).all() for e in envs: e.handle_jupyter_data() return envs def get_by_image_id(image_id): envs = Environment.query.filter_by(image_id=image_id).all() for e in envs: e.handle_jupyter_data() return envs def update(env_id, status=None, name=None, desc=None, raise_abort=True): e = get(env_id, raise_abort=raise_abort) User.check_request_for_logged_in_user(e.creator_id) if status and not e.status == status: if status == Environment.Status.running.value: dockerClient.containers.get(e.container_id).start() e.start_jupyter() elif status == Environment.Status.stopped.value: dockerClient.containers.get(e.container_id).stop() else: if raise_abort: abort(400, message="status {} is not allowed".format(status)) else: return None e.status = status if name: e.name = name if desc: e.description = desc db.session.commit() return e def delete(env_id, raise_abort=True): e = get(env_id, raise_abort=raise_abort) User.check_request_for_logged_in_user(e.creator_id) if not e.status == 'stopped': if raise_abort: abort(405, message="environment must be stopped before it can be deleted") else: return None container = dockerClient.containers.get(e.container_id) container.remove(force=True) db.session.delete(e) db.session.commit() return env_id

true

f736369cbe9d074b967f6f6e71550893a185fd79

4,636

py

Python

src/ball_window.py

gltchitm/desktop-pong

aff92e8fc683d72e6139b33b56736dc2fe9c3ca0

[ "MIT" ]

null

src/ball_window.py

gltchitm/desktop-pong

aff92e8fc683d72e6139b33b56736dc2fe9c3ca0

[ "MIT" ]

null

src/ball_window.py

gltchitm/desktop-pong

aff92e8fc683d72e6139b33b56736dc2fe9c3ca0

[ "MIT" ]

null

from gi.repository import Gtk, Gdk import cairo from random import getrandbits from ball_drawing_area import BallDrawingArea from store import store from near import near import config def rand_velocity(): return config.BALL_VELOCITY if getrandbits(1) else -config.BALL_VELOCITY class BallWindow(Gtk.Window): def __init__(self, window_size, x=None, y=None, x_velocity=None, y_velocity=None): Gtk.Window.__init__(self) self.window_size = window_size self.x = x self.y = y self.x_velocity = x_velocity if x_velocity != None else rand_velocity() self.y_velocity = y_velocity if y_velocity != None else rand_velocity() self.rebuilding = False self.set_app_paintable(True) self.set_decorated(False) self.set_accept_focus(False) self.set_keep_above(True) self.set_skip_taskbar_hint(True) self.set_skip_pager_hint(True) self.set_deletable(False) self.set_size_request(config.BALL_DIAMETER, config.BALL_DIAMETER) self.set_position(Gtk.WindowPosition.CENTER) self.set_visual(self.get_screen().get_rgba_visual()) self.add_tick_callback(self.tick) if self.x and self.y: self.move(self.x, self.y) self.connect('realize', self.realize) self.connect('draw', self.draw) self.connect('window-state-event', self.check_minimized) def rebuild(self, use_current_position): if not self.rebuilding: if config.USE_ANTI_TAMPER or not use_current_position: self.rebuilding = True self.destroy() if use_current_position: self = BallWindow(self.window_size, self.x, self.y, self.x_velocity, self.y_velocity) else: self = BallWindow(self.window_size) self.show_all() def tick(self, _widget, _frame_clock): if config.USE_ANTI_TAMPER: self.set_keep_above(True) self.get_window().move_to_desktop(0) current_x, current_y = self.get_position() ai_x, ai_y = store['ai_paddle'] player_x, player_y = store['player_paddle'] width, height = self.window_size if ai_x == None or player_x == None: return True if ( current_x < config.SCREEN_PADDING or current_x + config.BALL_DIAMETER > width - config.SCREEN_PADDING ): self.x -= self.x_velocity self.rebuild(False) if ( current_y < config.SCREEN_PADDING or current_y + config.BALL_DIAMETER > height - config.SCREEN_PADDING ): self.y -= self.y_velocity self.y_velocity *= -1 if ( self.x < ai_x + (config.BALL_DIAMETER / 2 + config.BALL_PADDING) and self.y + (config.BALL_DIAMETER + config.BALL_PADDING) > ai_y and self.y < ai_y + config.PADDLE_SIZE[1] + config.BALL_PADDING ): self.x_velocity *= -1 if config.USE_BALL_STUCK_IN_PADDLE_FIX: self.x = ai_x + (config.BALL_DIAMETER / 2 + config.BALL_PADDING) if ( self.x > player_x - (config.BALL_DIAMETER + config.BALL_PADDING) and self.y + (config.BALL_DIAMETER + config.BALL_PADDING) > player_y and self.y < player_y + config.PADDLE_SIZE[1] + config.BALL_PADDING ): self.x_velocity *= -1 if config.USE_BALL_STUCK_IN_PADDLE_FIX: self.x = player_x - (config.BALL_DIAMETER + config.BALL_PADDING) if not near(self.x, current_x, config.BALL_LEEWAY) or not near(self.y, current_y, config.BALL_LEEWAY): self.rebuild(True) self.x += self.x_velocity self.y += self.y_velocity self.move(self.x, self.y) store['ball_position'] = (self.x, self.y, self.x_velocity, self.y_velocity) return True def check_minimized(self, _widget, event): if event.new_window_state & Gdk.WindowState.ICONIFIED: self.rebuild(True) def realize(self, _widget): current_x, current_y = self.get_position() self.x = current_x self.y = current_y ball = BallDrawingArea(self.get_window()) self.add(ball) ball.show_all() cursor = Gdk.Cursor.new_from_name(Gdk.Display.get_default(), 'not-allowed') self.get_window().set_cursor(cursor) def draw(self, _widget, cr): cr.set_source_rgba(1.0, 1.0, 1.0, 0.0) cr.set_operator(cairo.OPERATOR_SOURCE) cr.paint() return False

34.597015

110

0.622951

from gi.repository import Gtk, Gdk import cairo from random import getrandbits from ball_drawing_area import BallDrawingArea from store import store from near import near import config def rand_velocity(): return config.BALL_VELOCITY if getrandbits(1) else -config.BALL_VELOCITY class BallWindow(Gtk.Window): def __init__(self, window_size, x=None, y=None, x_velocity=None, y_velocity=None): Gtk.Window.__init__(self) self.window_size = window_size self.x = x self.y = y self.x_velocity = x_velocity if x_velocity != None else rand_velocity() self.y_velocity = y_velocity if y_velocity != None else rand_velocity() self.rebuilding = False self.set_app_paintable(True) self.set_decorated(False) self.set_accept_focus(False) self.set_keep_above(True) self.set_skip_taskbar_hint(True) self.set_skip_pager_hint(True) self.set_deletable(False) self.set_size_request(config.BALL_DIAMETER, config.BALL_DIAMETER) self.set_position(Gtk.WindowPosition.CENTER) self.set_visual(self.get_screen().get_rgba_visual()) self.add_tick_callback(self.tick) if self.x and self.y: self.move(self.x, self.y) self.connect('realize', self.realize) self.connect('draw', self.draw) self.connect('window-state-event', self.check_minimized) def rebuild(self, use_current_position): if not self.rebuilding: if config.USE_ANTI_TAMPER or not use_current_position: self.rebuilding = True self.destroy() if use_current_position: self = BallWindow(self.window_size, self.x, self.y, self.x_velocity, self.y_velocity) else: self = BallWindow(self.window_size) self.show_all() def tick(self, _widget, _frame_clock): if config.USE_ANTI_TAMPER: self.set_keep_above(True) self.get_window().move_to_desktop(0) current_x, current_y = self.get_position() ai_x, ai_y = store['ai_paddle'] player_x, player_y = store['player_paddle'] width, height = self.window_size if ai_x == None or player_x == None: return True if ( current_x < config.SCREEN_PADDING or current_x + config.BALL_DIAMETER > width - config.SCREEN_PADDING ): self.x -= self.x_velocity self.rebuild(False) if ( current_y < config.SCREEN_PADDING or current_y + config.BALL_DIAMETER > height - config.SCREEN_PADDING ): self.y -= self.y_velocity self.y_velocity *= -1 if ( self.x < ai_x + (config.BALL_DIAMETER / 2 + config.BALL_PADDING) and self.y + (config.BALL_DIAMETER + config.BALL_PADDING) > ai_y and self.y < ai_y + config.PADDLE_SIZE[1] + config.BALL_PADDING ): self.x_velocity *= -1 if config.USE_BALL_STUCK_IN_PADDLE_FIX: self.x = ai_x + (config.BALL_DIAMETER / 2 + config.BALL_PADDING) if ( self.x > player_x - (config.BALL_DIAMETER + config.BALL_PADDING) and self.y + (config.BALL_DIAMETER + config.BALL_PADDING) > player_y and self.y < player_y + config.PADDLE_SIZE[1] + config.BALL_PADDING ): self.x_velocity *= -1 if config.USE_BALL_STUCK_IN_PADDLE_FIX: self.x = player_x - (config.BALL_DIAMETER + config.BALL_PADDING) if not near(self.x, current_x, config.BALL_LEEWAY) or not near(self.y, current_y, config.BALL_LEEWAY): self.rebuild(True) self.x += self.x_velocity self.y += self.y_velocity self.move(self.x, self.y) store['ball_position'] = (self.x, self.y, self.x_velocity, self.y_velocity) return True def check_minimized(self, _widget, event): if event.new_window_state & Gdk.WindowState.ICONIFIED: self.rebuild(True) def realize(self, _widget): current_x, current_y = self.get_position() self.x = current_x self.y = current_y ball = BallDrawingArea(self.get_window()) self.add(ball) ball.show_all() cursor = Gdk.Cursor.new_from_name(Gdk.Display.get_default(), 'not-allowed') self.get_window().set_cursor(cursor) def draw(self, _widget, cr): cr.set_source_rgba(1.0, 1.0, 1.0, 0.0) cr.set_operator(cairo.OPERATOR_SOURCE) cr.paint() return False

true

f73637169849c878d792f9992621b4bc541554d0

1,918

py

Python

task/collect_thirdparty_licenses.py

yshrk1597/nimclosedenv

2fc92e4e3b03e9f9aed92642af742ac4d4ffb1e6

[ "MIT" ]

null

task/collect_thirdparty_licenses.py

yshrk1597/nimclosedenv

2fc92e4e3b03e9f9aed92642af742ac4d4ffb1e6

[ "MIT" ]

null

task/collect_thirdparty_licenses.py

yshrk1597/nimclosedenv

2fc92e4e3b03e9f9aed92642af742ac4d4ffb1e6

[ "MIT" ]

null

# -*- coding: utf-8 -*- import sys import os import pathlib import json import urllib.parse import requests ''' json format [ { "name": "", "url": "", "category":"", "filename-format":"" }, ] ''' def download(session, url, filename): print(f"get {url}") r = session.get(url) if r.status_code == requests.codes.ok: print(f"save {filename}") data = r.text with open(filename, "w", encoding='utf-8') as f: f.write(data) else: print(f"failed {url}") print(f"status_code = {r.status_code}") sys.quit(1) if __name__ == '__main__': if len(sys.argv) >= 2: settingfilename = sys.argv[1] setting = None with open(settingfilename, encoding='utf-8') as f: setting = json.load(f) if setting is not None: with requests.Session() as session: session.headers.update({'Accept': '*/*'}) for p in setting: name = p["name"] url = p["url"] category = p["category"] urlfilename = urllib.parse.urlsplit(url).path.split('/')[-1] if "filename-format" in p and len(p["filename-format"]) > 0: f = p["filename-format"] else: if len(category) > 0: f = "{category}/{name}-{urlfilename}" else: f = "{name}-{urlfilename}" filename = f.format(name=name, category=category, urlfilename=urlfilename) parent_dir = pathlib.Path(filename).parent if str(parent_dir) != ".": parent_dir.mkdir(parents=True, exist_ok=True) download(session, url, filename) else: print("must arg \"jsonfile\"") sys.quit(1)

29.507692

94

0.487487

import sys import os import pathlib import json import urllib.parse import requests def download(session, url, filename): print(f"get {url}") r = session.get(url) if r.status_code == requests.codes.ok: print(f"save {filename}") data = r.text with open(filename, "w", encoding='utf-8') as f: f.write(data) else: print(f"failed {url}") print(f"status_code = {r.status_code}") sys.quit(1) if __name__ == '__main__': if len(sys.argv) >= 2: settingfilename = sys.argv[1] setting = None with open(settingfilename, encoding='utf-8') as f: setting = json.load(f) if setting is not None: with requests.Session() as session: session.headers.update({'Accept': '*/*'}) for p in setting: name = p["name"] url = p["url"] category = p["category"] urlfilename = urllib.parse.urlsplit(url).path.split('/')[-1] if "filename-format" in p and len(p["filename-format"]) > 0: f = p["filename-format"] else: if len(category) > 0: f = "{category}/{name}-{urlfilename}" else: f = "{name}-{urlfilename}" filename = f.format(name=name, category=category, urlfilename=urlfilename) parent_dir = pathlib.Path(filename).parent if str(parent_dir) != ".": parent_dir.mkdir(parents=True, exist_ok=True) download(session, url, filename) else: print("must arg \"jsonfile\"") sys.quit(1)

true

f7363798e2b7842fd9105ce3f50bac3499622d10

4,632

py

Python

fortnox/services/cost_center_services.py

xalien10/fortnox-python

7c5fe29a8adaa5a21288df4495996e20515ba8a7

[ "MIT" ]

21

2020-03-21T14:49:33.000Z

2022-02-02T12:46:08.000Z

fortnox/services/cost_center_services.py

xalien10/fortnox-python

7c5fe29a8adaa5a21288df4495996e20515ba8a7

[ "MIT" ]

5

2020-07-03T18:55:48.000Z

2021-11-02T10:25:32.000Z

fortnox/services/cost_center_services.py

xalien10/fortnox-python

7c5fe29a8adaa5a21288df4495996e20515ba8a7

[ "MIT" ]

3

2020-06-08T06:23:50.000Z

2021-06-10T18:28:32.000Z

class CostCenterService(object): """ :class:`fortnox.CostCenterService` is used by :class:`fortnox.Client` to make actions related to CostCenter resource. Normally you won't instantiate this class directly. """ """ Allowed attributes for CostCenter to send to Fortnox backend servers. """ OPTS_KEYS_TO_PERSIST = ['Code', 'Description'] SERVICE = "CostCenter" def __init__(self, http_client): """ :param :class:`fortnox.HttpClient` http_client: Pre configured high-level http client. """ self.__http_client = http_client @property def http_client(self): return self.__http_client def list(self, **params): """ Retrieve all CostCenter Returns all CostCenter available to the Company, according to the parameters provided :calls: ``get /costcenters`` :param dict params: (optional) Search options. :return: List of dictionaries that support attriubte-style access, which represent collection of CostCenter. :rtype: list """ _, _, cost_centers = self.http_client.get("/costcenters", params=params) return cost_centers def retrieve(self, code): """ Retrieve a single CostCenter Returns a single CostCenter according to the unique CostCenter ID provided If the specified CostCenter does not exist, this query returns an error :calls: ``get /costcenters/{code}`` :param int id: Unique identifier of a CostCenter. :return: Dictionary that support attriubte-style access and represent CostCenter resource. :rtype: dict """ _, _, cost_center = self.http_client.get("/costcenters/{code}".format(code=code)) return cost_center def create(self, *args, **kwargs): """ Create a CostCenter Creates a new CostCenter **Notice** the CostCenter's name **must** be unique within the scope of the resource_type :calls: ``post /costcenters`` :param tuple *args: (optional) Single object representing CostCenter resource. :param dict **kwargs: (optional) cost_center attributes. :return: Dictionary that support attriubte-style access and represents newely created CostCenter resource. :rtype: dict """ if not args and not kwargs: raise Exception('attributes for CostCenter are missing') initial_attributes = args[0] if args else kwargs attributes = dict((k, v) for k, v in initial_attributes.items()) attributes.update({'service': self.SERVICE}) _, _, cost_center = self.http_client.post("/costcenters", body=attributes) return cost_center def update(self, code, *args, **kwargs): """ Update a CostCenter Updates a CostCenter's information If the specified CostCenter does not exist, this query will return an error **Notice** if you want to update a CostCenter, you **must** make sure the CostCenter's name is unique within the scope of the specified resource :calls: ``put /costcenters/{code}`` :param int id: Unique identifier of a CostCenter. :param tuple *args: (optional) Single object representing CostCenter resource which attributes should be updated. :param dict **kwargs: (optional) CostCenter attributes to update. :return: Dictionary that support attriubte-style access and represents updated CostCenter resource. :rtype: dict """ if not args and not kwargs: raise Exception('attributes for CostCenter are missing') attributes = args[0] if args else kwargs attributes = dict((k, v) for k, v in attributes.items()) attributes.update({'service': self.SERVICE}) _, _, cost_center = self.http_client.put("/costcenters/{code}".format(code=code), body=attributes) return cost_center def destroy(self, code): """ Delete a CostCenter Deletes an existing CostCenter If the specified CostCenter is assigned to any resource, we will remove this CostCenter from all such resources If the specified CostCenter does not exist, this query will return an error This operation cannot be undone :calls: ``delete /costcenters/{code}`` :param int id: Unique identifier of a CostCenter. :return: True if the operation succeeded. :rtype: bool """ status_code, _, _ = self.http_client.delete("/costcenters/{code}".format(code=code)) return status_code == 204

38.280992

152

0.656952

class CostCenterService(object): OPTS_KEYS_TO_PERSIST = ['Code', 'Description'] SERVICE = "CostCenter" def __init__(self, http_client): self.__http_client = http_client @property def http_client(self): return self.__http_client def list(self, **params): _, _, cost_centers = self.http_client.get("/costcenters", params=params) return cost_centers def retrieve(self, code): _, _, cost_center = self.http_client.get("/costcenters/{code}".format(code=code)) return cost_center def create(self, *args, **kwargs): if not args and not kwargs: raise Exception('attributes for CostCenter are missing') initial_attributes = args[0] if args else kwargs attributes = dict((k, v) for k, v in initial_attributes.items()) attributes.update({'service': self.SERVICE}) _, _, cost_center = self.http_client.post("/costcenters", body=attributes) return cost_center def update(self, code, *args, **kwargs): if not args and not kwargs: raise Exception('attributes for CostCenter are missing') attributes = args[0] if args else kwargs attributes = dict((k, v) for k, v in attributes.items()) attributes.update({'service': self.SERVICE}) _, _, cost_center = self.http_client.put("/costcenters/{code}".format(code=code), body=attributes) return cost_center def destroy(self, code): status_code, _, _ = self.http_client.delete("/costcenters/{code}".format(code=code)) return status_code == 204

true

f736383903fae4d8d7d3cbcadabd5161e1f75a1b

5,125

py

Python

src/foolysh/fsm.py

tcdude/foolysh

dedd6eb655b9eab1d1ac90c6624b0ca92f54486f

[ "MIT" ]

2

2019-05-04T01:22:55.000Z

2019-10-04T10:48:05.000Z

src/foolysh/fsm.py

tcdude/foolysh

dedd6eb655b9eab1d1ac90c6624b0ca92f54486f

[ "MIT" ]

5

2020-03-09T16:15:13.000Z

2022-01-13T02:02:27.000Z

src/foolysh/fsm.py

tcdude/foolysh

dedd6eb655b9eab1d1ac90c6624b0ca92f54486f

[ "MIT" ]

null

""" Provides the FSM class, a rudimentary implementation of a Finite State Machine. """ from typing import Any, Callable, Dict, List, Optional, Tuple import sdl2 from .tools.common import to_snake_case __author__ = 'Tiziano Bettio' __license__ = 'MIT' __version__ = '0.1' __copyright__ = """Copyright (c) 2020 Tiziano Bettio Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.""" class FSM: """ Rudimentary Finite State Machine to organize state changes. If both a `enter_[my_class_name]` and `exit_[my_class_name]` are provided in a subclass of FSM, it will become a state that can be activated through the :meth:`self.request` method. `[my_class_name]` is a snake_case representation of the name of the subclass. It assumes PascalCase for class names (i.e. `MyClass` -> `my_class`). Use :func:`~foolysh.tools.common.to_snake_case` with your class name as parameter to determine the proper state name. When :meth:`FSM.request` is called the following actions are performed: 1. The `exit_` method from the current state gets called. 2. The `enter_` method from the requested state gets called. .. note:: Only provide `enter_` / `exit_` methods for subclasses that should be callable states. """ __states: Dict[str, Tuple[Callable, Callable]] = None __active_state: Optional[str] = None __history: List[str] = None __fsm_data: Dict[str, Any] = None def __setup_fsm(self): mro = [i.__name__ for i in self.__class__.__mro__] mro = mro[:mro.index('FSM')] self.__states = {} self.__history = [] self.__fsm_data = {} self.__fsm_data['-global-'] = {} for i in mro: name = to_snake_case(i) enterm = getattr(self, f'enter_{name}', False) exitm = getattr(self, f'exit_{name}', False) if enterm and exitm: self.__states[name] = enterm, exitm self.__fsm_data[name] = None else: Warning(f'Class "{i}" does not expose enter and exit methods. ' f'State not registered!') def request(self, state_name: str, back: bool = False) -> None: """ Performs the transition to a registered State. Raises a ValueError if the provided `state_name` is not registered. """ if not self.__states: self.__setup_fsm() if state_name not in self.__states: raise ValueError(f'Unknown state "{state_name}".') if self.__active_state == state_name: return if self.__active_state is not None: self.__states[self.__active_state][1]() if not back: self.__history.append(self.__active_state) self.__active_state = state_name self.__states[state_name][0]() sdl2.SDL_StopTextInput() # Ensure on-screen kbd gets hidden def fsm_back(self) -> None: """ Performs the transition to the last known state in the history. Does nothing if the history is empty. """ if not self.__history: return self.request(self.__history.pop(), True) @property def active_state(self) -> str: """The currently active state.""" return self.__active_state @property def previous_state(self) -> str: """The previous state before the last transition, or ``None``.""" if self.__history: return self.__history[-1] return None @property def fsm_data(self) -> Any: """The FSM data stored for the active state.""" if self.__active_state is None: raise ValueError(f'No state set yet.') if self.__fsm_data[self.__active_state] is None: self.__fsm_data[self.__active_state] = {} return self.__fsm_data[self.__active_state] @property def fsm_global_data(self) -> Dict[str, Any]: """ A data dict accessible from any state, potentially useful for passing information between states. """ return self.__fsm_data['-global-']

38.246269

79

0.658537

from typing import Any, Callable, Dict, List, Optional, Tuple import sdl2 from .tools.common import to_snake_case __author__ = 'Tiziano Bettio' __license__ = 'MIT' __version__ = '0.1' __copyright__ = """Copyright (c) 2020 Tiziano Bettio Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.""" class FSM: __states: Dict[str, Tuple[Callable, Callable]] = None __active_state: Optional[str] = None __history: List[str] = None __fsm_data: Dict[str, Any] = None def __setup_fsm(self): mro = [i.__name__ for i in self.__class__.__mro__] mro = mro[:mro.index('FSM')] self.__states = {} self.__history = [] self.__fsm_data = {} self.__fsm_data['-global-'] = {} for i in mro: name = to_snake_case(i) enterm = getattr(self, f'enter_{name}', False) exitm = getattr(self, f'exit_{name}', False) if enterm and exitm: self.__states[name] = enterm, exitm self.__fsm_data[name] = None else: Warning(f'Class "{i}" does not expose enter and exit methods. ' f'State not registered!') def request(self, state_name: str, back: bool = False) -> None: if not self.__states: self.__setup_fsm() if state_name not in self.__states: raise ValueError(f'Unknown state "{state_name}".') if self.__active_state == state_name: return if self.__active_state is not None: self.__states[self.__active_state][1]() if not back: self.__history.append(self.__active_state) self.__active_state = state_name self.__states[state_name][0]() sdl2.SDL_StopTextInput() def fsm_back(self) -> None: if not self.__history: return self.request(self.__history.pop(), True) @property def active_state(self) -> str: return self.__active_state @property def previous_state(self) -> str: if self.__history: return self.__history[-1] return None @property def fsm_data(self) -> Any: if self.__active_state is None: raise ValueError(f'No state set yet.') if self.__fsm_data[self.__active_state] is None: self.__fsm_data[self.__active_state] = {} return self.__fsm_data[self.__active_state] @property def fsm_global_data(self) -> Dict[str, Any]: return self.__fsm_data['-global-']

true

f736392d8c764845b6ae8e78f7c882654f9d241a

10,071

py

Python

impactutils/transfer/ftpsender.py

cbworden/earthquake-impact-utils

fff41e41668896110bebbc7054a36dd60779655b

[ "CC0-1.0" ]

3

2017-04-11T21:30:20.000Z

2018-02-28T23:17:30.000Z

impactutils/transfer/ftpsender.py

cbworden/earthquake-impact-utils

fff41e41668896110bebbc7054a36dd60779655b

[ "CC0-1.0" ]

103

2016-08-26T15:05:35.000Z

2021-02-10T16:24:55.000Z

impactutils/transfer/ftpsender.py

cbworden/earthquake-impact-utils

fff41e41668896110bebbc7054a36dd60779655b

[ "CC0-1.0" ]

9

2016-08-24T01:07:40.000Z

2022-03-01T10:03:27.000Z

#!/usr/bin/env python # stdlib imports from ftplib import FTP, error_perm import os.path import shutil import tempfile # local from .sender import Sender class FTPSender(Sender): '''Class for sending and deleting files and directories via FTP. PDLSender uses a local installation of Product Distribution Layer (PDL) (https://ehppdl1.cr.usgs.gov/index.html#documentation) to send a file or a directory, along with desired metadata to one or more PDL hubs. Required properties: - remote_host Name of FTP server. - remote_directory String path on remote_host where local files should be copied to. Optional properties: - user String user name, for FTP servers where anonymous login is not allowed. - password String password, for FTP servers where anonymous login is not allowed. Usage: sender = FTPSender(properties={'remote_host':'ftp.gov', 'remote_directory':'/pub/incoming/event1'}, local_directory = '/home/user/event1') sender.send() => Creates remote url: ftp://ftp.gov/pub/incoming/event1 with contents of /home/user/event1 in it. OR sender = FTPSender(properties={'remote_host':'ftp.gov', 'remote_directory':'/pub/incoming/event1'}, local_directory = '/home/user/event1/version1') sender.send() => Creates remote url: ftp://ftp.gov/pub/incoming/event1 with contents of /home/user/event1/version1 in it. OR sender = FTPSender(properties={'remote_host':'ftp.gov', 'remote_directory':'/pub/incoming/event1'}, local_files = ['/home/user/event1/version1/file1.txt','/home/user/event1/version1/file2.txt']) sender.send() => Creates remote files: ftp://ftp.gov/pub/incoming/event1/file1.txt AND ftp://ftp.gov/pub/incoming/event1/file1.txt ''' _required_properties = ['remote_directory', 'remote_host'] _optional_properties = ['user', 'password'] def send(self): ''' Send any files or folders that have been passed to constructor. Returns: Tuple of Number of files sent to remote SSH server and message describing success. Raises: Exception when files cannot be sent to remote FTP server for any reason. ''' remote_host = self._properties['remote_host'] remote_folder = self._properties['remote_directory'] try: # this should put us at the top level folder ftp = self._setup() # send any files we want nfiles = 0 for f in self._local_files: self.__sendfile(f, ftp) nfiles += 1 # send everything in the directories we specified if self._local_directory is not None: local_directory = self._local_directory allfiles = self.getAllLocalFiles() for filename in allfiles: try: self._copy_file_with_path( ftp, filename, remote_folder, local_folder=local_directory) nfiles += 1 except: x = 1 ftp.quit() return (nfiles, f'{int(nfiles):d} files were sent successfully to {remote_host} {remote_folder}') except Exception as obj: raise Exception( f'Could not send to {host}. Error "{str(obj)}"') def cancel(self): """ Create a cancel file (named as indicated in constructor "cancelfile" parameter) in remote_directory on remote_host. Args: cancel_content: String containing text that should be written to the cancelfile. Returns: A string message describing what has occurred. """ remote_host = self._properties['remote_host'] remote_folder = self._properties['remote_directory'] ftp = self._setup() # Create local .cancel file, then copy it to ftp server tempdir = tempfile.mkdtemp() try: tfile = os.path.join(tempdir, self._cancelfile) # local file f = open(tfile, 'wt') f.close() ftp.cwd(remote_folder) self.__sendfile(tfile, ftp) except Exception as e: raise Exception( f'Could not create .cancel file on {remote_host}/{remote_folder}') finally: shutil.rmtree(tempdir) return (f'{self._cancelfile} file succesfully placed on {remote_host} {remote_folder}') def _setup(self): """Initiate an ftp connection with properties passed to constructor. Navigate to/create directory (as necessary) specified by remote_directory property. Returns: Instance of the ftplib.FTP class. """ host = self._properties['remote_host'] remote_folder = self._properties['remote_directory'] # attempt to login to remote host try: dirparts = self._split(remote_folder) ftp = FTP(host) if 'user' in self._properties: user = self._properties['user'] else: user = '' if 'password' in self._properties: password = self._properties['password'] else: password = '' if user == '': ftp.login() else: ftp.login(user, password) except error_perm as msg: raise Exception(f'Could not login to remote host {host}') # attempt to cd to remote directory try: self._create_remote_directory(ftp, remote_folder) except Exception as e: ftp.quit() raise Exception( f'Could not navigate to directory "{remote_folder}" on remote host {host}') return ftp def _create_remote_directory(self, ftp, remote_directory): """Create directory (recursively) on remote_host. Args: ftp: ftplib.FTP instance. remote_directory: String path of directory on remote system which needs to be created. Raises: Exception when unable to create remote_directory. """ # attempt to cd to remote directory ftp.cwd('/') try: ftp.cwd(remote_directory) except error_perm as msg: dirparts = self._split(remote_directory) for directory in dirparts: try: ftp.cwd(directory) except error_perm as msg: try: ftp.mkd(directory) ftp.cwd(directory) except error_perm as msg: raise Exception( f'Unable to create subdirectory {directory}.') def _copy_file_with_path(self, ftp, local_file, remote_folder, local_folder=None): """ Copy local_file to remote_folder, preserving relative path and creating required sub-directories. Usage: local_file: /home/user/data/events/us2016abcd/data_files/datafile.txt remote_folder: /data/archive/events local_folder: /home/user/data/events/us2016abcd would create: /data/archive/events/us2016abcd/data_files/datafile.txt local_file: /home/user/data/events/us2016abcd/data_files/datafile.txt remote_folder: /data/archive/events/us2016abcd local_folder: None would create: /data/archive/events/us2016abcd/datafile.txt Args: local_file: Local file to copy. remote_folder: Remote folder to copy local files to. local_folder: Top of local directory where file copying started. If None, local_file should be copied to a file of the same name (not preserving path) into remote_folder. """ if local_folder is None: ftp.cwd(remote_folder) self.__sendfile(filename, ftp) else: local_parts = local_file.replace(local_folder, '').strip( os.path.sep).split(os.path.sep) remote_parts = self._split(remote_folder) all_parts = remote_parts + local_parts remote_file = '/' + '/'.join(all_parts) print(remote_file) remfolder, remfile = self._path_split(remote_file) try: ftp.cwd(remfolder) except error_perm as ep: self._create_remote_directory(ftp, remfolder) self.__sendfile(local_file, ftp) ftp.cwd(remote_folder) def __sendfile(self, filename, ftp): '''Internal function used to send a file using an FTP object. Args: filename: Local filename ftp: Instance of FTP object. ''' # in case somebody is polling for this file, # make a temporary file first, then rename it # so the poller doesn't grab it before its finished transferring. fbase, fpath = os.path.split(filename) # this is a local file tmpfile = fpath + '.tmp' cmd = "STOR " + tmpfile # we don't tell the ftp server about the local path to the file # actually send the file ftp.storbinary(cmd, open(filename, "rb"), 1024) # rename it to the desired destination ftp.rename(tmpfile, fpath) def _join(self, *path_parts): return '/' + '/'.join(path_parts) def _split(self, path): return path.strip('/').split('/') def _path_split(self, path): parts = path.strip('/').split('/') fname = parts[-1] fpath = '/' + '/'.join(parts[0:-1]) return (fpath, fname)

36.357401

125

0.577599

from ftplib import FTP, error_perm import os.path import shutil import tempfile from .sender import Sender class FTPSender(Sender): _required_properties = ['remote_directory', 'remote_host'] _optional_properties = ['user', 'password'] def send(self): remote_host = self._properties['remote_host'] remote_folder = self._properties['remote_directory'] try: ftp = self._setup() nfiles = 0 for f in self._local_files: self.__sendfile(f, ftp) nfiles += 1 if self._local_directory is not None: local_directory = self._local_directory allfiles = self.getAllLocalFiles() for filename in allfiles: try: self._copy_file_with_path( ftp, filename, remote_folder, local_folder=local_directory) nfiles += 1 except: x = 1 ftp.quit() return (nfiles, f'{int(nfiles):d} files were sent successfully to {remote_host} {remote_folder}') except Exception as obj: raise Exception( f'Could not send to {host}. Error "{str(obj)}"') def cancel(self): remote_host = self._properties['remote_host'] remote_folder = self._properties['remote_directory'] ftp = self._setup() tempdir = tempfile.mkdtemp() try: tfile = os.path.join(tempdir, self._cancelfile) f = open(tfile, 'wt') f.close() ftp.cwd(remote_folder) self.__sendfile(tfile, ftp) except Exception as e: raise Exception( f'Could not create .cancel file on {remote_host}/{remote_folder}') finally: shutil.rmtree(tempdir) return (f'{self._cancelfile} file succesfully placed on {remote_host} {remote_folder}') def _setup(self): host = self._properties['remote_host'] remote_folder = self._properties['remote_directory'] try: dirparts = self._split(remote_folder) ftp = FTP(host) if 'user' in self._properties: user = self._properties['user'] else: user = '' if 'password' in self._properties: password = self._properties['password'] else: password = '' if user == '': ftp.login() else: ftp.login(user, password) except error_perm as msg: raise Exception(f'Could not login to remote host {host}') try: self._create_remote_directory(ftp, remote_folder) except Exception as e: ftp.quit() raise Exception( f'Could not navigate to directory "{remote_folder}" on remote host {host}') return ftp def _create_remote_directory(self, ftp, remote_directory): ftp.cwd('/') try: ftp.cwd(remote_directory) except error_perm as msg: dirparts = self._split(remote_directory) for directory in dirparts: try: ftp.cwd(directory) except error_perm as msg: try: ftp.mkd(directory) ftp.cwd(directory) except error_perm as msg: raise Exception( f'Unable to create subdirectory {directory}.') def _copy_file_with_path(self, ftp, local_file, remote_folder, local_folder=None): if local_folder is None: ftp.cwd(remote_folder) self.__sendfile(filename, ftp) else: local_parts = local_file.replace(local_folder, '').strip( os.path.sep).split(os.path.sep) remote_parts = self._split(remote_folder) all_parts = remote_parts + local_parts remote_file = '/' + '/'.join(all_parts) print(remote_file) remfolder, remfile = self._path_split(remote_file) try: ftp.cwd(remfolder) except error_perm as ep: self._create_remote_directory(ftp, remfolder) self.__sendfile(local_file, ftp) ftp.cwd(remote_folder) def __sendfile(self, filename, ftp): fbase, fpath = os.path.split(filename) # this is a local file tmpfile = fpath + '.tmp' cmd = "STOR " + tmpfile # we don't tell the ftp server about the local path to the file ftp.storbinary(cmd, open(filename, "rb"), 1024) ftp.rename(tmpfile, fpath) def _join(self, *path_parts): return '/' + '/'.join(path_parts) def _split(self, path): return path.strip('/').split('/') def _path_split(self, path): parts = path.strip('/').split('/') fname = parts[-1] fpath = '/' + '/'.join(parts[0:-1]) return (fpath, fname)

true

f7363ac8e3c301222fb9085a20025b96b226a9eb

4,958

py

Python

Chapter9/Figure9-1.py

liloganle/Reinforcement-Learning

29ffb74a1c8e506c544245c9aff37e958e503f26

[ "MIT" ]

1

2018-08-27T10:09:06.000Z

Chapter9/Figure9-1.py

liloganle/Reinforcement-Learning

29ffb74a1c8e506c544245c9aff37e958e503f26

[ "MIT" ]

null

Chapter9/Figure9-1.py

liloganle/Reinforcement-Learning

29ffb74a1c8e506c544245c9aff37e958e503f26

[ "MIT" ]

null

# -*- coding:utf-8 -*- import numpy as np from tqdm import tqdm import matplotlib.pyplot as plt class RandomWalk(object): def __init__(self, num_states=1000, groups=10, alpha=2e-5): self.num_states = num_states # the number of states self.groups = groups # the number of groups self.alpha = alpha # the step size self.group_value = np.zeros(groups) # the value of each group self.group_size = int(num_states / groups) # the size of each group self.states = np.arange(1, num_states+1) # all states except terminal state self.start_state = int(num_states / 2) # the start state self.end_state = [0, num_states + 1] # the terminal states self.action = [-1, 1] # right:1, left:-1 self.neighbors = 100 # the neighboring states def select_action(self): """to select randomly an action""" if np.random.binomial(1, 0.5): return self.action[1] # select right action else: return self.action[0] # select left action def find_next_state(self, state, action): """to get the next state and reward""" move_step = np.random.randint(1, self.neighbors+1) # the step size of moving move_step *= action next_state = state + move_step # the next state next_state = max(min(next_state, self.end_state[1]), 0) if next_state == self.end_state[0]: # terminating on the left reward = -1 elif next_state == self.end_state[1]: # terminating on the right reward = 1 else: reward = 0 return next_state, reward def get_state_value(self, state): """to get the state value except for terminal states""" group_idx = (state - 1) // self.group_size return self.group_value[group_idx] def update_group_value(self, state, delta): """to update the group_value""" group_idx = (state - 1) // self.group_size self.group_value[group_idx] += delta def gradient_monte_carlo(self, state_distribution): """ the gradient-descent version of Monte Carlo state-value prediction""" state = self.start_state # initialize the state trajectory = [state] # track the transition state while state not in self.end_state: action = self.select_action() # select an action next_state, reward = self.find_next_state(state, action) # get the next state and reward trajectory.append(next_state) # record the transition state state = next_state for stat in trajectory[:-1]: delta = self.alpha * (reward - self.get_state_value(stat)) self.update_group_value(stat, delta) state_distribution[stat] += 1 def dp_compute_value(test_class): """using Dynamic programming to find the true state values""" value = np.arange(-test_class.end_state[1], test_class.end_state[1] + 1, 2) / test_class.end_state[1] print("Starting computing......") while True: value_temp = value.copy() for state in test_class.states: value[state] = 0 for act in test_class.action: for step in range(1, test_class.neighbors + 1): step *= act next_state = state + step next_state = max(min(next_state, test_class.end_state[1]), 0) # update the value value[state] += 1/(2*test_class.neighbors)*value[next_state] if np.linalg.norm(value - value_temp) < 0.001: break print("Completed!!!") return value if __name__ == "__main__": episodes = 100000 test_exam = RandomWalk() true_value = dp_compute_value(test_class=test_exam) distribution = np.zeros(test_exam.num_states + len(test_exam.end_state)) for itr in tqdm(range(episodes)): test_exam.gradient_monte_carlo(distribution) distribution /= np.sum(distribution) state_value = [test_exam.get_state_value(stat) for stat in test_exam.states] plt.figure(1) plt.plot(test_exam.states, true_value[1:-1], label="True value") plt.plot(test_exam.states, state_value, label="Approximate MC value") plt.xlabel("State") plt.ylabel("Value") plt.legend() plt.savefig("./images/Figure9-1-1.png") plt.show() plt.figure(2) plt.plot(test_exam.states, distribution[1:-1], label="State Distribution") plt.xlabel("State") plt.ylabel("Distribution") plt.legend() plt.savefig("./images/Figure9-1-2.png") plt.show() plt.close() print("Completed!!!You can check it in 'images' directory")

39.349206

107

0.594796

import numpy as np from tqdm import tqdm import matplotlib.pyplot as plt class RandomWalk(object): def __init__(self, num_states=1000, groups=10, alpha=2e-5): self.num_states = num_states self.groups = groups self.alpha = alpha self.group_value = np.zeros(groups) self.group_size = int(num_states / groups) self.states = np.arange(1, num_states+1) self.start_state = int(num_states / 2) self.end_state = [0, num_states + 1] self.action = [-1, 1] self.neighbors = 100 def select_action(self): if np.random.binomial(1, 0.5): return self.action[1] else: return self.action[0] def find_next_state(self, state, action): move_step = np.random.randint(1, self.neighbors+1) move_step *= action next_state = state + move_step next_state = max(min(next_state, self.end_state[1]), 0) if next_state == self.end_state[0]: reward = -1 elif next_state == self.end_state[1]: reward = 1 else: reward = 0 return next_state, reward def get_state_value(self, state): group_idx = (state - 1) // self.group_size return self.group_value[group_idx] def update_group_value(self, state, delta): group_idx = (state - 1) // self.group_size self.group_value[group_idx] += delta def gradient_monte_carlo(self, state_distribution): state = self.start_state trajectory = [state] while state not in self.end_state: action = self.select_action() next_state, reward = self.find_next_state(state, action) trajectory.append(next_state) state = next_state for stat in trajectory[:-1]: delta = self.alpha * (reward - self.get_state_value(stat)) self.update_group_value(stat, delta) state_distribution[stat] += 1 def dp_compute_value(test_class): value = np.arange(-test_class.end_state[1], test_class.end_state[1] + 1, 2) / test_class.end_state[1] print("Starting computing......") while True: value_temp = value.copy() for state in test_class.states: value[state] = 0 for act in test_class.action: for step in range(1, test_class.neighbors + 1): step *= act next_state = state + step next_state = max(min(next_state, test_class.end_state[1]), 0) value[state] += 1/(2*test_class.neighbors)*value[next_state] if np.linalg.norm(value - value_temp) < 0.001: break print("Completed!!!") return value if __name__ == "__main__": episodes = 100000 test_exam = RandomWalk() true_value = dp_compute_value(test_class=test_exam) distribution = np.zeros(test_exam.num_states + len(test_exam.end_state)) for itr in tqdm(range(episodes)): test_exam.gradient_monte_carlo(distribution) distribution /= np.sum(distribution) state_value = [test_exam.get_state_value(stat) for stat in test_exam.states] plt.figure(1) plt.plot(test_exam.states, true_value[1:-1], label="True value") plt.plot(test_exam.states, state_value, label="Approximate MC value") plt.xlabel("State") plt.ylabel("Value") plt.legend() plt.savefig("./images/Figure9-1-1.png") plt.show() plt.figure(2) plt.plot(test_exam.states, distribution[1:-1], label="State Distribution") plt.xlabel("State") plt.ylabel("Distribution") plt.legend() plt.savefig("./images/Figure9-1-2.png") plt.show() plt.close() print("Completed!!!You can check it in 'images' directory")

true

f7363b31d00a4b9a3ce297b2c0f2291a97db0556

3,344

py

Python

var/spack/repos/builtin/packages/spdlog/package.py

MiddelkoopT/spack

4d94c4c4600f42a7a3bb3d06ec879140bc259304

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

null

var/spack/repos/builtin/packages/spdlog/package.py

MiddelkoopT/spack

4d94c4c4600f42a7a3bb3d06ec879140bc259304

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

null

var/spack/repos/builtin/packages/spdlog/package.py

MiddelkoopT/spack

4d94c4c4600f42a7a3bb3d06ec879140bc259304

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

1

2022-01-18T23:39:24.000Z

# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class Spdlog(CMakePackage): """Very fast, header only, C++ logging library""" homepage = "https://github.com/gabime/spdlog" url = "https://github.com/gabime/spdlog/archive/v0.9.0.tar.gz" version('1.8.1', sha256='5197b3147cfcfaa67dd564db7b878e4a4b3d9f3443801722b3915cdeced656cb') version('1.8.0', sha256='1e68e9b40cf63bb022a4b18cdc1c9d88eb5d97e4fd64fa981950a9cacf57a4bf') version('1.7.0', sha256='f0114a4d3c88be9e696762f37a7c379619443ce9d668546c61b21d41affe5b62') version('1.6.1', sha256='378a040d91f787aec96d269b0c39189f58a6b852e4cbf9150ccfacbe85ebbbfc') version('1.6.0', sha256='0421667c9f2fc78e6548d44f7bc5921be0f03e612df384294c16cedb93d967f8') version('1.5.0', sha256='b38e0bbef7faac2b82fed550a0c19b0d4e7f6737d5321d4fd8f216b80f8aee8a') version('1.4.2', sha256='821c85b120ad15d87ca2bc44185fa9091409777c756029125a02f81354072157') version('1.4.1', sha256='3291958eb54ed942d1bd3aef1b4f8ccf70566cbc04d34296ec61eb96ceb73cff') version('1.2.1', sha256='867a4b7cedf9805e6f76d3ca41889679054f7e5a3b67722fe6d0eae41852a767') version('1.2.0', sha256='0ba31b9e7f8e43a7be328ab0236d57810e5d4fc8a1a7842df665ae22d5cbd128') version('1.1.0', sha256='3dbcbfd8c07e25f5e0d662b194d3a7772ef214358c49ada23c044c4747ce8b19') version('1.0.0', sha256='90d5365121bcd2c41ce94dfe6a460e89507a2dfef6133fe5fad5bb35ac4ef0a1') version('0.17.0', sha256='94f74fd1b3344733d1db3de2ec22e6cbeb769f93a8baa0d4a22b1f62dc7369f8') version('0.16.3', sha256='b88d7be261d9089c817fc8cee6c000d69f349b357828e4c7f66985bc5d5360b8') version('0.16.2', sha256='2081e5df5e87402398847431e16b87c71dd5c4d632314bb976ace8161f4d32de') version('0.16.1', sha256='733260e1fbdcf1b3dc307fc585e4476240026de8be28eb905731d2ab0942deae') version('0.16.0', sha256='9e64e3b10c2a3c54dfff63aa056057cf1db8a5fd506b3d9cf77207511820baac') version('0.14.0', sha256='eb5beb4e53f4bfff5b32eb4db8588484bdc15a17b90eeefef3a9fc74fec1d83d') version('0.13.0', sha256='d798a6ca19165f0a18a43938859359269f5a07fd8e0eb83ab8674739c9e8f361') version('0.12.0', sha256='5cfd6a0b3182a88e1eb35bcb65a7ef9035140d7c73b16ba6095939dbf07325b9') version('0.11.0', sha256='8c0f1810fb6b7d23fef70c2ea8b6fa6768ac8d18d6e0de39be1f48865e22916e') version('0.10.0', sha256='fbbc53c1cc09b93b4c3d76b683bbe9315e2efe3727701227374dce6aa4264075') version('0.9.0', sha256='bbbe5a855c8b309621352921d650449eb2f741d35d55ec50fb4d8122ddfb8f01') variant('shared', default=True, description='Build shared libraries (v1.4.0+)') depends_on('cmake@3.2:', type='build') def cmake_args(self): spec = self.spec args = [] if self.spec.version >= Version('1.4.0'): args.extend([ '-DSPDLOG_BUILD_SHARED:BOOL={0}'.format( 'ON' if '+shared' in spec else 'OFF'), # tests and examples '-DSPDLOG_BUILD_TESTS:BOOL={0}'.format( 'ON' if self.run_tests else 'OFF'), '-DSPDLOG_BUILD_EXAMPLE:BOOL={0}'.format( 'ON' if self.run_tests else 'OFF') ]) return args

54.819672

96

0.747309

from spack import * class Spdlog(CMakePackage): homepage = "https://github.com/gabime/spdlog" url = "https://github.com/gabime/spdlog/archive/v0.9.0.tar.gz" version('1.8.1', sha256='5197b3147cfcfaa67dd564db7b878e4a4b3d9f3443801722b3915cdeced656cb') version('1.8.0', sha256='1e68e9b40cf63bb022a4b18cdc1c9d88eb5d97e4fd64fa981950a9cacf57a4bf') version('1.7.0', sha256='f0114a4d3c88be9e696762f37a7c379619443ce9d668546c61b21d41affe5b62') version('1.6.1', sha256='378a040d91f787aec96d269b0c39189f58a6b852e4cbf9150ccfacbe85ebbbfc') version('1.6.0', sha256='0421667c9f2fc78e6548d44f7bc5921be0f03e612df384294c16cedb93d967f8') version('1.5.0', sha256='b38e0bbef7faac2b82fed550a0c19b0d4e7f6737d5321d4fd8f216b80f8aee8a') version('1.4.2', sha256='821c85b120ad15d87ca2bc44185fa9091409777c756029125a02f81354072157') version('1.4.1', sha256='3291958eb54ed942d1bd3aef1b4f8ccf70566cbc04d34296ec61eb96ceb73cff') version('1.2.1', sha256='867a4b7cedf9805e6f76d3ca41889679054f7e5a3b67722fe6d0eae41852a767') version('1.2.0', sha256='0ba31b9e7f8e43a7be328ab0236d57810e5d4fc8a1a7842df665ae22d5cbd128') version('1.1.0', sha256='3dbcbfd8c07e25f5e0d662b194d3a7772ef214358c49ada23c044c4747ce8b19') version('1.0.0', sha256='90d5365121bcd2c41ce94dfe6a460e89507a2dfef6133fe5fad5bb35ac4ef0a1') version('0.17.0', sha256='94f74fd1b3344733d1db3de2ec22e6cbeb769f93a8baa0d4a22b1f62dc7369f8') version('0.16.3', sha256='b88d7be261d9089c817fc8cee6c000d69f349b357828e4c7f66985bc5d5360b8') version('0.16.2', sha256='2081e5df5e87402398847431e16b87c71dd5c4d632314bb976ace8161f4d32de') version('0.16.1', sha256='733260e1fbdcf1b3dc307fc585e4476240026de8be28eb905731d2ab0942deae') version('0.16.0', sha256='9e64e3b10c2a3c54dfff63aa056057cf1db8a5fd506b3d9cf77207511820baac') version('0.14.0', sha256='eb5beb4e53f4bfff5b32eb4db8588484bdc15a17b90eeefef3a9fc74fec1d83d') version('0.13.0', sha256='d798a6ca19165f0a18a43938859359269f5a07fd8e0eb83ab8674739c9e8f361') version('0.12.0', sha256='5cfd6a0b3182a88e1eb35bcb65a7ef9035140d7c73b16ba6095939dbf07325b9') version('0.11.0', sha256='8c0f1810fb6b7d23fef70c2ea8b6fa6768ac8d18d6e0de39be1f48865e22916e') version('0.10.0', sha256='fbbc53c1cc09b93b4c3d76b683bbe9315e2efe3727701227374dce6aa4264075') version('0.9.0', sha256='bbbe5a855c8b309621352921d650449eb2f741d35d55ec50fb4d8122ddfb8f01') variant('shared', default=True, description='Build shared libraries (v1.4.0+)') depends_on('cmake@3.2:', type='build') def cmake_args(self): spec = self.spec args = [] if self.spec.version >= Version('1.4.0'): args.extend([ '-DSPDLOG_BUILD_SHARED:BOOL={0}'.format( 'ON' if '+shared' in spec else 'OFF'), '-DSPDLOG_BUILD_TESTS:BOOL={0}'.format( 'ON' if self.run_tests else 'OFF'), '-DSPDLOG_BUILD_EXAMPLE:BOOL={0}'.format( 'ON' if self.run_tests else 'OFF') ]) return args

true

f7363c67ecf0557ee81b88769cad34b8eafd3728

682

py

Python

Python Advanced/Advanced/Tuples and Sets/Exercise/Task06.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

1

2022-03-16T10:23:04.000Z

Python Advanced/Advanced/Tuples and Sets/Exercise/Task06.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

null

Python Advanced/Advanced/Tuples and Sets/Exercise/Task06.py

IvanTodorovBG/SoftUni

7b667f6905d9f695ab1484efbb02b6715f6d569e

[ "MIT" ]

null

n = int(input()) even_set = set() odd_set = set() for index in range(1, n + 1): name = input() ascii_value = 0 for char in name: ascii_value += ord(char) ascii_value /= index ascii_value = int(ascii_value) if ascii_value % 2 == 0: even_set.add(ascii_value) else: odd_set.add(ascii_value) if sum(even_set) == sum(odd_set): union = even_set.union(odd_set) print(", ".join([str(x) for x in union])) elif sum(odd_set) > sum(even_set): print(", ".join([str(x) for x in odd_set])) elif sum(odd_set) < sum(even_set): sym_dif = even_set.symmetric_difference(odd_set) print(", ".join([str(x) for x in sym_dif]))

22.733333

52

0.61437

n = int(input()) even_set = set() odd_set = set() for index in range(1, n + 1): name = input() ascii_value = 0 for char in name: ascii_value += ord(char) ascii_value /= index ascii_value = int(ascii_value) if ascii_value % 2 == 0: even_set.add(ascii_value) else: odd_set.add(ascii_value) if sum(even_set) == sum(odd_set): union = even_set.union(odd_set) print(", ".join([str(x) for x in union])) elif sum(odd_set) > sum(even_set): print(", ".join([str(x) for x in odd_set])) elif sum(odd_set) < sum(even_set): sym_dif = even_set.symmetric_difference(odd_set) print(", ".join([str(x) for x in sym_dif]))

true

f7363e2b72f9b4c0f235a44590c692b9349869a4

1,532

py

Python

server/canonicalization/utils/log.py

hotpxl/canonicalization-server

ac3c0e93adf35015d7f6cfc8c6cf2e6ec45cdeae

[ "MIT" ]

3

2015-10-29T21:43:27.000Z

2020-05-26T09:17:41.000Z

server/canonicalization/utils/log.py

hotpxl/canonicalization-server

ac3c0e93adf35015d7f6cfc8c6cf2e6ec45cdeae

[ "MIT" ]

null

server/canonicalization/utils/log.py

hotpxl/canonicalization-server

ac3c0e93adf35015d7f6cfc8c6cf2e6ec45cdeae

[ "MIT" ]

null

from __future__ import absolute_import from __future__ import print_function import logging class _Formatter(logging.Formatter): def __init__(self): datefmt = '%m%d %H:%M:%S' super(_Formatter, self).__init__(datefmt=datefmt) def get_color(self, level): if logging.WARNING <= level: return '\x1b[31m' elif logging.INFO <= level: return '\x1b[32m' else: return '\x1b[34m' def get_label(self, level): if level == logging.CRITICAL: return 'C' elif level == logging.ERROR: return 'E' elif level == logging.WARNING: return 'W' elif level == logging.INFO: return 'I' elif level == logging.DEBUG: return 'D' else: return 'U' def format(self, record): fmt = self.get_color(record.levelno) fmt += self.get_label(record.levelno) fmt += '%(asctime)s %(process)d %(filename)s:%(lineno)d:%(funcName)s' \ ' %(name)s]\x1b[0m' fmt += ' %(message)s' self._fmt = fmt return super(_Formatter, self).format(record) _handler = logging.StreamHandler() _handler.setFormatter(_Formatter()) def get_logger(name=None, level=logging.DEBUG): logger = logging.getLogger(name) if getattr(logger, '_init_done', None): return logger else: logger._init_done = True logger.addHandler(_handler) logger.setLevel(level) return logger

27.357143

79

0.580287

from __future__ import absolute_import from __future__ import print_function import logging class _Formatter(logging.Formatter): def __init__(self): datefmt = '%m%d %H:%M:%S' super(_Formatter, self).__init__(datefmt=datefmt) def get_color(self, level): if logging.WARNING <= level: return '\x1b[31m' elif logging.INFO <= level: return '\x1b[32m' else: return '\x1b[34m' def get_label(self, level): if level == logging.CRITICAL: return 'C' elif level == logging.ERROR: return 'E' elif level == logging.WARNING: return 'W' elif level == logging.INFO: return 'I' elif level == logging.DEBUG: return 'D' else: return 'U' def format(self, record): fmt = self.get_color(record.levelno) fmt += self.get_label(record.levelno) fmt += '%(asctime)s %(process)d %(filename)s:%(lineno)d:%(funcName)s' \ ' %(name)s]\x1b[0m' fmt += ' %(message)s' self._fmt = fmt return super(_Formatter, self).format(record) _handler = logging.StreamHandler() _handler.setFormatter(_Formatter()) def get_logger(name=None, level=logging.DEBUG): logger = logging.getLogger(name) if getattr(logger, '_init_done', None): return logger else: logger._init_done = True logger.addHandler(_handler) logger.setLevel(level) return logger

true

f7363ff34bb5175c2483c71a8f1bc69582de84b2

3,302

py

Python

techreview/settings.py

lcared/TechReview2021

96b5f9af3c35f2f1dc79e6c53b48623d84e21da8

[ "MIT" ]

1

2021-06-14T17:39:37.000Z

techreview/settings.py

mjelde/TechReview2021

96b5f9af3c35f2f1dc79e6c53b48623d84e21da8

[ "MIT" ]

null

techreview/settings.py

mjelde/TechReview2021

96b5f9af3c35f2f1dc79e6c53b48623d84e21da8

[ "MIT" ]

null

""" Django settings for techreview project. Generated by 'django-admin startproject' using Django 3.1.6. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ from pathlib import Path from decouple import config # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = config('SECRET_KEY') # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'tech', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'techreview.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'techreview.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE' : 'django.db.backends.postgresql', 'NAME' : config('DB_NAME'), 'USER' : config('DB_USER'), 'PASSWORD' : config('DB_PASSWORD'), 'HOST' : config('DB_HOST'), 'PORT' : config('DB_PORT'), } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/' LOGIN_REDIRECT_URL='loginmessage' LOGOUT_REDIRECT_URL='logoutmessage'

25.796875

91

0.692611

from pathlib import Path from decouple import config BASE_DIR = Path(__file__).resolve().parent.parent SECRET_KEY = config('SECRET_KEY') DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'tech', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'techreview.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'techreview.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE' : 'django.db.backends.postgresql', 'NAME' : config('DB_NAME'), 'USER' : config('DB_USER'), 'PASSWORD' : config('DB_PASSWORD'), 'HOST' : config('DB_HOST'), 'PORT' : config('DB_PORT'), } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/' LOGIN_REDIRECT_URL='loginmessage' LOGOUT_REDIRECT_URL='logoutmessage'

true

f73640ecbf844dab79db03ff22b848317b94f45e

3,023

py

Python

sphinx/source/tutorial/solutions/les_mis.py

timelyportfolio/bokeh

a976a85535cf137c6238ce9e90b41ab14ae8ce22

[ "BSD-3-Clause" ]

2

2021-09-01T12:36:06.000Z

2021-11-17T10:48:36.000Z

sphinx/source/tutorial/solutions/les_mis.py

brian15co/bokeh

6cecb7211277b9d838039d0eb15e50a10f9ac3d1

[ "BSD-3-Clause" ]

null

sphinx/source/tutorial/solutions/les_mis.py

brian15co/bokeh

6cecb7211277b9d838039d0eb15e50a10f9ac3d1

[ "BSD-3-Clause" ]

1

2016-03-18T03:01:59.000Z

import numpy as np from bokeh.plotting import figure, output_file, show from bokeh.models import HoverTool, ColumnDataSource from bokeh.sampledata.les_mis import data # EXERCISE: try out different sort orders for the names nodes = data['nodes'] names = [node['name'] for node in sorted(data['nodes'], key=lambda x: x['group'])] # store the links information in numpy N = len(nodes) counts = np.empty((N, N)) for link in data['links']: counts[link['source'], link['target']] = link['value'] counts[link['target'], link['source']] = link['value'] # We will use these colors to color each group by a different color colormap = [ "#444444", "#a6cee3", "#1f78b4", "#b2df8a", "#33a02c", "#fb9a99", "#e31a1c", "#fdbf6f", "#ff7f00", "#cab2d6", "#6a3d9a" ] # set up some data to plot! We will need to have values for every pair of names. The # co-occurrence count for a given pair of names is in `count[i,j]`. The strategy is # to color each rect by the group, and set its alpha based on the count. xname = [] yname = [] color = [] alpha = [] for i, n1 in enumerate(nodes): for j, n2 in enumerate(nodes): xname.append(n1['name']) yname.append(n2['name']) a = min(counts[i,j]/4.0, 0.9) + 0.1 alpha.append(a) if n1['group'] == n2['group']: color.append(colormap[n1['group']]) else: color.append('lightgrey') # EXERCISE: output static HTML file output_file("les_mis.html") # EXERCISE: create a ColumnDataSource to hold the xnames, ynames, colors, alphas, # and counts. NOTE: the counts array is 2D and will need to be flattened source = ColumnDataSource( data=dict( xname=xname, yname=yname, colors=color, alphas=alpha, count=counts.flatten(), ) ) # create a new figure p = figure(title="Les Mis Occurrences (one at a time)", x_axis_location="above", tools="resize,hover", x_range=list(reversed(names)), y_range=names, plot_width=800, plot_height=800) # EXERCISE: use the `p.rect` renderer to render a categorical heatmap of all the # data. Experiment with the widths and heights (use categorical percentage # unite) as well as colors and alphas. p.rect('xname', 'yname', 0.9, 0.9, source=source, color='colors', alpha='alphas', line_color=None) # EXERCISE: use p.grid, p.axis, etc. to style the plot. Some suggestions: # - remove the axis and grid lines # - remove the major ticks # - make the tick labels smaller # - set the x-axis orientation to vertical, or angled p.grid.grid_line_color = None p.axis.axis_line_color = None p.axis.major_tick_line_color = None p.axis.major_label_text_font_size = "5pt" p.axis.major_label_standoff = 0 p.xaxis.major_label_orientation = np.pi/3 # EXERCISE: configure the hover tool to display both names as well as # the count value as tooltips hover = p.select(dict(type=HoverTool)) hover.tooltips = [ ('names', '@yname, @xname'), ('count', '@count'), ] # EXERCISE: show the plot show(p)

32.505376

84

0.670526

import numpy as np from bokeh.plotting import figure, output_file, show from bokeh.models import HoverTool, ColumnDataSource from bokeh.sampledata.les_mis import data nodes = data['nodes'] names = [node['name'] for node in sorted(data['nodes'], key=lambda x: x['group'])] N = len(nodes) counts = np.empty((N, N)) for link in data['links']: counts[link['source'], link['target']] = link['value'] counts[link['target'], link['source']] = link['value'] colormap = [ "#444444", "#a6cee3", "#1f78b4", "#b2df8a", "#33a02c", "#fb9a99", "#e31a1c", "#fdbf6f", "#ff7f00", "#cab2d6", "#6a3d9a" ] xname = [] yname = [] color = [] alpha = [] for i, n1 in enumerate(nodes): for j, n2 in enumerate(nodes): xname.append(n1['name']) yname.append(n2['name']) a = min(counts[i,j]/4.0, 0.9) + 0.1 alpha.append(a) if n1['group'] == n2['group']: color.append(colormap[n1['group']]) else: color.append('lightgrey') output_file("les_mis.html") source = ColumnDataSource( data=dict( xname=xname, yname=yname, colors=color, alphas=alpha, count=counts.flatten(), ) ) p = figure(title="Les Mis Occurrences (one at a time)", x_axis_location="above", tools="resize,hover", x_range=list(reversed(names)), y_range=names, plot_width=800, plot_height=800) p.rect('xname', 'yname', 0.9, 0.9, source=source, color='colors', alpha='alphas', line_color=None) p.grid.grid_line_color = None p.axis.axis_line_color = None p.axis.major_tick_line_color = None p.axis.major_label_text_font_size = "5pt" p.axis.major_label_standoff = 0 p.xaxis.major_label_orientation = np.pi/3 hover = p.select(dict(type=HoverTool)) hover.tooltips = [ ('names', '@yname, @xname'), ('count', '@count'), ] show(p)

true

f73641597666934bb64b5e3940ed3742742977cd

3,013

py

Python

Scribe/other_estimators.py

aristoteleo/scribe-py

ea28d2b588f8648b9ce1679fe18c3142aee2aa58

[ "BSD-3-Clause" ]

32

2019-08-15T20:58:30.000Z

2022-03-17T14:16:31.000Z

Scribe/other_estimators.py

aristoteleo/scribe-py

ea28d2b588f8648b9ce1679fe18c3142aee2aa58

[ "BSD-3-Clause" ]

6

2020-03-06T06:02:08.000Z

2022-03-30T22:37:59.000Z

Scribe/other_estimators.py

aristoteleo/scribe-py

ea28d2b588f8648b9ce1679fe18c3142aee2aa58

[ "BSD-3-Clause" ]

10

2020-03-05T09:56:04.000Z

2021-03-14T12:16:54.000Z

import pandas import numpy as np from multiprocessing import Pool def __individual_corr(id1, id2, x, y): return (id1, id2, corr(x, y)[0]) def __individual_mi(id1, id2, x, y): return (id1, id2, mi(x, y)) def corr(self, number_of_processes=1): """Calculate pairwise correlation over the data Arguments --------- self: 'class causal_model object' An instance of a causal_model class object. This object can be converted from an AnnData object through load_anndata function. number_of_processes: `int` (Default: 1) Number of processes to use. Returns --------- corr_results: 'pd.core.frame.DataFrame' The correlation network inferred. """ self.corr_results = pandas.DataFrame({node_id: [np.nan for i in self.node_ids] for node_id in self.node_ids}, index=self.node_ids) if number_of_processes > 1: temp_input = [] for id1 in self.node_ids: for id2 in self.node_ids: if id1 == id2: continue if number_of_processes == 1: self.corr_results.loc[id1, id2] = __individual_corr((id1, id2, self.expression_concatenated.loc[id1], self.expression_concatenated.loc[id2]))[2] else: temp_input.append((id1, id2, self.expression_concatenated.loc[id1], self.expression_concatenated.loc[id2])) if number_of_processes > 1: tmp_results = Pool(number_of_processes).map(__individual_corr, temp_input) for t in tmp_results: self.corr_results.loc[t[0], t[1]] = t[2] return self.corr_results def mi(self, number_of_processes=1): """Calculate pairwise mutual information over the data Arguments --------- self: 'class causal_model object' An instance of a causal_model class object. This object can be converted from an AnnData object through load_anndata function. number_of_processes: `int` (Default: 1) Number of processes to use. Returns --------- mi_results: 'pd.core.frame.DataFrame' The mutual information network inferred. """ self.mi_results = pandas.DataFrame({node_id: [np.nan for i in self.node_ids] for node_id in self.node_ids}, index=self.node_ids) if number_of_processes > 1: temp_input = [] for id1 in self.node_ids: for id2 in self.node_ids: if id1 == id2: continue if number_of_processes == 1: self.mi_results.loc[id1, id2] = __individual_mi((id1, id2,[[i] for i in self.expression_concatenated.loc[id1]],[[j] for j in self.expression_concatenated.loc[id2]] ))[2] else: temp_input.append((id1, id2,[[i] for i in self.expression_concatenated.loc[id1]],[[j] for j in self.expression_concatenated.loc[id2]] )) if number_of_processes > 1: tmp_results = Pool(number_of_processes).map(__individual_mi, temp_input) for t in tmp_results: self.mi_results.loc[t[0], t[1]] = t[2] return self.mi_results

35.869048

185

0.654165

import pandas import numpy as np from multiprocessing import Pool def __individual_corr(id1, id2, x, y): return (id1, id2, corr(x, y)[0]) def __individual_mi(id1, id2, x, y): return (id1, id2, mi(x, y)) def corr(self, number_of_processes=1): self.corr_results = pandas.DataFrame({node_id: [np.nan for i in self.node_ids] for node_id in self.node_ids}, index=self.node_ids) if number_of_processes > 1: temp_input = [] for id1 in self.node_ids: for id2 in self.node_ids: if id1 == id2: continue if number_of_processes == 1: self.corr_results.loc[id1, id2] = __individual_corr((id1, id2, self.expression_concatenated.loc[id1], self.expression_concatenated.loc[id2]))[2] else: temp_input.append((id1, id2, self.expression_concatenated.loc[id1], self.expression_concatenated.loc[id2])) if number_of_processes > 1: tmp_results = Pool(number_of_processes).map(__individual_corr, temp_input) for t in tmp_results: self.corr_results.loc[t[0], t[1]] = t[2] return self.corr_results def mi(self, number_of_processes=1): self.mi_results = pandas.DataFrame({node_id: [np.nan for i in self.node_ids] for node_id in self.node_ids}, index=self.node_ids) if number_of_processes > 1: temp_input = [] for id1 in self.node_ids: for id2 in self.node_ids: if id1 == id2: continue if number_of_processes == 1: self.mi_results.loc[id1, id2] = __individual_mi((id1, id2,[[i] for i in self.expression_concatenated.loc[id1]],[[j] for j in self.expression_concatenated.loc[id2]] ))[2] else: temp_input.append((id1, id2,[[i] for i in self.expression_concatenated.loc[id1]],[[j] for j in self.expression_concatenated.loc[id2]] )) if number_of_processes > 1: tmp_results = Pool(number_of_processes).map(__individual_mi, temp_input) for t in tmp_results: self.mi_results.loc[t[0], t[1]] = t[2] return self.mi_results

true

f73642dae27409c6118a87490a8475aae4fb34f2

5,536

py

Python

liteflow/input.py

frankilepro/LiTeFlow

d07105ea00ad29b701e1b100d9cda2297eef19de

[ "Apache-2.0" ]

null

liteflow/input.py

frankilepro/LiTeFlow

d07105ea00ad29b701e1b100d9cda2297eef19de

[ "Apache-2.0" ]

null

liteflow/input.py

frankilepro/LiTeFlow

d07105ea00ad29b701e1b100d9cda2297eef19de

[ "Apache-2.0" ]

null

"""Utilities for input pipelines.""" import tensorflow as tf def shuffle(tensors, capacity=32, min_after_dequeue=16, num_threads=1, dtypes=None, shapes=None, seed=None, shared_name=None, name='shuffle'): """Wrapper around a `tf.RandomShuffleQueue` creation. Return a dequeue op that dequeues elements from `tensors` in a random order, through a `tf.RandomShuffleQueue` -- see for further documentation. Arguments: tensors: an iterable of tensors. capacity: (Optional) the capacity of the queue; default value set to 32. num_threads: (Optional) the number of threads to be used fo the queue runner; default value set to 1. min_after_dequeue: (Optional) minimum number of elements to remain in the queue after a `dequeue` or `dequeu_many` has been performend, in order to ensure better mixing of elements; default value set to 16. dtypes: (Optional) list of `DType` objects, one for each tensor in `tensors`; if not provided, will be inferred from `tensors`. shapes: (Optional) list of shapes, one for each tensor in `tensors`. seed: (Optional) seed for random shuffling. shared_name: (Optional) If non-empty, this queue will be shared under the given name across multiple sessions. name: Optional name scope for the ops. Returns: The tuple of tensors that was randomly dequeued from `tensors`. """ tensors = list(tensors) with tf.name_scope(name, values=tensors): dtypes = dtypes or list([t.dtype for t in tensors]) queue = tf.RandomShuffleQueue( seed=seed, shared_name=shared_name, name='random_shuffle_queue', dtypes=dtypes, shapes=shapes, capacity=capacity, min_after_dequeue=min_after_dequeue) enqueue = queue.enqueue(tensors) runner = tf.train.QueueRunner(queue, [enqueue] * num_threads) tf.train.add_queue_runner(runner) dequeue = queue.dequeue() return dequeue def shuffle_batch(tensors, batch_size, capacity=32, num_threads=1, min_after_dequeue=16, dtypes=None, shapes=None, seed=None, enqueue_many=False, dynamic_pad=True, allow_smaller_final_batch=False, shared_name=None, name='shuffle_batch'): """Create shuffled and padded batches of tensors in `tensors`. Dequeue elements from `tensors` shuffling, batching and dynamically padding them. First a `tf.RandomShuffleQueue` is created and fed with `tensors` (using the `dket.input.shuffle` function); the dequeued tensors shapes are then set and fed into a `tf.train.batch` function that provides batching and dynamic padding. Arguments: tensors: an iterable of tensors. batch_size: an `int` representing th batch size. capacity: (Optional) the capacity of the queues; default value set to 32. num_threads: (Optional) the number of threads to be used fo the queue runner; default value set to 1. min_after_dequeue: (Optional) minimum number of elements to remain in the shuffling queue after a `dequeue` or `dequeu_many` has been performend, in order to ensure better mixing of elements; default value set to 16. dtypes: (Optional) list of `DType` objects, one for each tensor in `tensors`; if not provided, will be inferred from `tensors`. shapes: (Optional) list of shapes, one for each tensor in `tensors`. seed: (Optional) seed for random shuffling. enqueue_many: Whether each tensor in tensors is a single example. dynamic_pad: Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes. allow_smaller_final_batch: (Optional) Boolean. If True, allow the final batch to be smaller if there are insufficient items left in the queue. shared_name: if set, the queues will be shared under the given name across different sessions. name: scope name for the given ops. Returns: A batch of tensors from `tensors`, shuffled and padded. """ tensors = list(tensors) with tf.name_scope(name, values=tensors): dtypes = dtypes or list([t.dtype for t in tensors]) shapes = shapes or list([t.get_shape() for t in tensors]) inputs = shuffle(tensors, seed=seed, dtypes=dtypes, capacity=capacity, num_threads=num_threads, min_after_dequeue=min_after_dequeue, shared_name=shared_name, name='shuffle') # fix the shapes for tensor, shape in zip(inputs, shapes): tensor.set_shape(shape) minibatch = tf.train.batch( tensors=inputs, batch_size=batch_size, num_threads=num_threads, capacity=capacity, dynamic_pad=dynamic_pad, allow_smaller_final_batch=allow_smaller_final_batch, shared_name=shared_name, enqueue_many=enqueue_many, name='batch') return minibatch

40.705882

83

0.627168

import tensorflow as tf def shuffle(tensors, capacity=32, min_after_dequeue=16, num_threads=1, dtypes=None, shapes=None, seed=None, shared_name=None, name='shuffle'): tensors = list(tensors) with tf.name_scope(name, values=tensors): dtypes = dtypes or list([t.dtype for t in tensors]) queue = tf.RandomShuffleQueue( seed=seed, shared_name=shared_name, name='random_shuffle_queue', dtypes=dtypes, shapes=shapes, capacity=capacity, min_after_dequeue=min_after_dequeue) enqueue = queue.enqueue(tensors) runner = tf.train.QueueRunner(queue, [enqueue] * num_threads) tf.train.add_queue_runner(runner) dequeue = queue.dequeue() return dequeue def shuffle_batch(tensors, batch_size, capacity=32, num_threads=1, min_after_dequeue=16, dtypes=None, shapes=None, seed=None, enqueue_many=False, dynamic_pad=True, allow_smaller_final_batch=False, shared_name=None, name='shuffle_batch'): tensors = list(tensors) with tf.name_scope(name, values=tensors): dtypes = dtypes or list([t.dtype for t in tensors]) shapes = shapes or list([t.get_shape() for t in tensors]) inputs = shuffle(tensors, seed=seed, dtypes=dtypes, capacity=capacity, num_threads=num_threads, min_after_dequeue=min_after_dequeue, shared_name=shared_name, name='shuffle') for tensor, shape in zip(inputs, shapes): tensor.set_shape(shape) minibatch = tf.train.batch( tensors=inputs, batch_size=batch_size, num_threads=num_threads, capacity=capacity, dynamic_pad=dynamic_pad, allow_smaller_final_batch=allow_smaller_final_batch, shared_name=shared_name, enqueue_many=enqueue_many, name='batch') return minibatch

true

f73642fd4c1a40e9dd300d78c5863ed4603901d6

12,577

py

Python

tsfresh/feature_selection/relevance.py

Nickwangpeng/tsfresh

48118627d9d4644906613e25b077ce2ec82ca2f9

[ "MIT" ]

1

2020-03-25T22:08:04.000Z

tsfresh/feature_selection/relevance.py

Chao-Jiang/tsfresh

48118627d9d4644906613e25b077ce2ec82ca2f9

[ "MIT" ]

null

tsfresh/feature_selection/relevance.py

Chao-Jiang/tsfresh

48118627d9d4644906613e25b077ce2ec82ca2f9

[ "MIT" ]

1

2019-12-30T14:07:12.000Z

# -*- coding: utf-8 -*- # This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt) # Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016 """ Contains a feature selection method that evaluates the importance of the different extracted features. To do so, for every feature the influence on the target is evaluated by an univariate tests and the p-Value is calculated. The methods that calculate the p-values are called feature selectors. Afterwards the Benjamini Hochberg procedure which is a multiple testing procedure decides which features to keep and which to cut off (solely based on the p-values). """ from multiprocessing import Pool import warnings import numpy as np import pandas as pd from functools import partial, reduce from tsfresh import defaults from tsfresh.feature_selection.benjamini_hochberg_test import benjamini_hochberg_test from tsfresh.feature_selection.significance_tests import target_binary_feature_real_test, \ target_real_feature_binary_test, target_real_feature_real_test, target_binary_feature_binary_test from tsfresh.utilities.distribution import initialize_warnings_in_workers def calculate_relevance_table(X, y, ml_task='auto', n_jobs=defaults.N_PROCESSES, show_warnings=defaults.SHOW_WARNINGS, chunksize=defaults.CHUNKSIZE, test_for_binary_target_binary_feature=defaults.TEST_FOR_BINARY_TARGET_BINARY_FEATURE, test_for_binary_target_real_feature=defaults.TEST_FOR_BINARY_TARGET_REAL_FEATURE, test_for_real_target_binary_feature=defaults.TEST_FOR_REAL_TARGET_BINARY_FEATURE, test_for_real_target_real_feature=defaults.TEST_FOR_REAL_TARGET_REAL_FEATURE, fdr_level=defaults.FDR_LEVEL, hypotheses_independent=defaults.HYPOTHESES_INDEPENDENT): """ Calculate the relevance table for the features contained in feature matrix `X` with respect to target vector `y`. The relevance table is calculated for the intended machine learning task `ml_task`. To accomplish this for each feature from the input pandas.DataFrame an univariate feature significance test is conducted. Those tests generate p values that are then evaluated by the Benjamini Hochberg procedure to decide which features to keep and which to delete. We are testing :math:`H_0` = the Feature is not relevant and should not be added against :math:`H_1` = the Feature is relevant and should be kept or in other words :math:`H_0` = Target and Feature are independent / the Feature has no influence on the target :math:`H_1` = Target and Feature are associated / dependent When the target is binary this becomes :math:`H_0 = \\left( F_{\\text{target}=1} = F_{\\text{target}=0} \\right)` :math:`H_1 = \\left( F_{\\text{target}=1} \\neq F_{\\text{target}=0} \\right)` Where :math:`F` is the distribution of the target. In the same way we can state the hypothesis when the feature is binary :math:`H_0 = \\left( T_{\\text{feature}=1} = T_{\\text{feature}=0} \\right)` :math:`H_1 = \\left( T_{\\text{feature}=1} \\neq T_{\\text{feature}=0} \\right)` Here :math:`T` is the distribution of the target. TODO: And for real valued? :param X: Feature matrix in the format mentioned before which will be reduced to only the relevant features. It can contain both binary or real-valued features at the same time. :type X: pandas.DataFrame :param y: Target vector which is needed to test which features are relevant. Can be binary or real-valued. :type y: pandas.Series or numpy.ndarray :param ml_task: The intended machine learning task. Either `'classification'`, `'regression'` or `'auto'`. Defaults to `'auto'`, meaning the intended task is inferred from `y`. If `y` has a boolean, integer or object dtype, the task is assumend to be classification, else regression. :type ml_task: str :param test_for_binary_target_binary_feature: Which test to be used for binary target, binary feature (currently unused) :type test_for_binary_target_binary_feature: str :param test_for_binary_target_real_feature: Which test to be used for binary target, real feature :type test_for_binary_target_real_feature: str :param test_for_real_target_binary_feature: Which test to be used for real target, binary feature (currently unused) :type test_for_real_target_binary_feature: str :param test_for_real_target_real_feature: Which test to be used for real target, real feature (currently unused) :type test_for_real_target_real_feature: str :param fdr_level: The FDR level that should be respected, this is the theoretical expected percentage of irrelevant features among all created features. :type fdr_level: float :param hypotheses_independent: Can the significance of the features be assumed to be independent? Normally, this should be set to False as the features are never independent (e.g. mean and median) :type hypotheses_independent: bool :param n_jobs: Number of processes to use during the p-value calculation :type n_jobs: int :param show_warnings: Show warnings during the p-value calculation (needed for debugging of calculators). :type show_warnings: bool :param chunksize: The size of one chunk that is submitted to the worker process for the parallelisation. Where one chunk is defined as a singular time series for one id and one kind. If you set the chunksize to 10, then it means that one task is to calculate all features for 10 time series. If it is set it to None, depending on distributor, heuristics are used to find the optimal chunksize. If you get out of memory exceptions, you can try it with the dask distributor and a smaller chunksize. :type chunksize: None or int :return: A pandas.DataFrame with each column of the input DataFrame X as index with information on the significance of this particular feature. The DataFrame has the columns "Feature", "type" (binary, real or const), "p_value" (the significance of this feature as a p-value, lower means more significant) "relevant" (True if the Benjamini Hochberg procedure rejected the null hypothesis [the feature is not relevant] for this feature) :rtype: pandas.DataFrame """ if ml_task not in ['auto', 'classification', 'regression']: raise ValueError('ml_task must be one of: \'auto\', \'classification\', \'regression\'') elif ml_task == 'auto': ml_task = infer_ml_task(y) with warnings.catch_warnings(): if not show_warnings: warnings.simplefilter("ignore") else: warnings.simplefilter("default") if n_jobs == 0: map_function = map else: pool = Pool(processes=n_jobs, initializer=initialize_warnings_in_workers, initargs=(show_warnings,)) map_function = partial(pool.map, chunksize=chunksize) relevance_table = pd.DataFrame(index=pd.Series(X.columns, name='feature')) relevance_table['feature'] = relevance_table.index relevance_table['type'] = pd.Series( map_function(get_feature_type, [X[feature] for feature in relevance_table.index]), index=relevance_table.index ) table_real = relevance_table[relevance_table.type == 'real'].copy() table_binary = relevance_table[relevance_table.type == 'binary'].copy() table_const = relevance_table[relevance_table.type == 'constant'].copy() table_const['p_value'] = np.NaN table_const['relevant'] = False if not table_const.empty: warnings.warn("[test_feature_significance] Constant features: {}" .format(", ".join(table_const.feature)), RuntimeWarning) if len(table_const) == len(relevance_table): if n_jobs != 0: pool.close() pool.terminate() pool.join() return table_const if ml_task == 'classification': tables = [] for label in y.unique(): _test_real_feature = partial(target_binary_feature_real_test, y=(y == label), test=test_for_binary_target_real_feature) _test_binary_feature = partial(target_binary_feature_binary_test, y=(y == label)) tmp = _calculate_relevance_table_for_implicit_target( table_real, table_binary, X, _test_real_feature, _test_binary_feature, hypotheses_independent, fdr_level, map_function ) tables.append(tmp) relevance_table = combine_relevance_tables(tables) elif ml_task == 'regression': _test_real_feature = partial(target_real_feature_real_test, y=y) _test_binary_feature = partial(target_real_feature_binary_test, y=y) relevance_table = _calculate_relevance_table_for_implicit_target( table_real, table_binary, X, _test_real_feature, _test_binary_feature, hypotheses_independent, fdr_level, map_function ) if n_jobs != 0: pool.close() pool.terminate() pool.join() relevance_table = pd.concat([relevance_table, table_const], axis=0) if sum(relevance_table['relevant']) == 0: warnings.warn( "No feature was found relevant for {} for fdr level = {} (which corresponds to the maximal percentage " "of irrelevant features, consider using an higher fdr level or add other features." .format(ml_task, fdr_level), RuntimeWarning) return relevance_table def _calculate_relevance_table_for_implicit_target(table_real, table_binary, X, test_real_feature, test_binary_feature, hypotheses_independent, fdr_level, map_function): table_real['p_value'] = pd.Series( map_function(test_real_feature, [X[feature] for feature in table_real.index]), index=table_real.index ) table_binary['p_value'] = pd.Series( map_function(test_binary_feature, [X[feature] for feature in table_binary.index]), index=table_binary.index ) relevance_table = pd.concat([table_real, table_binary]) return benjamini_hochberg_test(relevance_table, hypotheses_independent, fdr_level) def infer_ml_task(y): """ Infer the machine learning task to select for. The result will be either `'regression'` or `'classification'`. If the target vector only consists of integer typed values or objects, we assume the task is `'classification'`. Else `'regression'`. :param y: The target vector y. :type y: pandas.Series :return: 'classification' or 'regression' :rtype: str """ if y.dtype.kind in np.typecodes['AllInteger'] or y.dtype == np.object: ml_task = 'classification' else: ml_task = 'regression' return ml_task def combine_relevance_tables(relevance_tables): """ Create a combined relevance table out of a list of relevance tables, aggregating the p-values and the relevances. :param relevance_tables: A list of relevance tables :type relevance_tables: List[pd.DataFrame] :return: The combined relevance table :rtype: pandas.DataFrame """ def _combine(a, b): a.relevant |= b.relevant a.p_value = a.p_value.combine(b.p_value, min, 1) return a return reduce(_combine, relevance_tables) def get_feature_type(feature_column): """ For a given feature, determine if it is real, binary or constant. Here binary means that only two unique values occur in the feature. :param feature_column: The feature column :type feature_column: pandas.Series :return: 'constant', 'binary' or 'real' """ n_unique_values = len(set(feature_column.values)) if n_unique_values == 1: return 'constant' elif n_unique_values == 2: return 'binary' else: return 'real'

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120

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from multiprocessing import Pool import warnings import numpy as np import pandas as pd from functools import partial, reduce from tsfresh import defaults from tsfresh.feature_selection.benjamini_hochberg_test import benjamini_hochberg_test from tsfresh.feature_selection.significance_tests import target_binary_feature_real_test, \ target_real_feature_binary_test, target_real_feature_real_test, target_binary_feature_binary_test from tsfresh.utilities.distribution import initialize_warnings_in_workers def calculate_relevance_table(X, y, ml_task='auto', n_jobs=defaults.N_PROCESSES, show_warnings=defaults.SHOW_WARNINGS, chunksize=defaults.CHUNKSIZE, test_for_binary_target_binary_feature=defaults.TEST_FOR_BINARY_TARGET_BINARY_FEATURE, test_for_binary_target_real_feature=defaults.TEST_FOR_BINARY_TARGET_REAL_FEATURE, test_for_real_target_binary_feature=defaults.TEST_FOR_REAL_TARGET_BINARY_FEATURE, test_for_real_target_real_feature=defaults.TEST_FOR_REAL_TARGET_REAL_FEATURE, fdr_level=defaults.FDR_LEVEL, hypotheses_independent=defaults.HYPOTHESES_INDEPENDENT): if ml_task not in ['auto', 'classification', 'regression']: raise ValueError('ml_task must be one of: \'auto\', \'classification\', \'regression\'') elif ml_task == 'auto': ml_task = infer_ml_task(y) with warnings.catch_warnings(): if not show_warnings: warnings.simplefilter("ignore") else: warnings.simplefilter("default") if n_jobs == 0: map_function = map else: pool = Pool(processes=n_jobs, initializer=initialize_warnings_in_workers, initargs=(show_warnings,)) map_function = partial(pool.map, chunksize=chunksize) relevance_table = pd.DataFrame(index=pd.Series(X.columns, name='feature')) relevance_table['feature'] = relevance_table.index relevance_table['type'] = pd.Series( map_function(get_feature_type, [X[feature] for feature in relevance_table.index]), index=relevance_table.index ) table_real = relevance_table[relevance_table.type == 'real'].copy() table_binary = relevance_table[relevance_table.type == 'binary'].copy() table_const = relevance_table[relevance_table.type == 'constant'].copy() table_const['p_value'] = np.NaN table_const['relevant'] = False if not table_const.empty: warnings.warn("[test_feature_significance] Constant features: {}" .format(", ".join(table_const.feature)), RuntimeWarning) if len(table_const) == len(relevance_table): if n_jobs != 0: pool.close() pool.terminate() pool.join() return table_const if ml_task == 'classification': tables = [] for label in y.unique(): _test_real_feature = partial(target_binary_feature_real_test, y=(y == label), test=test_for_binary_target_real_feature) _test_binary_feature = partial(target_binary_feature_binary_test, y=(y == label)) tmp = _calculate_relevance_table_for_implicit_target( table_real, table_binary, X, _test_real_feature, _test_binary_feature, hypotheses_independent, fdr_level, map_function ) tables.append(tmp) relevance_table = combine_relevance_tables(tables) elif ml_task == 'regression': _test_real_feature = partial(target_real_feature_real_test, y=y) _test_binary_feature = partial(target_real_feature_binary_test, y=y) relevance_table = _calculate_relevance_table_for_implicit_target( table_real, table_binary, X, _test_real_feature, _test_binary_feature, hypotheses_independent, fdr_level, map_function ) if n_jobs != 0: pool.close() pool.terminate() pool.join() relevance_table = pd.concat([relevance_table, table_const], axis=0) if sum(relevance_table['relevant']) == 0: warnings.warn( "No feature was found relevant for {} for fdr level = {} (which corresponds to the maximal percentage " "of irrelevant features, consider using an higher fdr level or add other features." .format(ml_task, fdr_level), RuntimeWarning) return relevance_table def _calculate_relevance_table_for_implicit_target(table_real, table_binary, X, test_real_feature, test_binary_feature, hypotheses_independent, fdr_level, map_function): table_real['p_value'] = pd.Series( map_function(test_real_feature, [X[feature] for feature in table_real.index]), index=table_real.index ) table_binary['p_value'] = pd.Series( map_function(test_binary_feature, [X[feature] for feature in table_binary.index]), index=table_binary.index ) relevance_table = pd.concat([table_real, table_binary]) return benjamini_hochberg_test(relevance_table, hypotheses_independent, fdr_level) def infer_ml_task(y): if y.dtype.kind in np.typecodes['AllInteger'] or y.dtype == np.object: ml_task = 'classification' else: ml_task = 'regression' return ml_task def combine_relevance_tables(relevance_tables): def _combine(a, b): a.relevant |= b.relevant a.p_value = a.p_value.combine(b.p_value, min, 1) return a return reduce(_combine, relevance_tables) def get_feature_type(feature_column): n_unique_values = len(set(feature_column.values)) if n_unique_values == 1: return 'constant' elif n_unique_values == 2: return 'binary' else: return 'real'

true

f7364342837e8e9664cc1bea548231dfc7f87e39

5,173

py

Python

welib/FEM/reduction.py

moonieann/welib

0e430ad3ca034d0d2d60bdb7bbe06c947ce08f52

[ "MIT" ]

24

2019-07-24T23:37:10.000Z

2022-03-30T20:40:40.000Z

welib/FEM/reduction.py

moonieann/welib

0e430ad3ca034d0d2d60bdb7bbe06c947ce08f52

[ "MIT" ]

null

welib/FEM/reduction.py

moonieann/welib

0e430ad3ca034d0d2d60bdb7bbe06c947ce08f52

[ "MIT" ]

11

2019-03-14T13:47:04.000Z

2022-03-31T15:47:27.000Z

import numpy as np from welib.system.eva import eig def CraigBampton(MM, KK, Ileader, nModesCB=None, Ifollow=None, F=None, DD=None, fullModesOut=False): """ Performs the CraigBampton (CB) reduction of a system given some input master dofs index and a number of modes. Reduced matrices, and Guyan and Craig-Bampton modes are returned. INPUTS Ileader : index of leader DOFs nModesCB: number of CB modes to keep MM, KK : Maff and stiffness matrix INPUTS (Optional) nModesCB: number of CB modes to keep. Default: all Ifollow: indices of follower DOFs. Default: complementary set to Ileader fullModesOut: if true, the Guyan and CB modes OUTPUTS fc: critical frequency Mr,Kr,Fr,Dr: reduced mass, stiffness, force and damping matrices AUTHOR: E. Branlard """ # --- Input cleanup Ileader = np.asarray(Ileader).ravel() # --- Optional arguments if Ifollow is None: # Then we take the complementary to Ileader Iall = np.arange(len(MM)) Ifollow = [i for i in Iall if i not in Ileader] else: Ifollow = np.asarray(Ifollow).ravel() if nModesCB is None: nModesCB=len(Ifollow) # Partitioning - NOTE: leaders will be first in reduced matrix Mr and Kr Mll= MM[np.ix_(Ileader, Ileader)] Kll= KK[np.ix_(Ileader, Ileader)] Mff= MM[np.ix_(Ifollow, Ifollow)] Kff= KK[np.ix_(Ifollow, Ifollow)] Mlf= MM[np.ix_(Ileader, Ifollow)] Klf= KK[np.ix_(Ileader, Ifollow)] # --- Solve for Guyan modes Kff1Kfl = np.linalg.solve(Kff,(np.transpose(Klf))) # Kss1Ksm=Kss\(Kms'); #Kff1Kfl = np.linalg.inv(Kff).dot(Klf.T) Kff1Kfl = np.linalg.lstsq(Kff,Klf.T, rcond=None)[0] Phi_G = - Kff1Kfl; # --- Solve EVP for constrained system Phi_CB, Lambda_CB = eig(Kff,Mff) Omega2 = np.diag(Lambda_CB).copy() Omega2[Omega2<0]=0.0 f_CB = np.sqrt(Omega2)/(2*np.pi) # --- Taking only thefirst few modes Phi_CB = Phi_CB[:,:nModesCB] Lambda_CB = Lambda_CB[:,:nModesCB] f_CB = f_CB[:nModesCB] # --- Using the T matrix: # # T=[eye(nm) zeros(nm,nModesCB); -Kff1Kfl Phi_CB]; # # MM=[Mll Mlf; Mlf' Mff]; # # KK=[Kll Klf; Klf' Kff]; # # Mr=T' * MM * T; # # Kr=T' * KK * T; # --- Building reduced matrices #Mr11=Mmm-(Kss1Ksm')*Mms' - Mms*Kss1Ksm + (Kss1Ksm')*Mss*Kss1Ksm; #Kr11=Kmm-Kms*Kss1Ksm; #Mr12=(Mms-(Kss1Ksm')*Mss)*Psic; Mr11 = Mll - (np.transpose(Kff1Kfl)).dot(np.transpose(Mlf)) - Mlf.dot(Kff1Kfl) + (np.transpose(Kff1Kfl)).dot(Mff).dot(Kff1Kfl) Kr11 = Kll - Klf.dot(Kff1Kfl) Mr12 = (Mlf - (np.transpose(Kff1Kfl)).dot(Mff)).dot(Phi_CB) ZZ = np.zeros((len(Ileader),nModesCB)) # --- Guyan frequencies Phi_G2, Lambda_G = eig(Kr11,Mr11) Omega2 = np.diag(Lambda_G).copy() Omega2[Omega2<0]=0.0 f_G = np.sqrt(Omega2)/(2*np.pi) # Building reduced matrix Mr = np.block( [ [Mr11 , Mr12 ], [ Mr12.T, np.eye(nModesCB) ] ]) Kr = np.block( [ [Kr11 , ZZ ], [ ZZ.T , Lambda_CB[:nModesCB,:]] ]) # --- Augmenting modes so that they have the same dimension as MM # Add "1" for Guyan modes, and "0" for CB modes if fullModesOut: Phi_G, Phi_CB = augmentModes(Ileader, Phi_G, Phi_CB, Ifollow=Ifollow) if DD is not None: raise NotImplementedError('Not done') if F is not None: raise NotImplementedError('Not done') return Mr, Kr, Phi_G, Phi_CB, f_G, f_CB def augmentModes(Ileader, Phi_G, Phi_CB, Ifollow=None): """ Augment Guyan and Craig Bampton modes, so as to return full DOF vectors going back to the original size """ # --- Augment modes so that they go back to same size after BC nl = len(Ileader) nall = nl+Phi_G.shape[0] nf = nall-nl if Ifollow is None: Iall = np.arange(nall) Ifollow = list(np.setdiff1d(Iall, Ileader)) # Guyan Phi_G_aug = np.zeros((nall, nl)) Phi_G_aug[Ileader,:] = np.eye(nl) Phi_G_aug[Ifollow,:] = Phi_G # Phi_CB_aug = np.zeros((nall, Phi_CB.shape[1])) Phi_CB_aug[Ileader,:] = 0 Phi_CB_aug[Ifollow,:] = Phi_CB return Phi_G_aug, Phi_CB_aug if __name__=='__main__': np.set_printoptions(linewidth=500) L = 100 EI = 1868211939147.334 Maff = L * 8828.201296825122 KK = EI / (L ** 3) * np.array([[12,6 * L,- 12,6 * L],[6 * L,4 * L ** 2,- 6 * L,2 * L ** 2],[- 12,- 6 * L,12,- 6 * L],[6 * L,2 * L ** 2,- 6 * L,4 * L ** 2]]) MM = Maff / 420 * np.array([[156,22 * L,54,- 13 * L],[22 * L,4 * L ** 2,13 * L,- 3 * L ** 2],[54,13 * L,156,- 22 * L],[- 13 * L,- 3 * L ** 2,- 22 * L,4 * L ** 2]]) print(MM) Mr,Kr,Phi_G,Phi_CB,f_CB,f_G = CraigBampton(MM,KK,[2], nModesCB=2) print(Mr) print(Kr) print(Phi_G) print(Phi_CB) print(f_CB) ## --- Solve EVA __,Lambda = eig(Kr,Mr) f= np.sqrt(np.sort(np.diag(Lambda)))/(2*np.pi) print(f) # f = np.sqrt(Omega2) / (2 * pi) # for i in np.arange(1,np.amin(8,Mr.shape[1-1])+1).reshape(-1): # print('f%d=%8.3f Rayleigh Ratio=%.5f\n' % (i,f(i),(f(i) / fc) ** 2))

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167

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import numpy as np from welib.system.eva import eig def CraigBampton(MM, KK, Ileader, nModesCB=None, Ifollow=None, F=None, DD=None, fullModesOut=False): Ileader = np.asarray(Ileader).ravel() if Ifollow is None: Iall = np.arange(len(MM)) Ifollow = [i for i in Iall if i not in Ileader] else: Ifollow = np.asarray(Ifollow).ravel() if nModesCB is None: nModesCB=len(Ifollow) Mll= MM[np.ix_(Ileader, Ileader)] Kll= KK[np.ix_(Ileader, Ileader)] Mff= MM[np.ix_(Ifollow, Ifollow)] Kff= KK[np.ix_(Ifollow, Ifollow)] Mlf= MM[np.ix_(Ileader, Ifollow)] Klf= KK[np.ix_(Ileader, Ifollow)] Kff1Kfl = np.linalg.solve(Kff,(np.transpose(Klf))) #Kff1Kfl = np.linalg.inv(Kff).dot(Klf.T) Kff1Kfl = np.linalg.lstsq(Kff,Klf.T, rcond=None)[0] Phi_G = - Kff1Kfl; # --- Solve EVP for constrained system Phi_CB, Lambda_CB = eig(Kff,Mff) Omega2 = np.diag(Lambda_CB).copy() Omega2[Omega2<0]=0.0 f_CB = np.sqrt(Omega2)/(2*np.pi) # --- Taking only thefirst few modes Phi_CB = Phi_CB[:,:nModesCB] Lambda_CB = Lambda_CB[:,:nModesCB] f_CB = f_CB[:nModesCB] # --- Using the T matrix: # # T=[eye(nm) zeros(nm,nModesCB); -Kff1Kfl Phi_CB]; # # MM=[Mll Mlf; Mlf' Mff]; ing reduced matrices #Mr11=Mmm-(Kss1Ksm')*Mms' - Mms*Kss1Ksm + (Kss1Ksm')*Mss*Kss1Ksm; Mr11 = Mll - (np.transpose(Kff1Kfl)).dot(np.transpose(Mlf)) - Mlf.dot(Kff1Kfl) + (np.transpose(Kff1Kfl)).dot(Mff).dot(Kff1Kfl) Kr11 = Kll - Klf.dot(Kff1Kfl) Mr12 = (Mlf - (np.transpose(Kff1Kfl)).dot(Mff)).dot(Phi_CB) ZZ = np.zeros((len(Ileader),nModesCB)) # --- Guyan frequencies Phi_G2, Lambda_G = eig(Kr11,Mr11) Omega2 = np.diag(Lambda_G).copy() Omega2[Omega2<0]=0.0 f_G = np.sqrt(Omega2)/(2*np.pi) # Building reduced matrix Mr = np.block( [ [Mr11 , Mr12 ], [ Mr12.T, np.eye(nModesCB) ] ]) Kr = np.block( [ [Kr11 , ZZ ], [ ZZ.T , Lambda_CB[:nModesCB,:]] ]) # --- Augmenting modes so that they have the same dimension as MM # Add "1" for Guyan modes, and "0" for CB modes if fullModesOut: Phi_G, Phi_CB = augmentModes(Ileader, Phi_G, Phi_CB, Ifollow=Ifollow) if DD is not None: raise NotImplementedError('Not done') if F is not None: raise NotImplementedError('Not done') return Mr, Kr, Phi_G, Phi_CB, f_G, f_CB def augmentModes(Ileader, Phi_G, Phi_CB, Ifollow=None): # --- Augment modes so that they go back to same size after BC nl = len(Ileader) nall = nl+Phi_G.shape[0] nf = nall-nl if Ifollow is None: Iall = np.arange(nall) Ifollow = list(np.setdiff1d(Iall, Ileader)) # Guyan Phi_G_aug = np.zeros((nall, nl)) Phi_G_aug[Ileader,:] = np.eye(nl) Phi_G_aug[Ifollow,:] = Phi_G # Phi_CB_aug = np.zeros((nall, Phi_CB.shape[1])) Phi_CB_aug[Ileader,:] = 0 Phi_CB_aug[Ifollow,:] = Phi_CB return Phi_G_aug, Phi_CB_aug if __name__=='__main__': np.set_printoptions(linewidth=500) L = 100 EI = 1868211939147.334 Maff = L * 8828.201296825122 KK = EI / (L ** 3) * np.array([[12,6 * L,- 12,6 * L],[6 * L,4 * L ** 2,- 6 * L,2 * L ** 2],[- 12,- 6 * L,12,- 6 * L],[6 * L,2 * L ** 2,- 6 * L,4 * L ** 2]]) MM = Maff / 420 * np.array([[156,22 * L,54,- 13 * L],[22 * L,4 * L ** 2,13 * L,- 3 * L ** 2],[54,13 * L,156,- 22 * L],[- 13 * L,- 3 * L ** 2,- 22 * L,4 * L ** 2]]) print(MM) Mr,Kr,Phi_G,Phi_CB,f_CB,f_G = CraigBampton(MM,KK,[2], nModesCB=2) print(Mr) print(Kr) print(Phi_G) print(Phi_CB) print(f_CB) ## --- Solve EVA __,Lambda = eig(Kr,Mr) f= np.sqrt(np.sort(np.diag(Lambda)))/(2*np.pi) print(f) # f = np.sqrt(Omega2) / (2 * pi) # for i in np.arange(1,np.amin(8,Mr.shape[1-1])+1).reshape(-1): # print('f%d=%8.3f Rayleigh Ratio=%.5f\n' % (i,f(i),(f(i) / fc) ** 2))

true

f7364375968b371ac7c5110a2aae3ab9342ca3bb

2,884

py

Python

.venv/lib/python3.8/site-packages/pycrop/tools.py

amon-wanyonyi/publication

caae5e04f9191493408145ac80dc943dba7e93ca

[ "MIT" ]

null

.venv/lib/python3.8/site-packages/pycrop/tools.py

amon-wanyonyi/publication

caae5e04f9191493408145ac80dc943dba7e93ca

[ "MIT" ]

null

.venv/lib/python3.8/site-packages/pycrop/tools.py

amon-wanyonyi/publication

caae5e04f9191493408145ac80dc943dba7e93ca

[ "MIT" ]

null

import os import re from math import floor from PIL import Image def default_save_path(path, size): savepath, ext = os.path.splitext(path) savepath = '%s__w%dh%d%s' % (savepath, *size, ext) return savepath def assure_path_exists(path): if not os.path.exists(path): os.makedirs(path) def normilize_size(size, img_size): if None not in size: return size W, H = img_size w, h = size if w is None: return h * W / H, h return w, w * H / W def get_cover_size(from_size, to_size): p = max([ts / fs for ts, fs in zip(to_size, from_size)]) return tuple(floor(p * fs) for fs in from_size) def get_contain_size(from_size, to_size): p = min([ts / fs for ts, fs in zip(to_size, from_size)]) return tuple(floor(p * fs) for fs in from_size) def get_coords_from_center(from_size, to_size): return ( floor((from_size[0] - to_size[0]) / 2), floor((from_size[1] - to_size[1]) / 2), floor((from_size[0] + to_size[0]) / 2), floor((from_size[1] + to_size[1]) / 2) ) def adjust_coords(coords, size, point): vec = [ size[0] * (point[0] - 50) / 100, size[1] * (point[1] - 50) / 100 ] if coords[0] + vec[0] < 0: vec[0] = - coords[0] if coords[1] + vec[1] < 0: vec[1] = - coords[1] if coords[3] + vec[1] > size[1]: vec[1] = size[1] - coords[3] if coords[2] + vec[0] > size[0]: vec[0] = size[0] - coords[2] return tuple(floor(sum(coord)) for coord in zip(coords, 2 * vec)) def cover(path, size, point, savepath=None, quality=90): with Image.open(path) as img: size = normilize_size(size, img.size) if savepath is None: savepath = default_save_path(path, size) assure_path_exists(os.path.dirname(savepath)) cover_size = get_cover_size(img.size, size) coords = get_coords_from_center(cover_size, size) coords = adjust_coords(coords, cover_size, point) img = img.resize(cover_size, Image.ANTIALIAS) img = img.crop(coords) img.save(savepath, subsampling=0, quality=quality, optimize=True) return (True, savepath) return (False, '') def contain(path, size, savepath=None, quality=90): with Image.open(path) as img: size = normilize_size(size, img.size) if savepath is None: savepath = default_save_path(path, size) assure_path_exists(os.path.dirname(savepath)) contain_size = get_contain_size(img.size, size) img = img.resize(contain_size, Image.ANTIALIAS) img.save(savepath, subsampling=0, quality=quality, optimize=True) return (True, savepath) return (False, '') if __name__ == "__main__": img_path = os.path.abspath('img.jpg') cover(img_path, (500, None), (50, 50))

25.298246

69

0.601942

import os import re from math import floor from PIL import Image def default_save_path(path, size): savepath, ext = os.path.splitext(path) savepath = '%s__w%dh%d%s' % (savepath, *size, ext) return savepath def assure_path_exists(path): if not os.path.exists(path): os.makedirs(path) def normilize_size(size, img_size): if None not in size: return size W, H = img_size w, h = size if w is None: return h * W / H, h return w, w * H / W def get_cover_size(from_size, to_size): p = max([ts / fs for ts, fs in zip(to_size, from_size)]) return tuple(floor(p * fs) for fs in from_size) def get_contain_size(from_size, to_size): p = min([ts / fs for ts, fs in zip(to_size, from_size)]) return tuple(floor(p * fs) for fs in from_size) def get_coords_from_center(from_size, to_size): return ( floor((from_size[0] - to_size[0]) / 2), floor((from_size[1] - to_size[1]) / 2), floor((from_size[0] + to_size[0]) / 2), floor((from_size[1] + to_size[1]) / 2) ) def adjust_coords(coords, size, point): vec = [ size[0] * (point[0] - 50) / 100, size[1] * (point[1] - 50) / 100 ] if coords[0] + vec[0] < 0: vec[0] = - coords[0] if coords[1] + vec[1] < 0: vec[1] = - coords[1] if coords[3] + vec[1] > size[1]: vec[1] = size[1] - coords[3] if coords[2] + vec[0] > size[0]: vec[0] = size[0] - coords[2] return tuple(floor(sum(coord)) for coord in zip(coords, 2 * vec)) def cover(path, size, point, savepath=None, quality=90): with Image.open(path) as img: size = normilize_size(size, img.size) if savepath is None: savepath = default_save_path(path, size) assure_path_exists(os.path.dirname(savepath)) cover_size = get_cover_size(img.size, size) coords = get_coords_from_center(cover_size, size) coords = adjust_coords(coords, cover_size, point) img = img.resize(cover_size, Image.ANTIALIAS) img = img.crop(coords) img.save(savepath, subsampling=0, quality=quality, optimize=True) return (True, savepath) return (False, '') def contain(path, size, savepath=None, quality=90): with Image.open(path) as img: size = normilize_size(size, img.size) if savepath is None: savepath = default_save_path(path, size) assure_path_exists(os.path.dirname(savepath)) contain_size = get_contain_size(img.size, size) img = img.resize(contain_size, Image.ANTIALIAS) img.save(savepath, subsampling=0, quality=quality, optimize=True) return (True, savepath) return (False, '') if __name__ == "__main__": img_path = os.path.abspath('img.jpg') cover(img_path, (500, None), (50, 50))

true

f73644650dd1855dd44249090e286bf11f7c6b16

11,199

py

Python

mlbench_core/optim/pytorch/fp_optimizers.py

c4dt/mlbench-core

8a5cf6e00ff4535b2aea23b213241858a5ee5f00

[ "Apache-2.0" ]

null

mlbench_core/optim/pytorch/fp_optimizers.py

c4dt/mlbench-core

8a5cf6e00ff4535b2aea23b213241858a5ee5f00

[ "Apache-2.0" ]

null

mlbench_core/optim/pytorch/fp_optimizers.py

c4dt/mlbench-core

8a5cf6e00ff4535b2aea23b213241858a5ee5f00

[ "Apache-2.0" ]

null

# import ctypes import logging import math import torch import torch.distributed as dist from torch.nn.utils import clip_grad_norm_ from mlbench_core.utils.pytorch.distributed import ( AllReduceAggregation, AllReduceAggregationHVD, ) try: from apex.optimizers import FusedAdam from apex import amp except ImportError as e: pass logger = logging.getLogger("mlbench") class FP16Optimizer: """ Mixed precision optimizer with dynamic loss scaling and backoff. https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#scalefactor Args: fp16_model (`obj`:torch.nn.Module): model (previously casted to half) world_size (int): Distributed world size use_cuda (bool): Use cuda tensors for aggregation use_horovod (bool): Use Horovod for aggregation by_layer (bool): Aggregate by layer grad_clip (float): coefficient for gradient clipping, max L2 norm of the gradients loss_scale (int): initial loss scale dls_downscale (int): loss downscale factor, loss scale is divided by this factor when NaN/INF occurs in the gradients dls_upscale (int): loss upscale factor, loss scale is multiplied by this factor if previous dls_upscale_interval batches finished successfully dls_upscale_interval (int): interval for loss scale upscaling average_models (bool): Average the models """ def __init__( self, fp16_model, world_size, use_cuda=False, use_horovod=False, by_layer=False, grad_clip=float("inf"), loss_scale=1024, dls_downscale=2, dls_upscale=2, dls_upscale_interval=128, average_models=True, ): self.use_cuda = use_cuda self.fp16_model = fp16_model self.fp16_params, self.fp32_params = self.initialize_flat_fp32_weight() self.since_last_invalid = 0 self.loss_scale = loss_scale self.dls_downscale = dls_downscale self.dls_upscale = dls_upscale self.dls_upscale_interval = dls_upscale_interval self.grad_clip = grad_clip self.world_size = dist.get_world_size() self.optimizer = None if use_horovod: self.agg = AllReduceAggregationHVD( world_size=world_size, use_cuda=use_cuda ).agg_grad(by_layer=by_layer) else: self.agg = AllReduceAggregation( world_size=world_size, use_cuda=use_cuda ).agg_grad(by_layer=by_layer) if average_models: self.agg_mode = "avg" else: raise NotImplementedError("Only average model is supported right now.") def set_optimizer(self, optimizer): self.optimizer = optimizer # Flattening master weight def initialize_flat_fp32_weight(self): """ Initializes the model's parameters in fp32 and fp16 Returns: (torch.Tensor, torch.Tensor): The Parametrs in fp16 and fp32 """ # Set all gradients to None for p in self.fp16_model.parameters(): p.grad = None # Count number of parameters per layer nelem = 0 for p in self.fp16_model.parameters(): nelem += p.numel() fp32_params = torch.empty( nelem, dtype=torch.float32, device=torch.device("cuda" if self.use_cuda else "cpu"), ) fp16_params = torch.empty( nelem, dtype=torch.float16, device=torch.device("cuda" if self.use_cuda else "cpu"), ) pointer = 0 for p in self.fp16_model.parameters(): nelem = p.numel() fp32_params[pointer : pointer + nelem].copy_(p.data.view(-1)) fp16_params[pointer : pointer + nelem].copy_(p.data.view(-1)) pointer += nelem fp32_params = torch.nn.Parameter(fp32_params, requires_grad=True) fp32_params.grad = torch.autograd.Variable( fp32_params.data.new(*fp32_params.size()) ) fp16_params = torch.nn.Parameter(fp16_params, requires_grad=True) fp16_params.grad = torch.autograd.Variable( fp16_params.data.new(*fp16_params.size()) ) return fp16_params, fp32_params @staticmethod def fp16_to_fp32_flat_grad(fp32_params, fp16_model): """ Copies the parameters in `fp16_model` into `fp32_params` in-place Args: fp32_params (torch.Tensor): Parameters in fp32 fp16_model (torch.nn.Module): Model in fp16 """ pointer = 0 for p in fp16_model.parameters(): nelem = p.numel() fp32_params.grad.data[pointer : pointer + nelem].copy_(p.grad.data.view(-1)) pointer += nelem @staticmethod def fp32_to_fp16_grads(fp16_model, fp32_params): """ Copies the parameters in `fp32_params` into `fp16_model` in-place Args: fp16_model (torch.nn.Module): Model in fp16 fp32_params (torch.Tensor): Parameters in fp32 """ pointer = 0 for p in fp16_model.parameters(): nelem = p.numel() p.data.view(-1).copy_(fp32_params.data[pointer : pointer + nelem]) pointer += nelem def backward_loss(self, loss): """ Scales and performs backward on the given loss Args: loss (torch.nn.Module): The loss """ loss *= self.loss_scale loss.backward() def step(self, closure=None): """ Performs one step of the optimizer. Applies loss scaling, computes gradients in fp16, converts gradients to fp32, inverts scaling and applies optional gradient norm clipping. If gradients are finite, it applies update to fp32 master weights and copies updated parameters to fp16 model for the next iteration. If gradients are not finite, it skips the batch and adjusts scaling factor for the next iteration. Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ scaling_factor = self.loss_scale # Aggregate gradients self.agg(self.fp16_model, self.agg_mode) # Cast fp16 params to fp32 for optimizer self.fp16_to_fp32_flat_grad(self.fp32_params, self.fp16_model) if scaling_factor != 1.0: self.fp32_params.grad.data /= scaling_factor norm = clip_grad_norm_([self.fp32_params], self.grad_clip) updated = False if math.isfinite(norm): self.optimizer.step(closure=closure) self.fp32_to_fp16_grads(self.fp16_model, self.fp32_params) self.since_last_invalid += 1 updated = True else: self.loss_scale /= self.dls_downscale self.since_last_invalid = 0 logger.info(f"Skipped batch, new scale: {self.loss_scale}") if self.since_last_invalid >= self.dls_upscale_interval: self.loss_scale *= self.dls_upscale self.loss_scale = min(self.loss_scale, 8192.0) self.since_last_invalid = 0 for p in self.fp16_model.parameters(): p.grad = None return updated def zero_grad(self): self.optimizer.zero_grad() class FP32Optimizer: """ Standard optimizer, computes backward and applies weight update. Args: model (`obj`:torch.nn.Module): model world_size (int): Distributed world size use_cuda (bool): Use cuda tensors for aggregation by_layer (bool): Aggregate by layer grad_clip (float): coefficient for gradient clipping, max L2 norm of the gradients average_models (bool): Average the models """ def __init__( self, model, world_size, use_cuda=False, by_layer=False, grad_clip=None, average_models=True, ): self.model = model self.grad_clip = grad_clip self.optimizer = None self.agg = AllReduceAggregation( world_size=world_size, use_cuda=use_cuda ).agg_grad(by_layer=by_layer) if average_models: self.agg_mode = "avg" else: raise NotImplementedError("Only average model is supported right now.") def set_optimizer(self, optimizer): self.optimizer = optimizer def step(self, closure=None): """ Performs one step of the optimizer. """ if self.grad_clip != float("inf"): clip_grad_norm_(self.model.parameters(), self.grad_clip) self.agg(self.model, self.agg_mode) self.optimizer.step(closure=closure) return True def backward_loss(self, loss): loss.backward() def zero_grad(self): self.optimizer.zero_grad() class AMPOptimizer: """ Optimizer compatible with AMP. Uses AMP to apply loss scaling, computes backward and applies weight update. Args: model (`obj`:torch.nn.Module): model grad_clip (float): coefficient for gradient clipping, max L2 norm of the gradients loss_scale (int): initial loss scale dls_upscale_interval (int): interval for loss scale upscaling average_models (bool): Average the models world_size (int): Distributed world size use_cuda (bool): Use cuda tensors for aggregation by_layer (bool): Aggregate by layer use_horovod (bool): Use Horovod for aggregation """ def __init__( self, model, grad_clip=None, loss_scale=8192, dls_upscale_interval=128, average_models=True, world_size=1, use_cuda=False, by_layer=False, use_horovod=False, ): self.model = model self.grad_clip = grad_clip self.optimizer = None loss_scaler = amp._amp_state.loss_scalers[0] loss_scaler._loss_scale = loss_scale loss_scaler._scale_seq_len = dls_upscale_interval if average_models: self.agg_mode = "avg" else: raise NotImplementedError("Only average model is supported right now.") if use_horovod: self.agg = AllReduceAggregationHVD( world_size=world_size, use_cuda=use_cuda ).agg_grad(by_layer=by_layer) else: self.agg = AllReduceAggregation( world_size=world_size, use_cuda=use_cuda ).agg_grad(by_layer=by_layer) def set_optimizer(self, optimizer): self.optimizer = optimizer def backward_loss(self, loss): with amp.scale_loss(loss, self.optimizer) as scaled_loss: scaled_loss.backward() def step(self, closure=None): """ Performs one step of the optimizer. """ if self.grad_clip != float("inf"): clip_grad_norm_(amp.master_params(self.optimizer), self.grad_clip) self.agg(self.model, self.agg_mode) self.optimizer.step(closure=closure) return True def zero_grad(self): self.optimizer.zero_grad()

32.273775

150

0.628003

import logging import math import torch import torch.distributed as dist from torch.nn.utils import clip_grad_norm_ from mlbench_core.utils.pytorch.distributed import ( AllReduceAggregation, AllReduceAggregationHVD, ) try: from apex.optimizers import FusedAdam from apex import amp except ImportError as e: pass logger = logging.getLogger("mlbench") class FP16Optimizer: def __init__( self, fp16_model, world_size, use_cuda=False, use_horovod=False, by_layer=False, grad_clip=float("inf"), loss_scale=1024, dls_downscale=2, dls_upscale=2, dls_upscale_interval=128, average_models=True, ): self.use_cuda = use_cuda self.fp16_model = fp16_model self.fp16_params, self.fp32_params = self.initialize_flat_fp32_weight() self.since_last_invalid = 0 self.loss_scale = loss_scale self.dls_downscale = dls_downscale self.dls_upscale = dls_upscale self.dls_upscale_interval = dls_upscale_interval self.grad_clip = grad_clip self.world_size = dist.get_world_size() self.optimizer = None if use_horovod: self.agg = AllReduceAggregationHVD( world_size=world_size, use_cuda=use_cuda ).agg_grad(by_layer=by_layer) else: self.agg = AllReduceAggregation( world_size=world_size, use_cuda=use_cuda ).agg_grad(by_layer=by_layer) if average_models: self.agg_mode = "avg" else: raise NotImplementedError("Only average model is supported right now.") def set_optimizer(self, optimizer): self.optimizer = optimizer def initialize_flat_fp32_weight(self): for p in self.fp16_model.parameters(): p.grad = None nelem = 0 for p in self.fp16_model.parameters(): nelem += p.numel() fp32_params = torch.empty( nelem, dtype=torch.float32, device=torch.device("cuda" if self.use_cuda else "cpu"), ) fp16_params = torch.empty( nelem, dtype=torch.float16, device=torch.device("cuda" if self.use_cuda else "cpu"), ) pointer = 0 for p in self.fp16_model.parameters(): nelem = p.numel() fp32_params[pointer : pointer + nelem].copy_(p.data.view(-1)) fp16_params[pointer : pointer + nelem].copy_(p.data.view(-1)) pointer += nelem fp32_params = torch.nn.Parameter(fp32_params, requires_grad=True) fp32_params.grad = torch.autograd.Variable( fp32_params.data.new(*fp32_params.size()) ) fp16_params = torch.nn.Parameter(fp16_params, requires_grad=True) fp16_params.grad = torch.autograd.Variable( fp16_params.data.new(*fp16_params.size()) ) return fp16_params, fp32_params @staticmethod def fp16_to_fp32_flat_grad(fp32_params, fp16_model): pointer = 0 for p in fp16_model.parameters(): nelem = p.numel() fp32_params.grad.data[pointer : pointer + nelem].copy_(p.grad.data.view(-1)) pointer += nelem @staticmethod def fp32_to_fp16_grads(fp16_model, fp32_params): pointer = 0 for p in fp16_model.parameters(): nelem = p.numel() p.data.view(-1).copy_(fp32_params.data[pointer : pointer + nelem]) pointer += nelem def backward_loss(self, loss): loss *= self.loss_scale loss.backward() def step(self, closure=None): scaling_factor = self.loss_scale self.agg(self.fp16_model, self.agg_mode) self.fp16_to_fp32_flat_grad(self.fp32_params, self.fp16_model) if scaling_factor != 1.0: self.fp32_params.grad.data /= scaling_factor norm = clip_grad_norm_([self.fp32_params], self.grad_clip) updated = False if math.isfinite(norm): self.optimizer.step(closure=closure) self.fp32_to_fp16_grads(self.fp16_model, self.fp32_params) self.since_last_invalid += 1 updated = True else: self.loss_scale /= self.dls_downscale self.since_last_invalid = 0 logger.info(f"Skipped batch, new scale: {self.loss_scale}") if self.since_last_invalid >= self.dls_upscale_interval: self.loss_scale *= self.dls_upscale self.loss_scale = min(self.loss_scale, 8192.0) self.since_last_invalid = 0 for p in self.fp16_model.parameters(): p.grad = None return updated def zero_grad(self): self.optimizer.zero_grad() class FP32Optimizer: def __init__( self, model, world_size, use_cuda=False, by_layer=False, grad_clip=None, average_models=True, ): self.model = model self.grad_clip = grad_clip self.optimizer = None self.agg = AllReduceAggregation( world_size=world_size, use_cuda=use_cuda ).agg_grad(by_layer=by_layer) if average_models: self.agg_mode = "avg" else: raise NotImplementedError("Only average model is supported right now.") def set_optimizer(self, optimizer): self.optimizer = optimizer def step(self, closure=None): if self.grad_clip != float("inf"): clip_grad_norm_(self.model.parameters(), self.grad_clip) self.agg(self.model, self.agg_mode) self.optimizer.step(closure=closure) return True def backward_loss(self, loss): loss.backward() def zero_grad(self): self.optimizer.zero_grad() class AMPOptimizer: def __init__( self, model, grad_clip=None, loss_scale=8192, dls_upscale_interval=128, average_models=True, world_size=1, use_cuda=False, by_layer=False, use_horovod=False, ): self.model = model self.grad_clip = grad_clip self.optimizer = None loss_scaler = amp._amp_state.loss_scalers[0] loss_scaler._loss_scale = loss_scale loss_scaler._scale_seq_len = dls_upscale_interval if average_models: self.agg_mode = "avg" else: raise NotImplementedError("Only average model is supported right now.") if use_horovod: self.agg = AllReduceAggregationHVD( world_size=world_size, use_cuda=use_cuda ).agg_grad(by_layer=by_layer) else: self.agg = AllReduceAggregation( world_size=world_size, use_cuda=use_cuda ).agg_grad(by_layer=by_layer) def set_optimizer(self, optimizer): self.optimizer = optimizer def backward_loss(self, loss): with amp.scale_loss(loss, self.optimizer) as scaled_loss: scaled_loss.backward() def step(self, closure=None): if self.grad_clip != float("inf"): clip_grad_norm_(amp.master_params(self.optimizer), self.grad_clip) self.agg(self.model, self.agg_mode) self.optimizer.step(closure=closure) return True def zero_grad(self): self.optimizer.zero_grad()

true

f736477bb6aa66636e35c7f32ac0a2e1313242fa

8,375

py

Python

secmmf/mmf_data_loader/form_parsers.py

yj1990/sec_mmf

72a8c0d5a6aadb4362c07a5606c70e51b08a53cd

[ "MIT" ]

1

2019-12-20T17:52:14.000Z

secmmf/mmf_data_loader/form_parsers.py

yj1990/sec_mmf

72a8c0d5a6aadb4362c07a5606c70e51b08a53cd

[ "MIT" ]

null

secmmf/mmf_data_loader/form_parsers.py

yj1990/sec_mmf

72a8c0d5a6aadb4362c07a5606c70e51b08a53cd

[ "MIT" ]

null

import pandas as pd import bs4 as bs import untangle as ut import requests import urllib.request as rq from collections import OrderedDict from secmmf.mmf_data_loader.utils import get_edgar_url class N_MFP2: def __init__(self): self.select_cols() def born(self, tag): # if tag is a single-node tag contains a navigable string, return a list with that string # if tag has multiple element, needs to further born them childs = [] for x in tag: if (x != '\n') & (type(x) != bs.element.Comment): childs.append(x) return childs def dive(self, root, surname=''): name = surname + root.name sons = [] for son in self.born(root): if type(son) == bs.element.NavigableString: text = ': '.join([name, son]) sons.append(text) elif type(son) == bs.element.Tag: sons.extend(self.dive(son, surname=name + '_')) return sons def teach(self, root): sons = [] for son in self.born(root): if len(self.born(son)) == 1: sons.append((son.name, son.get_text().replace('\n', ''))) elif len(self.born(son)) > 1: for grandson in self.born(son): sons.append((son.name + '_' + grandson.name, grandson.get_text().replace('\n', ''))) return sons def teach_rec(self, root): sons = [] for son in self.born(root): if len(self.born(son)) == 1: sons.append((son.name, son.get_text().replace('\n', ''))) elif len(self.born(son)) > 1: sons.append(teach_rec(son)) return sons def parse(self, url='https://www.sec.gov/Archives/edgar/data/759667/000070217219000020/primary_doc.xml'): stubs = self.stubs #_tonum = self._tonum #series_level_names = self.series_level_names #class_level_names = self.class_level_names source = rq.urlopen(url).read() soup = bs.BeautifulSoup(source, 'xml') # parse XML info into a list of dictionaries mmf = [] for tag in self.born(soup.formData): if tag.name in ['classLevelInfo', 'generalInfo', 'seriesLevelInfo']: mmf.append((tag.name, self.teach(tag))) general_series_class = [] general_series = mmf[0][1] + mmf[1][1] for i, x in enumerate(general_series): if x[0] == 'numberOfSharesOutstanding': y = list(x) y[0] = 'series_numberOfSharesOutstanding' general_series[i] = tuple(y) for x in mmf[2:]: general_series_class.append(OrderedDict(general_series + x[1])) df = pd.DataFrame(general_series_class) if 'nameOfPersonDescExpensePay' in df.columns: df.drop(columns='nameOfPersonDescExpensePay', inplace=True) # rename those columns that have reversed patterns namemap = [] for x in ['weeklyGrossRedemptions', 'weeklyGrossSubscriptions']: namemap.append(dict([('fridayWeek' + str(i + 1) + '_' + x, x + '_' + 'fridayWeek' + str(i + 1)) for i in range(5)])) for x in ['totalValueDailyLiquidAssets', 'percentageDailyLiquidAssets']: namemap.append(dict([(x + '_' + 'fridayDay' + str(i + 1), x + '_' + 'fridayWeek' + str(i + 1)) for i in range(5)])) for i in range(4): df = df.rename(columns=namemap[i]) # make data wide to long on weekly holding statistics df = pd.wide_to_long(df, stubnames=self.stubs, i='classesId', j='week', sep='_', suffix='\w+') df.reset_index(inplace=True) df['week'] = df['week'].apply( lambda x: int(x.replace('fridayWeek', ''))) #df = df[['week']+series_level_names+class_level_names] # change the type of numeric data to float #df[_tonum] = df[_tonum].astype(dtype = float) return df def parse_csv(self, url): source = get_edgar_url(url).content soup = bs.BeautifulSoup(source, 'xml') return self.dive(soup.formData) def select_cols(self): self.stubs = ['totalValueDailyLiquidAssets', 'percentageDailyLiquidAssets', 'totalValueWeeklyLiquidAssets', 'percentageWeeklyLiquidAssets', 'netAssetValue', 'netAssetPerShare', 'weeklyGrossRedemptions', 'weeklyGrossSubscriptions'] self._tonum = ['totalShareClassesInSeries', 'averagePortfolioMaturity', 'averageLifeMaturity', 'cash', 'totalValuePortfolioSecurities', 'amortizedCostPortfolioSecurities', 'totalValueOtherAssets', 'totalValueLiabilities', 'netAssetOfSeries', 'numberOfSharesOutstanding', 'stablePricePerShare', 'sevenDayGrossYield', 'minInitialInvestment', 'netAssetsOfClass', 'totalForTheMonthReported_weeklyGrossSubscriptions', 'totalForTheMonthReported_weeklyGrossRedemptions', 'sevenDayNetYield'] + self.stubs self.series_level_names = ['reportDate', 'cik', 'seriesId', 'totalShareClassesInSeries', 'finalFilingFlag', 'fundAcqrdOrMrgdWthAnthrFlag', 'securitiesActFileNumber', 'adviser_adviserName', 'adviser_adviserFileNumber', 'indpPubAccountant_name', 'indpPubAccountant_city', 'indpPubAccountant_stateCountry', 'administrator', 'transferAgent_name', 'transferAgent_cik', 'transferAgent_fileNumber', 'feederFundFlag', 'masterFundFlag', 'seriesFundInsuCmpnySepAccntFlag', 'moneyMarketFundCategory', 'fundExemptRetailFlag', 'averagePortfolioMaturity', 'averageLifeMaturity', 'totalValueDailyLiquidAssets', 'totalValueWeeklyLiquidAssets', 'percentageDailyLiquidAssets', 'percentageWeeklyLiquidAssets', 'cash', 'totalValuePortfolioSecurities', 'amortizedCostPortfolioSecurities', 'totalValueOtherAssets', 'totalValueLiabilities', 'netAssetOfSeries', 'series_numberOfSharesOutstanding', 'stablePricePerShare', 'sevenDayGrossYield', 'netAssetValue'] self.class_level_names = ['classesId', 'minInitialInvestment', 'netAssetsOfClass', 'numberOfSharesOutstanding', 'netAssetPerShare', 'weeklyGrossSubscriptions', 'weeklyGrossRedemptions', 'totalForTheMonthReported_weeklyGrossSubscriptions', 'totalForTheMonthReported_weeklyGrossRedemptions', 'sevenDayNetYield', 'personPayForFundFlag']

43.848168

109

0.488597

import pandas as pd import bs4 as bs import untangle as ut import requests import urllib.request as rq from collections import OrderedDict from secmmf.mmf_data_loader.utils import get_edgar_url class N_MFP2: def __init__(self): self.select_cols() def born(self, tag): childs = [] for x in tag: if (x != '\n') & (type(x) != bs.element.Comment): childs.append(x) return childs def dive(self, root, surname=''): name = surname + root.name sons = [] for son in self.born(root): if type(son) == bs.element.NavigableString: text = ': '.join([name, son]) sons.append(text) elif type(son) == bs.element.Tag: sons.extend(self.dive(son, surname=name + '_')) return sons def teach(self, root): sons = [] for son in self.born(root): if len(self.born(son)) == 1: sons.append((son.name, son.get_text().replace('\n', ''))) elif len(self.born(son)) > 1: for grandson in self.born(son): sons.append((son.name + '_' + grandson.name, grandson.get_text().replace('\n', ''))) return sons def teach_rec(self, root): sons = [] for son in self.born(root): if len(self.born(son)) == 1: sons.append((son.name, son.get_text().replace('\n', ''))) elif len(self.born(son)) > 1: sons.append(teach_rec(son)) return sons def parse(self, url='https://www.sec.gov/Archives/edgar/data/759667/000070217219000020/primary_doc.xml'): stubs = self.stubs source = rq.urlopen(url).read() soup = bs.BeautifulSoup(source, 'xml') mmf = [] for tag in self.born(soup.formData): if tag.name in ['classLevelInfo', 'generalInfo', 'seriesLevelInfo']: mmf.append((tag.name, self.teach(tag))) general_series_class = [] general_series = mmf[0][1] + mmf[1][1] for i, x in enumerate(general_series): if x[0] == 'numberOfSharesOutstanding': y = list(x) y[0] = 'series_numberOfSharesOutstanding' general_series[i] = tuple(y) for x in mmf[2:]: general_series_class.append(OrderedDict(general_series + x[1])) df = pd.DataFrame(general_series_class) if 'nameOfPersonDescExpensePay' in df.columns: df.drop(columns='nameOfPersonDescExpensePay', inplace=True) namemap = [] for x in ['weeklyGrossRedemptions', 'weeklyGrossSubscriptions']: namemap.append(dict([('fridayWeek' + str(i + 1) + '_' + x, x + '_' + 'fridayWeek' + str(i + 1)) for i in range(5)])) for x in ['totalValueDailyLiquidAssets', 'percentageDailyLiquidAssets']: namemap.append(dict([(x + '_' + 'fridayDay' + str(i + 1), x + '_' + 'fridayWeek' + str(i + 1)) for i in range(5)])) for i in range(4): df = df.rename(columns=namemap[i]) df = pd.wide_to_long(df, stubnames=self.stubs, i='classesId', j='week', sep='_', suffix='\w+') df.reset_index(inplace=True) df['week'] = df['week'].apply( lambda x: int(x.replace('fridayWeek', ''))) return df def parse_csv(self, url): source = get_edgar_url(url).content soup = bs.BeautifulSoup(source, 'xml') return self.dive(soup.formData) def select_cols(self): self.stubs = ['totalValueDailyLiquidAssets', 'percentageDailyLiquidAssets', 'totalValueWeeklyLiquidAssets', 'percentageWeeklyLiquidAssets', 'netAssetValue', 'netAssetPerShare', 'weeklyGrossRedemptions', 'weeklyGrossSubscriptions'] self._tonum = ['totalShareClassesInSeries', 'averagePortfolioMaturity', 'averageLifeMaturity', 'cash', 'totalValuePortfolioSecurities', 'amortizedCostPortfolioSecurities', 'totalValueOtherAssets', 'totalValueLiabilities', 'netAssetOfSeries', 'numberOfSharesOutstanding', 'stablePricePerShare', 'sevenDayGrossYield', 'minInitialInvestment', 'netAssetsOfClass', 'totalForTheMonthReported_weeklyGrossSubscriptions', 'totalForTheMonthReported_weeklyGrossRedemptions', 'sevenDayNetYield'] + self.stubs self.series_level_names = ['reportDate', 'cik', 'seriesId', 'totalShareClassesInSeries', 'finalFilingFlag', 'fundAcqrdOrMrgdWthAnthrFlag', 'securitiesActFileNumber', 'adviser_adviserName', 'adviser_adviserFileNumber', 'indpPubAccountant_name', 'indpPubAccountant_city', 'indpPubAccountant_stateCountry', 'administrator', 'transferAgent_name', 'transferAgent_cik', 'transferAgent_fileNumber', 'feederFundFlag', 'masterFundFlag', 'seriesFundInsuCmpnySepAccntFlag', 'moneyMarketFundCategory', 'fundExemptRetailFlag', 'averagePortfolioMaturity', 'averageLifeMaturity', 'totalValueDailyLiquidAssets', 'totalValueWeeklyLiquidAssets', 'percentageDailyLiquidAssets', 'percentageWeeklyLiquidAssets', 'cash', 'totalValuePortfolioSecurities', 'amortizedCostPortfolioSecurities', 'totalValueOtherAssets', 'totalValueLiabilities', 'netAssetOfSeries', 'series_numberOfSharesOutstanding', 'stablePricePerShare', 'sevenDayGrossYield', 'netAssetValue'] self.class_level_names = ['classesId', 'minInitialInvestment', 'netAssetsOfClass', 'numberOfSharesOutstanding', 'netAssetPerShare', 'weeklyGrossSubscriptions', 'weeklyGrossRedemptions', 'totalForTheMonthReported_weeklyGrossSubscriptions', 'totalForTheMonthReported_weeklyGrossRedemptions', 'sevenDayNetYield', 'personPayForFundFlag']

true

f73647b2a6012065708253b687595d2d32a619e0

6,002

bzl

Python

go/private/actions/archive.bzl

tomlu/rules_go

28dd92b09c978ad09c74c18161a1da844235adfb

[ "Apache-2.0" ]

1

2020-12-02T20:04:26.000Z

go/private/actions/archive.bzl

tomlu/rules_go

28dd92b09c978ad09c74c18161a1da844235adfb

[ "Apache-2.0" ]

null

go/private/actions/archive.bzl

tomlu/rules_go

28dd92b09c978ad09c74c18161a1da844235adfb

[ "Apache-2.0" ]

null

# Copyright 2014 The Bazel 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. load( "@io_bazel_rules_go//go/private:common.bzl", "as_tuple", "split_srcs", ) load( "@io_bazel_rules_go//go/private:mode.bzl", "LINKMODE_C_ARCHIVE", "LINKMODE_C_SHARED", "mode_string", ) load( "@io_bazel_rules_go//go/private:providers.bzl", "GoArchive", "GoArchiveData", "effective_importpath_pkgpath", "get_archive", ) load( "@io_bazel_rules_go//go/private:rules/cgo.bzl", "cgo_configure", ) load( "@io_bazel_rules_go//go/private:actions/compilepkg.bzl", "emit_compilepkg", ) def emit_archive(go, source = None): """See go/toolchains.rst#archive for full documentation.""" if source == None: fail("source is a required parameter") split = split_srcs(source.srcs) testfilter = getattr(source.library, "testfilter", None) pre_ext = "" if go.mode.link == LINKMODE_C_ARCHIVE: pre_ext = "_" # avoid collision with go_binary output file with .a extension elif testfilter == "exclude": pre_ext = ".internal" elif testfilter == "only": pre_ext = ".external" out_lib = go.declare_file(go, ext = pre_ext + ".a") if go.nogo: # TODO(#1847): write nogo data into a new section in the .a file instead # of writing a separate file. out_export = go.declare_file(go, ext = pre_ext + ".x") else: out_export = None out_cgo_export_h = None # set if cgo used in c-shared or c-archive mode direct = [get_archive(dep) for dep in source.deps] runfiles = source.runfiles data_files = runfiles.files for a in direct: runfiles = runfiles.merge(a.runfiles) if a.source.mode != go.mode: fail("Archive mode does not match {} is {} expected {}".format(a.data.label, mode_string(a.source.mode), mode_string(go.mode))) importmap = "main" if source.library.is_main else source.library.importmap importpath, _ = effective_importpath_pkgpath(source.library) if source.cgo and not go.mode.pure: # TODO(jayconrod): do we need to do full Bourne tokenization here? cppopts = [f for fs in source.cppopts for f in fs.split(" ")] copts = [f for fs in source.copts for f in fs.split(" ")] cxxopts = [f for fs in source.cxxopts for f in fs.split(" ")] clinkopts = [f for fs in source.clinkopts for f in fs.split(" ")] cgo = cgo_configure( go, srcs = split.go + split.c + split.asm + split.cxx + split.objc + split.headers, cdeps = source.cdeps, cppopts = cppopts, copts = copts, cxxopts = cxxopts, clinkopts = clinkopts, ) if go.mode.link in (LINKMODE_C_SHARED, LINKMODE_C_ARCHIVE): out_cgo_export_h = go.declare_file(go, path = "_cgo_install.h") cgo_deps = cgo.deps runfiles = runfiles.merge(cgo.runfiles) emit_compilepkg( go, sources = split.go + split.c + split.asm + split.cxx + split.objc + split.headers, cover = source.cover, importpath = importpath, importmap = importmap, archives = direct, out_lib = out_lib, out_export = out_export, out_cgo_export_h = out_cgo_export_h, gc_goopts = source.gc_goopts, cgo = True, cgo_inputs = cgo.inputs, cppopts = cgo.cppopts, copts = cgo.copts, cxxopts = cgo.cxxopts, objcopts = cgo.objcopts, objcxxopts = cgo.objcxxopts, clinkopts = cgo.clinkopts, testfilter = testfilter, ) else: cgo_deps = depset() emit_compilepkg( go, sources = split.go + split.c + split.asm + split.cxx + split.objc + split.headers, cover = source.cover, importpath = importpath, importmap = importmap, archives = direct, out_lib = out_lib, out_export = out_export, gc_goopts = source.gc_goopts, cgo = False, testfilter = testfilter, ) data = GoArchiveData( name = source.library.name, label = source.library.label, importpath = source.library.importpath, importmap = source.library.importmap, importpath_aliases = source.library.importpath_aliases, pathtype = source.library.pathtype, file = out_lib, export_file = out_export, srcs = as_tuple(source.srcs), orig_srcs = as_tuple(source.orig_srcs), data_files = as_tuple(data_files), ) x_defs = dict(source.x_defs) for a in direct: x_defs.update(a.x_defs) cgo_exports_direct = list(source.cgo_exports) if out_cgo_export_h: cgo_exports_direct.append(out_cgo_export_h) cgo_exports = depset(direct = cgo_exports_direct, transitive = [a.cgo_exports for a in direct]) return GoArchive( source = source, data = data, direct = direct, libs = depset(direct = [out_lib], transitive = [a.libs for a in direct]), transitive = depset([data], transitive = [a.transitive for a in direct]), x_defs = x_defs, cgo_deps = depset(transitive = [cgo_deps] + [a.cgo_deps for a in direct]), cgo_exports = cgo_exports, runfiles = runfiles, mode = go.mode, )

36.375758

139

0.619294

load( "@io_bazel_rules_go//go/private:common.bzl", "as_tuple", "split_srcs", ) load( "@io_bazel_rules_go//go/private:mode.bzl", "LINKMODE_C_ARCHIVE", "LINKMODE_C_SHARED", "mode_string", ) load( "@io_bazel_rules_go//go/private:providers.bzl", "GoArchive", "GoArchiveData", "effective_importpath_pkgpath", "get_archive", ) load( "@io_bazel_rules_go//go/private:rules/cgo.bzl", "cgo_configure", ) load( "@io_bazel_rules_go//go/private:actions/compilepkg.bzl", "emit_compilepkg", ) def emit_archive(go, source = None): if source == None: fail("source is a required parameter") split = split_srcs(source.srcs) testfilter = getattr(source.library, "testfilter", None) pre_ext = "" if go.mode.link == LINKMODE_C_ARCHIVE: pre_ext = "_" elif testfilter == "exclude": pre_ext = ".internal" elif testfilter == "only": pre_ext = ".external" out_lib = go.declare_file(go, ext = pre_ext + ".a") if go.nogo: + ".x") else: out_export = None out_cgo_export_h = None direct = [get_archive(dep) for dep in source.deps] runfiles = source.runfiles data_files = runfiles.files for a in direct: runfiles = runfiles.merge(a.runfiles) if a.source.mode != go.mode: fail("Archive mode does not match {} is {} expected {}".format(a.data.label, mode_string(a.source.mode), mode_string(go.mode))) importmap = "main" if source.library.is_main else source.library.importmap importpath, _ = effective_importpath_pkgpath(source.library) if source.cgo and not go.mode.pure: cppopts = [f for fs in source.cppopts for f in fs.split(" ")] copts = [f for fs in source.copts for f in fs.split(" ")] cxxopts = [f for fs in source.cxxopts for f in fs.split(" ")] clinkopts = [f for fs in source.clinkopts for f in fs.split(" ")] cgo = cgo_configure( go, srcs = split.go + split.c + split.asm + split.cxx + split.objc + split.headers, cdeps = source.cdeps, cppopts = cppopts, copts = copts, cxxopts = cxxopts, clinkopts = clinkopts, ) if go.mode.link in (LINKMODE_C_SHARED, LINKMODE_C_ARCHIVE): out_cgo_export_h = go.declare_file(go, path = "_cgo_install.h") cgo_deps = cgo.deps runfiles = runfiles.merge(cgo.runfiles) emit_compilepkg( go, sources = split.go + split.c + split.asm + split.cxx + split.objc + split.headers, cover = source.cover, importpath = importpath, importmap = importmap, archives = direct, out_lib = out_lib, out_export = out_export, out_cgo_export_h = out_cgo_export_h, gc_goopts = source.gc_goopts, cgo = True, cgo_inputs = cgo.inputs, cppopts = cgo.cppopts, copts = cgo.copts, cxxopts = cgo.cxxopts, objcopts = cgo.objcopts, objcxxopts = cgo.objcxxopts, clinkopts = cgo.clinkopts, testfilter = testfilter, ) else: cgo_deps = depset() emit_compilepkg( go, sources = split.go + split.c + split.asm + split.cxx + split.objc + split.headers, cover = source.cover, importpath = importpath, importmap = importmap, archives = direct, out_lib = out_lib, out_export = out_export, gc_goopts = source.gc_goopts, cgo = False, testfilter = testfilter, ) data = GoArchiveData( name = source.library.name, label = source.library.label, importpath = source.library.importpath, importmap = source.library.importmap, importpath_aliases = source.library.importpath_aliases, pathtype = source.library.pathtype, file = out_lib, export_file = out_export, srcs = as_tuple(source.srcs), orig_srcs = as_tuple(source.orig_srcs), data_files = as_tuple(data_files), ) x_defs = dict(source.x_defs) for a in direct: x_defs.update(a.x_defs) cgo_exports_direct = list(source.cgo_exports) if out_cgo_export_h: cgo_exports_direct.append(out_cgo_export_h) cgo_exports = depset(direct = cgo_exports_direct, transitive = [a.cgo_exports for a in direct]) return GoArchive( source = source, data = data, direct = direct, libs = depset(direct = [out_lib], transitive = [a.libs for a in direct]), transitive = depset([data], transitive = [a.transitive for a in direct]), x_defs = x_defs, cgo_deps = depset(transitive = [cgo_deps] + [a.cgo_deps for a in direct]), cgo_exports = cgo_exports, runfiles = runfiles, mode = go.mode, )

true

f73647b504c490e3c41803e6e2a4d597213a1d43

1,900

py

Python

gsadjust/data/__init__.py

jkennedy-usgs/sgp-gsadjust

929d650674b942d33168bcdaae7da07db175a1c4

[ "CC0-1.0" ]

5

2019-01-08T13:51:23.000Z

2020-04-22T19:04:20.000Z

gsadjust/data/__init__.py

jkennedy-usgs/sgp-gsadjust

929d650674b942d33168bcdaae7da07db175a1c4

[ "CC0-1.0" ]

113

2018-06-14T21:39:59.000Z

2022-01-21T19:34:12.000Z

gsadjust/data/__init__.py

jkennedy-usgs/sgp-gsadjust

929d650674b942d33168bcdaae7da07db175a1c4

[ "CC0-1.0" ]

3

2021-01-26T07:10:42.000Z

2022-03-15T12:39:35.000Z

""" data ==== GSadjust objects for non-Survey | Loop | Station objects (those are represented as PyQt objects). -------------------------------------------------------------------------------- The main data objects are: ChannelList: Holds lists of observed data values (g, tilt, temp, etc.). The lists are copied to ObsTreeStation objects. Adjustment | AdjustmentOptions } Adjustment Results: The first contains instances of the latter two. Holds the input data, options, and results of the network adjustment. Datum: Absolute-gravity observation or other reference for the relative-gravity network. At least one datum is required for network adjustment. Delta: Relative-gravity difference calculated from two station occupations. May or may not include drift correction. Tare: Represents an offset applied to the data TimeSeries: Used only for tide correction. This software is preliminary, provisional, and is subject to revision. It is being provided to meet the need for timely best science. The software has not received final approval by the U.S. Geological Survey (USGS). No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. The software is provided on the condition tha neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the software. """ from . import adjustment, analysis, channel, correction, datum, delta, tare from .adjustment import ( AdjustedStation, Adjustment, AdjustmentOptions, AdjustmentResults, ) from .channel import ChannelList from .datum import Datum from .delta import ( Delta3Point, DeltaList, DeltaNormal, create_delta_by_type, ) from .tare import Tare

35.849057

81

0.728421

from . import adjustment, analysis, channel, correction, datum, delta, tare from .adjustment import ( AdjustedStation, Adjustment, AdjustmentOptions, AdjustmentResults, ) from .channel import ChannelList from .datum import Datum from .delta import ( Delta3Point, DeltaList, DeltaNormal, create_delta_by_type, ) from .tare import Tare

true

f73647cb9744604fcafbadc9ef4a6ae28b197403

937

py

Python

named_entity_recognition1.py

DreamFireworks/nlp

e96e6f505964f1886e3de5347f2c1179cf2578e5

[ "MIT" ]

null

named_entity_recognition1.py

DreamFireworks/nlp

e96e6f505964f1886e3de5347f2c1179cf2578e5

[ "MIT" ]

null

named_entity_recognition1.py

DreamFireworks/nlp

e96e6f505964f1886e3de5347f2c1179cf2578e5

[ "MIT" ]

null

import nltk text="""A biography of Yue Fei, the Eguo Jintuo Zubian (鄂國金佗稡编), was written 60 years after his death by his grandson, the poet and historian Yue Ke (岳柯) (1183–post 1240).[3][4][5] In 1346 it was incorporated into the History of Song, a 496-chapter record of historical events and biographies of noted Song dynasty individuals, compiled by Yuan dynasty prime minister Toqto'a and others.[6] Yue Fei's biography is found in the 365th chapter of the book and is numbered biography 124.[7] Some later historians including Deng Guangming (1907–1998) now doubt the veracity of many of Yue Ke's claims about his grandfather.[8] According to the History of Song, Yue Fei was named "Fei", meaning to fly, because at the time he was born, "a large bird like a swan landed on the roof of his house".""" tokenized = nltk.word_tokenize(text) tagged = nltk.pos_tag(tokenized) named_entity = nltk.ne_chunk(tagged) named_entity.draw()

72.076923

621

0.770544

import nltk text="""A biography of Yue Fei, the Eguo Jintuo Zubian (鄂國金佗稡编), was written 60 years after his death by his grandson, the poet and historian Yue Ke (岳柯) (1183–post 1240).[3][4][5] In 1346 it was incorporated into the History of Song, a 496-chapter record of historical events and biographies of noted Song dynasty individuals, compiled by Yuan dynasty prime minister Toqto'a and others.[6] Yue Fei's biography is found in the 365th chapter of the book and is numbered biography 124.[7] Some later historians including Deng Guangming (1907–1998) now doubt the veracity of many of Yue Ke's claims about his grandfather.[8] According to the History of Song, Yue Fei was named "Fei", meaning to fly, because at the time he was born, "a large bird like a swan landed on the roof of his house".""" tokenized = nltk.word_tokenize(text) tagged = nltk.pos_tag(tokenized) named_entity = nltk.ne_chunk(tagged) named_entity.draw()

true

f736498c71b92611343f1c80a567282dd6f753c1

413

py

Python

eHealth_Version1.0_01_13_2022_12_47_pm_Release/BUS/BUS_XRayPatient.py

kyvipro113/Graduation_Thesis

71e9e3aa2adf64cff7319d056592f8b6ef1fd5ec

[ "MIT" ]

null

eHealth_Version1.0_01_13_2022_12_47_pm_Release/BUS/BUS_XRayPatient.py

kyvipro113/Graduation_Thesis

71e9e3aa2adf64cff7319d056592f8b6ef1fd5ec

[ "MIT" ]

null

eHealth_Version1.0_01_13_2022_12_47_pm_Release/BUS/BUS_XRayPatient.py

kyvipro113/Graduation_Thesis

71e9e3aa2adf64cff7319d056592f8b6ef1fd5ec

[ "MIT" ]

null

from DAL.DAL_XRayPatient import DAL_XRayPatient class BUS_XRayPatient(): def __init__(self): self.dalXRayPatient = DAL_XRayPatient() def firstLoadLinkXRay(self, IDPatient): return self.dalXRayPatient.selectLinkXRayViaIDPatient(IDPatient=IDPatient) def loadLinkXRay(self, IDPatient, XRayType): return self.dalXRayPatient.selectLinkXRay(IDPatient=IDPatient, XRayType=XRayType)

37.545455

89

0.774818

from DAL.DAL_XRayPatient import DAL_XRayPatient class BUS_XRayPatient(): def __init__(self): self.dalXRayPatient = DAL_XRayPatient() def firstLoadLinkXRay(self, IDPatient): return self.dalXRayPatient.selectLinkXRayViaIDPatient(IDPatient=IDPatient) def loadLinkXRay(self, IDPatient, XRayType): return self.dalXRayPatient.selectLinkXRay(IDPatient=IDPatient, XRayType=XRayType)

true

f7364b16fc4142c59fd6ce8321417e4f024bf8de

1,543

py

Python

internal/notes/builtin-SAVE/packages/r-rodbc/package.py

HPCToolkit/hpctest

5ff4455582bf39e75530a31badcf6142081b386b

[ "BSD-3-Clause" ]

1

2019-01-17T20:07:19.000Z

internal/notes/builtin-SAVE/packages/r-rodbc/package.py

HPCToolkit/hpctest

5ff4455582bf39e75530a31badcf6142081b386b

[ "BSD-3-Clause" ]

null

internal/notes/builtin-SAVE/packages/r-rodbc/package.py

HPCToolkit/hpctest

5ff4455582bf39e75530a31badcf6142081b386b

[ "BSD-3-Clause" ]

2

2019-08-06T18:13:57.000Z

2021-11-05T18:19:49.000Z

############################################################################## # Copyright (c) 2013-2017, Lawrence Livermore National Security, LLC. # Produced at the Lawrence Livermore National Laboratory. # # This file is part of Spack. # Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved. # LLNL-CODE-647188 # # For details, see https://github.com/spack/spack # Please also see the NOTICE and LICENSE files for our notice and the LGPL. # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License (as # published by the Free Software Foundation) version 2.1, February 1999. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the IMPLIED WARRANTY OF # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the terms and # conditions of the GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ############################################################################## from spack import * class RRodbc(RPackage): """An ODBC database interface.""" homepage = "https://cran.rstudio.com/web/packages/RODBC/" url = "https://cran.rstudio.com/src/contrib/RODBC_1.3-13.tar.gz" version('1.3-13', 'c52ef9139c2ed85adc53ad6effa7d68e') depends_on('unixodbc')

41.702703

78

0.672715

true

f7364bb8d7801da82a198367f1f1d7ca19fe8f6b

373

py

Python

wordcloud/__init__.py

jcfr/word_cloud

d01847f973b45af9198c36b922a908b4374afe4d

[ "MIT" ]

null

wordcloud/__init__.py

jcfr/word_cloud

d01847f973b45af9198c36b922a908b4374afe4d

[ "MIT" ]

null

wordcloud/__init__.py

jcfr/word_cloud

d01847f973b45af9198c36b922a908b4374afe4d

[ "MIT" ]

null

from .wordcloud import (WordCloud, STOPWORDS, random_color_func, get_single_color_func) from .color_from_image import ImageColorGenerator __all__ = ['WordCloud', 'STOPWORDS', 'random_color_func', 'get_single_color_func', 'ImageColorGenerator'] from ._version import get_versions __version__ = get_versions()['version'] del get_versions

33.909091

64

0.742627

from .wordcloud import (WordCloud, STOPWORDS, random_color_func, get_single_color_func) from .color_from_image import ImageColorGenerator __all__ = ['WordCloud', 'STOPWORDS', 'random_color_func', 'get_single_color_func', 'ImageColorGenerator'] from ._version import get_versions __version__ = get_versions()['version'] del get_versions

true

f7364c56f83403121dd05f261447211892d91a9e

542

py

Python

I_love_Easin.py

mdhasan8/Problem_Solving

ac18f30ecc7d1baa4cea382c53aec16a544530be

[ "MIT" ]

null

I_love_Easin.py

mdhasan8/Problem_Solving

ac18f30ecc7d1baa4cea382c53aec16a544530be

[ "MIT" ]

null

I_love_Easin.py

mdhasan8/Problem_Solving

ac18f30ecc7d1baa4cea382c53aec16a544530be

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sat Aug 28 12:20:40 2021 @author: Easin """ in1 = input() in1 = int(in1) in2 = input().split() list1 = [] for elem in range(len(in2)): list1.append(int(in2[elem])) #print(list1) amazing = 0 min1 = list1[0] max1 = list1[0] for val in range(len(list1)-1): if list1[val +1] > min1: amazing +=1 min1 = list1[val+1] elif list1[val +1] < max1: amazing += 1 max1 = list1[val+1] print(amazing)

16.424242

36

0.498155

in1 = input() in1 = int(in1) in2 = input().split() list1 = [] for elem in range(len(in2)): list1.append(int(in2[elem])) amazing = 0 min1 = list1[0] max1 = list1[0] for val in range(len(list1)-1): if list1[val +1] > min1: amazing +=1 min1 = list1[val+1] elif list1[val +1] < max1: amazing += 1 max1 = list1[val+1] print(amazing)

true

f7364d5dfda7356574ef3f753491f24da0998c62

854

py

Python

triple_agent/reports/generation/generate_external_reports.py

andrewzwicky/TripleAgent

8d056df5c53a3d264dc778bad6771a0a2f62e7e7

[ "MIT" ]

3

2020-04-25T11:42:03.000Z

2020-07-08T16:38:26.000Z

triple_agent/reports/generation/generate_external_reports.py

andrewzwicky/TripleAgent

8d056df5c53a3d264dc778bad6771a0a2f62e7e7

[ "MIT" ]

17

2019-08-11T19:09:55.000Z

2021-03-30T17:12:28.000Z

triple_agent/reports/generation/generate_external_reports.py

andrewzwicky/TripleAgent

8d056df5c53a3d264dc778bad6771a0a2f62e7e7

[ "MIT" ]

null

import json from enum import Enum from triple_agent.reports.generation.generic_query import populate_data_properties def generate_external_reports( games, data_query_properties, json_file_path, html_file_path ): _, data_props = populate_data_properties(games, data_query_properties) # https://github.com/pandas-dev/pandas/issues/15273 # This means the normal .to_json doesn't work for a dataframe. if isinstance(data_props.frame.columns[0], Enum): data_props.frame.columns = data_props.frame.columns.map(lambda x: x.name) if isinstance(data_props.frame.index[0], Enum): data_props.frame.index = data_props.frame.index.map(lambda x: x.name) with open(json_file_path, "w") as at_json_out: json.dump(data_props.frame.to_dict(), at_json_out, indent=4) data_props.frame.T.to_html(html_file_path)

35.583333

82

0.75644

import json from enum import Enum from triple_agent.reports.generation.generic_query import populate_data_properties def generate_external_reports( games, data_query_properties, json_file_path, html_file_path ): _, data_props = populate_data_properties(games, data_query_properties) if isinstance(data_props.frame.columns[0], Enum): data_props.frame.columns = data_props.frame.columns.map(lambda x: x.name) if isinstance(data_props.frame.index[0], Enum): data_props.frame.index = data_props.frame.index.map(lambda x: x.name) with open(json_file_path, "w") as at_json_out: json.dump(data_props.frame.to_dict(), at_json_out, indent=4) data_props.frame.T.to_html(html_file_path)

true

f7364d9908dc590a28f74d0749e5123f5854f229

160,627

py

Python

IsoFit.py

wjwainwright/Capstone

a2ea661079ece6ff5008f4399b3f0f6d32c598d3

[ "MIT" ]

null

IsoFit.py

wjwainwright/Capstone

a2ea661079ece6ff5008f4399b3f0f6d32c598d3

[ "MIT" ]

null

IsoFit.py

wjwainwright/Capstone

a2ea661079ece6ff5008f4399b3f0f6d32c598d3

[ "MIT" ]

null

try: runCount += 1 except: isoIn = False clIn = False cataIn = False closePlots = False resultsIn = False clusterList = [] clusters=[] isochrones = [] isoList = [] catalogue = [] runCount = 1 class resultClusterObj: def __init__(self,cl): import numpy as np #Automatically populates variables based on those from the cluster it was given, except the data arrays global properties #List of all of the variables defined for the cluster cl, strips out the __functions__ properties = [a for a in dir(cl) if not a.startswith('_')] for prop in properties: #Saves all 'number' type variables to the memory of the result cluster object if eval(f"type(cl.{prop})") == float or eval(f"type(cl.{prop})") == np.float64 or eval(f"type(cl.{prop})") == int: exec(f"self.{prop} = float(cl.{prop})") elif eval(f"type(cl.{prop})") == str: exec(f"self.{prop} = cl.{prop}") #Manually defined properties self.name = cl.name self.clType = cl.clType class clusterObj: def __init__(self,name='genericCluster',basedir='clusters/',brightThreshold=15): #Declare instance variables self.basedir = basedir self.dataPath = self.basedir + f"{name}/data/" self.imgPath = self.basedir + f"{name}/plots/" self.unfilteredWide = [] self.unfilteredNarrow = [] self.filtered = [] self.mag = [] self.iso = [] self.condensed = [] self.condensed0 = [] self.condensedInit=[] self.unfilteredBright = [] self.filteredBright = [] self.brightmag = [] self.distFiltered = [] self.binaries = [] self.stars = [] self.brightThreshold = brightThreshold self.mean_par = 0 self.stdev_par = 0 self.mean_ra = 0 self.mean_dec = 0 self.stdev_ra = 0 self.stdev_dec = 0 self.mean_pmra = 0 self.stdev_pmra = 0 self.mean_pmdec = 0 self.stdev_pmdec = 0 self.mean_a_g = 0 self.stdev_a_g = 0 self.mean_e_bp_rp = 0 self.stdev_e_bp_rp = 0 self.mean_par_over_ra = 0 self.stdev_par_over_ra = 0 self.dist_mod = 0 self.turnPoint = 0 self.reddening = 0 self.radDist = 0 self.massLoaded = False #Catalogued properties self.name = name self.clType = "None" self.pmra_min = -99 self.pmra_max = -99 self.pmdec_min = -99 self.pmdec_max = -99 self.par_min = -99 self.par_max = -99 self.cltpx = -99 self.cltpy = -99 self.noise_cutoff = -99 #Check directory locations import os if not os.path.isdir(self.dataPath): os.mkdir(self.dataPath) if not os.path.isdir(self.imgPath): os.mkdir(self.imgPath) if not os.path.isdir(f"{self.imgPath}/png"): os.mkdir(f"{self.imgPath}/png") #Gaia DR2 Implementation # class starObj: # def __init__(self,name,ra,ra_err,dec,dec_err,par,par_err,par_over_err,pmra,pmra_err,pmdec,pmdec_err,ra_dec_corr,ra_par_corr,ra_pmra_corr,ra_pmdec_corr,dec_par_corr,dec_pmra_corr,dec_pmdec_corr,par_pmra_corr,par_pmdec_corr,pmra_pmdec_corr,astro_n_obs,astro_n_good_obs,astro_n_bad_obs,astro_gof,astro_chi2,astro_noise,astro_noise_sig,astro_match_obs,astro_sigma5d,match_obs,g_mag,b_mag,r_mag,b_r,b_g,g_r,radvel,radvel_err,variable,teff,a_g,e_bp_rp,lum): # #Declare instance variables # self.name = name # self.ra = float(ra) # self.ra_err = float(ra_err) # self.dec = float(dec) # self.dec_err = float(dec_err) # self.par = float(par) # self.par_err = float(par_err) # self.par_over_err = float(par_over_err) # self.pmra = float(pmra) # self.pmra_err = float(pmra_err) # self.pmdec = float(pmdec) # self.pmdec_err = float(pmdec_err) # self.ra_dec_corr = float(ra_dec_corr) # self.ra_par_corr = float(ra_par_corr) # self.ra_pmra_corr = float(ra_pmra_corr) # self.ra_pmdec_corr = float(ra_pmdec_corr) # self.dec_par_corr = float(dec_par_corr) # self.dec_pmra_corr = float(dec_pmra_corr) # self.dec_pmdec_corr = float(dec_pmdec_corr) # self.par_pmra_corr = float(par_pmra_corr) # self.par_pmdec_corr = float(par_pmdec_corr) # self.pmra_pmdec_corr = float(pmra_pmdec_corr) # self.astro_n_obs = float(astro_n_obs) # self.astro_n_good_obs = float(astro_n_good_obs) # self.astro_n_bad_obs = float(astro_n_bad_obs) # self.astro_gof = float(astro_gof) # self.astro_chi2 = float(astro_chi2) # self.astro_noise = float(astro_noise) # self.astro_noise_sig = float(astro_noise_sig) # self.astro_match_obs = float(astro_match_obs) # self.astro_sigma5d = float(astro_sigma5d) # self.match_obs = float(match_obs) # self.g_mag = float(g_mag) # self.b_mag = float(b_mag) # self.r_mag = float(r_mag) # self.b_r = float(b_r) # self.b_g = float(b_g) # self.g_r = float(g_r) # self.radvel = float(radvel) # self.radvel_err = float(radvel_err) # self.variable = variable # self.teff = float(teff) # self.a_g = float(a_g) # self.e_bp_rp = float(e_bp_rp) # self.lum = float(lum) # self.member = 0 # self.binary = 0 # self.radDist = 0 # self.par_over_ra = float(par)/float(ra) # self.par_over_dec = float(par)/float(dec) # self.par_over_pmra = float(par)/float(pmra) # self.par_over_pmdec = float(par)/float(pmdec) # self.vosaPoints = [] # self.excess = 0 #Gaia DR3 implementation class starObj: def __init__(self,name,source_id,ra,ra_err,dec,dec_err,par,par_err,par_over_err,pmra,pmra_err,pmdec,pmdec_err, #Basic astrometrics ra_dec_corr,ra_par_corr,ra_pmra_corr,ra_pmdec_corr,dec_par_corr,dec_pmra_corr,dec_pmdec_corr,par_pmra_corr,par_pmdec_corr,pmra_pmdec_corr, #Correlations astro_n_obs,astro_n_good_obs,astro_n_bad_obs,astro_gof,astro_chi2,astro_noise,astro_noise_sig,astro_nu_eff, #Assorted astrometric properties pseudocolor,pseudocolor_err,ra_pseudocolor_corr,dec_pseudocolor_corr,par_pseudocolor_corr,pmra_pseudoclor_corr,pmdec_pseudocolor_corr, #Pseudocolor astro_sigma5d,duplicated_source, #More assorted properties g_flux,g_flux_err,g_mag, #Gaia_G b_flux,b_flux_err,b_mag, #Gaia_BP r_flux,r_flux_err,r_mag, #Gaia_RP b_over_r_excess,b_r,b_g,g_r, #Color indices and excess radvel,radvel_err,radvel_num_transits,radvel_teff,radvel_feh, #Template Teff and Fe/H used to calculate the radvel l,b,long,lat): #Galactic l and b, ecliptic long and lat import numpy as np #Declare instance variables self.name = name self.source_id = source_id self.ra = float(ra) self.ra_err = float(ra_err) self.dec = float(dec) self.dec_err = float(dec_err) self.par = float(par) self.par_err = float(par_err) self.par_over_err = float(par_over_err) self.pmra = float(pmra) self.pmra_err = float(pmra_err) self.pmdec = float(pmdec) self.pmdec_err = float(pmdec_err) self.ra_dec_corr = float(ra_dec_corr) self.ra_par_corr = float(ra_par_corr) self.ra_pmra_corr = float(ra_pmra_corr) self.ra_pmdec_corr = float(ra_pmdec_corr) self.dec_par_corr = float(dec_par_corr) self.dec_pmra_corr = float(dec_pmra_corr) self.dec_pmdec_corr = float(dec_pmdec_corr) self.par_pmra_corr = float(par_pmra_corr) self.par_pmdec_corr = float(par_pmdec_corr) self.pmra_pmdec_corr = float(pmra_pmdec_corr) self.astro_n_obs = float(astro_n_obs) self.astro_n_good_obs = float(astro_n_good_obs) self.astro_n_bad_obs = float(astro_n_bad_obs) self.astro_gof = float(astro_gof) self.astro_chi2 = float(astro_chi2) self.astro_noise = float(astro_noise) self.astro_noise_sig = float(astro_noise_sig) self.astro_nu_eff = float(astro_nu_eff) self.astro_sigma5d = float(astro_sigma5d) self.duplicated_source = bool(duplicated_source) self.g_flux = float(g_flux) self.g_flux_err = float(g_flux_err) self.g_mag = float(g_mag) self.b_flux = float(b_flux) self.b_flux_err = float(b_flux_err) self.b_mag = float(b_mag) self.r_flux = float(r_flux) self.r_flux_err = float(r_flux_err) self.r_mag = float(r_mag) self.b_over_r_excess = float(b_over_r_excess) self.b_r = float(b_r) self.b_g = float(b_g) self.g_r = float(g_r) self.radvel = float(radvel) self.radvel_err = float(radvel_err) self.radvel_num_transits=float(radvel_num_transits) self.radvel_teff = float(radvel_teff) self.radvel_feh = float(radvel_feh) self.l = float(l) self.b = float(b) self.long = float(long) self.lat = float(lat) self.member = 0 self.binary = 0 self.radDist = 0 self.par_over_ra = float(par)/float(ra) self.par_over_dec = float(par)/float(dec) self.par_over_pmra = float(par)/float(pmra) self.par_over_pmdec = float(par)/float(pmdec) self.normRA = self.ra*np.cos(self.dec*np.pi/180) self.vosaPoints = [] self.excess = 0 class isochroneObj: def __init__(self,age=404,feh=404,afe=404,y=404,basedir='isochrones/',subdir='processed',isodir=''): #Declare instance variables self.basedir = basedir self.subdir = subdir self.isodir = isodir self.starList = [] self.age = age self.feh = feh self.afe = afe self.y = y self.name = f"feh_{feh}_afe_{afe}_age_{age}_y_{y}" self.distance = 0 self.coeff = [] self.g = [] self.br = [] class fakeStarObj: def __init__(self,g_mag,b_mag,r_mag): #Declare instance variables self.g_mag = g_mag self.b_mag = b_mag self.r_mag = r_mag self.b_r = self.b_mag-self.r_mag self.b_g = self.b_mag-self.g_mag self.g_r = self.g_mag-self.r_mag self.score = 0 class mistStar: def __init__(self,properties): #Declare instance variables for prop,val in properties: if "inf" in str(val): val = 50 exec(f"self.{prop} = {val}") class condensedPoint: def __init__(self,b_r,g_mag,weight): self.b_r = b_r self.g_mag = g_mag self.weight = weight class vosaPoint: def __init__(self,filterID,wavelength,obs_flux,obs_error,flux,flux_error,excess): self.filterID = filterID self.wavelength = wavelength self.obs_flux = obs_flux self.obs_error = obs_error self.flux = flux self.flux_error = flux_error self.excess = excess class cataloguedCluster(): def __init__(self,name,clType,pmra_min,pmra_max,pmdec_min,pmdec_max,par_min,par_max,cltpx,cltpy,noise_cutoff): #Catalogued properties self.name = str(name) self.clType = str(clType) self.pmra_min = float(pmra_min) self.pmra_max = float(pmra_max) self.pmdec_min = float(pmdec_min) self.pmdec_max = float(pmdec_max) self.par_min = float(par_min) self.par_max = float(par_max) self.cltpx = float(cltpx) self.cltpy = float(cltpy) self.noise_cutoff = float(noise_cutoff) class Datum: from matplotlib import colors as mcolors colorin = mcolors.to_rgba("red") colorout = mcolors.to_rgba("blue") def __init__(self, x, y, include=False): self.x = x self.y = y if include: self.color = self.colorin else: self.color = self.colorout class LassoManager: def __init__(self, ax, data, cluster): from matplotlib.collections import RegularPolyCollection self.axes = ax self.canvas = ax.figure.canvas self.data = data self.cluster = cluster self.Nxy = len(data) facecolors = [d.color for d in data] self.xys = [(d.x, d.y) for d in data] self.collection = RegularPolyCollection( 6, sizes=(5,), facecolors=facecolors, offsets=self.xys, transOffset=ax.transData) ax.add_collection(self.collection) self.cid = self.canvas.mpl_connect('button_press_event', self.on_press) def callback(self, verts): from matplotlib import path global coords global clusters cluster = clusters[self.cluster.name] facecolors = self.collection.get_facecolors() p = path.Path(verts) ind = p.contains_points(self.xys) cluster.binaries = [] for i in range(len(self.xys)): if ind[i]: facecolors[i] = Datum.colorin star = cluster.filtered[[a.b_r for a in cluster.filtered].index(self.xys[i][0])] cluster.binaries.append(star) else: facecolors[i] = Datum.colorout self.canvas.draw_idle() self.canvas.widgetlock.release(self.lasso) del self.lasso def on_press(self, event): from matplotlib.widgets import Lasso if self.canvas.widgetlock.locked(): return if event.inaxes is None: return self.lasso = Lasso(event.inaxes, (event.xdata, event.ydata), self.callback) # acquire a lock on the widget drawing self.canvas.widgetlock(self.lasso) def clusterCatalogue(types='all'): import numpy as np import pandas as pd global data global catalogue global cataIn data = pd.read_csv("catalogue.csv",sep=',',dtype=str) data = data.to_numpy(dtype=str) cata = [] for row in data: cata.append(cataloguedCluster(*row)) if types == 'all': catalogue = cata cataIn = True return def readClusters(cList=["M67"],basedir="clusters/",smRad=0.35): #Imports import numpy as np import pandas as pd global clusterList global clusters global stars global clIn global catalogue try: if clIn and len(clusterList) > 0: for clname in cList: if clname in clusters: unloadClusters([clname]) except: clusterList=[] #Check the cluster catalogue to load the catalogued properties if not cataIn: clusterCatalogue() #Loop through clusters for clname in cList: #Create cluster objects cluster = clusterObj(name=clname,basedir=basedir) reference = None for cl in catalogue: if str(cl.name) == str(clname): reference = cl print(f"Catalogue match for {clname} found") break if reference == None: print(f"Catalogue match for {clname} was not found, please create one") continue #Filter all of the methods out of the properties list properties = [a for a in dir(reference) if not a.startswith('_')] print(properties) #exec(f"print(reference.{properties[1]})") #print(properties) #Now we have a list of all the attributes assigned to the catalogue (the self.variables) for p in properties: prop = getattr(reference,p) #print(prop) exec(f"cluster.{p} = prop") try: if prop <= -98: print(f"{clname} does not have a specified catalogue value for {p}") except: continue # if cluster.name == 'NGC752' or cluster.name == 'NGC188': # cluster.brightThreshold=18 # if "M67" in clname: # cluster.type = "open" # if "M35" in clname: # cluster.type = "open" # if "NGC188" in clname: # cluster.type = "open" # if "NGC752" in clname: # cluster.type = "open" # if "IC4651" in clname: # cluster.type = "open" # if "NGC2451" in clname: # cluster.type = "open" # if "AlphaPer" in clname: # cluster.type = "open" # if "M12" in clname: # cluster.type = "globular" # if "M3" in clname: # cluster.type = "globular" # if "M5" in clname: # cluster.type = "globular" # if "M15" in clname: # cluster.type = "globular" # if "M53" in clname: # cluster.type = "globular" # if "NGC6426" in clname: # cluster.type = "globular" # if "NGC6934" in clname: # cluster.type = "globular" """ #Generate wide-field star list starlist = np.genfromtxt(cluster.dataPath+"narrow.csv", delimiter=",", skip_header=1, usecols=(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17)) starlist = preFilter(starlist) for s in starlist: star = starObj(s[0],s[1],s[2],s[3],s[4],s[5],s[6],s[7],s[8],s[9],s[10],s[11],s[12],s[13],s[14],s[15],s[16],s[17]) cluster.unfilteredNarrow.append(star) """ #Generate narrow-field star list starlist = pd.read_csv(cluster.dataPath+"wide.csv",sep=',',dtype=str) stars = pd.read_csv(cluster.dataPath+"wide.csv",sep=',',dtype=str) starlist = starlist.to_numpy(dtype=str) #starlist = np.genfromtxt(cluster.dataPath+"wide.csv", delimiter=",", skip_header=1) print(f"{clname} initial length: {len(starlist)}") starlist = preFilter(starlist) print(f"{clname} post-prefiltered length: {len(starlist)}") ramean = np.mean([float(x) for x in starlist[:,1]]) decmean = np.mean([float(x) for x in starlist[:,3]]) for s in starlist: star = starObj(*s) cluster.unfilteredWide.append(star) if np.less_equal(star.g_mag,cluster.brightThreshold): cluster.unfilteredBright.append(star) # if np.less_equal(np.sqrt(((star.ra-ramean)*np.cos(np.pi/180*star.dec))**2+(star.dec-decmean)**2),smRad): # cluster.unfilteredNarrow.append(star) clusterList.append(cluster) calcStats(cluster,mode='narrow') if not 'YSO' in clname: rmOutliers() clIn = True toDict() def pad(string, pads): spl = string.split(',') return '\n'.join([','.join(spl[i:i+pads]) for i in range(0,len(spl),pads)]) def readIso(basedir='isochrones/',subdir='MIST_raw/'): #Important note: The ages are rounded to a few decimal places in the Gyr range #This has the effect of making it such that a few dozen isochrones in the kyr range #are overwritten because they all round to the same value. I found this to be an issue #worth overlooking given that a cluster of that age hasn't been identified yet #Imports import os import re global isochrone_headers global isoList global isoIn path = basedir + subdir isoList = [] for fn in os.listdir(path): #Read in file main = open(path+fn).read() main = main.split("\n") #Relevant variables from headers N_iso = int(main[7].split("=")[1]) index = 13 varList = re.sub("\s+", ",", main[5].strip()).split(",") afe = varList[4] feh = varList[3] y = varList[1] z = varList[2] v_vcrit = varList[5] #Column labels #Replace any number of spaces with a single comma, then replace a few problematic phrases and split the list by commas isochrone_headers = re.sub("\s+", ",", main[12].replace("2MASS","TwoMASS").replace("[Fe/H]","feh").strip()).split(",")[1:] for idx in range(0,N_iso): N_stars = int(re.sub("\s+", "," , main[index-3].split("=")[1]).split(",")[1]) #print(f"Iso = {idx} N_stars = {N_stars}") #Populate a single isochrone stars = [] for i in range(index,index+N_stars): #Send the header and values to the mistStar object #print(f"i = {i}") values = [float(a) for a in re.sub("\s+", "," , main[i].strip()).split(",")] properties = zip(isochrone_headers,values) stars.append(mistStar(properties)) #Create the isochrone from the list of stars age = round(10**values[1]/1e9,3) iso = isochroneObj(age,feh,afe,y) iso.starList = stars iso.br = [star.Gaia_BP_EDR3-star.Gaia_RP_EDR3 for star in stars] iso.g = [star.Gaia_G_EDR3 for star in stars] isoList.append(iso) index += N_stars + 5 isoIn = True toDict() def checkIsoDupes(): global isochrones global isoList names = [] for iso in isoList: if iso.name in names: print(iso.name) else: names.append(iso.name) def processIso(basedir='isochrones/',subdir='raw/'): #Imports import os import re path = basedir + subdir for fn in os.listdir(path): main = open(path+fn).read() part = main.split('\n\n\n') part[0] = part[0].split('#----------------------------------------------------')[3].split('\n',1)[1] for a in range(len(part)): temp = part[a].split('#AGE=')[1].split(' EEPS=')[0] age = temp.strip() out = part[a].split('\n',2)[2] out = re.sub("\s+", ",", out.strip()) out = pad(out,8) filename = f"{basedir}processed/"+fn.split('.')[0]+'/'+age+".csv" os.makedirs(os.path.dirname(filename), exist_ok=True) with open(filename,"w") as f: f.write(out) def readIsochrones(basedir='isochrones/',subdir='processed/'): #Imports import os import numpy as np global isoList global isoIn isoList=[] for folder in os.listdir(basedir+subdir): for fn in os.listdir(basedir+subdir+folder): #Get the age and metallicities of the isochrones ageStr = fn.split('.csv')[0] fehStr = folder.split('feh')[1].split('afe')[0] afeStr = folder.split('afe')[1].split('y')[0] if 'y' in folder: yStr = folder.split('y')[1] else: yStr = '0' feh = float(fehStr[1]+fehStr[2])/10 afe = float(afeStr[1])/10 age = float(ageStr) y = int(yStr) if fehStr[0] == 'm': feh = feh*-1 if afeStr[0] == 'm': afe = afe*-1 #Debug #print(f"folder:{folder} fn:{fn} fehStr:{fehStr} feh:{feh} afeStr:{afeStr} afe:{afe} ageStr:{ageStr} age:{age}") #Create isochone object iso = isochroneObj(age=age,feh=feh,afe=afe,y=y,basedir=basedir,subdir=subdir,isodir=folder+'/') isoArr = np.genfromtxt(basedir+subdir+folder+"/"+fn, delimiter=",") for s in isoArr: star = fakeStarObj(s[5],s[6],s[7]) iso.starList.append(star) iso.br.append(s[6]-s[7]) iso.g.append(s[5]) isoList.append(iso) isoIn = True toDict() def preFilter(starList): #Imports import numpy as np final = [] #Columns to be checked for NaN values. If an NaN is present in this column, the entry(star) is discarded from the "unfiltered" list #2-12 is the astrometry #42,45,48 are the g,bp,rp magnitudes #50-52 are the color indices cols = list(range(2,13))+[42]+[45]+[48]+list(range(50,53)) #Filters out NaN values except for the last two columns for n,s in enumerate(starList): dump = False for c in cols: if np.isnan(float(s[c])): dump = True if not dump: final.append(starList[n]) #Reshapes array final = np.array(final) return final def rmOutliers(): #Imports global clusterList import numpy as np for cluster in clusterList: if cluster.clType.lower() == "globular": scale = 4 else: scale = 1.5 #Variables pmthreshold = 5 pmpthreshold = 50 parthreshold = 5 posthreshold = 5 toRemove=[] #print(cluster.mean_pmra,cluster.mean_pmdec,cluster.stdev_pmra,cluster.stdev_pmdec) #print(len(cluster.unfilteredWide)) #Classifies outliers for star in cluster.unfilteredWide: if cluster.name == "NGC188": if star.ra > 100: toRemove.append(star) #print(np.sqrt(((star.pmra-cluster.mean_pmra)*np.cos(np.pi/180*star.pmdec))**2+(star.pmdec-cluster.mean_pmdec)**2),star.pmra,star.pmdec) if np.greater(np.sqrt(((star.pmra-cluster.mean_pmra)*np.cos(np.pi/180*star.pmdec))**2+(star.pmdec-cluster.mean_pmdec)**2),pmthreshold) or np.greater(np.sqrt(((star.ra-cluster.mean_ra)*np.cos(np.pi/180*star.dec))**2+(star.dec-cluster.mean_dec)**2),posthreshold) or np.greater(abs(star.par),parthreshold): #if np.greater(np.sqrt((star.pmra-cluster.mean_pmra)**2+(star.pmdec-cluster.mean_pmdec)**2),threshold): toRemove.append(star) #Removes the outliers from the array for rm in toRemove: cluster.unfilteredWide.remove(rm) try: cluster.unfilteredNarrow.remove(rm) except ValueError: pass #print(len(cluster.unfilteredWide)) def calcStats(cluster,mode='filtered'): #Imports import numpy as np #Reads in all the values for a cluster par=[] par_err=[] ra=[] dec=[] pmra=[] pmdec=[] gmag = [] br = [] # a_g=[] # e_bp_rp=[] loopList=[] checkLoaded([cluster]) if type(cluster) == str: cluster = clusters[cluster] if mode == 'bright': loopList = cluster.filteredBright elif mode == 'narrow': loopList = cluster.unfilteredNarrow elif mode == 'filtered': loopList = cluster.filtered for star in loopList: par.append(star.par) par_err.append(star.par_err) pmra.append(star.pmra) pmdec.append(star.pmdec) ra.append(star.ra) dec.append(star.dec) gmag.append(star.g_mag) br.append(star.b_r) # if not np.isnan(star.a_g) and not star.a_g == 0: # a_g.append(star.a_g) # if not np.isnan(star.e_bp_rp) and not star.e_bp_rp == 0: # e_bp_rp.append(star.e_bp_rp) #Calculate the statistics cluster.mean_par = np.mean(par[:]) cluster.mean_ra = np.mean(ra[:]) cluster.mean_dec = np.mean(dec[:]) cluster.stdev_ra = np.std(ra[:]) cluster.stdev_dec = np.std(dec[:]) cluster.stdev_par = np.std(par[:]) cluster.mean_pmra = np.mean(pmra[:]) cluster.stdev_pmra = np.std(pmra[:]) cluster.mean_pmdec = np.mean(pmdec[:]) cluster.stdev_pmdec = np.std(pmdec[:]) # cluster.mean_a_g = np.mean(a_g[:]) # cluster.stdev_a_g = np.std(a_g[:]) # cluster.mean_e_bp_rp = np.mean(e_bp_rp[:]) # cluster.stdev_e_bp_rp = np.std(e_bp_rp[:]) cluster.mean_par_over_ra = np.mean([x/y for x,y in zip(par,ra)]) cluster.stdev_par_over_ra = np.std([x/y for x,y in zip(par,ra)]) cluster.mean_par_err = np.mean(par_err[:]) cluster.dist_mod = 5*np.log10(1000/cluster.mean_par)-5 for star in loopList: star.radDist = np.sqrt((star.ra-cluster.mean_ra)**2+(star.dec-cluster.mean_dec)**2) star.normRadDist = np.sqrt((star.ra*np.cos(star.dec*np.pi/180)-cluster.mean_ra*np.cos(cluster.mean_dec*np.pi/180))**2+(star.dec-cluster.mean_dec)**2) def saveClusters(cList): #Imports import dill saveResults(cList) #Creates a pickle file with all of the saved instances for cl in cList: cluster = clusters[cl] #print(cluster.name,id(cluster)) with open(f"{cluster.dataPath}filtered.pk1", 'wb') as output: dill.dump(cluster, output) def saveIsochrones(): #Imports import dill global clusterList #Creates a pickle file with all of the saved instances for iso in isoList: with open(f"{iso.basedir}pickled/{iso.name}.pk1", 'wb') as output: dill.dump(iso, output) def loadClusters(clusterNames=["M67"],basedir='clusters/'): #Imports import dill global clusterList global clusters global clIn for clusterName in clusterNames: if clusterName in clusters: unloadClusters([clusterName]) #Reads in instances from the saved pickle file with open(f"{basedir}{clusterName}/data/filtered.pk1",'rb') as input: cluster = dill.load(input) clusterList.append(cluster) clIn = True toDict() def loadIsochrones(basedir='isochrones/'): #Imports import dill import os global isoList global isoIn isoList=[] for fn in os.listdir(basedir+"pickled/"): #Reads in instances from the saved pickle file with open(f"{basedir}pickled/{fn}",'rb') as input: iso = dill.load(input) isoList.append(iso) isoIn = True toDict() def unloadClusters(cList=['all']): #Imports global clusterList global clusters if 'all' in cList: cList = [cluster.name for cluster in clusterList] for cl in cList: cluster = clusters[cl] clusterList.remove(cluster) clusters.pop(cl) del cluster def dataProcess(cList,load=False,fit=True,unload=True,plotting=True,member=True,save=True,close=True): #This method is largely intended for re-processing a bulk batch of clusters that have already been processed before, #meaning they already have condensed point lists or you are already aware of their fitting quality #Imports import matplotlib.pyplot as plt global clusterList global clusters global closePlots if not isoIn: loadIsochrones() loadList = ["M15","M12","M39","M46","M67","NGC188","NGC2355","NGC2158","IC4651","NGC6791","NGC2360","NGC2204"] for cl in cList: if cl in loadList: condensing = "load" else: condensing = "auto" if load: loadClusters([cl]) else: readClusters([cl]) turboFilter([cl]) if close: plt.close('all') if fit: turboFit([cl],condensing=condensing) if plotting: plot([cl],['pos','pm','cmd','quiver','iso']) if close: plt.close('all') if member: proxyMatch([cl]) boundedStats([cl],saveCl=False,unloadCl=False) membership(cl,mode='filtered') membership(cl,mode='bounded',N=75) plt.close('all') if save: saveClusters([cl]) saveResults([cl]) if unload: unloadClusters([cl]) def turboFilter(cl=["all"]): #Imports global clusterList cList = checkLoaded(cl) for clus in cList: cluster = clusters[clus] cluster.filteredBright,cluster.brightmag = pmFilter(cluster.unfilteredBright,cluster.name) print(f"==========================={cluster.name}===========================") print(f"bright unf/pm fil: {len(cluster.unfilteredBright)} / {len(cluster.filteredBright)}") calcStats(cluster,mode='bright') distFilter(cluster) print(f"dist(all): {len(cluster.distFiltered)}") cluster.filtered,cluster.mag = pmFilter(cluster.distFiltered,cluster.name) #Manual filtering of extraneous points cluster.filtered,cluster.mag = manualFilter(cluster) print(f"pm(all): {len(cluster.filtered)}") customPlot('b_r','g_mag',cluster.name,'filtered',iso=True,square=False,color='astro_sigma5d') magnitude = cutNoise(cluster) print(f"noise cutoff: mag {magnitude} length {len(cluster.filtered)}") customPlot('b_r','g_mag',cluster.name,'filtered',iso=True,square=False,color='astro_sigma5d') """ for i in range(10): print(f"{cluster.filtered[i].b_r} {cluster.mag[i,0]}") """ calcStats(cluster,mode='filtered') setFlag() def manualFilter(cluster): #This exists to remove any points that may or may not be relevant to the cluster but are prohibiting the fit from happening if "M35" in cluster.name: filtered = [star for star in cluster.filtered if star.g_mag > 9 or star.b_r < 1] return filtered,magList(filtered) else: return cluster.filtered,cluster.mag def magList(filtered): import numpy as np mag = np.empty((0,2)) for star in filtered: mag = np.r_[mag,[[star.b_r,star.g_mag]]] def pmFilter(starList,name): #Imports import numpy as np filtered = [] mag = np.empty((0,2)) cluster = clusters[name] assert cluster.name == name #Apply an elliptical filter to the proper motion space pmra_width = (cluster.pmra_max-cluster.pmra_min)/2 pmdec_width = (cluster.pmdec_max-cluster.pmdec_min)/2 pmra_center = cluster.pmra_min+pmra_width pmdec_center = cluster.pmdec_min+pmdec_width print(pmra_center,pmdec_center) for star in starList: if (star.pmra-pmra_center)**2/pmra_width**2 + (star.pmdec-pmdec_center)**2/pmdec_width**2 <= 1: filtered.append(star) mag = np.r_[mag,[[star.b_r,star.g_mag]]] assert len(filtered) > 1 print(len(filtered)) return filtered,mag def distFilter(cluster): #Imports import numpy as np if cluster.par_min == 0 or cluster.par_max == 0: threshold = 1.5*cluster.mean_par print(f"{cluster.name} filtered using mean parallax") for star in cluster.unfilteredWide: if not np.greater(np.abs(star.par-cluster.mean_par),threshold*cluster.stdev_par): cluster.distFiltered.append(star) else: print(f"{cluster.name} filtered using min & max parallax values") for star in cluster.unfilteredWide: if star.par > cluster.par_min and star.par < cluster.par_max: cluster.distFiltered.append(star) def cutNoise(cluster): #Imports import numpy as np stars = sorted(cluster.filtered,key=lambda x: x.g_mag) new = [] newMag = np.empty((0,2)) if cluster.noise_cutoff <= -98: threshold = 1 print(f"{cluster.name} noise cutoff undefined, using default") else: threshold = cluster.noise_cutoff bad = 0 badCut = 5 for i,s in enumerate(stars): if s.astro_sigma5d > threshold: bad += 1 if bad >= badCut: break else: new.append(s) newMag = np.r_[newMag,[[s.b_r,s.g_mag]]] cluster.filtered = new cluster.mag = newMag return s.g_mag def turboFit(cl=["all"],condensing='auto',weighting='pos',tp="catalogue",minScore=0.001): #Typical use cases are auto, pos, catalogue --OR-- manual, equal, catalogue #Imports import time global clusterList cList = checkLoaded(cl) print("=========================Fitting=========================") t0 = time.time() status = condense(cList,condensing,weighting,tp,minScore) if status == "Suspended": return for cluster in cList: redFitting(cluster,minScore,weighting) t1 = time.time() print(f"Total {cluster.name} fit runtime: {t1-t0} seconds") def redFitting(cluster,minScore,weighting): #Imports import numpy as np import math from sys import stdout from time import sleep global clusterList if type(cluster) == str: cluster = clusters[cluster] cluster.iso = [] redMin = 0 redMax = 0.7 step = 0.05 redList = [round(x,2) for x in np.arange(redMin,redMax+step,step)] for reddening in redList: stdout.write(f"\rCurrent reddening value for {cluster.name}: {reddening:.2f} / ({redList[0]:.2f}->{redList[-1]:.2f})") shapeFit(cluster,reddening,minScore,weighting) stdout.flush() sleep(0.1) cluster.iso = sorted(cluster.iso,key=lambda x: x[1]) best = float(cluster.iso[0][2]) print(f"\nCoarse-step reddening for {cluster.name}: {best}") subMin = best - 0.05 subMax = best + 0.05 substep = 0.01 if subMin < 0: subMin = 0 subList = [round(x,2) for x in np.arange(subMin,subMax+substep,substep) if not round(x,2) in redList and round(x,2) > subMin and round(x,2) < subMax] for reddening in subList: stdout.write(f"\rCurrent fine-step reddening value for {cluster.name}: {reddening:.2f} / ({subList[0]:.2f}->{subList[-1]:.2f})") shapeFit(cluster,reddening,minScore,weighting) stdout.flush() sleep(0.1) cluster.iso = sorted(cluster.iso,key=lambda x: x[1]) cluster.reddening = float(cluster.iso[0][2]) cluster.fit_age = float(isochrones[cluster.iso[0][0]].age) cluster.fit_feh = float(isochrones[cluster.iso[0][0]].feh) cluster.fit_afe = float(isochrones[cluster.iso[0][0]].afe) cluster.fit_y = float(isochrones[cluster.iso[0][0]].y) #Unrelated properties but I needed somewhere to assign them setattr(cluster,'meanDist',1000/cluster.mean_par) meanL = np.mean([a.l*np.pi/180 for a in cluster.filtered]) galDist = 8000 #pc gd = cluster.meanDist**2 + galDist**2 - 2*cluster.meanDist*galDist*np.cos(meanL) setattr(cluster,'meanGalacticDist',gd**0.5) print(f"\nReddening for {cluster.name}: {best}") def shapeFit(cluster,reddening,minScore,weighting): #Imports import numpy as np import shapely.geometry as geom global isoList conversion = 2.1 isoFitList = np.empty((0,3)) for iso in isoList: isoLine = geom.LineString(tuple(zip([x+reddening for x in iso.br],[x+cluster.dist_mod+conversion*reddening for x in iso.g]))) dist = [] for star in cluster.condensed: starPt = geom.Point(star.b_r,star.g_mag) #print(starPt.distance(isoLine)) pointDist = np.abs(starPt.distance(isoLine))*star.weight if pointDist < minScore*star.weight: pointDist = minScore*star.weight dist.append(pointDist**2) isoScore = np.sum(dist[:]) #print(isoScore,dist) #print(list(geom.shape(isoLine).coords)) isoFitList = np.r_[isoFitList,[[iso.name,float(isoScore),float(reddening)]]] #compareInstances(iso,cluster.iso[-1][0]) #print(isoScore) cluster.iso.extend(isoFitList) #best = cluster.iso[1][0] #specificPlot(cluster.name,best.name,reddening) #print(f"\nFirst point of best fit: {best.br[0]+reddening},{best.g[0]+conversion*reddening+cluster.dist_mod}") def onclick(x,y,fig,ax,cluster,minScore,weighting,newList): def func(event): import matplotlib.pyplot as plt global coords ix, iy = event.xdata, event.ydata if str(event.button) == "MouseButton.RIGHT": for i,(cx,cy) in enumerate(coords): if abs(ix-cx) <= 0.075 and abs(iy-cy) <= 0.25: coords.pop(i) ax.clear() ax.scatter(x,y,s=0.5,color='dimgray') ax.invert_yaxis() ax.scatter([a[0] for a in coords],[a[1] for a in coords],c='red',s=10) plt.gcf().canvas.draw_idle() if str(event.button) == "MouseButton.LEFT": coords.append((ix, iy)) ax.scatter(ix,iy,c='red',s=10) plt.gcf().canvas.draw_idle() if str(event.button) == "MouseButton.MIDDLE": fig.canvas.mpl_disconnect(cid) plt.close(fig) updateCondensed(cluster,minScore,weighting,newList) if len(coords) >= 100: fig.canvas.mpl_disconnect(cid) plt.close(fig) updateCondensed(cluster,minScore,weighting,newList) return return func def updateCondensed(cluster,minScore,weighting,newList): #Imports import numpy as np global coords condensed = [] for point in coords: if cluster.clType.lower() == "globular" or weighting.lower() == "equal": weight = 1 else: #Automatic weighting scheme currently unsupported for manual condensed point definition, #but the framework is here to be able to insert it without having to worry about it being #passed around from function to function weight = 1 condensed.append(condensedPoint(point[0],point[1],weight)) if cluster.reddening == 0: cluster.condensed0 = condensed cluster.condensed = condensed np.savetxt(f"{cluster.dataPath}condensed.csv",coords,delimiter=',') redFitting(cluster,minScore,weighting) if len(newList) > 0: turboFit(newList,'manual',weighting,'catalogue',minScore) return def find_nearest(array, value): #Imports import numpy as np array = np.asarray(array) idx = (np.abs(array - value)).argmin() return array[idx] def testCluster(name='feh_0.00_afe_0.00_age_0.141_y_0.2703'): #Imports import numpy as np global clusterList global clIn iso = isochrones[name] test = clusterObj('test') filtered = [starObj('fake',0,1,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,a.Gaia_G_EDR3,0,0,0,0,0,0,0,a.Gaia_BP_EDR3-a.Gaia_RP_EDR3,0,0,0,0,0,0,0,0,0,0,0) for a in iso.starList] test.filtered = filtered mag = np.empty((0,2)) for star in test.filtered: mag = np.r_[mag,[[star.b_r,star.g_mag]]] test.mag = mag if not 'test' in clusters: clusterList.append(test) else: idx = clusterList.index(clusters['test']) clusterList.pop(idx) clusterList.append(test) clIn = True toDict() def condense(cList,condensing,weighting,tp,minScore=0.001): #Imports import numpy as np global isoList global mag for cluster in cList: if type(cluster) == str: cluster = clusters[cluster] cList[cList.index(cluster.name)] = cluster #Creates mag arrays to be used in place of the filtered star objects mag = cluster.mag[:,:] mag[mag[:,1].argsort()] gmag = list(mag[:,1]) gmin = mag[0,1] gmax = mag[-1,1] div = 50 seg = (gmax-gmin)/div minpoints = 1 #The array that will become the condensed points list condensed = np.empty((0,3)) turnPoints = [] if condensing.lower() == "load": global pts pts = np.genfromtxt(f"{cluster.dataPath}condensed.csv",delimiter=',') condensed = [] for point in pts: #Missing alternate weighting schemes, but can be imlemented *here* condensed.append(condensedPoint(point[0],point[1],1)) cluster.condensed = condensed cluster.condensed0 = condensed continue #Manual point definition if condensing.lower() == "manual": import matplotlib.pyplot as plt global cid global coords coords = [] if len(cList) == 1: newList = [] else: newList = cList[cList.index(cluster)+1:] x,y = mag[:,0],mag[:,1] fig = plt.figure() ax = fig.add_subplot(111) ax.scatter(x,y,s=0.25,color='dimgray') ax.invert_yaxis() hook = onclick(x,y,fig,ax,cluster,minScore,weighting,newList) cid = fig.canvas.mpl_connect('button_press_event', hook) return "Suspended" #Vertically stacked slices in brightness for i in range(div): sliced = mag[gmag.index(find_nearest(gmag,gmin+i*seg)):gmag.index(find_nearest(gmag,gmin+(i+1)*seg))] #print(np.array(sliced).shape) #Skip forseen problems with empty arrays if len(sliced) < minpoints: continue condensed = np.r_[condensed,[[np.median(sliced[:,0]),np.median(sliced[:,1]),0]]] condensed = condensed[::-1] #Uses defined turning points in the cluster catalogue if tp.lower() == "catalogue": if cluster.cltpx <= -98 and cluster.cltpy <= -98: tp == "auto" #If no turning point is found, or auto is specified, then this section of code #attempts to find the turning point through steep gradient changes in the main sequence if tp.lower() == "auto": #Criteria for the line that forms the basis of the gradient change method start = 4 end = 11 theta_crit = 5 #Creates a slope-intercept fit for the lower main sequence basex = [a[0] for a in condensed[start:end]] basey = [a[1] for a in condensed[start:end]] base = np.polyfit(basex,basey,1) #Travels up the main sequence for i,point in enumerate(condensed): if i == start: continue #Creates a fit line between the start point and the current point x = [point[0],condensed[start,0]] y = [point[1],condensed[start,1]] lin = np.polyfit(x,y,1) #Calculates an angle between the new line and the lower main sequence point[2] = 180/np.pi*np.arctan(abs( (base[0]-lin[0])/(1+base[0]*lin[0]) )) #If the angle between the two lines is large enough, the point is considered #to be a candidate turning point, and is appended to the list of candidates if point[2] > theta_crit and i > end: turnPoints.append(point) #Analysis plot showing the theta value for each condensed point import matplotlib.pyplot as plt plt.figure() plt.scatter(condensed[:,0],condensed[:,1],c=condensed[:,2]) plt.set_cmap('brg') plt.gca().invert_yaxis() clb = plt.colorbar() clb.ax.set_title("Theta") plt.savefig(f'condensed_{cluster.name}') #If no automatic turning point is found, ends the method here if len(turnPoints) == 0: print("No turning point identified for {cluster.name}") return else: #Identifies the proper turning point as a 5% color offset of the dimmest turning point candidate turnPoints = sorted(turnPoints,key=lambda x: x[1]) tp = turnPoints[-1] tp[0] = tp[0] - 0.05*np.abs(tp[0]) cluster.turnPoint = tp #Stores the condensed point list cl = [] for point in condensed: cl.append(condensedPoint(point[0],point[1],point[2])) cluster.condensedInit = cl # [ B-R , G , Theta ] print(f"{cluster.name} Turning Point: {cluster.turnPoint}") #Assuming the undefined catch for manual would be caught the first time around if tp.lower() == "catalogue": cluster.turnPoint = [cluster.cltpx,cluster.cltpy] if cluster.clType.lower() == "open": #Recalc with the turnPoint limit enforced - Ignore blue stragglers condensed = np.empty((0,3)) condensed_giant = np.empty((0,3)) yList = [] #Vertically stacked slices in brightness for i in range(div): rawSliced = mag[gmag.index(find_nearest(gmag,gmin+i*seg)):gmag.index(find_nearest(gmag,gmin+(i+1)*seg))] sliced = np.empty((0,2)) sliced_giant = np.empty((0,2)) for point in rawSliced: #print(point) if point[0] >= cluster.turnPoint[0]: sliced = np.r_[sliced,[[point[0],point[1]]]] else: sliced_giant = np.r_[sliced_giant,[[point[0],point[1]]]] #Skip forseen problems with empty arrays if len(sliced) > 0: x = np.median(sliced[:,0]) y = np.median(sliced[:,1]) yList.append(y) condensed = np.r_[condensed,[[x,y,1]]] if len(sliced_giant) > 3: xg = np.median(sliced_giant[:,0]) yg = np.median(sliced_giant[:,1]) condensed_giant = np.r_[condensed_giant,[[xg,yg,1]]] #New turning point found from the reduced data set newTP = find_nearest(yList,cluster.turnPoint[1]) index = 0 for i,point in enumerate(condensed): if newTP == point[1]: index = i #print(f"{point} found to be TP") break assert not index == 0 #Binary star list tpcut = index + 3 xset = condensed[tpcut:-1,0] yset = condensed[tpcut:-1,1] #print(cluster.name,yset) fit = np.polyfit(xset,yset,1) #Distance from the main sequence linear fit for star in cluster.filtered: x0 = star.b_r y0 = star.g_mag dist = abs( y0 - fit[0]*x0 - fit[1] ) / np.sqrt(fit[0]**2 + 1) star.distance_MS = dist if dist > 0.05 and y0 < fit[0]*x0+fit[1] and x0 > xset[0] and y0 > condensed[index,1]: cluster.binaries.append(star) star.binary = 1 else: star.binary = 0 #Fit weight parameters N = len(condensed) beta = -2 index = index - 7 for i,point in enumerate(condensed): #point[2] = 5/(1+np.abs(index-i)) if weighting.lower() == 'pos': point[2] = np.exp(beta*((i-index)/N)**2) # if cluster.type == "globular": # condensed = np.vstack((condensed,condensed_giant)) condensed = condensed[::-1] cl = [] coords = [] for point in condensed: cl.append(condensedPoint(point[0],point[1],point[2])) coords.append((point[0],point[1])) np.savetxt(f"{cluster.dataPath}condensed.csv",coords,delimiter=',') if cluster.reddening == 0: cluster.condensed0 = cl cluster.condensed = cl # def checkLoaded(cList): # needsLoading = [] # loaded = [] # for cl in cList: # if not cl in clusters: # needsLoading.append(cl) # else: # loaded.append(cl) # return loaded,needsLoading() def toDict(): #Imports global clusterList global clusters global isoList global isochrones global resultList global results global clIn global isoIn global resultsIn if clIn: clName = [] for cluster in clusterList: clName.append(cluster.name) clusters = dict(zip(clName,clusterList)) if isoIn: isoName = [] for iso in isoList: isoName.append(iso.name) isochrones = dict(zip(isoName,isoList)) if resultsIn: resName=[] for res in resultList: resName.append(res.name) results = dict(zip(resName,resultList)) def plot(cList=['all'],modes=['pos','pm','cmd','quiver','iso'],closePlots=False): #Imports import matplotlib.pyplot as plt from matplotlib.patches import Rectangle import numpy as np import os global clusterList cList = checkLoaded(cList) for cl in cList: cluster = clusters[cl] if not os.path.isdir(f"{cluster.imgPath}/png"): os.mkdir(f"{cluster.imgPath}/png") #Position plots if 'pos' in modes: unfra=[star.ra for star in cluster.unfilteredWide] unfdec=[star.dec for star in cluster.unfilteredWide] ra=[star.ra for star in cluster.filtered] dec=[star.dec for star in cluster.filtered] unfnormra=[star.ra*np.cos(star.dec*np.pi/180) for star in cluster.unfilteredWide] normra=[star.ra*np.cos(star.dec*np.pi/180) for star in cluster.filtered] #Unfiltered position plot plt.figure(f"{cluster.name}_ra_dec_unfiltered") plt.xlabel('RA (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Unfiltered") plt.scatter(unfra[:],unfdec[:],s=0.5,c='dimgray') plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_unfiltered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_unfiltered.png",dpi=500) #Filtered position plot plt.figure(f"{cluster.name}_ra_dec_filtered") plt.xlabel('RA (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Filtered") plt.scatter(ra[:],dec[:],s=0.5,c='midnightblue') plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_filtered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_filtered.png",dpi=500) #Position overlay plt.figure(f"{cluster.name}_ra_dec_overlay") plt.xlabel('RA (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Overlay") plt.scatter(unfra[:],unfdec[:],s=0.5,c='lightgray') plt.scatter(ra[:],dec[:],s=1,c='midnightblue') plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_overlay.png",dpi=500) #Normalized #NormRA = RA*cos(DEC) #Unfiltered normalized position plot plt.figure(f"{cluster.name}_ra_dec_unfiltered_normalized") plt.xlabel('RA*cos(DEC) (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Unfiltered Normalized") plt.scatter(unfnormra[:],unfdec[:],s=0.5,c='dimgray') #plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_unfiltered_normalized.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_unfiltered_normalized.png",dpi=500) #Filtered normalized position plot plt.figure(f"{cluster.name}_ra_dec_filtered_normalized") plt.xlabel('RA*cos(DEC) (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Filtered Normalized") plt.scatter(normra[:],dec[:],s=0.5,c='midnightblue') #plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_filtered_normalized.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_filtered_normalized.png",dpi=500) #Position overlay normalized plt.figure(f"{cluster.name}_ra_dec_overlay_normalized") plt.xlabel('RA*cos(DEC) (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Overlay Normalized") plt.scatter(unfnormra[:],unfdec[:],s=0.5,c='lightgray') plt.scatter(normra[:],dec[:],s=1,c='midnightblue') #plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_overlay_normalized.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_overlay_normalized.png",dpi=500) #Proper motion plots if 'pm' in modes: unfpmra=[star.pmra for star in cluster.unfilteredWide] unfpmdec=[star.pmdec for star in cluster.unfilteredWide] pmra=[star.pmra for star in cluster.filtered] pmdec=[star.pmdec for star in cluster.filtered] unfpara=[star.par for star in cluster.unfilteredWide] para=[star.par for star in cluster.filtered] x0 = cluster.pmra_min x1 = cluster.pmra_max y0 = cluster.pmdec_min y1 = cluster.pmdec_max width = x1-x0 scale = 5 subscale = 2 xmin = x0-scale*width xmax = x1+scale*width ymin = y0-scale*width ymax = y1+scale*width sxmin = x0-subscale*width sxmax = x1+subscale*width symin = y0-subscale*width symax = y1+subscale*width #Unfiltered proper motion plot plt.figure(f"{cluster.name}_pm_unfiltered") plt.xlabel(r'PMRA ($mas*yr^{-1}$)') plt.ylabel(r'PMDEC ($mas*yr^{-1}$)') plt.title(f"{cluster.name} Unfiltered") plt.scatter(unfpmra[:],unfpmdec[:],s=0.5,c='dimgray') plt.xlim([xmin,xmax]) plt.ylim([ymin,ymax]) # plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_unfiltered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_unfiltered.png",dpi=500) plt.xlim([sxmin,sxmax]) plt.ylim([symin,symax]) # plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_unfiltered_closeup.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_unfiltered_closeup.png",dpi=500) #Filtered proper motion plot plt.figure(f"{cluster.name}_pm_filtered") plt.xlabel(r'PMRA ($mas*yr^{-1}$)') plt.ylabel(r'PMDEC ($mas*yr^{-1}$)') plt.title(f"{cluster.name} Filtered") plt.scatter(pmra[:],pmdec[:],s=0.5,c='midnightblue') # plt.xlim([xmin,xmax]) # plt.ylim([ymin,ymax]) plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_filtered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_filtered.png",dpi=500) #Proper motion overlay plt.figure(f"{cluster.name}_pm_overlay") plt.xlabel(r'PMRA ($mas*yr^{-1}$)') plt.ylabel(r'PMDEC ($mas*yr^{-1}$)') plt.title(f"{cluster.name} Overlay") plt.scatter(unfpmra[:],unfpmdec[:],s=0.5,c='lightgray') plt.scatter(pmra[:],pmdec[:],s=1,c='midnightblue') plt.xlim([xmin,xmax]) plt.ylim([ymin,ymax]) # plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_overlay.png",dpi=500) plt.xlim([sxmin,sxmax]) plt.ylim([symin,symax]) # plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_overlay_closeup.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_overlay_closeup.png",dpi=500) #Unfiltered PM/Parallax plt.figure(f"{cluster.name}_pm_over_parallax_unfiltered") plt.xlabel('PMRA / Parallax') plt.ylabel('PMDEC / Parallax') plt.title(f"{cluster.name} Unfiltered") plt.scatter([a/b for a,b in zip(unfpmra,unfpara)],[a/b for a,b in zip(unfpmdec,unfpara)],s=0.5,c='dimgray') plt.xlim([xmin,xmax]) plt.ylim([ymin,ymax]) # plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_over_parallax_unfiltered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_over_parallax_unfiltered.png",dpi=500) #Unfiltered PM*Parallax plt.figure(f"{cluster.name}_pm_times_parallax_unfiltered") plt.xlabel('PMRA * Parallax') plt.ylabel('PMDEC * Parallax') plt.title(f"{cluster.name} Unfiltered") plt.scatter([a*b for a,b in zip(unfpmra,unfpara)],[a*b for a,b in zip(unfpmdec,unfpara)],s=0.5,c='dimgray') plt.xlim([xmin,xmax]) plt.ylim([ymin,ymax]) # plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_times_parallax_unfiltered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_times_parallax_unfiltered.png",dpi=500) #CMD plots if 'cmd' in modes: unfgmag=[star.g_mag for star in cluster.unfilteredWide] unf_b_r=[star.b_r for star in cluster.unfilteredWide] gmag=[star.g_mag for star in cluster.filtered] b_r=[star.b_r for star in cluster.filtered] bright_b_r = [x.b_r for x in cluster.filteredBright] bright_gmag = [x.g_mag for x in cluster.filteredBright] par_b_r = [x.b_r for x in cluster.distFiltered] par_gmag = [x.g_mag for x in cluster.distFiltered] #Reddening Correction plt.figure(f"{cluster.name}_reddening_CMD") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('G Mag') plt.title(f"{cluster.name} Reddening = {cluster.reddening:.2f}") plt.scatter(b_r[:],gmag[:],s=0.5,c='dimgray',label='Observed') plt.arrow(b_r[int(len(b_r)/2)]-cluster.reddening,gmag[int(len(gmag)/2)]-2.1*cluster.reddening,cluster.reddening,2.1*cluster.reddening,color='red') plt.scatter([s-cluster.reddening for s in b_r[:]],[s-2.1*cluster.reddening for s in gmag[:]],s=1,c='midnightblue',label='Corrected') plt.legend() plt.savefig(f"{cluster.imgPath}{cluster.name}_reddening_CMD.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_reddening_CMD.png",dpi=500) #Unfiltered CMD plot plt.figure(f"{cluster.name}_CMD_unfiltered") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Unfiltered") plt.scatter(unf_b_r[:],unfgmag[:],s=0.5,c='dimgray') plt.savefig(f"{cluster.imgPath}{cluster.name}_CMD_unfiltered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_CMD_unfiltered.png",dpi=500) #Filtered CMD plot plt.figure(f"{cluster.name}_CMD_filtered") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Parallax & Proper Motion Filtered") plt.scatter(b_r[:],gmag[:],s=0.5,c='midnightblue') plt.savefig(f"{cluster.imgPath}{cluster.name}_CMD_filtered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_CMD_filtered.png",dpi=500) #CMD overlay plt.figure(f"{cluster.name}_CMD_overlay") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Overlay") plt.scatter(unf_b_r[:],unfgmag[:],s=0.5,c='dimgray') plt.scatter(b_r[:],gmag[:],s=1,c='midnightblue') plt.savefig(f"{cluster.imgPath}{cluster.name}_CMD_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_CMD_overlay.png",dpi=500) #Condensed CMD overlay plt.figure(f"{cluster.name}_condensed_CMD_overlay") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Condensed Overlay") plt.scatter([s - cluster.reddening for s in b_r],[s - 2.1*cluster.reddening for s in gmag],s=0.5,c='dimgray',label='Data') plt.scatter([s.b_r - cluster.reddening for s in cluster.condensed],[s.g_mag - 2.1*cluster.reddening for s in cluster.condensed],s=5,c='red',label='Proxy Points') try: plt.axvline(x=cluster.turnPoint[0] - cluster.reddening,linestyle='--',color='midnightblue',linewidth=0.8,label='95% of Turning Point') except: print(f"No turning point found for {cluster.name}") plt.legend() plt.savefig(f"{cluster.imgPath}{cluster.name}_condensed_CMD_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_condensed_CMD_overlay.png",dpi=500) #Weighted CMD overlay plt.figure(f"{cluster.name}_weighted_CMD_overlay") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Weighted Overlay") plt.scatter([s - cluster.reddening for s in b_r],[s - 2.1*cluster.reddening for s in gmag],s=0.5,c='dimgray',label='Data') plt.scatter([s.b_r - cluster.reddening for s in cluster.condensed],[s.g_mag - 2.1*cluster.reddening for s in cluster.condensed],s=5,c=[s.weight for s in cluster.condensed],label='Proxy Points') try: plt.axvline(x=cluster.turnPoint[0] - cluster.reddening,linestyle='--',color='midnightblue',linewidth=0.8,label='95% of Turning Point') except: print(f"No turning point found for {cluster.name}") plt.set_cmap('brg') clb = plt.colorbar() clb.ax.set_title("Weight") plt.legend() plt.savefig(f"{cluster.imgPath}{cluster.name}_weighted_CMD_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_weighted_CMD_overlay.png",dpi=500) #Initial Condensed CMD overlay plt.figure(f"{cluster.name}_initial_condensed_CMD_overlay") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Initial Condensed Overlay") plt.scatter(b_r,gmag,s=0.5,c='dimgray',label='Data') plt.scatter([s.b_r for s in cluster.condensedInit],[s.g_mag for s in cluster.condensedInit],s=5,c='red',label='Proxy Points') try: plt.axvline(x=cluster.turnPoint[0] - cluster.reddening,linestyle='--',color='midnightblue',linewidth=0.8,label='95% of Turning Point') except: print(f"No turning point found for {cluster.name}") plt.legend() plt.savefig(f"{cluster.imgPath}{cluster.name}_initial_condensed_CMD_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_initial_condensed_CMD_overlay.png",dpi=500) #Brightness-PM Filtered CMD plot plt.figure(f"{cluster.name}_CMD_bright_filtered") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Bright-Only Proper Motion Filtered") plt.scatter(bright_b_r[:],bright_gmag[:],s=0.5,c='midnightblue') plt.savefig(f"{cluster.imgPath}{cluster.name}_CMD_bright_filtered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_CMD_bright_filtered.png",dpi=500) #Parallax Filtered CMD plot plt.figure(f"{cluster.name}_CMD_parallax_filtered") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Parallax Filtered") plt.scatter(par_b_r[:],par_gmag[:],s=0.5,c='midnightblue') plt.savefig(f"{cluster.imgPath}{cluster.name}_CMD_parallax_filtered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_CMD_parallax_filtered.png",dpi=500) if 'quiver' in modes: unfra=[star.ra for star in cluster.unfilteredWide] unfdec=[star.dec for star in cluster.unfilteredWide] unfpmra=[star.pmra for star in cluster.unfilteredWide] unfpmdec=[star.pmdec for star in cluster.unfilteredWide] x0 = min([s.ra for s in cluster.filtered]) x1 = max([s.ra for s in cluster.filtered]) y0 = min([s.dec for s in cluster.filtered]) y1 = max([s.dec for s in cluster.filtered]) width = x1-x0 scale = 0.25 xmin = x0+scale*width xmax = x1-scale*width ymin = y0+scale*width ymax = y1-scale*width #Unfiltered position quiver plot plt.figure(f"{cluster.name}_ra_dec_unfiltered_quiver") plt.xlabel('RA (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Unfiltered") ax = plt.gca() ax.quiver(unfra[:],unfdec[:],unfpmra[:],unfpmdec[:],color='midnightblue',width=0.003,scale=400,scale_units='width') plt.axis("square") plt.gcf().set_size_inches(10,10) plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_unfiltered_pm_quiver.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_unfiltered_pm_quiver.png",dpi=500) plt.xlim([xmin,xmax]) plt.ylim([ymin,ymax]) plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_unfiltered_pm_quiver_zoom.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_unfiltered_pm_quiver_zoom.png",dpi=500) #Isochrone plots if 'iso' in modes: gmag=[star.g_mag for star in cluster.filtered] b_r=[star.b_r for star in cluster.filtered] isochrone = isochrones[cluster.iso[0][0]] #Isochrone best fit plt.figure(f"{cluster.name}_Iso_best") plt.gca().invert_yaxis() plt.xlabel('Dereddened BP-RP') plt.ylabel('Corrected Absolute G Mag') plt.title(f"{cluster.name} Isochrone Best Fit") plt.scatter([s - cluster.reddening for s in b_r],[s - 2.1*cluster.reddening-cluster.dist_mod for s in gmag],s=0.5,c='dimgray',label='Cluster') isoLabels = isochrone.name.split('_') isoLabel = r"$[\frac{Fe}{H}]$" + "=" + isoLabels[1] + "\n" \ + r"$[\frac{\alpha}{Fe}]$" + "=" + isoLabels[3] + "\n" \ + r"$[Y]$" + "=" + isoLabels[7] + "\n" \ + "Age" + "=" + isoLabels[5] + " Gyr" plt.plot(isochrone.br,isochrone.g,c='midnightblue',label=isoLabel) plt.scatter([s.b_r - cluster.reddening for s in cluster.condensed],[s.g_mag - 2.1*cluster.reddening-cluster.dist_mod for s in cluster.condensed],s=5,c='red',label='Cluster Proxy') extra = Rectangle((0, 0), 1, 1, fc="w", fill=False, edgecolor='none', linewidth=0) h,l = plt.gca().get_legend_handles_labels() h.insert(0,extra) l.insert(0,f"Reddening: {cluster.reddening}") plt.legend(h,l) plt.savefig(f"{cluster.imgPath}{cluster.name}_CMD_Iso_BestFit.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_CMD_Iso_BestFit.png",dpi=500) #Membership plots if 'membership' in modes: proxyMatch([cl]) boundedStats([cl],saveCl=False,unloadCl=False) membership(cl,mode='filtered') membership(cl,mode='bounded',N=50) #3D Position plots if '3D' in modes: A = [a.ra * np.pi/180 for a in cluster.filtered] B = [abs(b.dec) * np.pi/180 for b in cluster.filtered] C = [1/(1000*c.par) for c in cluster.filtered] x = [c*np.cos(b)*np.cos(a) for a,b,c in zip(A,B,C)] y = [c*np.cos(b)*np.sin(a) for a,b,c in zip(A,B,C)] z = [c*np.sin(b) for b,c in zip(B,C)] r = [np.sqrt(a**2+b**2) for a,b in zip(x,y)] theta = [np.arctan(b/a) for a,b in zip(x,y)] plt.figure(f"{cluster.name}_3D_Position") ax = plt.axes(projection='3d') ax.scatter3D(x,y,z) ax.scatter(0,0,0,color='red') scaling = np.array([getattr(ax, 'get_{}lim'.format(dim))() for dim in 'xyz']) ax.auto_scale_xyz(*[[np.min(scaling), np.max(scaling)]]*3) if closePlots: plt.close('all') # def Plot3D(cList): # #Imports # import matplotlib.pyplot as plt # import numpy as np # global clusterList # needsLoading=[] # plt.figure(f"3D_Position_Ensemble") # ax = plt.axes(projection='3d') # for cl in cList: # if not cl in clusters: # needsLoading.append(cl) # if not len(needsLoading) == 0: # loadClusters(needsLoading) # for cl in cList: # cluster = clusters[cl] # A = [a.ra * np.pi/180 for a in cluster.filtered] # B = [abs(b.dec) * np.pi/180 for b in cluster.filtered] # C = [1/(0.001*c.par) for c in cluster.filtered] # #Flatten radially # C = [np.mean(C)]*len(C) # x = [c*np.cos(b)*np.cos(a) for a,b,c in zip(A,B,C)] # y = [c*np.cos(b)*np.sin(a) for a,b,c in zip(A,B,C)] # z = [c*np.sin(b) for b,c in zip(B,C)] # #Force Cluster to origin # # x = [a-np.mean(x) for a in x] # # y = [a-np.mean(y) for a in y] # # z = [a-np.mean(z) for a in z] # ax.scatter3D(x,y,z,label=cluster.name) # scaling = np.array([getattr(ax, 'get_{}lim'.format(dim))() for dim in 'xyz']) # ax.auto_scale_xyz(*[[np.min(scaling), np.max(scaling)]]*3) # #ax.scatter(0,0,0,color='black') # plt.legend() def yso_lookup(): #Imports from astroquery.simbad import Simbad import numpy as np import os import re global names global sect global results global ra global dec main = open("Excess Examples/YSO_object_list.dat").read() main = main.split("\n")[:-1] #Get the names of all of the objects identified names = [] ra = [] dec = [] validNames = [] for row in main: sect = re.split('\s+',row) if sect[0] == '': sect = sect[1:] if sect[2] == 'none': continue name = sect[2] blacklist = ['A','Ab','AB','ABC','B','AaB'] for entry in sect[3:]: if '.' in entry or entry in blacklist: break name = name + " " + entry names.append(name) #Perform a SIMBAD query for the identified objects results = [] for name in names: result = Simbad.query_object(name) if not type(result) == type(None): results.append(result) validNames.append(name.replace(' ','')) ra1 = str(result.columns['RA']).split('\n')[-1] ra1 = re.split('\s+',ra1) if '' in ra1: ra.append('---') else: ra.append(str(round(float(ra1[0])*15+float(ra1[1])/4+float(ra1[2])/240,5))) dec1 = str(result.columns['DEC']).split('\n')[-1] dec1 = re.split('\s+',dec1) if '' in dec1: dec.append('---') else: dec.append(str(round(float(dec1[0])+float(dec1[1])/60+float(dec1[2])/3600,5))) #Create a text file in the VOSA readable format VOSAdata = [] gaiadata = [] for i in range(len(validNames)): line1 = f"{validNames[i]} {ra[i]} {dec[i]} --- --- --- --- --- --- ---" line2 = f"{ra[i]} {dec[i]}" VOSAdata.append(line1) if '-' in line2: continue gaiadata.append(line2) np.savetxt("Excess Examples/yso_vosa_output.txt",VOSAdata,fmt="%s") np.savetxt("Excess Examples/yso_gaia_output.txt",gaiadata,fmt="%s") def exportVOSA(cl): #Imports import numpy as np if not cl in clusters: loadClusters([cl]) cluster = clusters[cl] #objname RA DEC DIS Av Filter Flux Error PntOpts ObjOpts data = [] for star in cluster.filtered: name = star.name.replace(" ","") line = f"{name} {star.ra} {star.dec} {1000/star.par} --- --- --- --- --- ---" data.append(line) np.savetxt(f"{cluster.dataPath}{cluster.name}_VOSA.txt",data,fmt="%s") def readSED(cList=['all'],printMissing=False): #imports import numpy as np import re import os cList = checkLoaded(cList) for cl in cList: cluster = clusters[cl] objPath = cluster.dataPath + "vosa_results/objects/" names = [] for star in cluster.filtered: flat = star.name.replace(" ","").replace("DR2","").replace("EDR3","").replace("DR3","") names.append(flat) star.flatName = flat cluster.stars = dict(zip(names,cluster.filtered)) idx = 0 newStars = dict() #Each star in a cluster has its own folder, and each folder contains several data sets for folder in os.listdir(objPath): fileName = folder.replace("DR2","").replace("EDR3","").replace("DR3","") #Weed out VOSA stars not in current filtered members list if not fileName in cluster.stars: if printMissing: print(f"{fileName} is missing from filtered list, skipping it...") continue main = open(objPath+folder+"/sed/"+folder+".sed.dat").read() main = main.split("\n") data = main[10:-1] #Create a list of measurement object pointers to attach to the stars later measurements = [] #Convert every line of the data set into a vosaPoint object for row in data: sect = re.split('\s+',row)[1:-1] measurements.append(vosaPoint(str(sect[0]),float(sect[1]),float(sect[2]),float(sect[3]),float(sect[4]),float(sect[5]),float(sect[6]))) cluster.stars[fileName].vosaPoints = measurements #Weed out cluster.stars members who do not have a vosa table newStars[fileName] = cluster.stars[fileName] idx += 1 cluster.stars = newStars def checkBinary(cl): import numpy as np import matplotlib.pyplot as plt checkLoaded([cl]) cluster = clusters[cl] global lman data = [Datum(star.b_r,star.g_mag) for star in cluster.filtered] # ax = plt.axes(xlim=(cluster.min_b_r-0.25,cluster.max_b_r+0.25), ylim=(cluster.min_g_mag-1,cluster.max_g_mag+1),autoscale_on=False) ax = plt.axes(xlim=(0, 2.5), ylim=(8, 20), autoscale_on=False) ax.invert_yaxis() ax.set_title('Lasso points using left mouse button') lman = LassoManager(ax, data,cluster) plt.show() def vosaBinaries(cl): #Imports import numpy as np import matplotlib.pyplot as plt import os checkLoaded([cl]) cluster = clusters[cl] if not os.path.isdir(f"{cluster.imgPath}vosaBinaries/"): os.mkdir(f"{cluster.imgPath}vosaBinaries/") for star in cluster.stars.values(): if not star.binary == 1: return def excessIR(cl,plot=True): #Imports import numpy as np import matplotlib.pyplot as plt import os checkLoaded([cl]) cluster = clusters[cl] if not os.path.isdir(f"{cluster.imgPath}excessIR/"): os.mkdir(f"{cluster.imgPath}excessIR/") for star in cluster.stars.values(): excess = False for vp in star.vosaPoints: if vp.excess > 0: excess = True if excess: #print(f"{star.name} has {len(star.vosaPoints)} VOSA points") star.hasExcess = 1 if plot: plt.figure(f'{cluster.name} - {star.name}') plt.title(f'{cluster.name} : {star.name}') ax = plt.gca() ax.set_yscale('log') ax.set_xscale('log') plt.ylabel(r'Flux ($ergs^{-1}cm^{-2}\AA^{-1}$)') plt.xlabel(r'Wavelength ($\AA$)') plt.scatter([a.wavelength for a in star.vosaPoints],[a.flux for a in star.vosaPoints]) plt.savefig(f"{cluster.imgPath}excessIR/{star.name}.pdf") plt.savefig(f"{cluster.imgPath}excessIR/{star.name}.png",dpi=500) def proxyMatch(cList,plot=False): #Imports import matplotlib.pyplot as plt import numpy as np checkLoaded(cList) for cl in cList: cluster = clusters[cl] iso = isochrones[cluster.iso[0][0]] isoPoints = [] for pt in iso.starList: isoPoints.append(pt) # if pt.Gaia_G_EDR3+cluster.dist_mod > cluster.turnPoint[1]: # isoPoints.append(pt) for star in cluster.filtered: minDist = 0.2 smallestDist = 10 vertCutoff = 1 minPoint = None for point in isoPoints: dist = abs(point.Gaia_BP_EDR3-point.Gaia_RP_EDR3-star.b_r+cluster.reddening) if dist < minDist: if abs(point.Gaia_G_EDR3+cluster.dist_mod - star.g_mag + 2.1*cluster.reddening) < vertCutoff: minDist = dist minPoint = point elif dist < smallestDist: smallestDist = dist try: assert minDist < 0.2 except: print(f"[{cluster.name}] Star too distant from isochrone to make a good proxy: BP-RP: {star.b_r} | G: {star.g_mag} | Dist: {smallestDist}") star.proxyMass = 0 star.proxyLogTemp = 0 star.proxyFeH = 0 star.proxyLogAge = 0 star.proxy = None continue #print(minDist) star.proxyMass = minPoint.star_mass star.proxyLogTemp = minPoint.log_Teff star.proxyFeH = minPoint.feh star.proxyLogAge = minPoint.log10_isochrone_age_yr star.proxy = minPoint cluster.massLoaded = True cluster.meanProxyMass = np.mean([a.proxyMass for a in cluster.filtered]) cluster.totalProxyMass = np.sum([a.proxyMass for a in cluster.filtered]) cluster.min_g_mag = min([a.g_mag for a in cluster.filtered]) cluster.max_g_mag = max([a.g_mag for a in cluster.filtered]) cluster.min_b_r = min([a.b_r for a in cluster.filtered]) cluster.max_b_r = max([a.b_r for a in cluster.filtered]) # if plot: # plt.figure(f"{cluster.name}_proxy_fit") def variableHistogram(cl,var): #Imports import numpy as np import matplotlib.pyplot as plt checkLoaded([cl]) cluster = clusters[cl] plt.figure() plt.title(f"{cluster.name} Histogram of {var}") plt.xlabel(f"{var}") plt.ylabel("Count") plt.hist([eval(f"a.{var}") for a in cluster.filtered],bins='auto') def varHist2D(cl,var1,var2,color='default',listType='filtered'): #Imports import numpy as np import matplotlib.pyplot as plt checkLoaded([cl]) #Check allowed entries allowedTypes = ['filtered','unfilteredWide','unfilteredBright,filteredBright,binaries'] if not listType in allowedTypes: print(f"{listType} is not a valid list type, defaulting to filtered") listType = "filtered" cluster = clusters[cl] plt.figure(figsize=(8,8)) #Axis size and spacing left, width = 0.1, 0.65 bottom, height = 0.1, 0.65 spacing = 0.005 rect_scatter = [left, bottom, width, height] rect_histx = [left, bottom + height + spacing, width, 0.2] rect_histy = [left + width + spacing, bottom, 0.2, height] ax_scatter = plt.axes(rect_scatter) ax_scatter.tick_params(direction='in', top=True, right=True) ax_histx = plt.axes(rect_histx) ax_histx.tick_params(direction='in', labelbottom=False) ax_histy = plt.axes(rect_histy) ax_histy.tick_params(direction='in', labelleft=False) x = [eval(f"a.{var1}") for a in eval(f"cluster.{listType}")] y = [eval(f"a.{var2}") for a in eval(f"cluster.{listType}")] if color == 'default': ax_scatter.scatter(x, y, s=5) else: colorMap = plt.get_cmap('coolwarm')#.reversed() ax_scatter.scatter(x, y, s=5, c=[eval(f"a.{color}") for a in eval(f"cluster.{listType}")], cmap = colorMap) # clb = plt.colorbar(ax_scatter) # clb.ax.set_title(f"{color}") ax_histx.hist(x,bins='auto') ax_histy.hist(y,bins='auto',orientation='horizontal') ax_histx.set_title(f"Histogram of {listType} {cluster.name} in {var1} and {var2}") ax_scatter.set_xlabel(f"{var1}") ax_scatter.set_ylabel(f"{var2}") def Plot3D(cList=['all'],showEarth=True,flatten=True): #Imports import plotly.express as px import plotly.io as pio import numpy as np global clusterList pio.renderers.default='browser' fig = px.scatter_3d() if showEarth: fig.add_scatter3d(x=[0],y=[0],z=[0],marker=dict(color='lightblue'),name="Earth") cList = checkLoaded(cList) big = [] for cl in cList: cluster = clusters[cl] A = [a.ra * np.pi/180 for a in cluster.filtered] B = [abs(b.dec) * np.pi/180 for b in cluster.filtered] C = [1/(0.001*c.par) for c in cluster.filtered] #Flatten radially if flatten: C = [np.mean(C)]*len(C) x = [c*np.cos(b)*np.cos(a) for a,b,c in zip(A,B,C)] y = [c*np.cos(b)*np.sin(a) for a,b,c in zip(A,B,C)] z = [c*np.sin(b) for b,c in zip(B,C)] #Force Cluster to origin # x = [a-np.mean(x) for a in x] # y = [a-np.mean(y) for a in y] # z = [a-np.mean(z) for a in z] fig.add_scatter3d(x=x,y=y,z=z,name=cl,mode="markers",marker=dict(size=2)) big.append(np.amax(x)) big.append(np.amax(y)) big.append(np.amax(z)) #fig.layout.scene = dict(aspectmode="manual",aspectratio=dict(x=1,y=1,z=1)) #fig.update_layout(scene=dict(aspectmode="cube",xaxis=dict(showbackground=False,range=[-1*np.amax(big),np.amax(big)]),yaxis=dict(showbackground=False,range=[-1*np.amax(big),np.amax(big)]),zaxis=dict(showbackground=False,range=[-1*np.amax(big),np.amax(big)]))) fig.update_layout(scene=dict(aspectmode="cube",xaxis=dict(showbackground=False),yaxis=dict(showbackground=False),zaxis=dict(showbackground=False,visible=False))) fig.show() def specificPlot(cl,iso,reddening,score): #Imports import matplotlib.pyplot as plt from matplotlib.patches import Rectangle import os checkLoaded([cl]) cluster = clusters[f"{cl}"] isochrone = isochrones[f"{iso}"] #These are displayed on the plot # score = 0 reddening = float(reddening) #Directory for saving plot outputs if not os.path.isdir("SpecificPlots/pdf/"): os.makedirs("SpecificPlots/pdf/") if not os.path.isdir("SpecificPlots/png/"): os.makedirs("SpecificPlots/png/") # #Find the score of the associated isochrone # for chrone in cluster.iso: # if chrone[0] == iso and chrone[2] == reddening: # score = chrone[1] # break #Plots the CMD and the isochrone, with all of the points adjusted to reddening, extinction, and distance modulus plt.figure() plt.gca().invert_yaxis() plt.xlabel('B-R') plt.ylabel('G Mag') plt.title(f"{cl} {iso}") plt.scatter([s.b_r for s in cluster.filtered],[s.g_mag for s in cluster.filtered],s=0.05,c='dimgray',label='Cluster') plt.plot([x + reddening for x in isochrone.br],[x+cluster.dist_mod+2.1*reddening for x in isochrone.g],c='midnightblue',label=f"Score: {float(score):.7f}") plt.scatter([s.b_r for s in cluster.condensed],[s.g_mag for s in cluster.condensed],s=5,c=[s.weight for s in cluster.condensed],label='Cluster Proxy') #Colors the points by their fitting weight plt.set_cmap('brg') clb = plt.colorbar() clb.ax.set_title("Weight") #Label for the reddening extra = Rectangle((0, 0), 1, 1, fc="w", fill=False, edgecolor='none', linewidth=0) h,l = plt.gca().get_legend_handles_labels() h.insert(0,extra) l.insert(0,f"Reddening: {reddening}") plt.legend(h,l) #Save figure output to disk plt.savefig(f"SpecificPlots/pdf/Requested_Plot_{cl}_{iso}_Reddening_{reddening}.pdf") plt.savefig(f"SpecificPlots/png/Requested_Plot_{cl}_{iso}_Reddening_{reddening}.png",dpi=500) def plotRange(cl,a,b): global clusters checkLoaded([cl]) #Plots the top fitting isochrones over the range a to b for a given cluster #Does this by calling the specificPlot() method for each isochrone over the range for isochrone in clusters[f"{cl}"].iso[a:b]: specificPlot(cl,isochrones[isochrone[0]].name,isochrone[2],isochrone[1]) def getIsoScore(cl,iso,red,output=True): #Return the score for a given cluster's isochrone fit for i in cl.iso: if i[0] == iso.name and float(i[2]) == red: return i[1] if output: print(f"No score found for {cl.name} | {iso.name} | {red}") return 0 def onkey(x,y,cx,cy,fig,ax,cluster,iso,reddening): global curIso global curReddening curIso = iso curReddening = reddening def func(event): import matplotlib.patches as patches global curIso global curReddening global isochrones key = str(event.key) #print(key) ageSorted = [a for a in sorted(isoList,key=lambda x: float(x.age)) if a.feh == curIso.feh] fehSorted = [a for a in sorted(isoList,key=lambda x: float(x.feh)) if a.age == curIso.age] age_index = ageSorted.index(curIso) feh_index = fehSorted.index(curIso) #Move up or down in the desired variable space, with wrap-around at the ends of the lists if key == "w": #Increase metallicity try: curIso = fehSorted[feh_index+1] feh_index = feh_index+1 except: curIso = fehSorted[0] feh_index = 0 if key == "s": #Decrease metallicity curIso = fehSorted[feh_index-1] feh_index = feh_index-1 if feh_index < 0: feh_index = len(fehSorted)+feh_index if key == "a": #Increase age curIso = ageSorted[age_index-1] age_index = age_index-1 if age_index < 0: age_index = len(ageSorted)+age_index if key == "d": #Decrease age try: curIso = ageSorted[age_index+1] age_index = age_index+1 except: curIso = ageSorted[0] age_index = 0 if key == "q": #Decrease metallicity curReddening = round(curReddening-0.01,2) if key == "e": #Increase metalicity curReddening = round(curReddening+0.01,2) if key == "r": #Reset to originally requested isochrone curIso = iso ageSorted = [a for a in sorted(isoList,key=lambda x: float(x.age)) if a.feh == curIso.feh] fehSorted = [a for a in sorted(isoList,key=lambda x: float(x.feh)) if a.age == curIso.age] age_index = ageSorted.index(curIso) feh_index = fehSorted.index(curIso) if key == " ": #Print currently highlighted isochrone to console score = getIsoScore(cluster,curIso,curReddening) fig.savefig(f"Jamboree Images/frames/{curIso.name}.png",dpi=500) print(f"{curIso.name} | {curReddening} | {score}") score = getIsoScore(cluster,curIso,curReddening,output=False) #Replots everything with the updated isochrone ax.clear() ax.scatter(x,y,s=0.25,color='dimgray') ax.scatter(cx,cy,s=4,color='red') ax.plot([a.Gaia_BP_EDR3-a.Gaia_RP_EDR3+curReddening for a in curIso.starList],[a.Gaia_G_EDR3+cluster.dist_mod+2.1*curReddening for a in curIso.starList],color='darkblue') ax.set_title(f"{curIso.name}\n {curReddening}\n {score}") ax.set_xlabel("Apparent BP-RP") ax.set_ylabel("Apparent G Mag") ax.invert_yaxis() #Progress bar indicators for the interactive plot #Sets the dimensons of the boxes x0,x1 = ax.get_xlim() y0,y1 = ax.get_ylim() margin = 0.01 width = 0.05 * (x1-x0) height = 0.6 * (y1-y0) xmargin = margin * (x1-x0) ymargin = margin * (y1-y0) #The two main progress bars rect1 = patches.Rectangle((x1-width-xmargin,y0+ymargin),width,height,linewidth=1,edgecolor='black',facecolor='none',alpha=0.5) rect2 = patches.Rectangle((x1-2*width-2*xmargin,y0+ymargin),width,height,linewidth=1,edgecolor='black',facecolor='none',alpha=0.5) #rect3 = patches.Rectangle((x1-3*width-3*xmargin,y0+ymargin),width,height,linewidth=1,edgecolor='black',facecolor='none',alpha=0.5) ax.add_patch(rect1) ax.add_patch(rect2) #ax.add_patch(rect3) #The segments filling up the progress bars n = len(ageSorted) #Adds cells bottom to top for i in range(n): offset = i*height/n alpha = 0.25 if i == age_index: color = 'red' else: color = 'black' #Age progress bar ax.add_patch(patches.Rectangle((x1-2*width-2*xmargin,y0+ymargin+offset),width,height/n,linewidth=0.01,edgecolor='black',facecolor=color,alpha=alpha)) n = len(fehSorted) for i in range(n): offset = i*height/n alpha = 0.25 if i == feh_index: color = 'red' else: color = 'black' #Metallicity progress bar ax.add_patch(patches.Rectangle((x1-1*width-1*xmargin,y0+ymargin+offset),width,height/n,linewidth=0.01,edgecolor='black',facecolor=color,alpha=alpha)) fig.canvas.draw_idle() return func def interactivePlot(cl,iso=0,reddening="auto"): #Imports import matplotlib.pyplot as plt import matplotlib.patches as patches global clusters global isochrones global kid checkLoaded([cl]) cluster = clusters[f"{cl}"] #Select the starting isochrone based on user input if type(iso) == str: isochrone = isochrones[f"{iso}"] elif type(iso) == int: assert iso >= 0 isochrone = isochrones[cluster.iso[iso][0]] else: print("Invalid declaration of 'iso'") return name = isochrone.name #Get the reddening if not manually defined if reddening == "auto": reddening = cluster.reddening assert type(reddening) == float or type(reddening) == int score = getIsoScore(cluster,isochrone,reddening) # #Sorted and secondary-sorted isochrone lists # ageSorted = sorted(isoList,key=lambda x: (x.age,x.feh)) # fehSorted = sorted(isoList,key=lambda x: (x.feh,x.age)) ageSorted = [a for a in sorted(isoList,key=lambda x: float(x.age)) if a.feh == isochrone.feh] fehSorted = [a for a in sorted(isoList,key=lambda x: float(x.feh)) if a.age == isochrone.age] age_index = ageSorted.index(isochrone) feh_index = fehSorted.index(isochrone) #Coordinate lists to plot in addition to the isochrones x,y = cluster.mag[:,0],cluster.mag[:,1] cx,cy = [s.b_r for s in cluster.condensed],[s.g_mag for s in cluster.condensed] #Systematically remove some of the conflicting default keymaps in Pyplot letters = ['w','s','a','d','q','e','r'] for letter in letters: #Finds all keymap references in the rcParams for param in [key for key in plt.rcParams if key.startswith("keymap") ]: try: plt.rcParams[param].remove(letter) except: continue #Initialize the plot that will be updated every time fig = plt.figure(f"Interactive plot of {cl}") ax = fig.add_subplot(111) ax.scatter(x,y,s=0.25,color='dimgray') ax.scatter(cx,cy,s=4,color='red') ax.plot([a.Gaia_BP_EDR3-a.Gaia_RP_EDR3+reddening for a in isochrone.starList],[a.Gaia_G_EDR3+cluster.dist_mod+2.1*reddening for a in isochrone.starList],color='darkblue') ax.set_title(f"{name}\n {reddening}\n {score}") ax.set_xlabel("Apparent BP-RP") ax.set_ylabel("Apparent G Mag") ax.invert_yaxis() x0,x1 = ax.get_xlim() y0,y1 = ax.get_ylim() margin = 0.01 width = 0.05 * (x1-x0) height = 0.6 * (y1-y0) xmargin = margin * (x1-x0) ymargin = margin * (y1-y0) rect1 = patches.Rectangle((x1-width-xmargin,y0+ymargin),width,height,linewidth=1,edgecolor='black',facecolor='none',alpha=0.5) rect2 = patches.Rectangle((x1-2*width-2*xmargin,y0+ymargin),width,height,linewidth=1,edgecolor='black',facecolor='none',alpha=0.5) #rect3 = patches.Rectangle((x1-3*width-3*xmargin,y0+ymargin),width,height,linewidth=1,edgecolor='black',facecolor='none',alpha=0.5) ax.add_patch(rect1) ax.add_patch(rect2) #ax.add_patch(rect3) n = len(ageSorted) #Adds cells bottom to top for i in range(n): offset = i*height/n alpha = 0.25 if i == age_index: color = 'red' else: color = 'black' ax.add_patch(patches.Rectangle((x1-2*width-2*xmargin,y0+ymargin+offset),width,height/n,linewidth=0.01,edgecolor='black',facecolor=color,alpha=alpha)) n = len(fehSorted) for i in range(n): offset = i*height/n alpha = 0.25 if i == feh_index: color = 'red' else: color = 'black' ax.add_patch(patches.Rectangle((x1-1*width-1*xmargin,y0+ymargin+offset),width,height/n,linewidth=0.01,edgecolor='black',facecolor=color,alpha=alpha)) #Launch the key_press listener hook = onkey(x,y,cx,cy,fig,ax,cluster,isochrone,reddening) kid = fig.canvas.mpl_connect('key_press_event',hook) def printList(cList,varList): cList = checkLoaded(cList) for cl in cList: cluster = clusters[cl] for a in varList: clStr = f"[{cl}] {a} =" exec(f"print(clStr,cluster.{a})") def statRange(cl,a,b): import numpy as np global clusters checkLoaded([cl]) if not isoIn: loadIsochrones() ages = [] fehs = [] ys = [] reds = [] #Computes the mean age, metallicity, and reddening for the top fitting isochrones over the range a to b for a given cluster #For example, a=0, b=10 will average the top 10 isochrone fits for isochrone in clusters[cl].iso[a:b]: iso = isochrones[isochrone[0]] print(f"{iso.name} Reddening:{isochrone[2]}") ages.append(float(iso.age)) fehs.append(float(iso.feh)) ys.append(float(iso.y)) reds.append(float(isochrone[2])) print(f"[{cl}] Mean age= {np.mean(ages)} Mean feh= {np.mean(fehs)} Mean y= {np.mean(ys)} Mean Reddening= {np.mean(reds)}") def setFlag(): #Imports global clusterlist #Goes back and sets membership flags for all of the clusters loaded in memory to ensure that this tag can be used later #This takes place automatically after running turboFilter() #Example use case for this variable is in the customPlot() method for cluster in clusterList: for star in cluster.filtered: for unfStar in cluster.unfilteredWide: if star == unfStar: unfStar.member = 1 def customPlot(var1,var2,clname,mode='filtered',iso=False,square=True,color='default',title='default',close=False,save=True): #Imports import matplotlib.pyplot as plt global closePlots #Load the cluster if it isn't yet checkLoaded([clname]) cluster = clusters[f"{clname}"] #Set the list of stars to be used for the given cluster #Using a mode not specified will return a referenced before assignment error if mode == 'filtered': starlist = cluster.filtered elif mode == 'unfiltered': starlist = cluster.unfilteredWide elif mode == 'bright_filtered': starlist = cluster.filteredBright elif mode == 'dist_filtered': starlist = cluster.distFiltered elif mode == 'bright_unfiltered': starlist = cluster.unfilteredBright elif mode == 'duo': starlist = cluster.unfilteredWide starlistF = cluster.filtered elif mode == 'binary': starlist = cluster.binaries elif mode == 'duoBinary': starlist = cluster.filtered starlistF = cluster.binaries elif mode == 'duoBright': starlist = cluster.unfilteredBright starlistF = cluster.filteredBright elif mode == 'duoDist': starlist = cluster.distFiltered starlistF = cluster.filtered elif mode == 'condensed': starlist = cluster.condensed elif mode == 'duoCondensed': starlist = cluster.filtered starlistF = cluster.condensed elif mode == 'bounded': starlist = cluster.bounded elif mode == 'duoBounded': starlist = cluster.filtered starlistF = cluster.bounded else: print("No preset star list configuration found with that alias") return #Basic plot features with axis labels and a title plt.figure() if title == 'default': plt.title(f"{clname} {mode} | {var1} vs {var2} | {color} color") else: plt.title(f"{title}") plt.xlabel(f"{var1}".upper()) plt.ylabel(f"{var2}".upper()) #Plots differently depending on the mode #The color tag can be used to add distinction of a third variable while limited to two axes #If unspecified, filtered starlist with midnight blue coloring will be the result if iso: plt.gca().invert_yaxis() if 'duo' in mode: #plt.scatter([eval(f"x.{var1}") for x in starlist],[eval(f"y.{var2}") for y in starlist],s=[0.1+a.member*1.4 for a in starlist],c=[list(('lightgray',eval('z.par')))[z.member] for z in starlist]) plt.scatter([eval(f"x.{var1}") for x in starlist],[eval(f"y.{var2}") for y in starlist],s=2,c='gray') if color == 'default': plt.scatter([eval(f"x.{var1}") for x in starlistF],[eval(f"y.{var2}") for y in starlistF],s=2.5,c='red') else: plt.scatter([eval(f"x.{var1}") for x in starlistF],[eval(f"y.{var2}") for y in starlistF],s=2.5,c=[eval(f"z.{color}") for z in starlistF]) plt.set_cmap('brg') clb = plt.colorbar() clb.ax.set_title(f"{color}") else: if color == 'default': plt.scatter([eval(f"x.{var1}") for x in starlist],[eval(f"y.{var2}") for y in starlist],s=1,c='midnightblue') else: plt.scatter([eval(f"x.{var1}") for x in starlist],[eval(f"y.{var2}") for y in starlist],s=2,c=[eval(f"z.{color}") for z in starlist]) plt.set_cmap('cool') clb = plt.colorbar() clb.ax.set_title(f"{color}") #By default, squares the axes to avoid misinformation from stretched axes #Turn this off and iso to true for a color magnitude diagram if square: plt.axis("square") if save: plt.savefig(f"SpecificPlots/pdf/{clname}_{mode}_{var1}_{var2}.pdf") plt.savefig(f"SpecificPlots/png/{clname}_{mode}_{var1}_{var2}.png",dpi=500) if close or closePlots: plt.close() if save: print(f"Custom Plot {clname}_{mode}_{var1}_{var2} saved and closed") else: print(f"Custom Plot {clname}_{mode}_{var1}_{var2} closed") def splitMS(clname='M67',slope=3,offset=12.2): #Imports import numpy as np import matplotlib.pyplot as plt checkLoaded([clname]) cluster = clusters[clname] xlist = [s.b_r for s in cluster.filtered] ylist = [s.g_mag for s in cluster.filtered] x = np.linspace(1,2,100) #Create a diagram showing the lower edge and upper edge of the main sequence, which in theory are separated by 0.75mag plt.figure() plt.title('Main and Binary Sequences') plt.xlabel('B-R') plt.ylabel('Apparent G Mag') plt.scatter(xlist,ylist,s=0.5,label='Filtered Star Data') plt.plot(x,[slope*a + offset for a in x],color='r',label='Main Sequence') plt.plot(x,[slope*a + offset - 0.75 for a in x],'--',color='r',label='MS shifted 0.75 mag') plt.xlim(0.6,2.2) plt.ylim(13,19) plt.legend() plt.gca().invert_yaxis() plt.savefig(f"SpecificPlots/png/{clname}_MS_Spread.png",dpi=500) plt.savefig(f"SpecificPlots/pdf/{clname}_MS_Spread.pdf") def kingProfile(r,K,R): return K*(1+r**2/R**2)**(-1) def kingError(r,K,R,dK,dR): import numpy as np dfdK = (1+r**2/R**2)**(-1) dfdR = 2*K*r**2*R*(r**2+R**2)**(-2) return np.sqrt((dfdK*dK)**2 + (dfdR*dR)**2) def densityProfile(r,K,R): import numpy as np #The exponential that is fit for the membership profile #R is a characteristic radius, typically negative but the absolute value is used for comparison #K is a scalar constant return K*np.exp(-1*r/R) def densityError(r,K,R,dK,dR): import numpy as np dfdK = abs(np.exp(-1*r/R)) dfdR = abs(K*r/(R**2)*np.exp(-1*r/R)) return np.sqrt((dfdK*dK)**2 + (dfdR*dR)**2) def toIntensity(mag): msun = -26.74 #apparent magnitude Isun = 1360 #w/m^) return Isun*10**( 0.4*(msun-mag) ) def membership(clname='M67',N=100,mode='filtered',numPercentileBins=5,percentile=0.2,delta=5,normalize=True): #Imports import numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Circle import scipy.optimize as so import scipy.stats as st import math global volume checkLoaded([clname]) cluster = clusters[clname] mode = mode.lower() #Default mode is filtered, but unfiltered data can be processed if "filtered" in mode: starList = cluster.filtered elif "bounded" in mode: starList = cluster.bounded else: starList = cluster.unfilteredWide #Load mass estimates from isochrone fitting if not cluster.massLoaded: proxyMatch([cluster.name]) assert cluster.massLoaded assert len(starList) > 0 #Assign x and y lists based on normalization or not if normalize: starX = [a.ra*np.cos(a.dec*np.pi/180) for a in starList] starY = [a.dec for a in starList] mode = mode + "_normalized" else: starX = [a.ra for a in starList] starY = [a.dec for a in starList] #Determine bounds of the field of view (post-filtering) xmax = max(starX) ymax = max(starY) x0 = np.mean(starX) y0 = np.mean(starY) newN = N #Determine radius of the field of view rx = xmax-x0 ry = ymax-y0 #r = np.mean([rx,ry]) radiusFOV = ry #Using the mean ra and dec radius caused problems with clusters #like NGC188, which are close to the celestial pole and have #a very stretched mapping to the RA DEC space ringBins = list(np.linspace(0,radiusFOV,N)) #The bins are divided up such that 50% of the bins are located in the inner 25% of the cluster radius #The remaining 50% of the bins are divided from 25% to 100% of the radius rings = list(np.linspace(0,radiusFOV/4,math.ceil(N/2))) ring2 = list(np.linspace(radiusFOV/4,radiusFOV,math.floor(N/2)+1)) ring2 = ring2[1:-1] rings.extend(ring2) x=rings[:-1] # for i in range(0,len(rings[:-1])): # x.append((rings[i+1]+rings[i])/2) counts = list(np.zeros(N-1,dtype=int)) masses = list(np.zeros(N-1,dtype=int)) rads=[] for star in starList: #Radial distance from the mean RA and Dec of the cluster if normalize: rads.append(np.sqrt((star.ra*np.cos(star.dec*np.pi/180)-x0)**2+(star.dec-y0)**2)) else: rads.append(np.sqrt((star.ra-x0)**2+(star.dec-y0)**2)) #Find the nearest ring to the star r = find_nearest(rings, rads[-1]) i = rings.index(r) #Check bounds if i < len(counts): #If outside last ring, add to that count if r > rads[-1]: counts[i-1] += 1 masses [i-1] += star.proxyMass else: counts[i] += 1 masses [i] += star.proxyMass #Worth noting here that the way that this is set up, the rings don't actually mark the bounds of the bins but rather the midpoints. #There is no check to see if you are exterior or interior to the nearest ring, but rather what ring you are nearest to, #so the rings mark the midpoints of their bins not the boundaries #Histogram of the counts in each radial bin plt.figure(f"{clname}_membership_{mode}") plt.hist(rads,bins=ringBins) plt.xlabel("Radius (deg)") plt.ylabel("Number of Stars") plt.title(f"{clname} Membership") plt.savefig(f"{cluster.imgPath}{clname}_membership_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_membership_{mode}.png",dpi=500) #Calculates the volume of each region bounded by two concentric rings and the number density of the stars counted in those regions volume = [] for i in range(0,len(rings[:-1])): volume.append(np.pi*(rings[i+1]**2-rings[i]**2)) numDensity = [a/b for a,b in zip(counts,volume)] massDensity = [a/b for a,b in zip(masses,volume)] error_num = [np.sqrt(a)/b for a,b in zip(counts,volume)] error_mass = [np.sqrt(a)/b for a,b in zip(masses,volume)] for i in range(0,len(error_num)): if error_num[i] < 0.1: error_num[i] = 0.1 #Cut out the inner 5% because overbinning in the center of a circle doesn't help x = x[math.ceil(N/20):-1] counts = counts[math.ceil(N/20):-1] numDensity = numDensity[math.ceil(N/20):-1] massDensity = massDensity[math.ceil(N/20):-1] error_num = error_num[math.ceil(N/20):-1] error_mass = error_mass[math.ceil(N/20):-1] #Further filter the data based on outliers, either extremely low density or extremely big jumps in density from bin to bin i = 0 numSmall = 0 numGrad = 0 while i < len(x)-1: if numDensity[i] < 0.5 or numDensity[i] < numDensity[i+1]/delta or massDensity[i] < 0.1: x.pop(i) counts.pop(i) numDensity.pop(i) massDensity.pop(i) error_num.pop(i) error_mass.pop(i) numSmall += 1 newN -= 1 elif abs(numDensity[i]) > abs(numDensity[i+1])*delta:# or abs(numDensity[i]) < abs(numDensity[i-1])/3: x.pop(i) counts.pop(i) numDensity.pop(i) massDensity.pop(i) error_num.pop(i) error_mass.pop(i) numGrad += 1 newN -= 1 else: i += 1 if numDensity[-1] < 0.01 or massDensity[-1] < 0.01: x.pop(-1) counts.pop(-1) numDensity.pop(-1) massDensity.pop(-1) error_num.pop(-1) error_mass.pop(-1) numSmall += 1 newN -= 1 print(f"[{cluster.name}] Removed {numSmall} points with too small of a density and {numGrad} points with too extreme of a delta") #========= Number Density ========= #Number density vs radial bin plot plt.figure(f"{clname}_density_{mode}") plt.errorbar(x,numDensity,yerr=error_num,ls='None') plt.scatter(x,numDensity) plt.xlabel("Radius (deg)") plt.ylabel(r"Surface Number Density ($deg^{-2}$)") plt.title(f"{clname} {mode.capitalize()} Number Density".replace("_normalized",' Normalized')) #Fit an exponential curve to the density plot based on the densityProfile function defined above if "NGC2355" in cluster.name: p0=[5000,0.1] else: p0=[5000,0.1] #print([b/a for a,b in zip(numDensity,error_num)]) fit,var = so.curve_fit(kingProfile,x,numDensity,p0,maxfev=1000) #Std. Dev. from variance err = np.sqrt(var[1][1]) err_coeff = np.sqrt(var[0][0]) scale = np.abs(fit[1]*3600/206265)/(cluster.mean_par/1000) #scaleVar = (3600/206265)*(err/(cluster.mean_par/1000) ) + 2*fit[1]/(cluster.mean_par_err/1000) scaleVar = np.abs(scale*np.sqrt((var[1][1]/fit[1])**2 + (cluster.mean_par_err/cluster.mean_par)**2)) #Scale radius from count in parsecs setattr(cluster,f"scaleRad_{mode}",scale) setattr(cluster,f"scaleRad_err_{mode}",scaleVar) #Scale radius from count in degrees setattr(cluster,f"scaleAngle_{mode}",abs(fit[1])) setattr(cluster,f"scaleAngle_err_{mode}",err) setattr(cluster,f"numDensity_coeff_{mode}",fit[0]) setattr(cluster,f"numDensity_coeff_err_{mode}",err_coeff) #Plot the curve fit numLabel = ( f"N={newN} ({mode.capitalize()})".replace("_normalized",' Normalized')+"\n" + fr"K={fit[0]:.3f} $\pm$ {err_coeff:.3f}" + "\n" + fr"$\rho$={np.abs(fit[1]):.3f}$\degree$ $\pm$ {err:.3f}$\degree$"+ "\n" + fr"R={scale:.3f}pc $\pm$ {scaleVar:.3f}pc" ) plt.plot(x,[kingProfile(a,*fit) for a in x],color='red',label=numLabel) plt.fill_between(x,[kingProfile(a,*fit)-kingError(a,fit[0],fit[1],err_coeff,err) for a in x],[kingProfile(a,*fit)+kingError(a,fit[0],fit[1],err_coeff,err) for a in x],label=r'$1\sigma$',edgecolor='none',alpha=0.8,facecolor='salmon') plt.legend(fontsize=8,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_numDensity_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_numDensity_{mode}.png",dpi=500) plt.yscale('log') plt.savefig(f"{cluster.imgPath}{clname}_numDensity_log_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_numDensity_log_{mode}.png",dpi=500) #Double plot for bounded regions if "bounded" in mode: plt.figure(f"{clname}_density_filtered") plt.title(f"{clname} Overlaid Number Density") plt.errorbar(x,numDensity,yerr=error_num,ls='None',color='midnightblue') plt.scatter(x,numDensity,color='midnightblue') plt.plot(x,[kingProfile(a,*fit) for a in x],color='darkred',label=numLabel) plt.fill_between(x,[kingProfile(a,*fit)-kingError(a,fit[0],fit[1],err_coeff,err) for a in x],[kingProfile(a,*fit)+kingError(a,fit[0],fit[1],err_coeff,err) for a in x],edgecolor='none',alpha=0.8,facecolor='salmon') plt.legend(fontsize=8,loc='upper right') plt.yscale('linear') plt.savefig(f"{cluster.imgPath}{clname}_numDensity_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_numDensity_overlay.png",dpi=500) plt.yscale('log') plt.savefig(f"{cluster.imgPath}{clname}_numDensity_log_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_numDensity_log_overlay.png",dpi=500) #========= Mass Density ========= #Mass density vs radial bin plot plt.figure(f"{clname}_mass_density_{mode}") plt.errorbar(x,massDensity,yerr=error_mass,ls='None') plt.scatter(x,massDensity) plt.xlabel("Radius (deg)") plt.ylabel(r"Surface Mass Density ($M_{\odot}*deg^{-2}$)") plt.title(f"{clname} {mode.capitalize()} Mass Density".replace("_normalized",' Normalized')) #Fit an exponential curve to the density plot based on the densityProfile function defined above fit_mass,var_mass = so.curve_fit(kingProfile,x,massDensity,p0,maxfev=1000) #Std. Dev. from variance err_mass = np.sqrt(var[1][1]) err_mass_coeff = np.sqrt(var[0][0]) scale_mass = np.abs(fit_mass[1]*3600/206265)/(cluster.mean_par/1000) #scaleVar_mass = (3600/206265)*(err_mass/(cluster.mean_par/1000) ) + 2*fit_mass[1]/(cluster.mean_par_err/1000) scaleVar_mass = np.abs(scale_mass*np.sqrt((var_mass[1][1]/fit_mass[1])**2 + (cluster.mean_par_err/cluster.mean_par)**2)) #Scale radius from mass in parsecs setattr(cluster,f"scaleRad_mass_{mode}",scale_mass) setattr(cluster,f"scaleRad_mass_err_{mode}",scaleVar_mass) #Scale radius from mass in degrees setattr(cluster,f"scaleAngle_mass_{mode}",abs(fit_mass[1])) setattr(cluster,f"scaleAngle_mass_err_{mode}",err_mass) setattr(cluster,f"massDensity_coeff_{mode}",fit_mass[0]) setattr(cluster,f"massDensity_coeff_err_{mode}",err_mass_coeff) #Plot the curve fit massLabel = ( f"N={newN} ({mode.capitalize()})".replace("_normalized",' Normalized')+"\n" + fr"K={fit_mass[0]:.3f} $\pm$ {err_mass_coeff:.3f}" + "\n" + fr"$\rho$={np.abs(fit_mass[1]):.3f}$\degree$ $\pm$ {err_mass:.3f}$\degree$"+ "\n" + fr"R={scale_mass:.3f}pc $\pm$ {scaleVar_mass:.3f}pc" ) plt.plot(x,[kingProfile(a,*fit_mass) for a in x],color='red',label=massLabel) plt.fill_between(x,[kingProfile(a,*fit_mass)-kingError(a,fit_mass[0],fit_mass[1],err_mass_coeff,err_mass) for a in x],[kingProfile(a,*fit_mass)+kingError(a,fit_mass[0],fit_mass[1],err_mass_coeff,err_mass) for a in x],label=r'$1\sigma$',edgecolor='none',alpha=0.8,facecolor='salmon') plt.legend(fontsize=8,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_massDensity_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_massDensity_{mode}.png",dpi=500) plt.yscale('log') plt.savefig(f"{cluster.imgPath}{clname}_massDensity_log_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_massDensity_log_{mode}.png",dpi=500) #Double plot for bounded regions if "bounded" in mode: plt.figure(f"{clname}_mass_density_filtered") plt.title(f"{clname} Overlaid Mass Density") plt.errorbar(x,massDensity,yerr=error_mass,ls='None',color='midnightblue') plt.scatter(x,massDensity,color='midnightblue') plt.plot(x,[kingProfile(a,*fit_mass) for a in x],color='darkred',label=massLabel) plt.fill_between(x,[kingProfile(a,*fit_mass)-kingError(a,fit_mass[0],fit_mass[1],err_mass_coeff,err_mass) for a in x],[kingProfile(a,*fit_mass)+kingError(a,fit_mass[0],fit_mass[1],err_mass_coeff,err_mass) for a in x],edgecolor='none',alpha=0.8,facecolor='salmon') plt.legend(fontsize=8,loc='upper right') plt.yscale('linear') plt.savefig(f"{cluster.imgPath}{clname}_massDensity_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_massDensity_overlay.png",dpi=500) plt.yscale('log') plt.savefig(f"{cluster.imgPath}{clname}_massDensity_log_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_massDensity_log_overlay.png",dpi=500) #========= Average Mass ========= averageMass = [a/b for a,b in zip(massDensity,numDensity)] xDist = [np.abs(a*3600/206265)/(cluster.mean_par/1000) for a in x] #Average Mass plot plt.figure(f"{clname}_average_mass_{mode}") plt.scatter(xDist,averageMass,label=fr"N={newN} ({mode.capitalize()})".replace("_normalized",' Normalized')+"\n"+f"{numPercentileBins} Percentile Bins") plt.xlabel("Distance from Center (pc)") plt.ylabel(r"Average Stellar Mass ($M_{\odot}$)") plt.title(f"{clname} {mode.capitalize()} Average Mass".replace("_normalized",' Normalized')) #Split average mass data into numPercentileBins number of bins if "filtered" in mode: cluster.pMin = xDist[0] cluster.pMax = xDist[-1] pBins = np.linspace(cluster.pMin,cluster.pMax,numPercentileBins+1) xBins = [] for i in range(len(pBins)-1): xBins.append((pBins[i]+pBins[i+1])/2) pBins = np.delete(pBins,0) pBins = np.delete(pBins,-1) for b in pBins: plt.axvline(x=b,color='black',linestyle='--') binned = [] for n in range(numPercentileBins): binned.append([]) #Assign the average mass data points to the bins for i in range(len(xDist)): #Finds the nearest xBin to each x value and sorts the corresponding averageMass into that bin val = find_nearest(xBins,xDist[i]) idx = xBins.index(val) binned[idx].append(averageMass[i]) #Creates arrays that are numPercentileBins long that store the standard and quantile means of the points in those bins quantileMean = [] binMean = [] meanBins = [] for b in binned: if len(b) == 0: continue binSorted = sorted(b) #Finds the index of the lower percentile marker (ex. 20%) lower = binSorted.index(find_nearest(binSorted, np.quantile(b,percentile))) #Finds the index of the upper percentile marker (ex. 80%) upper = binSorted.index(find_nearest(binSorted, np.quantile(b,1-percentile))) #Means between lower and upper percentile markers quantileMean.append(np.mean(binSorted[lower:upper+1])) #Standard Mean binMean.append(np.mean(b)) #Bins meanBins.append(xBins[binned.index(b)]) try: fit, var = so.curve_fit(kingProfile,xDist,[kingProfile(a,*fit_mass)/kingProfile(a,*fit) for a in x]) residual_coeff, residual_scaleAngle = fit[0],fit[1] except: print(f"Unable to fit the residuals for {cluster.name}") residual_coeff, residual_scaleAngle = -99, -99 massFit = st.linregress(meanBins,quantileMean) fitslope, intercept, rval, pval, fitslope_err, intercept_err = massFit.slope, massFit.intercept, massFit.rvalue, massFit.pvalue, massFit.stderr, massFit.intercept_stderr residual_scaleRad = np.abs(residual_scaleAngle*3600/206265)/(cluster.mean_par/1000) setattr(cluster,f"residual_coeff_{mode}",residual_coeff) setattr(cluster,f"residual_scaleAngle_{mode}",residual_scaleAngle) setattr(cluster,f"residual_scaleRad_{mode}",residual_scaleRad) setattr(cluster,f"mass_slope_{mode}",fitslope) setattr(cluster,f"mass_slope_err_{mode}",fitslope_err) setattr(cluster,f"mass_intercept_{mode}",intercept) setattr(cluster,f"mass_intercept_err_{mode}",intercept_err) setattr(cluster,f"mass_fit_r2_{mode}",rval**2) setattr(cluster,f"mass_fit_p_{mode}",pval) fitLabel = ( fr"Slope = {fitslope:.3f} $\pm$ {fitslope_err:.3f}" + "\n" + fr"Intercept = {intercept:.3f} $\pm$ {intercept_err:.3f}" + "\n" + fr"$r^2$ = {rval**2:.3f} ({mode.capitalize()})".replace("_normalized",' Normalized')) #Plot the quantile and standard means on the existing average mass plot plt.scatter(meanBins,quantileMean,color='red',label=f'Interquartile Mean ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.plot(xDist,[fitslope*a+intercept for a in xDist],color='red',label=fitLabel) #plt.scatter(meanBins,binMean,color='dimgray',label=f'{mode.capitalize()} Standard Mean') plt.legend(fontsize=8,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_averageMass_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_averageMass_{mode}.png",dpi=500) #Double plot for bounded regions if "bounded" in mode: plt.figure(f"{clname}_average_mass_filtered") plt.title(f"{clname} Overlaid Average Mass") plt.scatter(xDist,averageMass,color='midnightblue',label=fr"N={newN} ({mode.capitalize()})".replace("_normalized",' Normalized')+"\n"+f"{numPercentileBins} Percentile Bins") plt.plot(xDist,[fitslope*a+intercept for a in xDist],color='darkred',label=fitLabel) plt.scatter(meanBins,quantileMean,color='darkred',label=f'Interquartile Mean ({mode.capitalize()})'.replace("_normalized",' Normalized')) #plt.scatter(meanBins,binMean,color='black',label=f'{mode.capitalize()} Standard Mean') plt.legend(fontsize=8,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_averageMass_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_averageMass_overlay.png",dpi=500) #========= Radius Plot ========= plt.figure(f"{clname}_characteristic_radius_{mode}") if normalize: plt.scatter([star.ra*np.cos(star.dec*np.pi/180) for star in cluster.unfilteredWide],[star.dec for star in cluster.unfilteredWide],s=1,c='lightgray',label='Unfiltered') plt.scatter([star.ra*np.cos(star.dec*np.pi/180) for star in cluster.filtered],[star.dec for star in cluster.filtered],s=2,c='midnightblue',label='Filtered') plt.xlabel("RA*cos(Dec) (Deg)") else: plt.scatter([star.ra for star in cluster.unfilteredWide],[star.dec for star in cluster.unfilteredWide],s=1,c='lightgray',label='Unfiltered') plt.scatter([star.ra for star in cluster.filtered],[star.dec for star in cluster.filtered],s=2,c='midnightblue',label='Filtered') plt.xlabel("RA (Deg)") pltRad = abs(getattr(cluster,f"scaleAngle_{mode}")) outline1 = Circle([x0,y0],1*pltRad,color='red',fill=False,ls='--',label=fr"$\rho$={1*pltRad:0.3f}$\degree$",alpha=0.7) outline2 = Circle([x0,y0],5*pltRad,color='red',fill=False,ls='--',label=fr"5$\rho$={5*pltRad:0.3f}$\degree$",alpha=0.7) #outline3 = Circle([x0,y0],10*abs(getattr(cluster,f"scaleAngle_{mode}")),color='red',fill=False,ls='--',label=fr"10$\rho$={3*abs(fit[1]):0.3f}$\degree$",alpha=0.7) plt.gca().add_patch(outline1) plt.gca().add_patch(outline2) #plt.gca().add_patch(outline3) plt.legend(fontsize=10,loc='upper right') plt.axis('square') plt.ylabel("DEC (Deg)") plt.title(f"{clname} {mode.capitalize()} Characteristic Radius".replace("_normalized",' Normalized')) plt.gcf().set_size_inches(8,8) plt.savefig(f"{cluster.imgPath}{clname}_radialMembership_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_radialMembership_{mode}.png",dpi=500) if "M67" in clname and "filtered" in mode: plt.figure(f"{clname}_rings_{mode}") if normalize: plt.scatter([star.ra*np.cos(star.dec*np.pi/180) for star in cluster.unfilteredWide],[star.dec for star in cluster.unfilteredWide],s=1,c='lightgray',label='Unfiltered') plt.scatter([star.ra*np.cos(star.dec*np.pi/180) for star in cluster.filtered],[star.dec for star in cluster.filtered],s=2,c='midnightblue',label='Filtered') plt.xlabel("RA*cos(Dec) (Deg)") else: plt.scatter([star.ra for star in cluster.unfilteredWide],[star.dec for star in cluster.unfilteredWide],s=1,c='lightgray',label='Unfiltered') plt.scatter([star.ra for star in cluster.filtered],[star.dec for star in cluster.filtered],s=2,c='midnightblue',label='Filtered') plt.xlabel("RA (Deg)") for i in range(0,len(rings)): outline = Circle([x0,y0],rings[i],color='red',fill=False) plt.gca().add_patch(outline) plt.legend(fontsize=10,loc='upper right') plt.axis('square') plt.ylabel("DEC (Deg)") plt.title(f"{clname} Radial Bins") plt.gcf().set_size_inches(8,8) plt.savefig(f"SpecificPlots/pdf/{clname}_radialBins_{mode}.pdf".replace("_filtered",'')) plt.savefig(f"SpecificPlots/png/{clname}_radialBins_{mode}.png".replace("_filtered",''),dpi=500) plt.xlim(x0-0.15,x0+0.15) plt.ylim(y0-0.15,y0+0.15) plt.savefig(f"SpecificPlots/pdf/{clname}_radialBins_center_{mode}.pdf".replace("_filtered",'')) plt.savefig(f"SpecificPlots/png/{clname}_radialBins_center_{mode}.png".replace("_filtered",''),dpi=500) #========= Stars by Mass ========= massList = [] innerMassList = [] for star in starList: massList.append(star.proxyMass) if normalize: if np.sqrt((star.ra*np.cos(star.dec*np.pi/180)-x0)**2+(star.dec-y0)**2) <= getattr(cluster,f"scaleAngle_{mode}"): innerMassList.append(star.proxyMass) else: if np.sqrt((star.ra-x0)**2+(star.dec-y0)**2) <= getattr(cluster,f"scaleAngle_{mode}"): innerMassList.append(star.proxyMass) mBins = np.arange(min(massList),max(massList)+0.1,0.1) inBins = np.arange(min(innerMassList),max(innerMassList)+0.1,0.1) plt.figure(f"{clname}_mass_frequency_{mode}") plt.xlabel(r"Stellar Mass ($M_{\odot}$)") plt.ylabel("Number of Stars") plt.title(f"{clname} {mode.capitalize()} Mass Frequency".replace("_normalized",' Normalized')) plt.hist(massList,bins=mBins,label=f"Total {mode.capitalize()}".replace("_normalized",' Normalized')) plt.hist(innerMassList,bins=inBins,color='midnightblue',label=f'Inside Core Radius ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.legend(fontsize=10,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_massFrequency_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_massFrequency_{mode}.png",dpi=500) #Double plot for bounded regions if "bounded" in mode: plt.figure(f"{clname}_mass_frequency_filtered") plt.title(f"{clname} Overlaid Mass Frequency") plt.hist(massList,bins=mBins,label=f"Total {mode.capitalize()}".replace("_normalized",' Normalized'),color='red') plt.hist(innerMassList,bins=inBins,color='darkred',label=f'Inside Core Radius ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.legend(fontsize=10,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_massFrequency_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_massFrequency_overlay.png",dpi=500) #========= Stars by Magnitude ========= magList = [] innerMagList = [] for star in starList: magList.append(star.g_mag-2.1*cluster.reddening-cluster.dist_mod) if normalize: if np.sqrt((star.ra*np.cos(star.dec*np.pi/180)-x0)**2+(star.dec-y0)**2) <= getattr(cluster,f"scaleAngle_{mode}"): innerMagList.append(star.g_mag-2.1*cluster.reddening-cluster.dist_mod) else: if np.sqrt((star.ra-x0)**2+(star.dec-y0)**2) <= getattr(cluster,f"scaleAngle_{mode}"): innerMagList.append(star.g_mag-2.1*cluster.reddening-cluster.dist_mod) mBins = np.arange(min(magList),max(magList)+0.1,0.1) inBins = np.arange(min(innerMagList),max(innerMagList)+0.1,0.1) plt.figure(f"{clname}_mag_frequency_{mode}") plt.xlabel(r"Absolute G Mag") plt.ylabel("Number of Stars") plt.title(f"{clname} {mode.capitalize()} Absolute Magnitude Frequency".replace("_normalized",' Normalized')) plt.hist(magList,bins=mBins,label=f"Total {mode.capitalize()}".replace("_normalized",' Normalized')) plt.hist(innerMagList,bins=inBins,color='midnightblue',label=f'Inside Core Radius ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.legend(fontsize=10,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_magFrequency_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_magFrequency_{mode}.png",dpi=500) #Double plot for bounded regions if "bounded" in mode: plt.figure(f"{clname}_mag_frequency_filtered") plt.title(f"{clname} Overlaid Absolute Magnitude Frequency") plt.hist(magList,bins=mBins,label=f"Total {mode.capitalize()}".replace("_normalized",' Normalized'),color='red') plt.hist(innerMagList,bins=inBins,color='darkred',label=f'Inside Core Radius ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.legend(fontsize=10,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_magFrequency_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_magFrequency_overlay.png",dpi=500) #========= Stars by Color ========= colorList = [] innerColorList = [] for star in starList: colorList.append(star.b_r-cluster.reddening) if normalize: if np.sqrt((star.ra*np.cos(star.dec*np.pi/180)-x0)**2+(star.dec-y0)**2) <= getattr(cluster,f"scaleAngle_{mode}"): innerColorList.append(star.b_r-cluster.reddening) else: if np.sqrt((star.ra-x0)**2+(star.dec-y0)**2) <= getattr(cluster,f"scaleAngle_{mode}"): innerColorList.append(star.b_r-cluster.reddening) mBins = np.arange(min(colorList),max(colorList)+0.1,0.1) inBins = np.arange(min(innerColorList),max(innerColorList)+0.1,0.1) plt.figure(f"{clname}_color_frequency_{mode}") plt.xlabel(r"Dereddened BP-RP") plt.ylabel("Number of Stars") plt.title(f"{clname} {mode.capitalize()} Dereddened Color Index Frequency".replace("_normalized",' Normalized')) plt.hist(colorList,bins=mBins,label=f"Total {mode.capitalize()}".replace("_normalized",' Normalized')) plt.hist(innerColorList,bins=inBins,color='midnightblue',label=f'Inside Core Radius ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.legend(fontsize=10,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_colorFrequency_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_colorFrequency_{mode}.png",dpi=500) #Double plot for bounded regions if "bounded" in mode: plt.figure(f"{clname}_color_frequency_filtered") plt.title(f"{clname} Overlaid Dereddened Color Index Frequency") plt.hist(colorList,bins=mBins,label=f"Total {mode.capitalize()}".replace("_normalized",' Normalized'),color='red') plt.hist(innerColorList,bins=inBins,color='darkred',label=f'Inside Core Radius ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.legend(fontsize=10,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_colorFrequency_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_colorFrequency_overlay.png",dpi=500) #========= Other Radii ========= massSum = np.sum([star.proxyMass for star in starList]) intensitySum = np.sum([toIntensity(star.g_mag) for star in starList]) curMassSum = 0 curIntSum = 0 massFound = False intFound = False if normalize: setattr(cluster,f"medianRad_{mode}",np.median([np.abs(star.normRadDist*3600/206265)/(cluster.mean_par/1000) for star in starList])) setattr(cluster,f"medianAngle_{mode}",np.median([star.normRadDist for star in starList])) radialStarList = sorted(starList,key=lambda x: x.normRadDist) for star in radialStarList: curMassSum += star.proxyMass curIntSum += toIntensity(star.g_mag) if curMassSum > massSum/2 and not massFound: setattr(cluster,f"halfMassRad_{mode}",np.abs(star.normRadDist*3600/206265)/(cluster.mean_par/1000)) setattr(cluster,f"halfMassAngle_{mode}",star.normRadDist) massFound = True if curIntSum > intensitySum/2 and not intFound: setattr(cluster,f"halfLightRad_{mode}",np.abs(star.normRadDist*3600/206265)/(cluster.mean_par/1000)) setattr(cluster,f"halfLightAngle_{mode}",star.normRadDist) intFound = True if massFound and intFound: break plt.figure(f"{clname}_other_radii_{mode}") plt.scatter([star.ra*np.cos(star.dec*np.pi/180) for star in cluster.unfilteredWide],[star.dec for star in cluster.unfilteredWide],s=1,c='lightgray',label='Unfiltered') plt.scatter([star.ra*np.cos(star.dec*np.pi/180) for star in cluster.filtered],[star.dec for star in cluster.filtered],s=2,c='midnightblue',label='Filtered') plt.xlabel("RA*cos(Dec) (deg)") else: setattr(cluster,f"medianRad_{mode}",np.median([np.abs(star.radDist*3600/206265)/(cluster.mean_par/1000) for star in starList])) setattr(cluster,f"medianAngle_{mode}",np.median([star.radDist for star in starList])) radialStarList = sorted(starList,key=lambda x: x.radDist) for star in radialStarList: curMassSum += star.proxyMass curIntSum += toIntensity(star.g_mag) if curMassSum > massSum/2 and not massFound: setattr(cluster,f"halfMassRad_{mode}",np.abs(star.radDist*3600/206265)/(cluster.mean_par/1000)) setattr(cluster,f"halfMassAngle_{mode}",star.radDist) massFound = True if curIntSum > intensitySum/2 and not intFound: setattr(cluster,f"halfLightRad_{mode}",np.abs(star.radDist*3600/206265)/(cluster.mean_par/1000)) setattr(cluster,f"halfLightAngle_{mode}",star.radDist) intFound = True if massFound and intFound: break plt.figure(f"{clname}_other_radii_{mode}") plt.scatter([star.ra for star in cluster.unfilteredWide],[star.dec for star in cluster.unfilteredWide],s=1,c='lightgray',label='Unfiltered') plt.scatter([star.ra for star in cluster.filtered],[star.dec for star in cluster.filtered],s=2,c='midnightblue',label='Filtered') plt.xlabel("RA (deg)") medRad = getattr(cluster,f"medianRad_{mode}") medAngle = getattr(cluster,f"medianAngle_{mode}") mRad = getattr(cluster,f"halfMassRad_{mode}") mAngle = getattr(cluster,f"halfMassAngle_{mode}") lRad = getattr(cluster,f"halfLightRad_{mode}") lAngle = getattr(cluster,f"halfLightAngle_{mode}") print(medAngle) outline1 = Circle([x0,y0],medAngle,color='red',fill=False,ls='--',label=fr"Median Star Distance = {medAngle:.3f}$\degree$, {medRad:.3f}pc",alpha=1) outline2 = Circle([x0,y0],mAngle,color='darkgreen',fill=False,ls='--',label=fr"Half Mass Radius = {mAngle:.3f}$\degree$, {mRad:.3f}pc",alpha=1) outline3 = Circle([x0,y0],lAngle,color='purple',fill=False,ls='--',label=fr"Half Light Radius = {lAngle:.3f}$\degree$, {lRad:.3f}pc",alpha=1) plt.gca().add_patch(outline1) plt.gca().add_patch(outline2) plt.gca().add_patch(outline3) plt.legend(fontsize=10,loc='upper right') plt.axis('square') plt.ylabel("DEC (Deg)") plt.title(f"{clname} {mode.capitalize()} Various Radii".replace("_normalized",' Normalized')) plt.gcf().set_size_inches(8,8) plt.savefig(f"{cluster.imgPath}{clname}_otherRadii_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_otherRadii_{mode}.png",dpi=500) def checkLoaded(cList): if 'all' in cList: cList = [c.name for c in clusterList] else: for cl in cList: if not cl in clusters: loadClusters([cl]) return cList def saveResults(cList,outdir="results"): #Imports import numpy as np import dill import os global clusters global clusterList checkLoaded(cList) #Check and create the relevant directory paths to save/load the results if not os.path.isdir(f"{outdir}/"): os.mkdir(f"{outdir}/") if not os.path.isdir(f"{outdir}/pickled/"): os.mkdir(f"{outdir}/pickled/") else: for cl in cList: cluster = clusters[cl] #Creates a "result cluster" object from the cluster, effectively just stripping away lists rCl = resultClusterObj(cluster) #Pickle the result cluster object with open(f"{outdir}/pickled/{cluster.name}.pk1", 'wb') as output: dill.dump(rCl, output) #Store variables into an array to be printed as csv properties = [a for a in dir(rCl) if not a.startswith('_')] res = [getattr(rCl,p) for p in properties] #Stack into an array of 2 rows with variable names and values fin = np.vstack((properties,res)) np.savetxt(f"{outdir}/{cluster.name}.csv",fin,delimiter=',',fmt='%s') def loadResults(filter="None",indir="results"): #Imports import numpy as np import dill import os global resultList global resultsIn assert os.path.isdir("results/") resultList = [] for fn in os.listdir(indir+"/pickled/"): #Reads in instances from the saved pickle file with open(f"{indir}/pickled/{fn}",'rb') as input: res = dill.load(input) resultList.append(res) resultsIn = True toDict() def refreshProperties(cList=['all']): import numpy as np global catalogue global clusterList global clusters clusterCatalogue() checkLoaded(cList) #Loop through clusters for cluster in cList: reference = None for cl in catalogue: if str(cl.name) == str(cluster.name): reference = cl print(f"Catalogue match for {cluster.name} found") break if reference == None: print(f"Catalogue match for {cluster.name} was not found, please create one") continue #Filter all of the methods out of the properties list properties = [a for a in dir(reference) if not a.startswith('_')] #print(properties) #exec(f"print(reference.{properties[1]})") #print(properties) #Now we have a list of all the attributes assigned to the catalogue (the self.variables) for p in properties: prop = getattr(reference,p) #print(prop) exec(f"cluster.{p} = prop") try: if prop <= -98: print(f"{cluster.name} does not have a specified catalogue value for {p}") except: continue #Additional properties that may be useful for star in cluster.filtered: star.normRA = star.pmra*np.cos(star.dec*np.pi/180) print(f"{cluster.name} properties refreshed from catalogue") def statPlot(statX,statY,population="open",color="default",square=True,invertY=False,logX=False,logY=False,pointLabels=True,linFit=False,directory='default'): #Create plots of stat X vs stat Y across a population of clusters, similar to customPlot() #Can be set to use a custom list of clusters, or all clusters of a given type # import matplotlib import matplotlib.pyplot as plt import numpy as np from scipy.stats import linregress global clusters global clusterList global catalogue global resultsIn global resultList if not resultsIn: loadResults() #Filter out incorrect inputs if type(population) == str: population = population.lower() try: assert population == "open" or population == "globular" except: print("Specified population type not recognized") else: try: assert type(population) == list assert type(population[0]) == str except: print("Population type given is not valid, must be either a list of cluster name strings or a single string \'open\' or \'closed\'") return try: assert len(population) > 1 except: print("Population statistic plots cannot be made with fewer than 2 clusters given") return #Load cluster information from cList #This is going to involve using the resultCluster object to read data from each cluster folder in the cList cList = [] banList = ['NGC2204'] if type(population) == str: for res in resultList: if res.clType.lower() == population and not res.name in banList: cList.append(res) else: for res in resultList: if res.name in population: cList.append(res) if statX.lower() == "b_r" and statY.lower() == "g_mag": #Corrected CMD overlay NUM_COLORS = len(cList) cm = plt.get_cmap('nipy_spectral') plt.figure("uncorrected") plt.title("Cluster Overlay") plt.xlabel("Observed B-R") plt.ylabel("Apparent G Mag") plt.gca().invert_yaxis() plt.gca().set_prop_cycle('color', [cm(1.025*i/NUM_COLORS) for i in range(NUM_COLORS)]) plt.figure("unshifted") plt.title("Corrected Cluster Overlay") plt.xlabel("Dereddened B-R") plt.ylabel("Absolute G Mag") plt.gca().invert_yaxis() plt.gca().set_prop_cycle('color', [cm(1.025*i/NUM_COLORS) for i in range(NUM_COLORS)]) plt.figure("shifted") plt.title("Corrected Cluster Overlay - Offset") plt.xlabel("Dereddened B-R") plt.ylabel("Absolute G Mag") plt.gca().invert_yaxis() plt.gca().set_prop_cycle('color', [cm(1.025*i/NUM_COLORS) for i in range(NUM_COLORS)]) index = 0 offset = 2.5 for cluster in cList: try: path = cluster.dataPath except: path = f"clusters/{cluster.name}/data/" condensed = np.genfromtxt(f"{path}condensed.csv",delimiter=",") cluster.condensed = condensed #Adjust by cluster.reddening and cluster.dist_mod x1 = [a[0] for a in condensed] y1 = [a[1] for a in condensed] x2 = [a[0]-cluster.reddening for a in condensed] y2 = [a[1]-2.1*cluster.reddening-cluster.dist_mod for a in condensed] x3 = [a[0]-cluster.reddening for a in condensed] y3 = [a[1]-2.1*cluster.reddening-cluster.dist_mod+index*offset for a in condensed] index += 1 plt.figure("uncorrected") plt.scatter(x1,y1,label=f"{cluster.name}") plt.figure("unshifted") plt.axvline(x=1.6,ymax=0.5,color='black',linestyle='--') plt.axhline(y=4,xmin=0.59,color='black',linestyle='--') plt.scatter(x2,y2,label=f"{cluster.name}") plt.figure("shifted") plt.scatter(x3,y3,label=f"{cluster.name}") plt.axvline(x=1.6,color='black',linestyle='--') # if 'NGC2301' in cluster.name: # for a,b in zip(x2,y2): # print(f"{a},{b}") plt.figure("uncorrected") plt.legend(fontsize=10,loc='upper right') plt.gcf().set_size_inches(8,6) plt.savefig(f"results/plots/pdf/{population}_clusters_stacked_cmd_apparent.pdf") plt.savefig(f"results/plots/png/{population}_clusters_stacked_cmd_apparent.png",dpi=500) plt.figure("unshifted") plt.legend(fontsize=10,loc='upper right') plt.gcf().set_size_inches(8,6) plt.savefig(f"results/plots/pdf/{population}_clusters_stacked_cmd_absolute.pdf") plt.savefig(f"results/plots/png/{population}_clusters_stacked_cmd_absolute.png",dpi=500) plt.figure("shifted") plt.legend(fontsize=10,loc='upper right') plt.gcf().set_size_inches(8,6) plt.savefig(f"results/plots/pdf/{population}_clusters_stacked_cmd_shifted.pdf") plt.savefig(f"results/plots/png/{population}_clusters_stacked_cmd_shifted.png",dpi=500) else: x = [getattr(a, statX) for a in cList] y = [getattr(a, statY) for a in cList] plt.figure() plt.xlabel(f"{statX}") plt.ylabel(f"{statY}") if pointLabels: for cluster in cList: plt.scatter(getattr(cluster, statX),getattr(cluster, statY),label=cluster.name) plt.legend(fontsize="small") else: plt.scatter(x,y) if linFit: reg = linregress(x,y) plt.plot(x,[reg[0]*a+reg[1] for a in x]) plt.savefig(f"SpecificPlots/pdf/{population}_{statX}_{statY}.pdf") plt.savefig(f"SpecificPlots/png/{population}_{statX}_{statY}.png",dpi=500) return def ageMassFit(t,m0,k): import numpy as np return 1 + m0*np.exp(-1*k*t) def extinctionLaw(d,M0): import numpy as np return M0 -2.5*np.log10(1/(4*np.pi*d**2)) def resultPlots(): #Imports import matplotlib.pyplot as plt import numpy as np from scipy.stats import linregress from scipy.optimize import curve_fit global clusters global clusterList global catalogue global resultsIn global resultList if not resultsIn: loadResults() #Select open clusters from resultList banList = ['NGC2204'] cList = [] for res in resultList: if res.clType.lower() == "open" and not res.name in banList: cList.append(res) #Filtered mass versus age fname = "mass_vs_age_filtered" plt.figure(fname) plt.title(f"{len(cList)} Open Clusters") plt.xlabel("Fit Age (Gyr)") plt.ylabel(r"Mean Cluster Member Mass ($M_{\odot}$)") plt.scatter([c.fit_age for c in cList],[c.meanProxyMass for c in cList]) plt.savefig(f"results/plots/pdf/{fname}.pdf") plt.savefig(f"results/plots/png/{fname}.png",dpi=500) #Bounded mass versus age fname = "mass_vs_age_bounded" plt.figure(fname) plt.title(f"{len(cList)} Open Clusters - BR-RP Limit Enforced") plt.xlabel("Fit Age (Gyr)") plt.ylabel(r"Mean Cluster Member Mass ($M_{\odot}$)") x,y = [c.fit_age for c in cList],[c.meanBoundedProxyMass for c in cList] plt.scatter(x,y) fit,var = curve_fit(ageMassFit,x,y,p0=[8,1],maxfev=1000) xr = list(np.linspace(min(x),max(x),101)) fitLabel = fr"$y = 1+{fit[0]:.3f}e^{{-{fit[1]:.3f}t}}$" + "\n" + fr"Uncertainties = $\pm{var[0][0]:.3f}, \pm{var[1][1]:.3f}$" plt.plot(xr,[ageMassFit(a,fit[0],fit[1]) for a in xr],label=fitLabel) plt.legend() plt.savefig(f"results/plots/pdf/{fname}.pdf") plt.savefig(f"results/plots/png/{fname}.png",dpi=500) #Mass intercept versus age fname = "mass_intercept_vs_age_bounded" plt.figure(fname) plt.title(f"{len(cList)} Open Clusters - BR-RP Limit Enforced") plt.xlabel("Fit Age (Gyr)") plt.ylabel(r"Mean Stellar Mass in Core ($M_{\odot}$)") x,y = [c.fit_age for c in cList],[c.mass_intercept_bounded for c in cList] plt.scatter(x,y) fit,var = curve_fit(ageMassFit,x,y,p0=[8,1],maxfev=1000) xr = list(np.linspace(min(x),max(x),101)) fitLabel = fr"$y = 1+{fit[0]:.3f}e^{{-{fit[1]:.3f}t}}$" + "\n" + fr"Uncertainties = $\pm{var[0][0]:.3f}, \pm{var[1][1]:.3f}$" plt.plot(xr,[ageMassFit(a,fit[0],fit[1]) for a in xr],label=fitLabel) plt.legend() plt.savefig(f"results/plots/pdf/{fname}.pdf") plt.savefig(f"results/plots/png/{fname}.png",dpi=500) #Mass slope versus age fname = "mass_slop_vs_age_bounded" plt.figure(fname) plt.title(f"{len(cList)} Open Clusters - BR-RP Limit Enforced") plt.xlabel("Fit Age (Gyr)") plt.ylabel(r"IQM Stellar Mass Dropoff ($\frac{M_{\odot}}{pc}$)") x,y = [c.fit_age for c in cList],[c.mass_slope_bounded for c in cList] plt.scatter(x,y) plt.savefig(f"results/plots/pdf/{fname}.pdf") plt.savefig(f"results/plots/png/{fname}.png",dpi=500) #Magnitude versus distance (Extinction law) fname = "mag_vs_dist_bounded" plt.figure(fname) plt.title(f"{len(cList)} Open Clusters - BR-RP Limit Enforced") plt.xlabel("Cluster Distance from Earth (pc)") plt.ylabel(r"Mean Apparent G Magnitude") x,y = [c.meanDist for c in cList],[c.mean_bounded_g_mag for c in cList] plt.scatter(x,y) fit,var = curve_fit(extinctionLaw,x,y,maxfev=1000) xr = list(np.linspace(min(x),max(x),101)) plt.plot(xr,[extinctionLaw(a,fit[0]) for a in xr],label="Inverse Square Law \n" + fr" $M_0 = {fit[0]:.3f} \pm {var[0][0]:.3f}$") plt.gca().invert_yaxis() plt.legend() plt.savefig(f"results/plots/pdf/{fname}.pdf") plt.savefig(f"results/plots/png/{fname}.png",dpi=500) #Bounded fraction versus distance fname = "bounded_fraction_vs_dist" plt.figure(fname) plt.title(f"{len(cList)} Open Clusters - BR-RP Limit Enforced") plt.xlabel("Cluster Distance from Earth (pc)") plt.ylabel("Fraction Unaffected by BP-RP Limit") x,y = [c.meanDist for c in cList],[c.fractionBounded for c in cList] plt.scatter(x,y) plt.savefig(f"results/plots/pdf/{fname}.pdf") plt.savefig(f"results/plots/png/{fname}.png",dpi=500) #Radii plt.figure() plt.scatter([c.meanGalacticDist for c in cList],[c.halfLightRad_bounded/c.medianRad_bounded for c in cList]) def boundedStats(cList,xmax=1.6,saveCl=True,unloadCl=True): import numpy as np global clusters global subList for cl in cList: checkLoaded([cl]) cluster = clusters[cl] subList = [star for star in cluster.filtered if not (star.b_r-cluster.reddening > xmax and star.g_mag > cluster.cltpy)] cluster.bounded = subList #Windowed properties (over the xmin to xmax range) cluster.meanBoundedProxyMass = np.mean([a.proxyMass for a in subList]) cluster.totalBoundedProxyMass = np.sum([a.proxyMass for a in subList]) cluster.numBounded = len(subList) cluster.fractionBounded = len(subList)/len(cluster.filtered) cluster.mean_bounded_b_r = np.mean([a.b_r for a in subList]) cluster.mean_bounded_g_mag = np.mean([a.g_mag for a in subList]) if saveCl: saveClusters([cl]) saveResults([cl]) if unloadCl: unloadClusters([cl]) def tryFits(fitVar='fit_age'): from scipy.stats import linregress global resultsIn global resultList global props global r2 if not resultsIn: loadResults() cList = [] for res in resultList: if res.clType.lower() == "open": cList.append(res) if 'all' in fitVar: #List of plottable variables props = dir(cList[0]) props = [a for a in props if not '__' in a] propList = [a for a in props if type(getattr(cList[0],a)) == float] propList.remove('turnPoint') r2 = [] for pr in propList: #List of plottable variables props = dir(cList[0]) props = [a for a in props if not '__' in a] props = [a for a in props if type(getattr(cList[0],a)) == float] props.remove('turnPoint') props.remove(pr) for prop in props: x = [getattr(a, pr) for a in cList] y = [getattr(a, prop) for a in cList] reg = linregress(x,y) r2.append((pr,prop,reg[2]**2)) r2 = sorted(r2,key = lambda x: x[2],reverse=True) print("Top 100 r^2 values:") for r in r2[:200]: print(f"{r[0]} | {r[1]} | {r[2]}") else: #List of plottable variables props = dir(cList[0]) props = [a for a in props if not '__' in a] props = [a for a in props if type(getattr(cList[0],a)) == float] props.remove('turnPoint') props.remove(fitVar) r2 = [] for prop in props: x = [getattr(a, fitVar) for a in cList] y = [getattr(a, prop) for a in cList] reg = linregress(x,y) r2.append((prop,reg[2]**2)) r2 = sorted(r2,key = lambda x: x[1],reverse=True) print("Top 20 r^2 values:") for r in r2[:20]: print(f"{r[0]} | {r[1]}") def prelimPlot(cl): import matplotlib.pyplot as plt cluster = clusters[cl] plt.scatter([a.ra for a in cluster.unfilteredWide],[a.dec for a in cluster.unfilteredWide],s=0.1) plt.figure() plt.scatter([a.pmra for a in cluster.unfilteredWide],[a.pmdec for a in cluster.unfilteredWide],s=0.1) # plt.figure() # plt.scatter([a.pmra for a in cluster.unfilteredWide],[a.pmdec for a in cluster.unfilteredWide],s=0.1,c=[a.par for a in cluster.unfilteredWide]) # plt.set_cmap('cool') # clb = plt.colorbar() plt.figure() plt.scatter([a.b_r for a in cluster.unfilteredWide],[a.g_mag for a in cluster.unfilteredWide],s=0.1) plt.gca().invert_yaxis() # plt.figure() # plt.scatter([a.par for a in cluster.unfilteredWide],[a.par for a in cluster.unfilteredWide],s=0.1,c=[(a.pmra**2 + a.pmdec**2)**0.5 for a in cluster.unfilteredWide]) # plt.set_cmap('cool')

37.599953

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0.588674

try: runCount += 1 except: isoIn = False clIn = False cataIn = False closePlots = False resultsIn = False clusterList = [] clusters=[] isochrones = [] isoList = [] catalogue = [] runCount = 1 class resultClusterObj: def __init__(self,cl): import numpy as np global properties properties = [a for a in dir(cl) if not a.startswith('_')] for prop in properties: if eval(f"type(cl.{prop})") == float or eval(f"type(cl.{prop})") == np.float64 or eval(f"type(cl.{prop})") == int: exec(f"self.{prop} = float(cl.{prop})") elif eval(f"type(cl.{prop})") == str: exec(f"self.{prop} = cl.{prop}") self.name = cl.name self.clType = cl.clType class clusterObj: def __init__(self,name='genericCluster',basedir='clusters/',brightThreshold=15): self.basedir = basedir self.dataPath = self.basedir + f"{name}/data/" self.imgPath = self.basedir + f"{name}/plots/" self.unfilteredWide = [] self.unfilteredNarrow = [] self.filtered = [] self.mag = [] self.iso = [] self.condensed = [] self.condensed0 = [] self.condensedInit=[] self.unfilteredBright = [] self.filteredBright = [] self.brightmag = [] self.distFiltered = [] self.binaries = [] self.stars = [] self.brightThreshold = brightThreshold self.mean_par = 0 self.stdev_par = 0 self.mean_ra = 0 self.mean_dec = 0 self.stdev_ra = 0 self.stdev_dec = 0 self.mean_pmra = 0 self.stdev_pmra = 0 self.mean_pmdec = 0 self.stdev_pmdec = 0 self.mean_a_g = 0 self.stdev_a_g = 0 self.mean_e_bp_rp = 0 self.stdev_e_bp_rp = 0 self.mean_par_over_ra = 0 self.stdev_par_over_ra = 0 self.dist_mod = 0 self.turnPoint = 0 self.reddening = 0 self.radDist = 0 self.massLoaded = False self.name = name self.clType = "None" self.pmra_min = -99 self.pmra_max = -99 self.pmdec_min = -99 self.pmdec_max = -99 self.par_min = -99 self.par_max = -99 self.cltpx = -99 self.cltpy = -99 self.noise_cutoff = -99 import os if not os.path.isdir(self.dataPath): os.mkdir(self.dataPath) if not os.path.isdir(self.imgPath): os.mkdir(self.imgPath) if not os.path.isdir(f"{self.imgPath}/png"): os.mkdir(f"{self.imgPath}/png") class starObj: def __init__(self,name,source_id,ra,ra_err,dec,dec_err,par,par_err,par_over_err,pmra,pmra_err,pmdec,pmdec_err, ra_dec_corr,ra_par_corr,ra_pmra_corr,ra_pmdec_corr,dec_par_corr,dec_pmra_corr,dec_pmdec_corr,par_pmra_corr,par_pmdec_corr,pmra_pmdec_corr, astro_n_obs,astro_n_good_obs,astro_n_bad_obs,astro_gof,astro_chi2,astro_noise,astro_noise_sig,astro_nu_eff, pseudocolor,pseudocolor_err,ra_pseudocolor_corr,dec_pseudocolor_corr,par_pseudocolor_corr,pmra_pseudoclor_corr,pmdec_pseudocolor_corr, astro_sigma5d,duplicated_source, g_flux,g_flux_err,g_mag, b_flux,b_flux_err,b_mag, r_flux,r_flux_err,r_mag, b_over_r_excess,b_r,b_g,g_r, radvel,radvel_err,radvel_num_transits,radvel_teff,radvel_feh, l,b,long,lat): import numpy as np self.name = name self.source_id = source_id self.ra = float(ra) self.ra_err = float(ra_err) self.dec = float(dec) self.dec_err = float(dec_err) self.par = float(par) self.par_err = float(par_err) self.par_over_err = float(par_over_err) self.pmra = float(pmra) self.pmra_err = float(pmra_err) self.pmdec = float(pmdec) self.pmdec_err = float(pmdec_err) self.ra_dec_corr = float(ra_dec_corr) self.ra_par_corr = float(ra_par_corr) self.ra_pmra_corr = float(ra_pmra_corr) self.ra_pmdec_corr = float(ra_pmdec_corr) self.dec_par_corr = float(dec_par_corr) self.dec_pmra_corr = float(dec_pmra_corr) self.dec_pmdec_corr = float(dec_pmdec_corr) self.par_pmra_corr = float(par_pmra_corr) self.par_pmdec_corr = float(par_pmdec_corr) self.pmra_pmdec_corr = float(pmra_pmdec_corr) self.astro_n_obs = float(astro_n_obs) self.astro_n_good_obs = float(astro_n_good_obs) self.astro_n_bad_obs = float(astro_n_bad_obs) self.astro_gof = float(astro_gof) self.astro_chi2 = float(astro_chi2) self.astro_noise = float(astro_noise) self.astro_noise_sig = float(astro_noise_sig) self.astro_nu_eff = float(astro_nu_eff) self.astro_sigma5d = float(astro_sigma5d) self.duplicated_source = bool(duplicated_source) self.g_flux = float(g_flux) self.g_flux_err = float(g_flux_err) self.g_mag = float(g_mag) self.b_flux = float(b_flux) self.b_flux_err = float(b_flux_err) self.b_mag = float(b_mag) self.r_flux = float(r_flux) self.r_flux_err = float(r_flux_err) self.r_mag = float(r_mag) self.b_over_r_excess = float(b_over_r_excess) self.b_r = float(b_r) self.b_g = float(b_g) self.g_r = float(g_r) self.radvel = float(radvel) self.radvel_err = float(radvel_err) self.radvel_num_transits=float(radvel_num_transits) self.radvel_teff = float(radvel_teff) self.radvel_feh = float(radvel_feh) self.l = float(l) self.b = float(b) self.long = float(long) self.lat = float(lat) self.member = 0 self.binary = 0 self.radDist = 0 self.par_over_ra = float(par)/float(ra) self.par_over_dec = float(par)/float(dec) self.par_over_pmra = float(par)/float(pmra) self.par_over_pmdec = float(par)/float(pmdec) self.normRA = self.ra*np.cos(self.dec*np.pi/180) self.vosaPoints = [] self.excess = 0 class isochroneObj: def __init__(self,age=404,feh=404,afe=404,y=404,basedir='isochrones/',subdir='processed',isodir=''): self.basedir = basedir self.subdir = subdir self.isodir = isodir self.starList = [] self.age = age self.feh = feh self.afe = afe self.y = y self.name = f"feh_{feh}_afe_{afe}_age_{age}_y_{y}" self.distance = 0 self.coeff = [] self.g = [] self.br = [] class fakeStarObj: def __init__(self,g_mag,b_mag,r_mag): self.g_mag = g_mag self.b_mag = b_mag self.r_mag = r_mag self.b_r = self.b_mag-self.r_mag self.b_g = self.b_mag-self.g_mag self.g_r = self.g_mag-self.r_mag self.score = 0 class mistStar: def __init__(self,properties): for prop,val in properties: if "inf" in str(val): val = 50 exec(f"self.{prop} = {val}") class condensedPoint: def __init__(self,b_r,g_mag,weight): self.b_r = b_r self.g_mag = g_mag self.weight = weight class vosaPoint: def __init__(self,filterID,wavelength,obs_flux,obs_error,flux,flux_error,excess): self.filterID = filterID self.wavelength = wavelength self.obs_flux = obs_flux self.obs_error = obs_error self.flux = flux self.flux_error = flux_error self.excess = excess class cataloguedCluster(): def __init__(self,name,clType,pmra_min,pmra_max,pmdec_min,pmdec_max,par_min,par_max,cltpx,cltpy,noise_cutoff): self.name = str(name) self.clType = str(clType) self.pmra_min = float(pmra_min) self.pmra_max = float(pmra_max) self.pmdec_min = float(pmdec_min) self.pmdec_max = float(pmdec_max) self.par_min = float(par_min) self.par_max = float(par_max) self.cltpx = float(cltpx) self.cltpy = float(cltpy) self.noise_cutoff = float(noise_cutoff) class Datum: from matplotlib import colors as mcolors colorin = mcolors.to_rgba("red") colorout = mcolors.to_rgba("blue") def __init__(self, x, y, include=False): self.x = x self.y = y if include: self.color = self.colorin else: self.color = self.colorout class LassoManager: def __init__(self, ax, data, cluster): from matplotlib.collections import RegularPolyCollection self.axes = ax self.canvas = ax.figure.canvas self.data = data self.cluster = cluster self.Nxy = len(data) facecolors = [d.color for d in data] self.xys = [(d.x, d.y) for d in data] self.collection = RegularPolyCollection( 6, sizes=(5,), facecolors=facecolors, offsets=self.xys, transOffset=ax.transData) ax.add_collection(self.collection) self.cid = self.canvas.mpl_connect('button_press_event', self.on_press) def callback(self, verts): from matplotlib import path global coords global clusters cluster = clusters[self.cluster.name] facecolors = self.collection.get_facecolors() p = path.Path(verts) ind = p.contains_points(self.xys) cluster.binaries = [] for i in range(len(self.xys)): if ind[i]: facecolors[i] = Datum.colorin star = cluster.filtered[[a.b_r for a in cluster.filtered].index(self.xys[i][0])] cluster.binaries.append(star) else: facecolors[i] = Datum.colorout self.canvas.draw_idle() self.canvas.widgetlock.release(self.lasso) del self.lasso def on_press(self, event): from matplotlib.widgets import Lasso if self.canvas.widgetlock.locked(): return if event.inaxes is None: return self.lasso = Lasso(event.inaxes, (event.xdata, event.ydata), self.callback) self.canvas.widgetlock(self.lasso) def clusterCatalogue(types='all'): import numpy as np import pandas as pd global data global catalogue global cataIn data = pd.read_csv("catalogue.csv",sep=',',dtype=str) data = data.to_numpy(dtype=str) cata = [] for row in data: cata.append(cataloguedCluster(*row)) if types == 'all': catalogue = cata cataIn = True return def readClusters(cList=["M67"],basedir="clusters/",smRad=0.35): import numpy as np import pandas as pd global clusterList global clusters global stars global clIn global catalogue try: if clIn and len(clusterList) > 0: for clname in cList: if clname in clusters: unloadClusters([clname]) except: clusterList=[] if not cataIn: clusterCatalogue() for clname in cList: cluster = clusterObj(name=clname,basedir=basedir) reference = None for cl in catalogue: if str(cl.name) == str(clname): reference = cl print(f"Catalogue match for {clname} found") break if reference == None: print(f"Catalogue match for {clname} was not found, please create one") continue properties = [a for a in dir(reference) if not a.startswith('_')] print(properties) for p in properties: prop = getattr(reference,p) exec(f"cluster.{p} = prop") try: if prop <= -98: print(f"{clname} does not have a specified catalogue value for {p}") except: continue starlist = pd.read_csv(cluster.dataPath+"wide.csv",sep=',',dtype=str) stars = pd.read_csv(cluster.dataPath+"wide.csv",sep=',',dtype=str) starlist = starlist.to_numpy(dtype=str) print(f"{clname} initial length: {len(starlist)}") starlist = preFilter(starlist) print(f"{clname} post-prefiltered length: {len(starlist)}") ramean = np.mean([float(x) for x in starlist[:,1]]) decmean = np.mean([float(x) for x in starlist[:,3]]) for s in starlist: star = starObj(*s) cluster.unfilteredWide.append(star) if np.less_equal(star.g_mag,cluster.brightThreshold): cluster.unfilteredBright.append(star) clusterList.append(cluster) calcStats(cluster,mode='narrow') if not 'YSO' in clname: rmOutliers() clIn = True toDict() def pad(string, pads): spl = string.split(',') return '\n'.join([','.join(spl[i:i+pads]) for i in range(0,len(spl),pads)]) def readIso(basedir='isochrones/',subdir='MIST_raw/'): #Imports import os import re global isochrone_headers global isoList global isoIn path = basedir + subdir isoList = [] for fn in os.listdir(path): #Read in file main = open(path+fn).read() main = main.split("\n") #Relevant variables from headers N_iso = int(main[7].split("=")[1]) index = 13 varList = re.sub("\s+", ",", main[5].strip()).split(",") afe = varList[4] feh = varList[3] y = varList[1] z = varList[2] v_vcrit = varList[5] #Column labels #Replace any number of spaces with a single comma, then replace a few problematic phrases and split the list by commas isochrone_headers = re.sub("\s+", ",", main[12].replace("2MASS","TwoMASS").replace("[Fe/H]","feh").strip()).split(",")[1:] for idx in range(0,N_iso): N_stars = int(re.sub("\s+", "," , main[index-3].split("=")[1]).split(",")[1]) #print(f"Iso = {idx} N_stars = {N_stars}") #Populate a single isochrone stars = [] for i in range(index,index+N_stars): #Send the header and values to the mistStar object #print(f"i = {i}") values = [float(a) for a in re.sub("\s+", "," , main[i].strip()).split(",")] properties = zip(isochrone_headers,values) stars.append(mistStar(properties)) #Create the isochrone from the list of stars age = round(10**values[1]/1e9,3) iso = isochroneObj(age,feh,afe,y) iso.starList = stars iso.br = [star.Gaia_BP_EDR3-star.Gaia_RP_EDR3 for star in stars] iso.g = [star.Gaia_G_EDR3 for star in stars] isoList.append(iso) index += N_stars + 5 isoIn = True toDict() def checkIsoDupes(): global isochrones global isoList names = [] for iso in isoList: if iso.name in names: print(iso.name) else: names.append(iso.name) def processIso(basedir='isochrones/',subdir='raw/'): #Imports import os import re path = basedir + subdir for fn in os.listdir(path): main = open(path+fn).read() part = main.split('\n\n\n') part[0] = part[0].split(' for a in range(len(part)): temp = part[a].split(' age = temp.strip() out = part[a].split('\n',2)[2] out = re.sub("\s+", ",", out.strip()) out = pad(out,8) filename = f"{basedir}processed/"+fn.split('.')[0]+'/'+age+".csv" os.makedirs(os.path.dirname(filename), exist_ok=True) with open(filename,"w") as f: f.write(out) def readIsochrones(basedir='isochrones/',subdir='processed/'): #Imports import os import numpy as np global isoList global isoIn isoList=[] for folder in os.listdir(basedir+subdir): for fn in os.listdir(basedir+subdir+folder): #Get the age and metallicities of the isochrones ageStr = fn.split('.csv')[0] fehStr = folder.split('feh')[1].split('afe')[0] afeStr = folder.split('afe')[1].split('y')[0] if 'y' in folder: yStr = folder.split('y')[1] else: yStr = '0' feh = float(fehStr[1]+fehStr[2])/10 afe = float(afeStr[1])/10 age = float(ageStr) y = int(yStr) if fehStr[0] == 'm': feh = feh*-1 if afeStr[0] == 'm': afe = afe*-1 #Debug #print(f"folder:{folder} fn:{fn} fehStr:{fehStr} feh:{feh} afeStr:{afeStr} afe:{afe} ageStr:{ageStr} age:{age}") #Create isochone object iso = isochroneObj(age=age,feh=feh,afe=afe,y=y,basedir=basedir,subdir=subdir,isodir=folder+'/') isoArr = np.genfromtxt(basedir+subdir+folder+"/"+fn, delimiter=",") for s in isoArr: star = fakeStarObj(s[5],s[6],s[7]) iso.starList.append(star) iso.br.append(s[6]-s[7]) iso.g.append(s[5]) isoList.append(iso) isoIn = True toDict() def preFilter(starList): #Imports import numpy as np final = [] #Columns to be checked for NaN values. If an NaN is present in this column, the entry(star) is discarded from the "unfiltered" list #2-12 is the astrometry #42,45,48 are the g,bp,rp magnitudes #50-52 are the color indices cols = list(range(2,13))+[42]+[45]+[48]+list(range(50,53)) #Filters out NaN values except for the last two columns for n,s in enumerate(starList): dump = False for c in cols: if np.isnan(float(s[c])): dump = True if not dump: final.append(starList[n]) #Reshapes array final = np.array(final) return final def rmOutliers(): #Imports global clusterList import numpy as np for cluster in clusterList: if cluster.clType.lower() == "globular": scale = 4 else: scale = 1.5 #Variables pmthreshold = 5 pmpthreshold = 50 parthreshold = 5 posthreshold = 5 toRemove=[] #print(cluster.mean_pmra,cluster.mean_pmdec,cluster.stdev_pmra,cluster.stdev_pmdec) #print(len(cluster.unfilteredWide)) #Classifies outliers for star in cluster.unfilteredWide: if cluster.name == "NGC188": if star.ra > 100: toRemove.append(star) #print(np.sqrt(((star.pmra-cluster.mean_pmra)*np.cos(np.pi/180*star.pmdec))**2+(star.pmdec-cluster.mean_pmdec)**2),star.pmra,star.pmdec) if np.greater(np.sqrt(((star.pmra-cluster.mean_pmra)*np.cos(np.pi/180*star.pmdec))**2+(star.pmdec-cluster.mean_pmdec)**2),pmthreshold) or np.greater(np.sqrt(((star.ra-cluster.mean_ra)*np.cos(np.pi/180*star.dec))**2+(star.dec-cluster.mean_dec)**2),posthreshold) or np.greater(abs(star.par),parthreshold): #if np.greater(np.sqrt((star.pmra-cluster.mean_pmra)**2+(star.pmdec-cluster.mean_pmdec)**2),threshold): toRemove.append(star) #Removes the outliers from the array for rm in toRemove: cluster.unfilteredWide.remove(rm) try: cluster.unfilteredNarrow.remove(rm) except ValueError: pass #print(len(cluster.unfilteredWide)) def calcStats(cluster,mode='filtered'): #Imports import numpy as np #Reads in all the values for a cluster par=[] par_err=[] ra=[] dec=[] pmra=[] pmdec=[] gmag = [] br = [] # a_g=[] # e_bp_rp=[] loopList=[] checkLoaded([cluster]) if type(cluster) == str: cluster = clusters[cluster] if mode == 'bright': loopList = cluster.filteredBright elif mode == 'narrow': loopList = cluster.unfilteredNarrow elif mode == 'filtered': loopList = cluster.filtered for star in loopList: par.append(star.par) par_err.append(star.par_err) pmra.append(star.pmra) pmdec.append(star.pmdec) ra.append(star.ra) dec.append(star.dec) gmag.append(star.g_mag) br.append(star.b_r) # if not np.isnan(star.a_g) and not star.a_g == 0: # a_g.append(star.a_g) # if not np.isnan(star.e_bp_rp) and not star.e_bp_rp == 0: # e_bp_rp.append(star.e_bp_rp) #Calculate the statistics cluster.mean_par = np.mean(par[:]) cluster.mean_ra = np.mean(ra[:]) cluster.mean_dec = np.mean(dec[:]) cluster.stdev_ra = np.std(ra[:]) cluster.stdev_dec = np.std(dec[:]) cluster.stdev_par = np.std(par[:]) cluster.mean_pmra = np.mean(pmra[:]) cluster.stdev_pmra = np.std(pmra[:]) cluster.mean_pmdec = np.mean(pmdec[:]) cluster.stdev_pmdec = np.std(pmdec[:]) # cluster.mean_a_g = np.mean(a_g[:]) # cluster.stdev_a_g = np.std(a_g[:]) # cluster.mean_e_bp_rp = np.mean(e_bp_rp[:]) # cluster.stdev_e_bp_rp = np.std(e_bp_rp[:]) cluster.mean_par_over_ra = np.mean([x/y for x,y in zip(par,ra)]) cluster.stdev_par_over_ra = np.std([x/y for x,y in zip(par,ra)]) cluster.mean_par_err = np.mean(par_err[:]) cluster.dist_mod = 5*np.log10(1000/cluster.mean_par)-5 for star in loopList: star.radDist = np.sqrt((star.ra-cluster.mean_ra)**2+(star.dec-cluster.mean_dec)**2) star.normRadDist = np.sqrt((star.ra*np.cos(star.dec*np.pi/180)-cluster.mean_ra*np.cos(cluster.mean_dec*np.pi/180))**2+(star.dec-cluster.mean_dec)**2) def saveClusters(cList): #Imports import dill saveResults(cList) #Creates a pickle file with all of the saved instances for cl in cList: cluster = clusters[cl] #print(cluster.name,id(cluster)) with open(f"{cluster.dataPath}filtered.pk1", 'wb') as output: dill.dump(cluster, output) def saveIsochrones(): #Imports import dill global clusterList #Creates a pickle file with all of the saved instances for iso in isoList: with open(f"{iso.basedir}pickled/{iso.name}.pk1", 'wb') as output: dill.dump(iso, output) def loadClusters(clusterNames=["M67"],basedir='clusters/'): #Imports import dill global clusterList global clusters global clIn for clusterName in clusterNames: if clusterName in clusters: unloadClusters([clusterName]) #Reads in instances from the saved pickle file with open(f"{basedir}{clusterName}/data/filtered.pk1",'rb') as input: cluster = dill.load(input) clusterList.append(cluster) clIn = True toDict() def loadIsochrones(basedir='isochrones/'): #Imports import dill import os global isoList global isoIn isoList=[] for fn in os.listdir(basedir+"pickled/"): #Reads in instances from the saved pickle file with open(f"{basedir}pickled/{fn}",'rb') as input: iso = dill.load(input) isoList.append(iso) isoIn = True toDict() def unloadClusters(cList=['all']): #Imports global clusterList global clusters if 'all' in cList: cList = [cluster.name for cluster in clusterList] for cl in cList: cluster = clusters[cl] clusterList.remove(cluster) clusters.pop(cl) del cluster def dataProcess(cList,load=False,fit=True,unload=True,plotting=True,member=True,save=True,close=True): #This method is largely intended for re-processing a bulk batch of clusters that have already been processed before, #meaning they already have condensed point lists or you are already aware of their fitting quality #Imports import matplotlib.pyplot as plt global clusterList global clusters global closePlots if not isoIn: loadIsochrones() loadList = ["M15","M12","M39","M46","M67","NGC188","NGC2355","NGC2158","IC4651","NGC6791","NGC2360","NGC2204"] for cl in cList: if cl in loadList: condensing = "load" else: condensing = "auto" if load: loadClusters([cl]) else: readClusters([cl]) turboFilter([cl]) if close: plt.close('all') if fit: turboFit([cl],condensing=condensing) if plotting: plot([cl],['pos','pm','cmd','quiver','iso']) if close: plt.close('all') if member: proxyMatch([cl]) boundedStats([cl],saveCl=False,unloadCl=False) membership(cl,mode='filtered') membership(cl,mode='bounded',N=75) plt.close('all') if save: saveClusters([cl]) saveResults([cl]) if unload: unloadClusters([cl]) def turboFilter(cl=["all"]): #Imports global clusterList cList = checkLoaded(cl) for clus in cList: cluster = clusters[clus] cluster.filteredBright,cluster.brightmag = pmFilter(cluster.unfilteredBright,cluster.name) print(f"==========================={cluster.name}===========================") print(f"bright unf/pm fil: {len(cluster.unfilteredBright)} / {len(cluster.filteredBright)}") calcStats(cluster,mode='bright') distFilter(cluster) print(f"dist(all): {len(cluster.distFiltered)}") cluster.filtered,cluster.mag = pmFilter(cluster.distFiltered,cluster.name) #Manual filtering of extraneous points cluster.filtered,cluster.mag = manualFilter(cluster) print(f"pm(all): {len(cluster.filtered)}") customPlot('b_r','g_mag',cluster.name,'filtered',iso=True,square=False,color='astro_sigma5d') magnitude = cutNoise(cluster) print(f"noise cutoff: mag {magnitude} length {len(cluster.filtered)}") customPlot('b_r','g_mag',cluster.name,'filtered',iso=True,square=False,color='astro_sigma5d') calcStats(cluster,mode='filtered') setFlag() def manualFilter(cluster): #This exists to remove any points that may or may not be relevant to the cluster but are prohibiting the fit from happening if "M35" in cluster.name: filtered = [star for star in cluster.filtered if star.g_mag > 9 or star.b_r < 1] return filtered,magList(filtered) else: return cluster.filtered,cluster.mag def magList(filtered): import numpy as np mag = np.empty((0,2)) for star in filtered: mag = np.r_[mag,[[star.b_r,star.g_mag]]] def pmFilter(starList,name): #Imports import numpy as np filtered = [] mag = np.empty((0,2)) cluster = clusters[name] assert cluster.name == name #Apply an elliptical filter to the proper motion space pmra_width = (cluster.pmra_max-cluster.pmra_min)/2 pmdec_width = (cluster.pmdec_max-cluster.pmdec_min)/2 pmra_center = cluster.pmra_min+pmra_width pmdec_center = cluster.pmdec_min+pmdec_width print(pmra_center,pmdec_center) for star in starList: if (star.pmra-pmra_center)**2/pmra_width**2 + (star.pmdec-pmdec_center)**2/pmdec_width**2 <= 1: filtered.append(star) mag = np.r_[mag,[[star.b_r,star.g_mag]]] assert len(filtered) > 1 print(len(filtered)) return filtered,mag def distFilter(cluster): #Imports import numpy as np if cluster.par_min == 0 or cluster.par_max == 0: threshold = 1.5*cluster.mean_par print(f"{cluster.name} filtered using mean parallax") for star in cluster.unfilteredWide: if not np.greater(np.abs(star.par-cluster.mean_par),threshold*cluster.stdev_par): cluster.distFiltered.append(star) else: print(f"{cluster.name} filtered using min & max parallax values") for star in cluster.unfilteredWide: if star.par > cluster.par_min and star.par < cluster.par_max: cluster.distFiltered.append(star) def cutNoise(cluster): #Imports import numpy as np stars = sorted(cluster.filtered,key=lambda x: x.g_mag) new = [] newMag = np.empty((0,2)) if cluster.noise_cutoff <= -98: threshold = 1 print(f"{cluster.name} noise cutoff undefined, using default") else: threshold = cluster.noise_cutoff bad = 0 badCut = 5 for i,s in enumerate(stars): if s.astro_sigma5d > threshold: bad += 1 if bad >= badCut: break else: new.append(s) newMag = np.r_[newMag,[[s.b_r,s.g_mag]]] cluster.filtered = new cluster.mag = newMag return s.g_mag def turboFit(cl=["all"],condensing='auto',weighting='pos',tp="catalogue",minScore=0.001): #Typical use cases are auto, pos, catalogue --OR-- manual, equal, catalogue #Imports import time global clusterList cList = checkLoaded(cl) print("=========================Fitting=========================") t0 = time.time() status = condense(cList,condensing,weighting,tp,minScore) if status == "Suspended": return for cluster in cList: redFitting(cluster,minScore,weighting) t1 = time.time() print(f"Total {cluster.name} fit runtime: {t1-t0} seconds") def redFitting(cluster,minScore,weighting): #Imports import numpy as np import math from sys import stdout from time import sleep global clusterList if type(cluster) == str: cluster = clusters[cluster] cluster.iso = [] redMin = 0 redMax = 0.7 step = 0.05 redList = [round(x,2) for x in np.arange(redMin,redMax+step,step)] for reddening in redList: stdout.write(f"\rCurrent reddening value for {cluster.name}: {reddening:.2f} / ({redList[0]:.2f}->{redList[-1]:.2f})") shapeFit(cluster,reddening,minScore,weighting) stdout.flush() sleep(0.1) cluster.iso = sorted(cluster.iso,key=lambda x: x[1]) best = float(cluster.iso[0][2]) print(f"\nCoarse-step reddening for {cluster.name}: {best}") subMin = best - 0.05 subMax = best + 0.05 substep = 0.01 if subMin < 0: subMin = 0 subList = [round(x,2) for x in np.arange(subMin,subMax+substep,substep) if not round(x,2) in redList and round(x,2) > subMin and round(x,2) < subMax] for reddening in subList: stdout.write(f"\rCurrent fine-step reddening value for {cluster.name}: {reddening:.2f} / ({subList[0]:.2f}->{subList[-1]:.2f})") shapeFit(cluster,reddening,minScore,weighting) stdout.flush() sleep(0.1) cluster.iso = sorted(cluster.iso,key=lambda x: x[1]) cluster.reddening = float(cluster.iso[0][2]) cluster.fit_age = float(isochrones[cluster.iso[0][0]].age) cluster.fit_feh = float(isochrones[cluster.iso[0][0]].feh) cluster.fit_afe = float(isochrones[cluster.iso[0][0]].afe) cluster.fit_y = float(isochrones[cluster.iso[0][0]].y) #Unrelated properties but I needed somewhere to assign them setattr(cluster,'meanDist',1000/cluster.mean_par) meanL = np.mean([a.l*np.pi/180 for a in cluster.filtered]) galDist = 8000 #pc gd = cluster.meanDist**2 + galDist**2 - 2*cluster.meanDist*galDist*np.cos(meanL) setattr(cluster,'meanGalacticDist',gd**0.5) print(f"\nReddening for {cluster.name}: {best}") def shapeFit(cluster,reddening,minScore,weighting): #Imports import numpy as np import shapely.geometry as geom global isoList conversion = 2.1 isoFitList = np.empty((0,3)) for iso in isoList: isoLine = geom.LineString(tuple(zip([x+reddening for x in iso.br],[x+cluster.dist_mod+conversion*reddening for x in iso.g]))) dist = [] for star in cluster.condensed: starPt = geom.Point(star.b_r,star.g_mag) #print(starPt.distance(isoLine)) pointDist = np.abs(starPt.distance(isoLine))*star.weight if pointDist < minScore*star.weight: pointDist = minScore*star.weight dist.append(pointDist**2) isoScore = np.sum(dist[:]) #print(isoScore,dist) #print(list(geom.shape(isoLine).coords)) isoFitList = np.r_[isoFitList,[[iso.name,float(isoScore),float(reddening)]]] #compareInstances(iso,cluster.iso[-1][0]) #print(isoScore) cluster.iso.extend(isoFitList) #best = cluster.iso[1][0] #specificPlot(cluster.name,best.name,reddening) #print(f"\nFirst point of best fit: {best.br[0]+reddening},{best.g[0]+conversion*reddening+cluster.dist_mod}") def onclick(x,y,fig,ax,cluster,minScore,weighting,newList): def func(event): import matplotlib.pyplot as plt global coords ix, iy = event.xdata, event.ydata if str(event.button) == "MouseButton.RIGHT": for i,(cx,cy) in enumerate(coords): if abs(ix-cx) <= 0.075 and abs(iy-cy) <= 0.25: coords.pop(i) ax.clear() ax.scatter(x,y,s=0.5,color='dimgray') ax.invert_yaxis() ax.scatter([a[0] for a in coords],[a[1] for a in coords],c='red',s=10) plt.gcf().canvas.draw_idle() if str(event.button) == "MouseButton.LEFT": coords.append((ix, iy)) ax.scatter(ix,iy,c='red',s=10) plt.gcf().canvas.draw_idle() if str(event.button) == "MouseButton.MIDDLE": fig.canvas.mpl_disconnect(cid) plt.close(fig) updateCondensed(cluster,minScore,weighting,newList) if len(coords) >= 100: fig.canvas.mpl_disconnect(cid) plt.close(fig) updateCondensed(cluster,minScore,weighting,newList) return return func def updateCondensed(cluster,minScore,weighting,newList): #Imports import numpy as np global coords condensed = [] for point in coords: if cluster.clType.lower() == "globular" or weighting.lower() == "equal": weight = 1 else: #Automatic weighting scheme currently unsupported for manual condensed point definition, #but the framework is here to be able to insert it without having to worry about it being #passed around from function to function weight = 1 condensed.append(condensedPoint(point[0],point[1],weight)) if cluster.reddening == 0: cluster.condensed0 = condensed cluster.condensed = condensed np.savetxt(f"{cluster.dataPath}condensed.csv",coords,delimiter=',') redFitting(cluster,minScore,weighting) if len(newList) > 0: turboFit(newList,'manual',weighting,'catalogue',minScore) return def find_nearest(array, value): #Imports import numpy as np array = np.asarray(array) idx = (np.abs(array - value)).argmin() return array[idx] def testCluster(name='feh_0.00_afe_0.00_age_0.141_y_0.2703'): #Imports import numpy as np global clusterList global clIn iso = isochrones[name] test = clusterObj('test') filtered = [starObj('fake',0,1,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,a.Gaia_G_EDR3,0,0,0,0,0,0,0,a.Gaia_BP_EDR3-a.Gaia_RP_EDR3,0,0,0,0,0,0,0,0,0,0,0) for a in iso.starList] test.filtered = filtered mag = np.empty((0,2)) for star in test.filtered: mag = np.r_[mag,[[star.b_r,star.g_mag]]] test.mag = mag if not 'test' in clusters: clusterList.append(test) else: idx = clusterList.index(clusters['test']) clusterList.pop(idx) clusterList.append(test) clIn = True toDict() def condense(cList,condensing,weighting,tp,minScore=0.001): #Imports import numpy as np global isoList global mag for cluster in cList: if type(cluster) == str: cluster = clusters[cluster] cList[cList.index(cluster.name)] = cluster #Creates mag arrays to be used in place of the filtered star objects mag = cluster.mag[:,:] mag[mag[:,1].argsort()] gmag = list(mag[:,1]) gmin = mag[0,1] gmax = mag[-1,1] div = 50 seg = (gmax-gmin)/div minpoints = 1 #The array that will become the condensed points list condensed = np.empty((0,3)) turnPoints = [] if condensing.lower() == "load": global pts pts = np.genfromtxt(f"{cluster.dataPath}condensed.csv",delimiter=',') condensed = [] for point in pts: #Missing alternate weighting schemes, but can be imlemented *here* condensed.append(condensedPoint(point[0],point[1],1)) cluster.condensed = condensed cluster.condensed0 = condensed continue #Manual point definition if condensing.lower() == "manual": import matplotlib.pyplot as plt global cid global coords coords = [] if len(cList) == 1: newList = [] else: newList = cList[cList.index(cluster)+1:] x,y = mag[:,0],mag[:,1] fig = plt.figure() ax = fig.add_subplot(111) ax.scatter(x,y,s=0.25,color='dimgray') ax.invert_yaxis() hook = onclick(x,y,fig,ax,cluster,minScore,weighting,newList) cid = fig.canvas.mpl_connect('button_press_event', hook) return "Suspended" #Vertically stacked slices in brightness for i in range(div): sliced = mag[gmag.index(find_nearest(gmag,gmin+i*seg)):gmag.index(find_nearest(gmag,gmin+(i+1)*seg))] #print(np.array(sliced).shape) #Skip forseen problems with empty arrays if len(sliced) < minpoints: continue condensed = np.r_[condensed,[[np.median(sliced[:,0]),np.median(sliced[:,1]),0]]] condensed = condensed[::-1] #Uses defined turning points in the cluster catalogue if tp.lower() == "catalogue": if cluster.cltpx <= -98 and cluster.cltpy <= -98: tp == "auto" #If no turning point is found, or auto is specified, then this section of code #attempts to find the turning point through steep gradient changes in the main sequence if tp.lower() == "auto": #Criteria for the line that forms the basis of the gradient change method start = 4 end = 11 theta_crit = 5 #Creates a slope-intercept fit for the lower main sequence basex = [a[0] for a in condensed[start:end]] basey = [a[1] for a in condensed[start:end]] base = np.polyfit(basex,basey,1) #Travels up the main sequence for i,point in enumerate(condensed): if i == start: continue #Creates a fit line between the start point and the current point x = [point[0],condensed[start,0]] y = [point[1],condensed[start,1]] lin = np.polyfit(x,y,1) #Calculates an angle between the new line and the lower main sequence point[2] = 180/np.pi*np.arctan(abs( (base[0]-lin[0])/(1+base[0]*lin[0]) )) #If the angle between the two lines is large enough, the point is considered #to be a candidate turning point, and is appended to the list of candidates if point[2] > theta_crit and i > end: turnPoints.append(point) #Analysis plot showing the theta value for each condensed point import matplotlib.pyplot as plt plt.figure() plt.scatter(condensed[:,0],condensed[:,1],c=condensed[:,2]) plt.set_cmap('brg') plt.gca().invert_yaxis() clb = plt.colorbar() clb.ax.set_title("Theta") plt.savefig(f'condensed_{cluster.name}') #If no automatic turning point is found, ends the method here if len(turnPoints) == 0: print("No turning point identified for {cluster.name}") return else: #Identifies the proper turning point as a 5% color offset of the dimmest turning point candidate turnPoints = sorted(turnPoints,key=lambda x: x[1]) tp = turnPoints[-1] tp[0] = tp[0] - 0.05*np.abs(tp[0]) cluster.turnPoint = tp #Stores the condensed point list cl = [] for point in condensed: cl.append(condensedPoint(point[0],point[1],point[2])) cluster.condensedInit = cl # [ B-R , G , Theta ] print(f"{cluster.name} Turning Point: {cluster.turnPoint}") #Assuming the undefined catch for manual would be caught the first time around if tp.lower() == "catalogue": cluster.turnPoint = [cluster.cltpx,cluster.cltpy] if cluster.clType.lower() == "open": #Recalc with the turnPoint limit enforced - Ignore blue stragglers condensed = np.empty((0,3)) condensed_giant = np.empty((0,3)) yList = [] #Vertically stacked slices in brightness for i in range(div): rawSliced = mag[gmag.index(find_nearest(gmag,gmin+i*seg)):gmag.index(find_nearest(gmag,gmin+(i+1)*seg))] sliced = np.empty((0,2)) sliced_giant = np.empty((0,2)) for point in rawSliced: #print(point) if point[0] >= cluster.turnPoint[0]: sliced = np.r_[sliced,[[point[0],point[1]]]] else: sliced_giant = np.r_[sliced_giant,[[point[0],point[1]]]] #Skip forseen problems with empty arrays if len(sliced) > 0: x = np.median(sliced[:,0]) y = np.median(sliced[:,1]) yList.append(y) condensed = np.r_[condensed,[[x,y,1]]] if len(sliced_giant) > 3: xg = np.median(sliced_giant[:,0]) yg = np.median(sliced_giant[:,1]) condensed_giant = np.r_[condensed_giant,[[xg,yg,1]]] #New turning point found from the reduced data set newTP = find_nearest(yList,cluster.turnPoint[1]) index = 0 for i,point in enumerate(condensed): if newTP == point[1]: index = i #print(f"{point} found to be TP") break assert not index == 0 #Binary star list tpcut = index + 3 xset = condensed[tpcut:-1,0] yset = condensed[tpcut:-1,1] #print(cluster.name,yset) fit = np.polyfit(xset,yset,1) #Distance from the main sequence linear fit for star in cluster.filtered: x0 = star.b_r y0 = star.g_mag dist = abs( y0 - fit[0]*x0 - fit[1] ) / np.sqrt(fit[0]**2 + 1) star.distance_MS = dist if dist > 0.05 and y0 < fit[0]*x0+fit[1] and x0 > xset[0] and y0 > condensed[index,1]: cluster.binaries.append(star) star.binary = 1 else: star.binary = 0 #Fit weight parameters N = len(condensed) beta = -2 index = index - 7 for i,point in enumerate(condensed): #point[2] = 5/(1+np.abs(index-i)) if weighting.lower() == 'pos': point[2] = np.exp(beta*((i-index)/N)**2) # if cluster.type == "globular": # condensed = np.vstack((condensed,condensed_giant)) condensed = condensed[::-1] cl = [] coords = [] for point in condensed: cl.append(condensedPoint(point[0],point[1],point[2])) coords.append((point[0],point[1])) np.savetxt(f"{cluster.dataPath}condensed.csv",coords,delimiter=',') if cluster.reddening == 0: cluster.condensed0 = cl cluster.condensed = cl # def checkLoaded(cList): # needsLoading = [] # loaded = [] # for cl in cList: # if not cl in clusters: # needsLoading.append(cl) # else: # loaded.append(cl) # return loaded,needsLoading() def toDict(): #Imports global clusterList global clusters global isoList global isochrones global resultList global results global clIn global isoIn global resultsIn if clIn: clName = [] for cluster in clusterList: clName.append(cluster.name) clusters = dict(zip(clName,clusterList)) if isoIn: isoName = [] for iso in isoList: isoName.append(iso.name) isochrones = dict(zip(isoName,isoList)) if resultsIn: resName=[] for res in resultList: resName.append(res.name) results = dict(zip(resName,resultList)) def plot(cList=['all'],modes=['pos','pm','cmd','quiver','iso'],closePlots=False): #Imports import matplotlib.pyplot as plt from matplotlib.patches import Rectangle import numpy as np import os global clusterList cList = checkLoaded(cList) for cl in cList: cluster = clusters[cl] if not os.path.isdir(f"{cluster.imgPath}/png"): os.mkdir(f"{cluster.imgPath}/png") #Position plots if 'pos' in modes: unfra=[star.ra for star in cluster.unfilteredWide] unfdec=[star.dec for star in cluster.unfilteredWide] ra=[star.ra for star in cluster.filtered] dec=[star.dec for star in cluster.filtered] unfnormra=[star.ra*np.cos(star.dec*np.pi/180) for star in cluster.unfilteredWide] normra=[star.ra*np.cos(star.dec*np.pi/180) for star in cluster.filtered] #Unfiltered position plot plt.figure(f"{cluster.name}_ra_dec_unfiltered") plt.xlabel('RA (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Unfiltered") plt.scatter(unfra[:],unfdec[:],s=0.5,c='dimgray') plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_unfiltered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_unfiltered.png",dpi=500) #Filtered position plot plt.figure(f"{cluster.name}_ra_dec_filtered") plt.xlabel('RA (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Filtered") plt.scatter(ra[:],dec[:],s=0.5,c='midnightblue') plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_filtered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_filtered.png",dpi=500) #Position overlay plt.figure(f"{cluster.name}_ra_dec_overlay") plt.xlabel('RA (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Overlay") plt.scatter(unfra[:],unfdec[:],s=0.5,c='lightgray') plt.scatter(ra[:],dec[:],s=1,c='midnightblue') plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_overlay.png",dpi=500) #Normalized #NormRA = RA*cos(DEC) #Unfiltered normalized position plot plt.figure(f"{cluster.name}_ra_dec_unfiltered_normalized") plt.xlabel('RA*cos(DEC) (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Unfiltered Normalized") plt.scatter(unfnormra[:],unfdec[:],s=0.5,c='dimgray') #plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_unfiltered_normalized.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_unfiltered_normalized.png",dpi=500) #Filtered normalized position plot plt.figure(f"{cluster.name}_ra_dec_filtered_normalized") plt.xlabel('RA*cos(DEC) (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Filtered Normalized") plt.scatter(normra[:],dec[:],s=0.5,c='midnightblue') #plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_filtered_normalized.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_filtered_normalized.png",dpi=500) #Position overlay normalized plt.figure(f"{cluster.name}_ra_dec_overlay_normalized") plt.xlabel('RA*cos(DEC) (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Overlay Normalized") plt.scatter(unfnormra[:],unfdec[:],s=0.5,c='lightgray') plt.scatter(normra[:],dec[:],s=1,c='midnightblue') #plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_overlay_normalized.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_overlay_normalized.png",dpi=500) #Proper motion plots if 'pm' in modes: unfpmra=[star.pmra for star in cluster.unfilteredWide] unfpmdec=[star.pmdec for star in cluster.unfilteredWide] pmra=[star.pmra for star in cluster.filtered] pmdec=[star.pmdec for star in cluster.filtered] unfpara=[star.par for star in cluster.unfilteredWide] para=[star.par for star in cluster.filtered] x0 = cluster.pmra_min x1 = cluster.pmra_max y0 = cluster.pmdec_min y1 = cluster.pmdec_max width = x1-x0 scale = 5 subscale = 2 xmin = x0-scale*width xmax = x1+scale*width ymin = y0-scale*width ymax = y1+scale*width sxmin = x0-subscale*width sxmax = x1+subscale*width symin = y0-subscale*width symax = y1+subscale*width #Unfiltered proper motion plot plt.figure(f"{cluster.name}_pm_unfiltered") plt.xlabel(r'PMRA ($mas*yr^{-1}$)') plt.ylabel(r'PMDEC ($mas*yr^{-1}$)') plt.title(f"{cluster.name} Unfiltered") plt.scatter(unfpmra[:],unfpmdec[:],s=0.5,c='dimgray') plt.xlim([xmin,xmax]) plt.ylim([ymin,ymax]) # plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_unfiltered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_unfiltered.png",dpi=500) plt.xlim([sxmin,sxmax]) plt.ylim([symin,symax]) # plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_unfiltered_closeup.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_unfiltered_closeup.png",dpi=500) #Filtered proper motion plot plt.figure(f"{cluster.name}_pm_filtered") plt.xlabel(r'PMRA ($mas*yr^{-1}$)') plt.ylabel(r'PMDEC ($mas*yr^{-1}$)') plt.title(f"{cluster.name} Filtered") plt.scatter(pmra[:],pmdec[:],s=0.5,c='midnightblue') # plt.xlim([xmin,xmax]) # plt.ylim([ymin,ymax]) plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_filtered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_filtered.png",dpi=500) #Proper motion overlay plt.figure(f"{cluster.name}_pm_overlay") plt.xlabel(r'PMRA ($mas*yr^{-1}$)') plt.ylabel(r'PMDEC ($mas*yr^{-1}$)') plt.title(f"{cluster.name} Overlay") plt.scatter(unfpmra[:],unfpmdec[:],s=0.5,c='lightgray') plt.scatter(pmra[:],pmdec[:],s=1,c='midnightblue') plt.xlim([xmin,xmax]) plt.ylim([ymin,ymax]) # plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_overlay.png",dpi=500) plt.xlim([sxmin,sxmax]) plt.ylim([symin,symax]) # plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_overlay_closeup.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_overlay_closeup.png",dpi=500) #Unfiltered PM/Parallax plt.figure(f"{cluster.name}_pm_over_parallax_unfiltered") plt.xlabel('PMRA / Parallax') plt.ylabel('PMDEC / Parallax') plt.title(f"{cluster.name} Unfiltered") plt.scatter([a/b for a,b in zip(unfpmra,unfpara)],[a/b for a,b in zip(unfpmdec,unfpara)],s=0.5,c='dimgray') plt.xlim([xmin,xmax]) plt.ylim([ymin,ymax]) # plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_over_parallax_unfiltered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_over_parallax_unfiltered.png",dpi=500) #Unfiltered PM*Parallax plt.figure(f"{cluster.name}_pm_times_parallax_unfiltered") plt.xlabel('PMRA * Parallax') plt.ylabel('PMDEC * Parallax') plt.title(f"{cluster.name} Unfiltered") plt.scatter([a*b for a,b in zip(unfpmra,unfpara)],[a*b for a,b in zip(unfpmdec,unfpara)],s=0.5,c='dimgray') plt.xlim([xmin,xmax]) plt.ylim([ymin,ymax]) # plt.axis("square") plt.savefig(f"{cluster.imgPath}{cluster.name}_pm_times_parallax_unfiltered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_pm_times_parallax_unfiltered.png",dpi=500) #CMD plots if 'cmd' in modes: unfgmag=[star.g_mag for star in cluster.unfilteredWide] unf_b_r=[star.b_r for star in cluster.unfilteredWide] gmag=[star.g_mag for star in cluster.filtered] b_r=[star.b_r for star in cluster.filtered] bright_b_r = [x.b_r for x in cluster.filteredBright] bright_gmag = [x.g_mag for x in cluster.filteredBright] par_b_r = [x.b_r for x in cluster.distFiltered] par_gmag = [x.g_mag for x in cluster.distFiltered] #Reddening Correction plt.figure(f"{cluster.name}_reddening_CMD") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('G Mag') plt.title(f"{cluster.name} Reddening = {cluster.reddening:.2f}") plt.scatter(b_r[:],gmag[:],s=0.5,c='dimgray',label='Observed') plt.arrow(b_r[int(len(b_r)/2)]-cluster.reddening,gmag[int(len(gmag)/2)]-2.1*cluster.reddening,cluster.reddening,2.1*cluster.reddening,color='red') plt.scatter([s-cluster.reddening for s in b_r[:]],[s-2.1*cluster.reddening for s in gmag[:]],s=1,c='midnightblue',label='Corrected') plt.legend() plt.savefig(f"{cluster.imgPath}{cluster.name}_reddening_CMD.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_reddening_CMD.png",dpi=500) #Unfiltered CMD plot plt.figure(f"{cluster.name}_CMD_unfiltered") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Unfiltered") plt.scatter(unf_b_r[:],unfgmag[:],s=0.5,c='dimgray') plt.savefig(f"{cluster.imgPath}{cluster.name}_CMD_unfiltered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_CMD_unfiltered.png",dpi=500) #Filtered CMD plot plt.figure(f"{cluster.name}_CMD_filtered") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Parallax & Proper Motion Filtered") plt.scatter(b_r[:],gmag[:],s=0.5,c='midnightblue') plt.savefig(f"{cluster.imgPath}{cluster.name}_CMD_filtered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_CMD_filtered.png",dpi=500) #CMD overlay plt.figure(f"{cluster.name}_CMD_overlay") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Overlay") plt.scatter(unf_b_r[:],unfgmag[:],s=0.5,c='dimgray') plt.scatter(b_r[:],gmag[:],s=1,c='midnightblue') plt.savefig(f"{cluster.imgPath}{cluster.name}_CMD_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_CMD_overlay.png",dpi=500) #Condensed CMD overlay plt.figure(f"{cluster.name}_condensed_CMD_overlay") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Condensed Overlay") plt.scatter([s - cluster.reddening for s in b_r],[s - 2.1*cluster.reddening for s in gmag],s=0.5,c='dimgray',label='Data') plt.scatter([s.b_r - cluster.reddening for s in cluster.condensed],[s.g_mag - 2.1*cluster.reddening for s in cluster.condensed],s=5,c='red',label='Proxy Points') try: plt.axvline(x=cluster.turnPoint[0] - cluster.reddening,linestyle='--',color='midnightblue',linewidth=0.8,label='95% of Turning Point') except: print(f"No turning point found for {cluster.name}") plt.legend() plt.savefig(f"{cluster.imgPath}{cluster.name}_condensed_CMD_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_condensed_CMD_overlay.png",dpi=500) #Weighted CMD overlay plt.figure(f"{cluster.name}_weighted_CMD_overlay") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Weighted Overlay") plt.scatter([s - cluster.reddening for s in b_r],[s - 2.1*cluster.reddening for s in gmag],s=0.5,c='dimgray',label='Data') plt.scatter([s.b_r - cluster.reddening for s in cluster.condensed],[s.g_mag - 2.1*cluster.reddening for s in cluster.condensed],s=5,c=[s.weight for s in cluster.condensed],label='Proxy Points') try: plt.axvline(x=cluster.turnPoint[0] - cluster.reddening,linestyle='--',color='midnightblue',linewidth=0.8,label='95% of Turning Point') except: print(f"No turning point found for {cluster.name}") plt.set_cmap('brg') clb = plt.colorbar() clb.ax.set_title("Weight") plt.legend() plt.savefig(f"{cluster.imgPath}{cluster.name}_weighted_CMD_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_weighted_CMD_overlay.png",dpi=500) #Initial Condensed CMD overlay plt.figure(f"{cluster.name}_initial_condensed_CMD_overlay") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Initial Condensed Overlay") plt.scatter(b_r,gmag,s=0.5,c='dimgray',label='Data') plt.scatter([s.b_r for s in cluster.condensedInit],[s.g_mag for s in cluster.condensedInit],s=5,c='red',label='Proxy Points') try: plt.axvline(x=cluster.turnPoint[0] - cluster.reddening,linestyle='--',color='midnightblue',linewidth=0.8,label='95% of Turning Point') except: print(f"No turning point found for {cluster.name}") plt.legend() plt.savefig(f"{cluster.imgPath}{cluster.name}_initial_condensed_CMD_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_initial_condensed_CMD_overlay.png",dpi=500) #Brightness-PM Filtered CMD plot plt.figure(f"{cluster.name}_CMD_bright_filtered") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Bright-Only Proper Motion Filtered") plt.scatter(bright_b_r[:],bright_gmag[:],s=0.5,c='midnightblue') plt.savefig(f"{cluster.imgPath}{cluster.name}_CMD_bright_filtered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_CMD_bright_filtered.png",dpi=500) #Parallax Filtered CMD plot plt.figure(f"{cluster.name}_CMD_parallax_filtered") plt.gca().invert_yaxis() plt.xlabel('BP-RP') plt.ylabel('Apparent G Mag') plt.title(f"{cluster.name} Parallax Filtered") plt.scatter(par_b_r[:],par_gmag[:],s=0.5,c='midnightblue') plt.savefig(f"{cluster.imgPath}{cluster.name}_CMD_parallax_filtered.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_CMD_parallax_filtered.png",dpi=500) if 'quiver' in modes: unfra=[star.ra for star in cluster.unfilteredWide] unfdec=[star.dec for star in cluster.unfilteredWide] unfpmra=[star.pmra for star in cluster.unfilteredWide] unfpmdec=[star.pmdec for star in cluster.unfilteredWide] x0 = min([s.ra for s in cluster.filtered]) x1 = max([s.ra for s in cluster.filtered]) y0 = min([s.dec for s in cluster.filtered]) y1 = max([s.dec for s in cluster.filtered]) width = x1-x0 scale = 0.25 xmin = x0+scale*width xmax = x1-scale*width ymin = y0+scale*width ymax = y1-scale*width #Unfiltered position quiver plot plt.figure(f"{cluster.name}_ra_dec_unfiltered_quiver") plt.xlabel('RA (Deg)') plt.ylabel('DEC (Deg)') plt.title(f"{cluster.name} Unfiltered") ax = plt.gca() ax.quiver(unfra[:],unfdec[:],unfpmra[:],unfpmdec[:],color='midnightblue',width=0.003,scale=400,scale_units='width') plt.axis("square") plt.gcf().set_size_inches(10,10) plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_unfiltered_pm_quiver.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_unfiltered_pm_quiver.png",dpi=500) plt.xlim([xmin,xmax]) plt.ylim([ymin,ymax]) plt.savefig(f"{cluster.imgPath}{cluster.name}_ra_dec_unfiltered_pm_quiver_zoom.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_ra_dec_unfiltered_pm_quiver_zoom.png",dpi=500) #Isochrone plots if 'iso' in modes: gmag=[star.g_mag for star in cluster.filtered] b_r=[star.b_r for star in cluster.filtered] isochrone = isochrones[cluster.iso[0][0]] #Isochrone best fit plt.figure(f"{cluster.name}_Iso_best") plt.gca().invert_yaxis() plt.xlabel('Dereddened BP-RP') plt.ylabel('Corrected Absolute G Mag') plt.title(f"{cluster.name} Isochrone Best Fit") plt.scatter([s - cluster.reddening for s in b_r],[s - 2.1*cluster.reddening-cluster.dist_mod for s in gmag],s=0.5,c='dimgray',label='Cluster') isoLabels = isochrone.name.split('_') isoLabel = r"$[\frac{Fe}{H}]$" + "=" + isoLabels[1] + "\n" \ + r"$[\frac{\alpha}{Fe}]$" + "=" + isoLabels[3] + "\n" \ + r"$[Y]$" + "=" + isoLabels[7] + "\n" \ + "Age" + "=" + isoLabels[5] + " Gyr" plt.plot(isochrone.br,isochrone.g,c='midnightblue',label=isoLabel) plt.scatter([s.b_r - cluster.reddening for s in cluster.condensed],[s.g_mag - 2.1*cluster.reddening-cluster.dist_mod for s in cluster.condensed],s=5,c='red',label='Cluster Proxy') extra = Rectangle((0, 0), 1, 1, fc="w", fill=False, edgecolor='none', linewidth=0) h,l = plt.gca().get_legend_handles_labels() h.insert(0,extra) l.insert(0,f"Reddening: {cluster.reddening}") plt.legend(h,l) plt.savefig(f"{cluster.imgPath}{cluster.name}_CMD_Iso_BestFit.pdf") plt.savefig(f"{cluster.imgPath}png/{cluster.name}_CMD_Iso_BestFit.png",dpi=500) #Membership plots if 'membership' in modes: proxyMatch([cl]) boundedStats([cl],saveCl=False,unloadCl=False) membership(cl,mode='filtered') membership(cl,mode='bounded',N=50) #3D Position plots if '3D' in modes: A = [a.ra * np.pi/180 for a in cluster.filtered] B = [abs(b.dec) * np.pi/180 for b in cluster.filtered] C = [1/(1000*c.par) for c in cluster.filtered] x = [c*np.cos(b)*np.cos(a) for a,b,c in zip(A,B,C)] y = [c*np.cos(b)*np.sin(a) for a,b,c in zip(A,B,C)] z = [c*np.sin(b) for b,c in zip(B,C)] r = [np.sqrt(a**2+b**2) for a,b in zip(x,y)] theta = [np.arctan(b/a) for a,b in zip(x,y)] plt.figure(f"{cluster.name}_3D_Position") ax = plt.axes(projection='3d') ax.scatter3D(x,y,z) ax.scatter(0,0,0,color='red') scaling = np.array([getattr(ax, 'get_{}lim'.format(dim))() for dim in 'xyz']) ax.auto_scale_xyz(*[[np.min(scaling), np.max(scaling)]]*3) if closePlots: plt.close('all') # def Plot3D(cList): # #Imports # import matplotlib.pyplot as plt # import numpy as np # global clusterList # needsLoading=[] # plt.figure(f"3D_Position_Ensemble") # ax = plt.axes(projection='3d') # for cl in cList: # if not cl in clusters: # needsLoading.append(cl) # if not len(needsLoading) == 0: # loadClusters(needsLoading) # for cl in cList: # cluster = clusters[cl] # A = [a.ra * np.pi/180 for a in cluster.filtered] # B = [abs(b.dec) * np.pi/180 for b in cluster.filtered] # C = [1/(0.001*c.par) for c in cluster.filtered] # #Flatten radially # C = [np.mean(C)]*len(C) # x = [c*np.cos(b)*np.cos(a) for a,b,c in zip(A,B,C)] # y = [c*np.cos(b)*np.sin(a) for a,b,c in zip(A,B,C)] # z = [c*np.sin(b) for b,c in zip(B,C)] # #Force Cluster to origin # # x = [a-np.mean(x) for a in x] # # y = [a-np.mean(y) for a in y] # # z = [a-np.mean(z) for a in z] # ax.scatter3D(x,y,z,label=cluster.name) # scaling = np.array([getattr(ax, 'get_{}lim'.format(dim))() for dim in 'xyz']) # ax.auto_scale_xyz(*[[np.min(scaling), np.max(scaling)]]*3) # #ax.scatter(0,0,0,color='black') # plt.legend() def yso_lookup(): #Imports from astroquery.simbad import Simbad import numpy as np import os import re global names global sect global results global ra global dec main = open("Excess Examples/YSO_object_list.dat").read() main = main.split("\n")[:-1] #Get the names of all of the objects identified names = [] ra = [] dec = [] validNames = [] for row in main: sect = re.split('\s+',row) if sect[0] == '': sect = sect[1:] if sect[2] == 'none': continue name = sect[2] blacklist = ['A','Ab','AB','ABC','B','AaB'] for entry in sect[3:]: if '.' in entry or entry in blacklist: break name = name + " " + entry names.append(name) #Perform a SIMBAD query for the identified objects results = [] for name in names: result = Simbad.query_object(name) if not type(result) == type(None): results.append(result) validNames.append(name.replace(' ','')) ra1 = str(result.columns['RA']).split('\n')[-1] ra1 = re.split('\s+',ra1) if '' in ra1: ra.append('---') else: ra.append(str(round(float(ra1[0])*15+float(ra1[1])/4+float(ra1[2])/240,5))) dec1 = str(result.columns['DEC']).split('\n')[-1] dec1 = re.split('\s+',dec1) if '' in dec1: dec.append('---') else: dec.append(str(round(float(dec1[0])+float(dec1[1])/60+float(dec1[2])/3600,5))) #Create a text file in the VOSA readable format VOSAdata = [] gaiadata = [] for i in range(len(validNames)): line1 = f"{validNames[i]} {ra[i]} {dec[i]} --- --- --- --- --- --- ---" line2 = f"{ra[i]} {dec[i]}" VOSAdata.append(line1) if '-' in line2: continue gaiadata.append(line2) np.savetxt("Excess Examples/yso_vosa_output.txt",VOSAdata,fmt="%s") np.savetxt("Excess Examples/yso_gaia_output.txt",gaiadata,fmt="%s") def exportVOSA(cl): #Imports import numpy as np if not cl in clusters: loadClusters([cl]) cluster = clusters[cl] #objname RA DEC DIS Av Filter Flux Error PntOpts ObjOpts data = [] for star in cluster.filtered: name = star.name.replace(" ","") line = f"{name} {star.ra} {star.dec} {1000/star.par} --- --- --- --- --- ---" data.append(line) np.savetxt(f"{cluster.dataPath}{cluster.name}_VOSA.txt",data,fmt="%s") def readSED(cList=['all'],printMissing=False): #imports import numpy as np import re import os cList = checkLoaded(cList) for cl in cList: cluster = clusters[cl] objPath = cluster.dataPath + "vosa_results/objects/" names = [] for star in cluster.filtered: flat = star.name.replace(" ","").replace("DR2","").replace("EDR3","").replace("DR3","") names.append(flat) star.flatName = flat cluster.stars = dict(zip(names,cluster.filtered)) idx = 0 newStars = dict() #Each star in a cluster has its own folder, and each folder contains several data sets for folder in os.listdir(objPath): fileName = folder.replace("DR2","").replace("EDR3","").replace("DR3","") #Weed out VOSA stars not in current filtered members list if not fileName in cluster.stars: if printMissing: print(f"{fileName} is missing from filtered list, skipping it...") continue main = open(objPath+folder+"/sed/"+folder+".sed.dat").read() main = main.split("\n") data = main[10:-1] #Create a list of measurement object pointers to attach to the stars later measurements = [] #Convert every line of the data set into a vosaPoint object for row in data: sect = re.split('\s+',row)[1:-1] measurements.append(vosaPoint(str(sect[0]),float(sect[1]),float(sect[2]),float(sect[3]),float(sect[4]),float(sect[5]),float(sect[6]))) cluster.stars[fileName].vosaPoints = measurements #Weed out cluster.stars members who do not have a vosa table newStars[fileName] = cluster.stars[fileName] idx += 1 cluster.stars = newStars def checkBinary(cl): import numpy as np import matplotlib.pyplot as plt checkLoaded([cl]) cluster = clusters[cl] global lman data = [Datum(star.b_r,star.g_mag) for star in cluster.filtered] # ax = plt.axes(xlim=(cluster.min_b_r-0.25,cluster.max_b_r+0.25), ylim=(cluster.min_g_mag-1,cluster.max_g_mag+1),autoscale_on=False) ax = plt.axes(xlim=(0, 2.5), ylim=(8, 20), autoscale_on=False) ax.invert_yaxis() ax.set_title('Lasso points using left mouse button') lman = LassoManager(ax, data,cluster) plt.show() def vosaBinaries(cl): #Imports import numpy as np import matplotlib.pyplot as plt import os checkLoaded([cl]) cluster = clusters[cl] if not os.path.isdir(f"{cluster.imgPath}vosaBinaries/"): os.mkdir(f"{cluster.imgPath}vosaBinaries/") for star in cluster.stars.values(): if not star.binary == 1: return def excessIR(cl,plot=True): #Imports import numpy as np import matplotlib.pyplot as plt import os checkLoaded([cl]) cluster = clusters[cl] if not os.path.isdir(f"{cluster.imgPath}excessIR/"): os.mkdir(f"{cluster.imgPath}excessIR/") for star in cluster.stars.values(): excess = False for vp in star.vosaPoints: if vp.excess > 0: excess = True if excess: #print(f"{star.name} has {len(star.vosaPoints)} VOSA points") star.hasExcess = 1 if plot: plt.figure(f'{cluster.name} - {star.name}') plt.title(f'{cluster.name} : {star.name}') ax = plt.gca() ax.set_yscale('log') ax.set_xscale('log') plt.ylabel(r'Flux ($ergs^{-1}cm^{-2}\AA^{-1}$)') plt.xlabel(r'Wavelength ($\AA$)') plt.scatter([a.wavelength for a in star.vosaPoints],[a.flux for a in star.vosaPoints]) plt.savefig(f"{cluster.imgPath}excessIR/{star.name}.pdf") plt.savefig(f"{cluster.imgPath}excessIR/{star.name}.png",dpi=500) def proxyMatch(cList,plot=False): #Imports import matplotlib.pyplot as plt import numpy as np checkLoaded(cList) for cl in cList: cluster = clusters[cl] iso = isochrones[cluster.iso[0][0]] isoPoints = [] for pt in iso.starList: isoPoints.append(pt) # if pt.Gaia_G_EDR3+cluster.dist_mod > cluster.turnPoint[1]: # isoPoints.append(pt) for star in cluster.filtered: minDist = 0.2 smallestDist = 10 vertCutoff = 1 minPoint = None for point in isoPoints: dist = abs(point.Gaia_BP_EDR3-point.Gaia_RP_EDR3-star.b_r+cluster.reddening) if dist < minDist: if abs(point.Gaia_G_EDR3+cluster.dist_mod - star.g_mag + 2.1*cluster.reddening) < vertCutoff: minDist = dist minPoint = point elif dist < smallestDist: smallestDist = dist try: assert minDist < 0.2 except: print(f"[{cluster.name}] Star too distant from isochrone to make a good proxy: BP-RP: {star.b_r} | G: {star.g_mag} | Dist: {smallestDist}") star.proxyMass = 0 star.proxyLogTemp = 0 star.proxyFeH = 0 star.proxyLogAge = 0 star.proxy = None continue #print(minDist) star.proxyMass = minPoint.star_mass star.proxyLogTemp = minPoint.log_Teff star.proxyFeH = minPoint.feh star.proxyLogAge = minPoint.log10_isochrone_age_yr star.proxy = minPoint cluster.massLoaded = True cluster.meanProxyMass = np.mean([a.proxyMass for a in cluster.filtered]) cluster.totalProxyMass = np.sum([a.proxyMass for a in cluster.filtered]) cluster.min_g_mag = min([a.g_mag for a in cluster.filtered]) cluster.max_g_mag = max([a.g_mag for a in cluster.filtered]) cluster.min_b_r = min([a.b_r for a in cluster.filtered]) cluster.max_b_r = max([a.b_r for a in cluster.filtered]) # if plot: # plt.figure(f"{cluster.name}_proxy_fit") def variableHistogram(cl,var): #Imports import numpy as np import matplotlib.pyplot as plt checkLoaded([cl]) cluster = clusters[cl] plt.figure() plt.title(f"{cluster.name} Histogram of {var}") plt.xlabel(f"{var}") plt.ylabel("Count") plt.hist([eval(f"a.{var}") for a in cluster.filtered],bins='auto') def varHist2D(cl,var1,var2,color='default',listType='filtered'): #Imports import numpy as np import matplotlib.pyplot as plt checkLoaded([cl]) #Check allowed entries allowedTypes = ['filtered','unfilteredWide','unfilteredBright,filteredBright,binaries'] if not listType in allowedTypes: print(f"{listType} is not a valid list type, defaulting to filtered") listType = "filtered" cluster = clusters[cl] plt.figure(figsize=(8,8)) #Axis size and spacing left, width = 0.1, 0.65 bottom, height = 0.1, 0.65 spacing = 0.005 rect_scatter = [left, bottom, width, height] rect_histx = [left, bottom + height + spacing, width, 0.2] rect_histy = [left + width + spacing, bottom, 0.2, height] ax_scatter = plt.axes(rect_scatter) ax_scatter.tick_params(direction='in', top=True, right=True) ax_histx = plt.axes(rect_histx) ax_histx.tick_params(direction='in', labelbottom=False) ax_histy = plt.axes(rect_histy) ax_histy.tick_params(direction='in', labelleft=False) x = [eval(f"a.{var1}") for a in eval(f"cluster.{listType}")] y = [eval(f"a.{var2}") for a in eval(f"cluster.{listType}")] if color == 'default': ax_scatter.scatter(x, y, s=5) else: colorMap = plt.get_cmap('coolwarm')#.reversed() ax_scatter.scatter(x, y, s=5, c=[eval(f"a.{color}") for a in eval(f"cluster.{listType}")], cmap = colorMap) # clb = plt.colorbar(ax_scatter) # clb.ax.set_title(f"{color}") ax_histx.hist(x,bins='auto') ax_histy.hist(y,bins='auto',orientation='horizontal') ax_histx.set_title(f"Histogram of {listType} {cluster.name} in {var1} and {var2}") ax_scatter.set_xlabel(f"{var1}") ax_scatter.set_ylabel(f"{var2}") def Plot3D(cList=['all'],showEarth=True,flatten=True): #Imports import plotly.express as px import plotly.io as pio import numpy as np global clusterList pio.renderers.default='browser' fig = px.scatter_3d() if showEarth: fig.add_scatter3d(x=[0],y=[0],z=[0],marker=dict(color='lightblue'),name="Earth") cList = checkLoaded(cList) big = [] for cl in cList: cluster = clusters[cl] A = [a.ra * np.pi/180 for a in cluster.filtered] B = [abs(b.dec) * np.pi/180 for b in cluster.filtered] C = [1/(0.001*c.par) for c in cluster.filtered] #Flatten radially if flatten: C = [np.mean(C)]*len(C) x = [c*np.cos(b)*np.cos(a) for a,b,c in zip(A,B,C)] y = [c*np.cos(b)*np.sin(a) for a,b,c in zip(A,B,C)] z = [c*np.sin(b) for b,c in zip(B,C)] #Force Cluster to origin # x = [a-np.mean(x) for a in x] # y = [a-np.mean(y) for a in y] # z = [a-np.mean(z) for a in z] fig.add_scatter3d(x=x,y=y,z=z,name=cl,mode="markers",marker=dict(size=2)) big.append(np.amax(x)) big.append(np.amax(y)) big.append(np.amax(z)) #fig.layout.scene = dict(aspectmode="manual",aspectratio=dict(x=1,y=1,z=1)) #fig.update_layout(scene=dict(aspectmode="cube",xaxis=dict(showbackground=False,range=[-1*np.amax(big),np.amax(big)]),yaxis=dict(showbackground=False,range=[-1*np.amax(big),np.amax(big)]),zaxis=dict(showbackground=False,range=[-1*np.amax(big),np.amax(big)]))) fig.update_layout(scene=dict(aspectmode="cube",xaxis=dict(showbackground=False),yaxis=dict(showbackground=False),zaxis=dict(showbackground=False,visible=False))) fig.show() def specificPlot(cl,iso,reddening,score): #Imports import matplotlib.pyplot as plt from matplotlib.patches import Rectangle import os checkLoaded([cl]) cluster = clusters[f"{cl}"] isochrone = isochrones[f"{iso}"] #These are displayed on the plot # score = 0 reddening = float(reddening) #Directory for saving plot outputs if not os.path.isdir("SpecificPlots/pdf/"): os.makedirs("SpecificPlots/pdf/") if not os.path.isdir("SpecificPlots/png/"): os.makedirs("SpecificPlots/png/") # #Find the score of the associated isochrone # for chrone in cluster.iso: # if chrone[0] == iso and chrone[2] == reddening: # score = chrone[1] # break #Plots the CMD and the isochrone, with all of the points adjusted to reddening, extinction, and distance modulus plt.figure() plt.gca().invert_yaxis() plt.xlabel('B-R') plt.ylabel('G Mag') plt.title(f"{cl} {iso}") plt.scatter([s.b_r for s in cluster.filtered],[s.g_mag for s in cluster.filtered],s=0.05,c='dimgray',label='Cluster') plt.plot([x + reddening for x in isochrone.br],[x+cluster.dist_mod+2.1*reddening for x in isochrone.g],c='midnightblue',label=f"Score: {float(score):.7f}") plt.scatter([s.b_r for s in cluster.condensed],[s.g_mag for s in cluster.condensed],s=5,c=[s.weight for s in cluster.condensed],label='Cluster Proxy') #Colors the points by their fitting weight plt.set_cmap('brg') clb = plt.colorbar() clb.ax.set_title("Weight") #Label for the reddening extra = Rectangle((0, 0), 1, 1, fc="w", fill=False, edgecolor='none', linewidth=0) h,l = plt.gca().get_legend_handles_labels() h.insert(0,extra) l.insert(0,f"Reddening: {reddening}") plt.legend(h,l) #Save figure output to disk plt.savefig(f"SpecificPlots/pdf/Requested_Plot_{cl}_{iso}_Reddening_{reddening}.pdf") plt.savefig(f"SpecificPlots/png/Requested_Plot_{cl}_{iso}_Reddening_{reddening}.png",dpi=500) def plotRange(cl,a,b): global clusters checkLoaded([cl]) #Plots the top fitting isochrones over the range a to b for a given cluster #Does this by calling the specificPlot() method for each isochrone over the range for isochrone in clusters[f"{cl}"].iso[a:b]: specificPlot(cl,isochrones[isochrone[0]].name,isochrone[2],isochrone[1]) def getIsoScore(cl,iso,red,output=True): #Return the score for a given cluster's isochrone fit for i in cl.iso: if i[0] == iso.name and float(i[2]) == red: return i[1] if output: print(f"No score found for {cl.name} | {iso.name} | {red}") return 0 def onkey(x,y,cx,cy,fig,ax,cluster,iso,reddening): global curIso global curReddening curIso = iso curReddening = reddening def func(event): import matplotlib.patches as patches global curIso global curReddening global isochrones key = str(event.key) ageSorted = [a for a in sorted(isoList,key=lambda x: float(x.age)) if a.feh == curIso.feh] fehSorted = [a for a in sorted(isoList,key=lambda x: float(x.feh)) if a.age == curIso.age] age_index = ageSorted.index(curIso) feh_index = fehSorted.index(curIso) if key == "w": try: curIso = fehSorted[feh_index+1] feh_index = feh_index+1 except: curIso = fehSorted[0] feh_index = 0 if key == "s": curIso = fehSorted[feh_index-1] feh_index = feh_index-1 if feh_index < 0: feh_index = len(fehSorted)+feh_index if key == "a": curIso = ageSorted[age_index-1] age_index = age_index-1 if age_index < 0: age_index = len(ageSorted)+age_index if key == "d": try: curIso = ageSorted[age_index+1] age_index = age_index+1 except: curIso = ageSorted[0] age_index = 0 if key == "q": curReddening = round(curReddening-0.01,2) if key == "e": curReddening = round(curReddening+0.01,2) if key == "r": curIso = iso ageSorted = [a for a in sorted(isoList,key=lambda x: float(x.age)) if a.feh == curIso.feh] fehSorted = [a for a in sorted(isoList,key=lambda x: float(x.feh)) if a.age == curIso.age] age_index = ageSorted.index(curIso) feh_index = fehSorted.index(curIso) if key == " ": score = getIsoScore(cluster,curIso,curReddening) fig.savefig(f"Jamboree Images/frames/{curIso.name}.png",dpi=500) print(f"{curIso.name} | {curReddening} | {score}") score = getIsoScore(cluster,curIso,curReddening,output=False) ax.clear() ax.scatter(x,y,s=0.25,color='dimgray') ax.scatter(cx,cy,s=4,color='red') ax.plot([a.Gaia_BP_EDR3-a.Gaia_RP_EDR3+curReddening for a in curIso.starList],[a.Gaia_G_EDR3+cluster.dist_mod+2.1*curReddening for a in curIso.starList],color='darkblue') ax.set_title(f"{curIso.name}\n {curReddening}\n {score}") ax.set_xlabel("Apparent BP-RP") ax.set_ylabel("Apparent G Mag") ax.invert_yaxis() x0,x1 = ax.get_xlim() y0,y1 = ax.get_ylim() margin = 0.01 width = 0.05 * (x1-x0) height = 0.6 * (y1-y0) xmargin = margin * (x1-x0) ymargin = margin * (y1-y0) rect1 = patches.Rectangle((x1-width-xmargin,y0+ymargin),width,height,linewidth=1,edgecolor='black',facecolor='none',alpha=0.5) rect2 = patches.Rectangle((x1-2*width-2*xmargin,y0+ymargin),width,height,linewidth=1,edgecolor='black',facecolor='none',alpha=0.5) ax.add_patch(rect1) ax.add_patch(rect2) n = len(ageSorted) for i in range(n): offset = i*height/n alpha = 0.25 if i == age_index: color = 'red' else: color = 'black' ax.add_patch(patches.Rectangle((x1-2*width-2*xmargin,y0+ymargin+offset),width,height/n,linewidth=0.01,edgecolor='black',facecolor=color,alpha=alpha)) n = len(fehSorted) for i in range(n): offset = i*height/n alpha = 0.25 if i == feh_index: color = 'red' else: color = 'black' ax.add_patch(patches.Rectangle((x1-1*width-1*xmargin,y0+ymargin+offset),width,height/n,linewidth=0.01,edgecolor='black',facecolor=color,alpha=alpha)) fig.canvas.draw_idle() return func def interactivePlot(cl,iso=0,reddening="auto"): import matplotlib.pyplot as plt import matplotlib.patches as patches global clusters global isochrones global kid checkLoaded([cl]) cluster = clusters[f"{cl}"] if type(iso) == str: isochrone = isochrones[f"{iso}"] elif type(iso) == int: assert iso >= 0 isochrone = isochrones[cluster.iso[iso][0]] else: print("Invalid declaration of 'iso'") return name = isochrone.name if reddening == "auto": reddening = cluster.reddening assert type(reddening) == float or type(reddening) == int score = getIsoScore(cluster,isochrone,reddening) d(isoList,key=lambda x: float(x.age)) if a.feh == isochrone.feh] fehSorted = [a for a in sorted(isoList,key=lambda x: float(x.feh)) if a.age == isochrone.age] age_index = ageSorted.index(isochrone) feh_index = fehSorted.index(isochrone) x,y = cluster.mag[:,0],cluster.mag[:,1] cx,cy = [s.b_r for s in cluster.condensed],[s.g_mag for s in cluster.condensed] letters = ['w','s','a','d','q','e','r'] for letter in letters: for param in [key for key in plt.rcParams if key.startswith("keymap") ]: try: plt.rcParams[param].remove(letter) except: continue fig = plt.figure(f"Interactive plot of {cl}") ax = fig.add_subplot(111) ax.scatter(x,y,s=0.25,color='dimgray') ax.scatter(cx,cy,s=4,color='red') ax.plot([a.Gaia_BP_EDR3-a.Gaia_RP_EDR3+reddening for a in isochrone.starList],[a.Gaia_G_EDR3+cluster.dist_mod+2.1*reddening for a in isochrone.starList],color='darkblue') ax.set_title(f"{name}\n {reddening}\n {score}") ax.set_xlabel("Apparent BP-RP") ax.set_ylabel("Apparent G Mag") ax.invert_yaxis() x0,x1 = ax.get_xlim() y0,y1 = ax.get_ylim() margin = 0.01 width = 0.05 * (x1-x0) height = 0.6 * (y1-y0) xmargin = margin * (x1-x0) ymargin = margin * (y1-y0) rect1 = patches.Rectangle((x1-width-xmargin,y0+ymargin),width,height,linewidth=1,edgecolor='black',facecolor='none',alpha=0.5) rect2 = patches.Rectangle((x1-2*width-2*xmargin,y0+ymargin),width,height,linewidth=1,edgecolor='black',facecolor='none',alpha=0.5) ax.add_patch(rect1) ax.add_patch(rect2) n = len(ageSorted) for i in range(n): offset = i*height/n alpha = 0.25 if i == age_index: color = 'red' else: color = 'black' ax.add_patch(patches.Rectangle((x1-2*width-2*xmargin,y0+ymargin+offset),width,height/n,linewidth=0.01,edgecolor='black',facecolor=color,alpha=alpha)) n = len(fehSorted) for i in range(n): offset = i*height/n alpha = 0.25 if i == feh_index: color = 'red' else: color = 'black' ax.add_patch(patches.Rectangle((x1-1*width-1*xmargin,y0+ymargin+offset),width,height/n,linewidth=0.01,edgecolor='black',facecolor=color,alpha=alpha)) hook = onkey(x,y,cx,cy,fig,ax,cluster,isochrone,reddening) kid = fig.canvas.mpl_connect('key_press_event',hook) def printList(cList,varList): cList = checkLoaded(cList) for cl in cList: cluster = clusters[cl] for a in varList: clStr = f"[{cl}] {a} =" exec(f"print(clStr,cluster.{a})") def statRange(cl,a,b): import numpy as np global clusters checkLoaded([cl]) if not isoIn: loadIsochrones() ages = [] fehs = [] ys = [] reds = [] for isochrone in clusters[cl].iso[a:b]: iso = isochrones[isochrone[0]] print(f"{iso.name} Reddening:{isochrone[2]}") ages.append(float(iso.age)) fehs.append(float(iso.feh)) ys.append(float(iso.y)) reds.append(float(isochrone[2])) print(f"[{cl}] Mean age= {np.mean(ages)} Mean feh= {np.mean(fehs)} Mean y= {np.mean(ys)} Mean Reddening= {np.mean(reds)}") def setFlag(): global clusterlist for cluster in clusterList: for star in cluster.filtered: for unfStar in cluster.unfilteredWide: if star == unfStar: unfStar.member = 1 def customPlot(var1,var2,clname,mode='filtered',iso=False,square=True,color='default',title='default',close=False,save=True): import matplotlib.pyplot as plt global closePlots checkLoaded([clname]) cluster = clusters[f"{clname}"] #Set the list of stars to be used for the given cluster #Using a mode not specified will return a referenced before assignment error if mode == 'filtered': starlist = cluster.filtered elif mode == 'unfiltered': starlist = cluster.unfilteredWide elif mode == 'bright_filtered': starlist = cluster.filteredBright elif mode == 'dist_filtered': starlist = cluster.distFiltered elif mode == 'bright_unfiltered': starlist = cluster.unfilteredBright elif mode == 'duo': starlist = cluster.unfilteredWide starlistF = cluster.filtered elif mode == 'binary': starlist = cluster.binaries elif mode == 'duoBinary': starlist = cluster.filtered starlistF = cluster.binaries elif mode == 'duoBright': starlist = cluster.unfilteredBright starlistF = cluster.filteredBright elif mode == 'duoDist': starlist = cluster.distFiltered starlistF = cluster.filtered elif mode == 'condensed': starlist = cluster.condensed elif mode == 'duoCondensed': starlist = cluster.filtered starlistF = cluster.condensed elif mode == 'bounded': starlist = cluster.bounded elif mode == 'duoBounded': starlist = cluster.filtered starlistF = cluster.bounded else: print("No preset star list configuration found with that alias") return #Basic plot features with axis labels and a title plt.figure() if title == 'default': plt.title(f"{clname} {mode} | {var1} vs {var2} | {color} color") else: plt.title(f"{title}") plt.xlabel(f"{var1}".upper()) plt.ylabel(f"{var2}".upper()) #Plots differently depending on the mode #The color tag can be used to add distinction of a third variable while limited to two axes #If unspecified, filtered starlist with midnight blue coloring will be the result if iso: plt.gca().invert_yaxis() if 'duo' in mode: #plt.scatter([eval(f"x.{var1}") for x in starlist],[eval(f"y.{var2}") for y in starlist],s=[0.1+a.member*1.4 for a in starlist],c=[list(('lightgray',eval('z.par')))[z.member] for z in starlist]) plt.scatter([eval(f"x.{var1}") for x in starlist],[eval(f"y.{var2}") for y in starlist],s=2,c='gray') if color == 'default': plt.scatter([eval(f"x.{var1}") for x in starlistF],[eval(f"y.{var2}") for y in starlistF],s=2.5,c='red') else: plt.scatter([eval(f"x.{var1}") for x in starlistF],[eval(f"y.{var2}") for y in starlistF],s=2.5,c=[eval(f"z.{color}") for z in starlistF]) plt.set_cmap('brg') clb = plt.colorbar() clb.ax.set_title(f"{color}") else: if color == 'default': plt.scatter([eval(f"x.{var1}") for x in starlist],[eval(f"y.{var2}") for y in starlist],s=1,c='midnightblue') else: plt.scatter([eval(f"x.{var1}") for x in starlist],[eval(f"y.{var2}") for y in starlist],s=2,c=[eval(f"z.{color}") for z in starlist]) plt.set_cmap('cool') clb = plt.colorbar() clb.ax.set_title(f"{color}") #By default, squares the axes to avoid misinformation from stretched axes #Turn this off and iso to true for a color magnitude diagram if square: plt.axis("square") if save: plt.savefig(f"SpecificPlots/pdf/{clname}_{mode}_{var1}_{var2}.pdf") plt.savefig(f"SpecificPlots/png/{clname}_{mode}_{var1}_{var2}.png",dpi=500) if close or closePlots: plt.close() if save: print(f"Custom Plot {clname}_{mode}_{var1}_{var2} saved and closed") else: print(f"Custom Plot {clname}_{mode}_{var1}_{var2} closed") def splitMS(clname='M67',slope=3,offset=12.2): #Imports import numpy as np import matplotlib.pyplot as plt checkLoaded([clname]) cluster = clusters[clname] xlist = [s.b_r for s in cluster.filtered] ylist = [s.g_mag for s in cluster.filtered] x = np.linspace(1,2,100) #Create a diagram showing the lower edge and upper edge of the main sequence, which in theory are separated by 0.75mag plt.figure() plt.title('Main and Binary Sequences') plt.xlabel('B-R') plt.ylabel('Apparent G Mag') plt.scatter(xlist,ylist,s=0.5,label='Filtered Star Data') plt.plot(x,[slope*a + offset for a in x],color='r',label='Main Sequence') plt.plot(x,[slope*a + offset - 0.75 for a in x],'--',color='r',label='MS shifted 0.75 mag') plt.xlim(0.6,2.2) plt.ylim(13,19) plt.legend() plt.gca().invert_yaxis() plt.savefig(f"SpecificPlots/png/{clname}_MS_Spread.png",dpi=500) plt.savefig(f"SpecificPlots/pdf/{clname}_MS_Spread.pdf") def kingProfile(r,K,R): return K*(1+r**2/R**2)**(-1) def kingError(r,K,R,dK,dR): import numpy as np dfdK = (1+r**2/R**2)**(-1) dfdR = 2*K*r**2*R*(r**2+R**2)**(-2) return np.sqrt((dfdK*dK)**2 + (dfdR*dR)**2) def densityProfile(r,K,R): import numpy as np #The exponential that is fit for the membership profile #R is a characteristic radius, typically negative but the absolute value is used for comparison #K is a scalar constant return K*np.exp(-1*r/R) def densityError(r,K,R,dK,dR): import numpy as np dfdK = abs(np.exp(-1*r/R)) dfdR = abs(K*r/(R**2)*np.exp(-1*r/R)) return np.sqrt((dfdK*dK)**2 + (dfdR*dR)**2) def toIntensity(mag): msun = -26.74 #apparent magnitude Isun = 1360 #w/m^) return Isun*10**( 0.4*(msun-mag) ) def membership(clname='M67',N=100,mode='filtered',numPercentileBins=5,percentile=0.2,delta=5,normalize=True): #Imports import numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Circle import scipy.optimize as so import scipy.stats as st import math global volume checkLoaded([clname]) cluster = clusters[clname] mode = mode.lower() #Default mode is filtered, but unfiltered data can be processed if "filtered" in mode: starList = cluster.filtered elif "bounded" in mode: starList = cluster.bounded else: starList = cluster.unfilteredWide #Load mass estimates from isochrone fitting if not cluster.massLoaded: proxyMatch([cluster.name]) assert cluster.massLoaded assert len(starList) > 0 #Assign x and y lists based on normalization or not if normalize: starX = [a.ra*np.cos(a.dec*np.pi/180) for a in starList] starY = [a.dec for a in starList] mode = mode + "_normalized" else: starX = [a.ra for a in starList] starY = [a.dec for a in starList] #Determine bounds of the field of view (post-filtering) xmax = max(starX) ymax = max(starY) x0 = np.mean(starX) y0 = np.mean(starY) newN = N #Determine radius of the field of view rx = xmax-x0 ry = ymax-y0 #r = np.mean([rx,ry]) radiusFOV = ry #Using the mean ra and dec radius caused problems with clusters #like NGC188, which are close to the celestial pole and have #a very stretched mapping to the RA DEC space ringBins = list(np.linspace(0,radiusFOV,N)) #The bins are divided up such that 50% of the bins are located in the inner 25% of the cluster radius #The remaining 50% of the bins are divided from 25% to 100% of the radius rings = list(np.linspace(0,radiusFOV/4,math.ceil(N/2))) ring2 = list(np.linspace(radiusFOV/4,radiusFOV,math.floor(N/2)+1)) ring2 = ring2[1:-1] rings.extend(ring2) x=rings[:-1] # for i in range(0,len(rings[:-1])): # x.append((rings[i+1]+rings[i])/2) counts = list(np.zeros(N-1,dtype=int)) masses = list(np.zeros(N-1,dtype=int)) rads=[] for star in starList: #Radial distance from the mean RA and Dec of the cluster if normalize: rads.append(np.sqrt((star.ra*np.cos(star.dec*np.pi/180)-x0)**2+(star.dec-y0)**2)) else: rads.append(np.sqrt((star.ra-x0)**2+(star.dec-y0)**2)) #Find the nearest ring to the star r = find_nearest(rings, rads[-1]) i = rings.index(r) #Check bounds if i < len(counts): #If outside last ring, add to that count if r > rads[-1]: counts[i-1] += 1 masses [i-1] += star.proxyMass else: counts[i] += 1 masses [i] += star.proxyMass #Worth noting here that the way that this is set up, the rings don't actually mark the bounds of the bins but rather the midpoints. plt.figure(f"{clname}_membership_{mode}") plt.hist(rads,bins=ringBins) plt.xlabel("Radius (deg)") plt.ylabel("Number of Stars") plt.title(f"{clname} Membership") plt.savefig(f"{cluster.imgPath}{clname}_membership_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_membership_{mode}.png",dpi=500) volume = [] for i in range(0,len(rings[:-1])): volume.append(np.pi*(rings[i+1]**2-rings[i]**2)) numDensity = [a/b for a,b in zip(counts,volume)] massDensity = [a/b for a,b in zip(masses,volume)] error_num = [np.sqrt(a)/b for a,b in zip(counts,volume)] error_mass = [np.sqrt(a)/b for a,b in zip(masses,volume)] for i in range(0,len(error_num)): if error_num[i] < 0.1: error_num[i] = 0.1 x = x[math.ceil(N/20):-1] counts = counts[math.ceil(N/20):-1] numDensity = numDensity[math.ceil(N/20):-1] massDensity = massDensity[math.ceil(N/20):-1] error_num = error_num[math.ceil(N/20):-1] error_mass = error_mass[math.ceil(N/20):-1] #Further filter the data based on outliers, either extremely low density or extremely big jumps in density from bin to bin i = 0 numSmall = 0 numGrad = 0 while i < len(x)-1: if numDensity[i] < 0.5 or numDensity[i] < numDensity[i+1]/delta or massDensity[i] < 0.1: x.pop(i) counts.pop(i) numDensity.pop(i) massDensity.pop(i) error_num.pop(i) error_mass.pop(i) numSmall += 1 newN -= 1 elif abs(numDensity[i]) > abs(numDensity[i+1])*delta:# or abs(numDensity[i]) < abs(numDensity[i-1])/3: x.pop(i) counts.pop(i) numDensity.pop(i) massDensity.pop(i) error_num.pop(i) error_mass.pop(i) numGrad += 1 newN -= 1 else: i += 1 if numDensity[-1] < 0.01 or massDensity[-1] < 0.01: x.pop(-1) counts.pop(-1) numDensity.pop(-1) massDensity.pop(-1) error_num.pop(-1) error_mass.pop(-1) numSmall += 1 newN -= 1 print(f"[{cluster.name}] Removed {numSmall} points with too small of a density and {numGrad} points with too extreme of a delta") #========= Number Density ========= #Number density vs radial bin plot plt.figure(f"{clname}_density_{mode}") plt.errorbar(x,numDensity,yerr=error_num,ls='None') plt.scatter(x,numDensity) plt.xlabel("Radius (deg)") plt.ylabel(r"Surface Number Density ($deg^{-2}$)") plt.title(f"{clname} {mode.capitalize()} Number Density".replace("_normalized",' Normalized')) #Fit an exponential curve to the density plot based on the densityProfile function defined above if "NGC2355" in cluster.name: p0=[5000,0.1] else: p0=[5000,0.1] #print([b/a for a,b in zip(numDensity,error_num)]) fit,var = so.curve_fit(kingProfile,x,numDensity,p0,maxfev=1000) #Std. Dev. from variance err = np.sqrt(var[1][1]) err_coeff = np.sqrt(var[0][0]) scale = np.abs(fit[1]*3600/206265)/(cluster.mean_par/1000) #scaleVar = (3600/206265)*(err/(cluster.mean_par/1000) ) + 2*fit[1]/(cluster.mean_par_err/1000) scaleVar = np.abs(scale*np.sqrt((var[1][1]/fit[1])**2 + (cluster.mean_par_err/cluster.mean_par)**2)) #Scale radius from count in parsecs setattr(cluster,f"scaleRad_{mode}",scale) setattr(cluster,f"scaleRad_err_{mode}",scaleVar) #Scale radius from count in degrees setattr(cluster,f"scaleAngle_{mode}",abs(fit[1])) setattr(cluster,f"scaleAngle_err_{mode}",err) setattr(cluster,f"numDensity_coeff_{mode}",fit[0]) setattr(cluster,f"numDensity_coeff_err_{mode}",err_coeff) #Plot the curve fit numLabel = ( f"N={newN} ({mode.capitalize()})".replace("_normalized",' Normalized')+"\n" + fr"K={fit[0]:.3f} $\pm$ {err_coeff:.3f}" + "\n" + fr"$\rho$={np.abs(fit[1]):.3f}$\degree$ $\pm$ {err:.3f}$\degree$"+ "\n" + fr"R={scale:.3f}pc $\pm$ {scaleVar:.3f}pc" ) plt.plot(x,[kingProfile(a,*fit) for a in x],color='red',label=numLabel) plt.fill_between(x,[kingProfile(a,*fit)-kingError(a,fit[0],fit[1],err_coeff,err) for a in x],[kingProfile(a,*fit)+kingError(a,fit[0],fit[1],err_coeff,err) for a in x],label=r'$1\sigma$',edgecolor='none',alpha=0.8,facecolor='salmon') plt.legend(fontsize=8,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_numDensity_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_numDensity_{mode}.png",dpi=500) plt.yscale('log') plt.savefig(f"{cluster.imgPath}{clname}_numDensity_log_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_numDensity_log_{mode}.png",dpi=500) #Double plot for bounded regions if "bounded" in mode: plt.figure(f"{clname}_density_filtered") plt.title(f"{clname} Overlaid Number Density") plt.errorbar(x,numDensity,yerr=error_num,ls='None',color='midnightblue') plt.scatter(x,numDensity,color='midnightblue') plt.plot(x,[kingProfile(a,*fit) for a in x],color='darkred',label=numLabel) plt.fill_between(x,[kingProfile(a,*fit)-kingError(a,fit[0],fit[1],err_coeff,err) for a in x],[kingProfile(a,*fit)+kingError(a,fit[0],fit[1],err_coeff,err) for a in x],edgecolor='none',alpha=0.8,facecolor='salmon') plt.legend(fontsize=8,loc='upper right') plt.yscale('linear') plt.savefig(f"{cluster.imgPath}{clname}_numDensity_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_numDensity_overlay.png",dpi=500) plt.yscale('log') plt.savefig(f"{cluster.imgPath}{clname}_numDensity_log_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_numDensity_log_overlay.png",dpi=500) #========= Mass Density ========= #Mass density vs radial bin plot plt.figure(f"{clname}_mass_density_{mode}") plt.errorbar(x,massDensity,yerr=error_mass,ls='None') plt.scatter(x,massDensity) plt.xlabel("Radius (deg)") plt.ylabel(r"Surface Mass Density ($M_{\odot}*deg^{-2}$)") plt.title(f"{clname} {mode.capitalize()} Mass Density".replace("_normalized",' Normalized')) #Fit an exponential curve to the density plot based on the densityProfile function defined above fit_mass,var_mass = so.curve_fit(kingProfile,x,massDensity,p0,maxfev=1000) #Std. Dev. from variance err_mass = np.sqrt(var[1][1]) err_mass_coeff = np.sqrt(var[0][0]) scale_mass = np.abs(fit_mass[1]*3600/206265)/(cluster.mean_par/1000) #scaleVar_mass = (3600/206265)*(err_mass/(cluster.mean_par/1000) ) + 2*fit_mass[1]/(cluster.mean_par_err/1000) scaleVar_mass = np.abs(scale_mass*np.sqrt((var_mass[1][1]/fit_mass[1])**2 + (cluster.mean_par_err/cluster.mean_par)**2)) #Scale radius from mass in parsecs setattr(cluster,f"scaleRad_mass_{mode}",scale_mass) setattr(cluster,f"scaleRad_mass_err_{mode}",scaleVar_mass) #Scale radius from mass in degrees setattr(cluster,f"scaleAngle_mass_{mode}",abs(fit_mass[1])) setattr(cluster,f"scaleAngle_mass_err_{mode}",err_mass) setattr(cluster,f"massDensity_coeff_{mode}",fit_mass[0]) setattr(cluster,f"massDensity_coeff_err_{mode}",err_mass_coeff) #Plot the curve fit massLabel = ( f"N={newN} ({mode.capitalize()})".replace("_normalized",' Normalized')+"\n" + fr"K={fit_mass[0]:.3f} $\pm$ {err_mass_coeff:.3f}" + "\n" + fr"$\rho$={np.abs(fit_mass[1]):.3f}$\degree$ $\pm$ {err_mass:.3f}$\degree$"+ "\n" + fr"R={scale_mass:.3f}pc $\pm$ {scaleVar_mass:.3f}pc" ) plt.plot(x,[kingProfile(a,*fit_mass) for a in x],color='red',label=massLabel) plt.fill_between(x,[kingProfile(a,*fit_mass)-kingError(a,fit_mass[0],fit_mass[1],err_mass_coeff,err_mass) for a in x],[kingProfile(a,*fit_mass)+kingError(a,fit_mass[0],fit_mass[1],err_mass_coeff,err_mass) for a in x],label=r'$1\sigma$',edgecolor='none',alpha=0.8,facecolor='salmon') plt.legend(fontsize=8,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_massDensity_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_massDensity_{mode}.png",dpi=500) plt.yscale('log') plt.savefig(f"{cluster.imgPath}{clname}_massDensity_log_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_massDensity_log_{mode}.png",dpi=500) #Double plot for bounded regions if "bounded" in mode: plt.figure(f"{clname}_mass_density_filtered") plt.title(f"{clname} Overlaid Mass Density") plt.errorbar(x,massDensity,yerr=error_mass,ls='None',color='midnightblue') plt.scatter(x,massDensity,color='midnightblue') plt.plot(x,[kingProfile(a,*fit_mass) for a in x],color='darkred',label=massLabel) plt.fill_between(x,[kingProfile(a,*fit_mass)-kingError(a,fit_mass[0],fit_mass[1],err_mass_coeff,err_mass) for a in x],[kingProfile(a,*fit_mass)+kingError(a,fit_mass[0],fit_mass[1],err_mass_coeff,err_mass) for a in x],edgecolor='none',alpha=0.8,facecolor='salmon') plt.legend(fontsize=8,loc='upper right') plt.yscale('linear') plt.savefig(f"{cluster.imgPath}{clname}_massDensity_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_massDensity_overlay.png",dpi=500) plt.yscale('log') plt.savefig(f"{cluster.imgPath}{clname}_massDensity_log_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_massDensity_log_overlay.png",dpi=500) #========= Average Mass ========= averageMass = [a/b for a,b in zip(massDensity,numDensity)] xDist = [np.abs(a*3600/206265)/(cluster.mean_par/1000) for a in x] #Average Mass plot plt.figure(f"{clname}_average_mass_{mode}") plt.scatter(xDist,averageMass,label=fr"N={newN} ({mode.capitalize()})".replace("_normalized",' Normalized')+"\n"+f"{numPercentileBins} Percentile Bins") plt.xlabel("Distance from Center (pc)") plt.ylabel(r"Average Stellar Mass ($M_{\odot}$)") plt.title(f"{clname} {mode.capitalize()} Average Mass".replace("_normalized",' Normalized')) #Split average mass data into numPercentileBins number of bins if "filtered" in mode: cluster.pMin = xDist[0] cluster.pMax = xDist[-1] pBins = np.linspace(cluster.pMin,cluster.pMax,numPercentileBins+1) xBins = [] for i in range(len(pBins)-1): xBins.append((pBins[i]+pBins[i+1])/2) pBins = np.delete(pBins,0) pBins = np.delete(pBins,-1) for b in pBins: plt.axvline(x=b,color='black',linestyle='--') binned = [] for n in range(numPercentileBins): binned.append([]) #Assign the average mass data points to the bins for i in range(len(xDist)): #Finds the nearest xBin to each x value and sorts the corresponding averageMass into that bin val = find_nearest(xBins,xDist[i]) idx = xBins.index(val) binned[idx].append(averageMass[i]) #Creates arrays that are numPercentileBins long that store the standard and quantile means of the points in those bins quantileMean = [] binMean = [] meanBins = [] for b in binned: if len(b) == 0: continue binSorted = sorted(b) #Finds the index of the lower percentile marker (ex. 20%) lower = binSorted.index(find_nearest(binSorted, np.quantile(b,percentile))) #Finds the index of the upper percentile marker (ex. 80%) upper = binSorted.index(find_nearest(binSorted, np.quantile(b,1-percentile))) #Means between lower and upper percentile markers quantileMean.append(np.mean(binSorted[lower:upper+1])) #Standard Mean binMean.append(np.mean(b)) #Bins meanBins.append(xBins[binned.index(b)]) try: fit, var = so.curve_fit(kingProfile,xDist,[kingProfile(a,*fit_mass)/kingProfile(a,*fit) for a in x]) residual_coeff, residual_scaleAngle = fit[0],fit[1] except: print(f"Unable to fit the residuals for {cluster.name}") residual_coeff, residual_scaleAngle = -99, -99 massFit = st.linregress(meanBins,quantileMean) fitslope, intercept, rval, pval, fitslope_err, intercept_err = massFit.slope, massFit.intercept, massFit.rvalue, massFit.pvalue, massFit.stderr, massFit.intercept_stderr residual_scaleRad = np.abs(residual_scaleAngle*3600/206265)/(cluster.mean_par/1000) setattr(cluster,f"residual_coeff_{mode}",residual_coeff) setattr(cluster,f"residual_scaleAngle_{mode}",residual_scaleAngle) setattr(cluster,f"residual_scaleRad_{mode}",residual_scaleRad) setattr(cluster,f"mass_slope_{mode}",fitslope) setattr(cluster,f"mass_slope_err_{mode}",fitslope_err) setattr(cluster,f"mass_intercept_{mode}",intercept) setattr(cluster,f"mass_intercept_err_{mode}",intercept_err) setattr(cluster,f"mass_fit_r2_{mode}",rval**2) setattr(cluster,f"mass_fit_p_{mode}",pval) fitLabel = ( fr"Slope = {fitslope:.3f} $\pm$ {fitslope_err:.3f}" + "\n" + fr"Intercept = {intercept:.3f} $\pm$ {intercept_err:.3f}" + "\n" + fr"$r^2$ = {rval**2:.3f} ({mode.capitalize()})".replace("_normalized",' Normalized')) #Plot the quantile and standard means on the existing average mass plot plt.scatter(meanBins,quantileMean,color='red',label=f'Interquartile Mean ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.plot(xDist,[fitslope*a+intercept for a in xDist],color='red',label=fitLabel) #plt.scatter(meanBins,binMean,color='dimgray',label=f'{mode.capitalize()} Standard Mean') plt.legend(fontsize=8,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_averageMass_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_averageMass_{mode}.png",dpi=500) #Double plot for bounded regions if "bounded" in mode: plt.figure(f"{clname}_average_mass_filtered") plt.title(f"{clname} Overlaid Average Mass") plt.scatter(xDist,averageMass,color='midnightblue',label=fr"N={newN} ({mode.capitalize()})".replace("_normalized",' Normalized')+"\n"+f"{numPercentileBins} Percentile Bins") plt.plot(xDist,[fitslope*a+intercept for a in xDist],color='darkred',label=fitLabel) plt.scatter(meanBins,quantileMean,color='darkred',label=f'Interquartile Mean ({mode.capitalize()})'.replace("_normalized",' Normalized')) #plt.scatter(meanBins,binMean,color='black',label=f'{mode.capitalize()} Standard Mean') plt.legend(fontsize=8,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_averageMass_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_averageMass_overlay.png",dpi=500) #========= Radius Plot ========= plt.figure(f"{clname}_characteristic_radius_{mode}") if normalize: plt.scatter([star.ra*np.cos(star.dec*np.pi/180) for star in cluster.unfilteredWide],[star.dec for star in cluster.unfilteredWide],s=1,c='lightgray',label='Unfiltered') plt.scatter([star.ra*np.cos(star.dec*np.pi/180) for star in cluster.filtered],[star.dec for star in cluster.filtered],s=2,c='midnightblue',label='Filtered') plt.xlabel("RA*cos(Dec) (Deg)") else: plt.scatter([star.ra for star in cluster.unfilteredWide],[star.dec for star in cluster.unfilteredWide],s=1,c='lightgray',label='Unfiltered') plt.scatter([star.ra for star in cluster.filtered],[star.dec for star in cluster.filtered],s=2,c='midnightblue',label='Filtered') plt.xlabel("RA (Deg)") pltRad = abs(getattr(cluster,f"scaleAngle_{mode}")) outline1 = Circle([x0,y0],1*pltRad,color='red',fill=False,ls='--',label=fr"$\rho$={1*pltRad:0.3f}$\degree$",alpha=0.7) outline2 = Circle([x0,y0],5*pltRad,color='red',fill=False,ls='--',label=fr"5$\rho$={5*pltRad:0.3f}$\degree$",alpha=0.7) #outline3 = Circle([x0,y0],10*abs(getattr(cluster,f"scaleAngle_{mode}")),color='red',fill=False,ls='--',label=fr"10$\rho$={3*abs(fit[1]):0.3f}$\degree$",alpha=0.7) plt.gca().add_patch(outline1) plt.gca().add_patch(outline2) #plt.gca().add_patch(outline3) plt.legend(fontsize=10,loc='upper right') plt.axis('square') plt.ylabel("DEC (Deg)") plt.title(f"{clname} {mode.capitalize()} Characteristic Radius".replace("_normalized",' Normalized')) plt.gcf().set_size_inches(8,8) plt.savefig(f"{cluster.imgPath}{clname}_radialMembership_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_radialMembership_{mode}.png",dpi=500) if "M67" in clname and "filtered" in mode: plt.figure(f"{clname}_rings_{mode}") if normalize: plt.scatter([star.ra*np.cos(star.dec*np.pi/180) for star in cluster.unfilteredWide],[star.dec for star in cluster.unfilteredWide],s=1,c='lightgray',label='Unfiltered') plt.scatter([star.ra*np.cos(star.dec*np.pi/180) for star in cluster.filtered],[star.dec for star in cluster.filtered],s=2,c='midnightblue',label='Filtered') plt.xlabel("RA*cos(Dec) (Deg)") else: plt.scatter([star.ra for star in cluster.unfilteredWide],[star.dec for star in cluster.unfilteredWide],s=1,c='lightgray',label='Unfiltered') plt.scatter([star.ra for star in cluster.filtered],[star.dec for star in cluster.filtered],s=2,c='midnightblue',label='Filtered') plt.xlabel("RA (Deg)") for i in range(0,len(rings)): outline = Circle([x0,y0],rings[i],color='red',fill=False) plt.gca().add_patch(outline) plt.legend(fontsize=10,loc='upper right') plt.axis('square') plt.ylabel("DEC (Deg)") plt.title(f"{clname} Radial Bins") plt.gcf().set_size_inches(8,8) plt.savefig(f"SpecificPlots/pdf/{clname}_radialBins_{mode}.pdf".replace("_filtered",'')) plt.savefig(f"SpecificPlots/png/{clname}_radialBins_{mode}.png".replace("_filtered",''),dpi=500) plt.xlim(x0-0.15,x0+0.15) plt.ylim(y0-0.15,y0+0.15) plt.savefig(f"SpecificPlots/pdf/{clname}_radialBins_center_{mode}.pdf".replace("_filtered",'')) plt.savefig(f"SpecificPlots/png/{clname}_radialBins_center_{mode}.png".replace("_filtered",''),dpi=500) #========= Stars by Mass ========= massList = [] innerMassList = [] for star in starList: massList.append(star.proxyMass) if normalize: if np.sqrt((star.ra*np.cos(star.dec*np.pi/180)-x0)**2+(star.dec-y0)**2) <= getattr(cluster,f"scaleAngle_{mode}"): innerMassList.append(star.proxyMass) else: if np.sqrt((star.ra-x0)**2+(star.dec-y0)**2) <= getattr(cluster,f"scaleAngle_{mode}"): innerMassList.append(star.proxyMass) mBins = np.arange(min(massList),max(massList)+0.1,0.1) inBins = np.arange(min(innerMassList),max(innerMassList)+0.1,0.1) plt.figure(f"{clname}_mass_frequency_{mode}") plt.xlabel(r"Stellar Mass ($M_{\odot}$)") plt.ylabel("Number of Stars") plt.title(f"{clname} {mode.capitalize()} Mass Frequency".replace("_normalized",' Normalized')) plt.hist(massList,bins=mBins,label=f"Total {mode.capitalize()}".replace("_normalized",' Normalized')) plt.hist(innerMassList,bins=inBins,color='midnightblue',label=f'Inside Core Radius ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.legend(fontsize=10,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_massFrequency_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_massFrequency_{mode}.png",dpi=500) #Double plot for bounded regions if "bounded" in mode: plt.figure(f"{clname}_mass_frequency_filtered") plt.title(f"{clname} Overlaid Mass Frequency") plt.hist(massList,bins=mBins,label=f"Total {mode.capitalize()}".replace("_normalized",' Normalized'),color='red') plt.hist(innerMassList,bins=inBins,color='darkred',label=f'Inside Core Radius ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.legend(fontsize=10,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_massFrequency_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_massFrequency_overlay.png",dpi=500) #========= Stars by Magnitude ========= magList = [] innerMagList = [] for star in starList: magList.append(star.g_mag-2.1*cluster.reddening-cluster.dist_mod) if normalize: if np.sqrt((star.ra*np.cos(star.dec*np.pi/180)-x0)**2+(star.dec-y0)**2) <= getattr(cluster,f"scaleAngle_{mode}"): innerMagList.append(star.g_mag-2.1*cluster.reddening-cluster.dist_mod) else: if np.sqrt((star.ra-x0)**2+(star.dec-y0)**2) <= getattr(cluster,f"scaleAngle_{mode}"): innerMagList.append(star.g_mag-2.1*cluster.reddening-cluster.dist_mod) mBins = np.arange(min(magList),max(magList)+0.1,0.1) inBins = np.arange(min(innerMagList),max(innerMagList)+0.1,0.1) plt.figure(f"{clname}_mag_frequency_{mode}") plt.xlabel(r"Absolute G Mag") plt.ylabel("Number of Stars") plt.title(f"{clname} {mode.capitalize()} Absolute Magnitude Frequency".replace("_normalized",' Normalized')) plt.hist(magList,bins=mBins,label=f"Total {mode.capitalize()}".replace("_normalized",' Normalized')) plt.hist(innerMagList,bins=inBins,color='midnightblue',label=f'Inside Core Radius ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.legend(fontsize=10,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_magFrequency_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_magFrequency_{mode}.png",dpi=500) #Double plot for bounded regions if "bounded" in mode: plt.figure(f"{clname}_mag_frequency_filtered") plt.title(f"{clname} Overlaid Absolute Magnitude Frequency") plt.hist(magList,bins=mBins,label=f"Total {mode.capitalize()}".replace("_normalized",' Normalized'),color='red') plt.hist(innerMagList,bins=inBins,color='darkred',label=f'Inside Core Radius ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.legend(fontsize=10,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_magFrequency_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_magFrequency_overlay.png",dpi=500) #========= Stars by Color ========= colorList = [] innerColorList = [] for star in starList: colorList.append(star.b_r-cluster.reddening) if normalize: if np.sqrt((star.ra*np.cos(star.dec*np.pi/180)-x0)**2+(star.dec-y0)**2) <= getattr(cluster,f"scaleAngle_{mode}"): innerColorList.append(star.b_r-cluster.reddening) else: if np.sqrt((star.ra-x0)**2+(star.dec-y0)**2) <= getattr(cluster,f"scaleAngle_{mode}"): innerColorList.append(star.b_r-cluster.reddening) mBins = np.arange(min(colorList),max(colorList)+0.1,0.1) inBins = np.arange(min(innerColorList),max(innerColorList)+0.1,0.1) plt.figure(f"{clname}_color_frequency_{mode}") plt.xlabel(r"Dereddened BP-RP") plt.ylabel("Number of Stars") plt.title(f"{clname} {mode.capitalize()} Dereddened Color Index Frequency".replace("_normalized",' Normalized')) plt.hist(colorList,bins=mBins,label=f"Total {mode.capitalize()}".replace("_normalized",' Normalized')) plt.hist(innerColorList,bins=inBins,color='midnightblue',label=f'Inside Core Radius ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.legend(fontsize=10,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_colorFrequency_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_colorFrequency_{mode}.png",dpi=500) #Double plot for bounded regions if "bounded" in mode: plt.figure(f"{clname}_color_frequency_filtered") plt.title(f"{clname} Overlaid Dereddened Color Index Frequency") plt.hist(colorList,bins=mBins,label=f"Total {mode.capitalize()}".replace("_normalized",' Normalized'),color='red') plt.hist(innerColorList,bins=inBins,color='darkred',label=f'Inside Core Radius ({mode.capitalize()})'.replace("_normalized",' Normalized')) plt.legend(fontsize=10,loc='upper right') plt.savefig(f"{cluster.imgPath}{clname}_colorFrequency_overlay.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_colorFrequency_overlay.png",dpi=500) #========= Other Radii ========= massSum = np.sum([star.proxyMass for star in starList]) intensitySum = np.sum([toIntensity(star.g_mag) for star in starList]) curMassSum = 0 curIntSum = 0 massFound = False intFound = False if normalize: setattr(cluster,f"medianRad_{mode}",np.median([np.abs(star.normRadDist*3600/206265)/(cluster.mean_par/1000) for star in starList])) setattr(cluster,f"medianAngle_{mode}",np.median([star.normRadDist for star in starList])) radialStarList = sorted(starList,key=lambda x: x.normRadDist) for star in radialStarList: curMassSum += star.proxyMass curIntSum += toIntensity(star.g_mag) if curMassSum > massSum/2 and not massFound: setattr(cluster,f"halfMassRad_{mode}",np.abs(star.normRadDist*3600/206265)/(cluster.mean_par/1000)) setattr(cluster,f"halfMassAngle_{mode}",star.normRadDist) massFound = True if curIntSum > intensitySum/2 and not intFound: setattr(cluster,f"halfLightRad_{mode}",np.abs(star.normRadDist*3600/206265)/(cluster.mean_par/1000)) setattr(cluster,f"halfLightAngle_{mode}",star.normRadDist) intFound = True if massFound and intFound: break plt.figure(f"{clname}_other_radii_{mode}") plt.scatter([star.ra*np.cos(star.dec*np.pi/180) for star in cluster.unfilteredWide],[star.dec for star in cluster.unfilteredWide],s=1,c='lightgray',label='Unfiltered') plt.scatter([star.ra*np.cos(star.dec*np.pi/180) for star in cluster.filtered],[star.dec for star in cluster.filtered],s=2,c='midnightblue',label='Filtered') plt.xlabel("RA*cos(Dec) (deg)") else: setattr(cluster,f"medianRad_{mode}",np.median([np.abs(star.radDist*3600/206265)/(cluster.mean_par/1000) for star in starList])) setattr(cluster,f"medianAngle_{mode}",np.median([star.radDist for star in starList])) radialStarList = sorted(starList,key=lambda x: x.radDist) for star in radialStarList: curMassSum += star.proxyMass curIntSum += toIntensity(star.g_mag) if curMassSum > massSum/2 and not massFound: setattr(cluster,f"halfMassRad_{mode}",np.abs(star.radDist*3600/206265)/(cluster.mean_par/1000)) setattr(cluster,f"halfMassAngle_{mode}",star.radDist) massFound = True if curIntSum > intensitySum/2 and not intFound: setattr(cluster,f"halfLightRad_{mode}",np.abs(star.radDist*3600/206265)/(cluster.mean_par/1000)) setattr(cluster,f"halfLightAngle_{mode}",star.radDist) intFound = True if massFound and intFound: break plt.figure(f"{clname}_other_radii_{mode}") plt.scatter([star.ra for star in cluster.unfilteredWide],[star.dec for star in cluster.unfilteredWide],s=1,c='lightgray',label='Unfiltered') plt.scatter([star.ra for star in cluster.filtered],[star.dec for star in cluster.filtered],s=2,c='midnightblue',label='Filtered') plt.xlabel("RA (deg)") medRad = getattr(cluster,f"medianRad_{mode}") medAngle = getattr(cluster,f"medianAngle_{mode}") mRad = getattr(cluster,f"halfMassRad_{mode}") mAngle = getattr(cluster,f"halfMassAngle_{mode}") lRad = getattr(cluster,f"halfLightRad_{mode}") lAngle = getattr(cluster,f"halfLightAngle_{mode}") print(medAngle) outline1 = Circle([x0,y0],medAngle,color='red',fill=False,ls='--',label=fr"Median Star Distance = {medAngle:.3f}$\degree$, {medRad:.3f}pc",alpha=1) outline2 = Circle([x0,y0],mAngle,color='darkgreen',fill=False,ls='--',label=fr"Half Mass Radius = {mAngle:.3f}$\degree$, {mRad:.3f}pc",alpha=1) outline3 = Circle([x0,y0],lAngle,color='purple',fill=False,ls='--',label=fr"Half Light Radius = {lAngle:.3f}$\degree$, {lRad:.3f}pc",alpha=1) plt.gca().add_patch(outline1) plt.gca().add_patch(outline2) plt.gca().add_patch(outline3) plt.legend(fontsize=10,loc='upper right') plt.axis('square') plt.ylabel("DEC (Deg)") plt.title(f"{clname} {mode.capitalize()} Various Radii".replace("_normalized",' Normalized')) plt.gcf().set_size_inches(8,8) plt.savefig(f"{cluster.imgPath}{clname}_otherRadii_{mode}.pdf") plt.savefig(f"{cluster.imgPath}png/{clname}_otherRadii_{mode}.png",dpi=500) def checkLoaded(cList): if 'all' in cList: cList = [c.name for c in clusterList] else: for cl in cList: if not cl in clusters: loadClusters([cl]) return cList def saveResults(cList,outdir="results"): #Imports import numpy as np import dill import os global clusters global clusterList checkLoaded(cList) #Check and create the relevant directory paths to save/load the results if not os.path.isdir(f"{outdir}/"): os.mkdir(f"{outdir}/") if not os.path.isdir(f"{outdir}/pickled/"): os.mkdir(f"{outdir}/pickled/") else: for cl in cList: cluster = clusters[cl] #Creates a "result cluster" object from the cluster, effectively just stripping away lists rCl = resultClusterObj(cluster) #Pickle the result cluster object with open(f"{outdir}/pickled/{cluster.name}.pk1", 'wb') as output: dill.dump(rCl, output) #Store variables into an array to be printed as csv properties = [a for a in dir(rCl) if not a.startswith('_')] res = [getattr(rCl,p) for p in properties] #Stack into an array of 2 rows with variable names and values fin = np.vstack((properties,res)) np.savetxt(f"{outdir}/{cluster.name}.csv",fin,delimiter=',',fmt='%s') def loadResults(filter="None",indir="results"): #Imports import numpy as np import dill import os global resultList global resultsIn assert os.path.isdir("results/") resultList = [] for fn in os.listdir(indir+"/pickled/"): #Reads in instances from the saved pickle file with open(f"{indir}/pickled/{fn}",'rb') as input: res = dill.load(input) resultList.append(res) resultsIn = True toDict() def refreshProperties(cList=['all']): import numpy as np global catalogue global clusterList global clusters clusterCatalogue() checkLoaded(cList) #Loop through clusters for cluster in cList: reference = None for cl in catalogue: if str(cl.name) == str(cluster.name): reference = cl print(f"Catalogue match for {cluster.name} found") break if reference == None: print(f"Catalogue match for {cluster.name} was not found, please create one") continue #Filter all of the methods out of the properties list properties = [a for a in dir(reference) if not a.startswith('_')] #print(properties) #exec(f"print(reference.{properties[1]})") #print(properties) #Now we have a list of all the attributes assigned to the catalogue (the self.variables) for p in properties: prop = getattr(reference,p) #print(prop) exec(f"cluster.{p} = prop") try: if prop <= -98: print(f"{cluster.name} does not have a specified catalogue value for {p}") except: continue #Additional properties that may be useful for star in cluster.filtered: star.normRA = star.pmra*np.cos(star.dec*np.pi/180) print(f"{cluster.name} properties refreshed from catalogue") def statPlot(statX,statY,population="open",color="default",square=True,invertY=False,logX=False,logY=False,pointLabels=True,linFit=False,directory='default'): #Create plots of stat X vs stat Y across a population of clusters, similar to customPlot() #Can be set to use a custom list of clusters, or all clusters of a given type # import matplotlib import matplotlib.pyplot as plt import numpy as np from scipy.stats import linregress global clusters global clusterList global catalogue global resultsIn global resultList if not resultsIn: loadResults() #Filter out incorrect inputs if type(population) == str: population = population.lower() try: assert population == "open" or population == "globular" except: print("Specified population type not recognized") else: try: assert type(population) == list assert type(population[0]) == str except: print("Population type given is not valid, must be either a list of cluster name strings or a single string \'open\' or \'closed\'") return try: assert len(population) > 1 except: print("Population statistic plots cannot be made with fewer than 2 clusters given") return #Load cluster information from cList #This is going to involve using the resultCluster object to read data from each cluster folder in the cList cList = [] banList = ['NGC2204'] if type(population) == str: for res in resultList: if res.clType.lower() == population and not res.name in banList: cList.append(res) else: for res in resultList: if res.name in population: cList.append(res) if statX.lower() == "b_r" and statY.lower() == "g_mag": #Corrected CMD overlay NUM_COLORS = len(cList) cm = plt.get_cmap('nipy_spectral') plt.figure("uncorrected") plt.title("Cluster Overlay") plt.xlabel("Observed B-R") plt.ylabel("Apparent G Mag") plt.gca().invert_yaxis() plt.gca().set_prop_cycle('color', [cm(1.025*i/NUM_COLORS) for i in range(NUM_COLORS)]) plt.figure("unshifted") plt.title("Corrected Cluster Overlay") plt.xlabel("Dereddened B-R") plt.ylabel("Absolute G Mag") plt.gca().invert_yaxis() plt.gca().set_prop_cycle('color', [cm(1.025*i/NUM_COLORS) for i in range(NUM_COLORS)]) plt.figure("shifted") plt.title("Corrected Cluster Overlay - Offset") plt.xlabel("Dereddened B-R") plt.ylabel("Absolute G Mag") plt.gca().invert_yaxis() plt.gca().set_prop_cycle('color', [cm(1.025*i/NUM_COLORS) for i in range(NUM_COLORS)]) index = 0 offset = 2.5 for cluster in cList: try: path = cluster.dataPath except: path = f"clusters/{cluster.name}/data/" condensed = np.genfromtxt(f"{path}condensed.csv",delimiter=",") cluster.condensed = condensed #Adjust by cluster.reddening and cluster.dist_mod x1 = [a[0] for a in condensed] y1 = [a[1] for a in condensed] x2 = [a[0]-cluster.reddening for a in condensed] y2 = [a[1]-2.1*cluster.reddening-cluster.dist_mod for a in condensed] x3 = [a[0]-cluster.reddening for a in condensed] y3 = [a[1]-2.1*cluster.reddening-cluster.dist_mod+index*offset for a in condensed] index += 1 plt.figure("uncorrected") plt.scatter(x1,y1,label=f"{cluster.name}") plt.figure("unshifted") plt.axvline(x=1.6,ymax=0.5,color='black',linestyle='--') plt.axhline(y=4,xmin=0.59,color='black',linestyle='--') plt.scatter(x2,y2,label=f"{cluster.name}") plt.figure("shifted") plt.scatter(x3,y3,label=f"{cluster.name}") plt.axvline(x=1.6,color='black',linestyle='--') # if 'NGC2301' in cluster.name: # for a,b in zip(x2,y2): # print(f"{a},{b}") plt.figure("uncorrected") plt.legend(fontsize=10,loc='upper right') plt.gcf().set_size_inches(8,6) plt.savefig(f"results/plots/pdf/{population}_clusters_stacked_cmd_apparent.pdf") plt.savefig(f"results/plots/png/{population}_clusters_stacked_cmd_apparent.png",dpi=500) plt.figure("unshifted") plt.legend(fontsize=10,loc='upper right') plt.gcf().set_size_inches(8,6) plt.savefig(f"results/plots/pdf/{population}_clusters_stacked_cmd_absolute.pdf") plt.savefig(f"results/plots/png/{population}_clusters_stacked_cmd_absolute.png",dpi=500) plt.figure("shifted") plt.legend(fontsize=10,loc='upper right') plt.gcf().set_size_inches(8,6) plt.savefig(f"results/plots/pdf/{population}_clusters_stacked_cmd_shifted.pdf") plt.savefig(f"results/plots/png/{population}_clusters_stacked_cmd_shifted.png",dpi=500) else: x = [getattr(a, statX) for a in cList] y = [getattr(a, statY) for a in cList] plt.figure() plt.xlabel(f"{statX}") plt.ylabel(f"{statY}") if pointLabels: for cluster in cList: plt.scatter(getattr(cluster, statX),getattr(cluster, statY),label=cluster.name) plt.legend(fontsize="small") else: plt.scatter(x,y) if linFit: reg = linregress(x,y) plt.plot(x,[reg[0]*a+reg[1] for a in x]) plt.savefig(f"SpecificPlots/pdf/{population}_{statX}_{statY}.pdf") plt.savefig(f"SpecificPlots/png/{population}_{statX}_{statY}.png",dpi=500) return def ageMassFit(t,m0,k): import numpy as np return 1 + m0*np.exp(-1*k*t) def extinctionLaw(d,M0): import numpy as np return M0 -2.5*np.log10(1/(4*np.pi*d**2)) def resultPlots(): #Imports import matplotlib.pyplot as plt import numpy as np from scipy.stats import linregress from scipy.optimize import curve_fit global clusters global clusterList global catalogue global resultsIn global resultList if not resultsIn: loadResults() #Select open clusters from resultList banList = ['NGC2204'] cList = [] for res in resultList: if res.clType.lower() == "open" and not res.name in banList: cList.append(res) #Filtered mass versus age fname = "mass_vs_age_filtered" plt.figure(fname) plt.title(f"{len(cList)} Open Clusters") plt.xlabel("Fit Age (Gyr)") plt.ylabel(r"Mean Cluster Member Mass ($M_{\odot}$)") plt.scatter([c.fit_age for c in cList],[c.meanProxyMass for c in cList]) plt.savefig(f"results/plots/pdf/{fname}.pdf") plt.savefig(f"results/plots/png/{fname}.png",dpi=500) #Bounded mass versus age fname = "mass_vs_age_bounded" plt.figure(fname) plt.title(f"{len(cList)} Open Clusters - BR-RP Limit Enforced") plt.xlabel("Fit Age (Gyr)") plt.ylabel(r"Mean Cluster Member Mass ($M_{\odot}$)") x,y = [c.fit_age for c in cList],[c.meanBoundedProxyMass for c in cList] plt.scatter(x,y) fit,var = curve_fit(ageMassFit,x,y,p0=[8,1],maxfev=1000) xr = list(np.linspace(min(x),max(x),101)) fitLabel = fr"$y = 1+{fit[0]:.3f}e^{{-{fit[1]:.3f}t}}$" + "\n" + fr"Uncertainties = $\pm{var[0][0]:.3f}, \pm{var[1][1]:.3f}$" plt.plot(xr,[ageMassFit(a,fit[0],fit[1]) for a in xr],label=fitLabel) plt.legend() plt.savefig(f"results/plots/pdf/{fname}.pdf") plt.savefig(f"results/plots/png/{fname}.png",dpi=500) #Mass intercept versus age fname = "mass_intercept_vs_age_bounded" plt.figure(fname) plt.title(f"{len(cList)} Open Clusters - BR-RP Limit Enforced") plt.xlabel("Fit Age (Gyr)") plt.ylabel(r"Mean Stellar Mass in Core ($M_{\odot}$)") x,y = [c.fit_age for c in cList],[c.mass_intercept_bounded for c in cList] plt.scatter(x,y) fit,var = curve_fit(ageMassFit,x,y,p0=[8,1],maxfev=1000) xr = list(np.linspace(min(x),max(x),101)) fitLabel = fr"$y = 1+{fit[0]:.3f}e^{{-{fit[1]:.3f}t}}$" + "\n" + fr"Uncertainties = $\pm{var[0][0]:.3f}, \pm{var[1][1]:.3f}$" plt.plot(xr,[ageMassFit(a,fit[0],fit[1]) for a in xr],label=fitLabel) plt.legend() plt.savefig(f"results/plots/pdf/{fname}.pdf") plt.savefig(f"results/plots/png/{fname}.png",dpi=500) #Mass slope versus age fname = "mass_slop_vs_age_bounded" plt.figure(fname) plt.title(f"{len(cList)} Open Clusters - BR-RP Limit Enforced") plt.xlabel("Fit Age (Gyr)") plt.ylabel(r"IQM Stellar Mass Dropoff ($\frac{M_{\odot}}{pc}$)") x,y = [c.fit_age for c in cList],[c.mass_slope_bounded for c in cList] plt.scatter(x,y) plt.savefig(f"results/plots/pdf/{fname}.pdf") plt.savefig(f"results/plots/png/{fname}.png",dpi=500) #Magnitude versus distance (Extinction law) fname = "mag_vs_dist_bounded" plt.figure(fname) plt.title(f"{len(cList)} Open Clusters - BR-RP Limit Enforced") plt.xlabel("Cluster Distance from Earth (pc)") plt.ylabel(r"Mean Apparent G Magnitude") x,y = [c.meanDist for c in cList],[c.mean_bounded_g_mag for c in cList] plt.scatter(x,y) fit,var = curve_fit(extinctionLaw,x,y,maxfev=1000) xr = list(np.linspace(min(x),max(x),101)) plt.plot(xr,[extinctionLaw(a,fit[0]) for a in xr],label="Inverse Square Law \n" + fr" $M_0 = {fit[0]:.3f} \pm {var[0][0]:.3f}$") plt.gca().invert_yaxis() plt.legend() plt.savefig(f"results/plots/pdf/{fname}.pdf") plt.savefig(f"results/plots/png/{fname}.png",dpi=500) #Bounded fraction versus distance fname = "bounded_fraction_vs_dist" plt.figure(fname) plt.title(f"{len(cList)} Open Clusters - BR-RP Limit Enforced") plt.xlabel("Cluster Distance from Earth (pc)") plt.ylabel("Fraction Unaffected by BP-RP Limit") x,y = [c.meanDist for c in cList],[c.fractionBounded for c in cList] plt.scatter(x,y) plt.savefig(f"results/plots/pdf/{fname}.pdf") plt.savefig(f"results/plots/png/{fname}.png",dpi=500) #Radii plt.figure() plt.scatter([c.meanGalacticDist for c in cList],[c.halfLightRad_bounded/c.medianRad_bounded for c in cList]) def boundedStats(cList,xmax=1.6,saveCl=True,unloadCl=True): import numpy as np global clusters global subList for cl in cList: checkLoaded([cl]) cluster = clusters[cl] subList = [star for star in cluster.filtered if not (star.b_r-cluster.reddening > xmax and star.g_mag > cluster.cltpy)] cluster.bounded = subList #Windowed properties (over the xmin to xmax range) cluster.meanBoundedProxyMass = np.mean([a.proxyMass for a in subList]) cluster.totalBoundedProxyMass = np.sum([a.proxyMass for a in subList]) cluster.numBounded = len(subList) cluster.fractionBounded = len(subList)/len(cluster.filtered) cluster.mean_bounded_b_r = np.mean([a.b_r for a in subList]) cluster.mean_bounded_g_mag = np.mean([a.g_mag for a in subList]) if saveCl: saveClusters([cl]) saveResults([cl]) if unloadCl: unloadClusters([cl]) def tryFits(fitVar='fit_age'): from scipy.stats import linregress global resultsIn global resultList global props global r2 if not resultsIn: loadResults() cList = [] for res in resultList: if res.clType.lower() == "open": cList.append(res) if 'all' in fitVar: #List of plottable variables props = dir(cList[0]) props = [a for a in props if not '__' in a] propList = [a for a in props if type(getattr(cList[0],a)) == float] propList.remove('turnPoint') r2 = [] for pr in propList: #List of plottable variables props = dir(cList[0]) props = [a for a in props if not '__' in a] props = [a for a in props if type(getattr(cList[0],a)) == float] props.remove('turnPoint') props.remove(pr) for prop in props: x = [getattr(a, pr) for a in cList] y = [getattr(a, prop) for a in cList] reg = linregress(x,y) r2.append((pr,prop,reg[2]**2)) r2 = sorted(r2,key = lambda x: x[2],reverse=True) print("Top 100 r^2 values:") for r in r2[:200]: print(f"{r[0]} | {r[1]} | {r[2]}") else: #List of plottable variables props = dir(cList[0]) props = [a for a in props if not '__' in a] props = [a for a in props if type(getattr(cList[0],a)) == float] props.remove('turnPoint') props.remove(fitVar) r2 = [] for prop in props: x = [getattr(a, fitVar) for a in cList] y = [getattr(a, prop) for a in cList] reg = linregress(x,y) r2.append((prop,reg[2]**2)) r2 = sorted(r2,key = lambda x: x[1],reverse=True) print("Top 20 r^2 values:") for r in r2[:20]: print(f"{r[0]} | {r[1]}") def prelimPlot(cl): import matplotlib.pyplot as plt cluster = clusters[cl] plt.scatter([a.ra for a in cluster.unfilteredWide],[a.dec for a in cluster.unfilteredWide],s=0.1) plt.figure() plt.scatter([a.pmra for a in cluster.unfilteredWide],[a.pmdec for a in cluster.unfilteredWide],s=0.1) # plt.figure() # plt.scatter([a.pmra for a in cluster.unfilteredWide],[a.pmdec for a in cluster.unfilteredWide],s=0.1,c=[a.par for a in cluster.unfilteredWide]) # plt.set_cmap('cool') # clb = plt.colorbar() plt.figure() plt.scatter([a.b_r for a in cluster.unfilteredWide],[a.g_mag for a in cluster.unfilteredWide],s=0.1) plt.gca().invert_yaxis() # plt.figure() # plt.scatter([a.par for a in cluster.unfilteredWide],[a.par for a in cluster.unfilteredWide],s=0.1,c=[(a.pmra**2 + a.pmdec**2)**0.5 for a in cluster.unfilteredWide]) # plt.set_cmap('cool')

true

f7364db0a9b0f7a13ada988a208e0f0859847424

2,476

py

Python

neurolib/utils/saver.py

Ronjaa95/neurolib

6d066a6428718ec85c9b1b87707ea73bc179ebf9

[ "MIT" ]

258

2020-01-26T14:38:09.000Z

2022-03-31T14:54:04.000Z

neurolib/utils/saver.py

Ronjaa95/neurolib

6d066a6428718ec85c9b1b87707ea73bc179ebf9

[ "MIT" ]

172

2020-01-27T11:02:28.000Z

2022-03-22T22:25:38.000Z

neurolib/utils/saver.py

Ronjaa95/neurolib

6d066a6428718ec85c9b1b87707ea73bc179ebf9

[ "MIT" ]

49

2020-02-04T08:34:44.000Z

2022-03-28T09:29:12.000Z

""" Saving model output. """ import json import pickle from copy import deepcopy import os import numpy as np import xarray as xr def save_to_pickle(datafield, filename): """ Save datafield to pickle file. Keep in mind that restoring a pickle requires that the internal structure of the types for the pickled data remain unchanged, o.e. not recommended for long-term storage. :param datafield: datafield or dataarray to save :type datafield: xr.Dataset|xr.DataArray :param filename: filename :type filename: str """ assert isinstance(datafield, (xr.DataArray, xr.Dataset)) if not filename.endswith(".pkl"): filename += ".pkl" with open(filename, "wb") as handle: pickle.dump(datafield, handle, protocol=pickle.HIGHEST_PROTOCOL) def save_to_netcdf(datafield, filename): """ Save datafield to NetCDF. NetCDF cannot handle structured attributes, hence they are stripped and if there are some, they are saved as json with the same filename. :param datafield: datafield or dataarray to save :type datafield: xr.Dataset|xr.DataArray :param filename: filename :type filename: str """ assert isinstance(datafield, (xr.DataArray, xr.Dataset)) datafield = deepcopy(datafield) if not filename.endswith(".nc"): filename += ".nc" if datafield.attrs: attributes_copy = deepcopy(datafield.attrs) _save_attrs_json(attributes_copy, filename) datafield.attrs = {} datafield.to_netcdf(filename) def _save_attrs_json(attrs, filename): """ Save attributes to json. :param attrs: attributes to save :type attrs: dict :param filename: filename for the json file :type filename: str """ def sanitise_attrs(attrs): sanitised = {} for k, v in attrs.items(): if isinstance(v, list): sanitised[k] = [ sanitise_attrs(vv) if isinstance(vv, dict) else vv.tolist() if isinstance(vv, np.ndarray) else vv for vv in v ] elif isinstance(v, dict): sanitised[k] = sanitise_attrs(v) elif isinstance(v, np.ndarray): sanitised[k] = v.tolist() else: sanitised[k] = v return sanitised filename = os.path.splitext(filename)[0] + ".json" with open(filename, "w") as handle: json.dump(sanitise_attrs(attrs), handle)

29.47619

117

0.640549

import json import pickle from copy import deepcopy import os import numpy as np import xarray as xr def save_to_pickle(datafield, filename): assert isinstance(datafield, (xr.DataArray, xr.Dataset)) if not filename.endswith(".pkl"): filename += ".pkl" with open(filename, "wb") as handle: pickle.dump(datafield, handle, protocol=pickle.HIGHEST_PROTOCOL) def save_to_netcdf(datafield, filename): assert isinstance(datafield, (xr.DataArray, xr.Dataset)) datafield = deepcopy(datafield) if not filename.endswith(".nc"): filename += ".nc" if datafield.attrs: attributes_copy = deepcopy(datafield.attrs) _save_attrs_json(attributes_copy, filename) datafield.attrs = {} datafield.to_netcdf(filename) def _save_attrs_json(attrs, filename): def sanitise_attrs(attrs): sanitised = {} for k, v in attrs.items(): if isinstance(v, list): sanitised[k] = [ sanitise_attrs(vv) if isinstance(vv, dict) else vv.tolist() if isinstance(vv, np.ndarray) else vv for vv in v ] elif isinstance(v, dict): sanitised[k] = sanitise_attrs(v) elif isinstance(v, np.ndarray): sanitised[k] = v.tolist() else: sanitised[k] = v return sanitised filename = os.path.splitext(filename)[0] + ".json" with open(filename, "w") as handle: json.dump(sanitise_attrs(attrs), handle)

true

f7364ddf2333b397212e2ec8913c3f5019780df6

163

py

Python

vizdoomgym/envs/vizdoomtakecover.py

AlexGonRo/vizdoomgym

231b5f07d878f4e8cab49e09db5cbc38c2d3abfb

[ "MIT" ]

1

2021-04-13T19:05:27.000Z

vizdoomgym/envs/vizdoomtakecover.py

safijari/vizdoomgym

4ba08296d528a40ce865f30f7abfef93417614f4

[ "MIT" ]

12

2019-10-21T04:53:12.000Z

2022-02-10T00:28:37.000Z

vizdoomgym/envs/vizdoomtakecover.py

safijari/vizdoomgym

4ba08296d528a40ce865f30f7abfef93417614f4

[ "MIT" ]

1

2021-12-14T12:20:39.000Z

from vizdoomgym.envs.vizdoomenv import VizdoomEnv class VizdoomTakeCover(VizdoomEnv): def __init__(self): super(VizdoomTakeCover, self).__init__(7)

20.375

49

0.760736

from vizdoomgym.envs.vizdoomenv import VizdoomEnv class VizdoomTakeCover(VizdoomEnv): def __init__(self): super(VizdoomTakeCover, self).__init__(7)

true

f7364f6d6829dd6dc289c75df401165fac819425

2,399

py

Python

tests/test_tensor_transform.py

aripekka/tbcalc

a0337db245f5391bfa9a42123994832c299b1fbe

[ "MIT" ]

1

2020-05-03T00:10:39.000Z

tests/test_tensor_transform.py

aripekka/tbcalc

a0337db245f5391bfa9a42123994832c299b1fbe

[ "MIT" ]

null

tests/test_tensor_transform.py

aripekka/tbcalc

a0337db245f5391bfa9a42123994832c299b1fbe

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Tests for the tensor transform functions. Run with pytest. Created on Sat May 9 00:09:00 2020 @author: aripekka """ import sys import os.path import numpy as np sys.path.insert(1, os.path.join(os.path.dirname(__file__),'..')) from tbcalc.transverse_deformation import * from tbcalc import cartesian_tensors_to_cylindrical from pyTTE import TTcrystal, Quantity def test_isotropic_circular(): #Calculate the reference stresses and strains as implemented in the #deprecated sbcalc package E = 165 nu = 0.22 thickness = 0.1 Rx = 1000.0 Ry = 500.0 R = np.sqrt(Rx*Ry) L = 100.0 x=np.linspace(-L/2,L/2,150) X,Y=np.meshgrid(x,x) RR = np.sqrt(X**2 + Y**2) PHI = np.arctan2(Y,X) stress, strain, P_imp = isotropic_circular(Rx, Ry, L, thickness, nu, E) stress_cyl = cartesian_tensors_to_cylindrical(stress) strain_cyl = cartesian_tensors_to_cylindrical(strain) stress_cyl_ref = {} stress_cyl_ref['rr'] = E/(16*R**2)*(L**2/4-RR**2)+stress['xx'](X,Y)*0 stress_cyl_ref['phiphi'] = E/(16*R**2)*(L**2/4-3*RR**2)+stress['xx'](X,Y)*0 stress_cyl_ref['rphi'] = stress['xx'](X,Y)*0 stress_cyl_ref['phir'] = stress['xx'](X,Y)*0 strain_cyl_ref = {} strain_cyl_ref['rr'] = 1/(16*R**2)*((1-nu)*L**2/4-(1-3*nu)*RR**2)+stress['xx'](X,Y)*0 strain_cyl_ref['phiphi'] = 1/(16*R**2)*((1-nu)*L**2/4-(3-nu)*RR**2)+stress['xx'](X,Y)*0 strain_cyl_ref['rphi'] = stress['xx'](X,Y)*0 strain_cyl_ref['phir'] = stress['xx'](X,Y)*0 strain_cyl_ref['zphi'] = stress['xx'](X,Y)*0 strain_cyl_ref['phiz'] = stress['xx'](X,Y)*0 strain_cyl_ref['rz'] = stress['xx'](X,Y)*0 strain_cyl_ref['zr'] = stress['xx'](X,Y)*0 strain_cyl_ref['zz'] = nu/(4*R**2)*(RR**2-L**2/8)+stress['xx'](X,Y)*0 meps = np.finfo(np.float).eps #m for i in ['r','phi']: for j in ['r','phi']: assert np.all(np.logical_or(np.abs(stress_cyl_ref[i+j] - stress_cyl[i+j](RR,PHI)) < meps, np.logical_and(np.isnan(stress_cyl_ref[i+j]), np.isnan(stress_cyl[i+j](RR,PHI))))) for i in ['r','phi','z']: for j in ['r','phi','z']: assert np.all(np.logical_or(np.abs(strain_cyl_ref[i+j] - strain_cyl[i+j](RR,PHI)) < meps, np.logical_and(np.isnan(strain_cyl_ref[i+j]), np.isnan(strain_cyl[i+j](RR,PHI)))))

31.155844

108

0.60025

import sys import os.path import numpy as np sys.path.insert(1, os.path.join(os.path.dirname(__file__),'..')) from tbcalc.transverse_deformation import * from tbcalc import cartesian_tensors_to_cylindrical from pyTTE import TTcrystal, Quantity def test_isotropic_circular(): E = 165 nu = 0.22 thickness = 0.1 Rx = 1000.0 Ry = 500.0 R = np.sqrt(Rx*Ry) L = 100.0 x=np.linspace(-L/2,L/2,150) X,Y=np.meshgrid(x,x) RR = np.sqrt(X**2 + Y**2) PHI = np.arctan2(Y,X) stress, strain, P_imp = isotropic_circular(Rx, Ry, L, thickness, nu, E) stress_cyl = cartesian_tensors_to_cylindrical(stress) strain_cyl = cartesian_tensors_to_cylindrical(strain) stress_cyl_ref = {} stress_cyl_ref['rr'] = E/(16*R**2)*(L**2/4-RR**2)+stress['xx'](X,Y)*0 stress_cyl_ref['phiphi'] = E/(16*R**2)*(L**2/4-3*RR**2)+stress['xx'](X,Y)*0 stress_cyl_ref['rphi'] = stress['xx'](X,Y)*0 stress_cyl_ref['phir'] = stress['xx'](X,Y)*0 strain_cyl_ref = {} strain_cyl_ref['rr'] = 1/(16*R**2)*((1-nu)*L**2/4-(1-3*nu)*RR**2)+stress['xx'](X,Y)*0 strain_cyl_ref['phiphi'] = 1/(16*R**2)*((1-nu)*L**2/4-(3-nu)*RR**2)+stress['xx'](X,Y)*0 strain_cyl_ref['rphi'] = stress['xx'](X,Y)*0 strain_cyl_ref['phir'] = stress['xx'](X,Y)*0 strain_cyl_ref['zphi'] = stress['xx'](X,Y)*0 strain_cyl_ref['phiz'] = stress['xx'](X,Y)*0 strain_cyl_ref['rz'] = stress['xx'](X,Y)*0 strain_cyl_ref['zr'] = stress['xx'](X,Y)*0 strain_cyl_ref['zz'] = nu/(4*R**2)*(RR**2-L**2/8)+stress['xx'](X,Y)*0 meps = np.finfo(np.float).eps for i in ['r','phi']: for j in ['r','phi']: assert np.all(np.logical_or(np.abs(stress_cyl_ref[i+j] - stress_cyl[i+j](RR,PHI)) < meps, np.logical_and(np.isnan(stress_cyl_ref[i+j]), np.isnan(stress_cyl[i+j](RR,PHI))))) for i in ['r','phi','z']: for j in ['r','phi','z']: assert np.all(np.logical_or(np.abs(strain_cyl_ref[i+j] - strain_cyl[i+j](RR,PHI)) < meps, np.logical_and(np.isnan(strain_cyl_ref[i+j]), np.isnan(strain_cyl[i+j](RR,PHI)))))

true

f736512234f6783eb63953d115f2c91aeee1b5bf

23,644

py

Python

src/trapi/trapi.py

guilhermehott/trapi

7f44df1872b2e253bc423fa92a52efdd6d4fdbf1

[ "MIT" ]

1

2022-03-23T23:16:04.000Z

src/trapi/trapi.py

guilhermehott/trapi

7f44df1872b2e253bc423fa92a52efdd6d4fdbf1

[ "MIT" ]

null

src/trapi/trapi.py

guilhermehott/trapi

7f44df1872b2e253bc423fa92a52efdd6d4fdbf1

[ "MIT" ]

null

import asyncio import base64 import hashlib import json import logging import time import urllib.parse import uuid import requests import websockets from ecdsa import NIST256p, SigningKey from ecdsa.util import sigencode_der logging.basicConfig( level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", # handlers=[ # logging.FileHandler("src/tmp/debug.log"), # logging.StreamHandler() # ] ) logger = logging.getLogger(__name__) # home = pathlib.Path.home() # Necessary when this lib is called from a flask endpoint because it's not on the main thread. def get_or_create_eventloop(): try: return asyncio.get_event_loop() except RuntimeError as ex: if "There is no current event loop in thread" in str(ex): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) return asyncio.get_event_loop() class TradeRepublicApi: _default_headers = {'User-Agent': 'TradeRepublic/Android 24/App Version 1.1.2875'} _host = "https://api.traderepublic.com" _refresh_token = None _session_token = None _session_token_expires_at = None credentials_file = "credentials" _ws = None get_or_create_eventloop() _lock = asyncio.Lock() _subscription_id_counter = 1 _previous_responses = {} subscriptions = {} @property def session_token(self): if not self._refresh_token: self.login() elif self._refresh_token and time.time() > self._session_token_expires_at: self.refresh_access_token() return self._session_token @session_token.setter def session_token(self, val): self._session_token_expires_at = time.time() + 290 self._session_token = val def __init__(self, phone_no=None, pin=None, keyfile=None, locale="de"): self._locale = locale if not (phone_no and pin): try: with open(self.credentials_file, 'r') as f: lines = f.readlines() self.phone_no = lines[0].strip() self.pin = lines[1].strip() except FileNotFoundError: raise ValueError(f"phone_no and pin must be specified explicitly or via credentials file") else: self.phone_no = phone_no self.pin = pin self.keyfile = keyfile if keyfile else "keyfile.pem" try: with open(self.keyfile, 'rb') as f: self.sk = SigningKey.from_pem(f.read(), hashfunc=hashlib.sha512) except FileNotFoundError as ex: logger.warn('File not found %s', self.keyfile) pass def interactive_device_reset(self): self.sk = SigningKey.generate(curve=NIST256p, hashfunc=hashlib.sha512) r = requests.post(f"{self._host}/api/v1/auth/account/reset/device", json={"phoneNumber": self.phone_no, "pin": self.pin}, headers=self._default_headers) if r.status_code == 200: process_id = r.json()['processId'] else: self.print_error_response(r) token = input("Please enter the sms code: ") self.pair_device(process_id, token) def initiate_device_reset(self): self.sk = SigningKey.generate(curve=NIST256p, hashfunc=hashlib.sha512) r = requests.post(f"{self._host}/api/v1/auth/account/reset/device", json={"phoneNumber": self.phone_no, "pin": self.pin}, headers=self._default_headers) if r.status_code == 200: # save processId with open(self.credentials_file, 'a') as f: f.write("\n") f.write(r.json()['processId']) logger.info("processId %s", r.json()['processId']) # save signature key with open(self.keyfile, 'wb') as f: f.write(self.sk.to_pem()) logger.info("writing to pem file") logger.info(self.sk.to_pem()) else: self.print_error_response(r) raise Exception(r.json()) def complete_device_reset(self, token): with open(self.credentials_file, 'r') as f: lines = f.readlines() process_id = lines[len(lines)-1].strip() # get the latest process_id with open(self.keyfile, 'rb') as f: self.sk = SigningKey.from_pem(f.read(), hashfunc=hashlib.sha512) if not process_id or not self.sk: raise ValueError("Initiate Device Reset first.") else: self.pair_device(process_id, token) def pair_device(self, process_id, token): logger.info("pairing device") pubkey_bytes = self.sk.get_verifying_key().to_string('uncompressed') pubkey_string = base64.b64encode(pubkey_bytes).decode('ascii') r = requests.post(f"{self._host}/api/v1/auth/account/reset/device/{process_id}/key", json={"code": token, "deviceKey": pubkey_string}, headers=self._default_headers) if r.status_code != 200: self.print_error_response(r) raise Exception(r.json()) @staticmethod def print_error_response(r): logger.error("%s %s", r.request.method, r.request.url) logger.error("%s %s", r.status_code, r.json()) def login(self): logging.info("Logging in") r = self._sign_request("/api/v1/auth/login", payload={"phoneNumber": self.phone_no, "pin": self.pin}) if r.status_code == 200: self._refresh_token = r.json()['refreshToken'] self.session_token = r.json()['sessionToken'] # else: # # the device lost the session, must be reseted # self.interactive_device_reset() # self.login() def refresh_access_token(self): logger.info("Refreshing access token") r = self._sign_request("/api/v1/auth/session", method="GET") self.session_token = r.json()['sessionToken'] def _sign_request(self, url_path, payload=None, method="POST"): ts = int(time.time() * 1000) payload_string = json.dumps(payload) if payload else "" signature_payload = f"{ts}.{payload_string}" signature = self.sk.sign(bytes(signature_payload, "utf-8"), hashfunc=hashlib.sha512, sigencode=sigencode_der) signature_string = base64.b64encode(signature).decode('ascii') headers = self._default_headers.copy() headers["X-Zeta-Timestamp"] = str(ts) headers["X-Zeta-Signature"] = signature_string headers['Content-Type'] = 'application/json' if url_path == "/api/v1/auth/login": pass elif url_path == "/api/v1/auth/session": headers['Authorization'] = f'Bearer {self._refresh_token}' elif self.session_token: headers['Authorization'] = f'Bearer {self.session_token}' return requests.request(method=method, url=f"{self._host}{url_path}", data=payload_string, headers=headers) async def _get_ws(self): if self._ws and self._ws.open: return self._ws logger.info(f"Connecting to websocket ...") self._ws = await websockets.connect("wss://api.traderepublic.com") connection_message = {'locale': self._locale} await self._ws.send(f"connect 21 {json.dumps(connection_message)}") response = await self._ws.recv() if not response == 'connected': raise ValueError(f"Connection Error: {response}") logger.info(f"Connected to websocket ...") return self._ws async def _next_subscription_id(self): async with self._lock: subscription_id = self._subscription_id_counter self._subscription_id_counter += 1 return str(subscription_id) async def subscribe(self, payload): subscription_id = await self._next_subscription_id() ws = await self._get_ws() logger.info(f"Subscribing: 'sub {subscription_id} {json.dumps(payload)}'") self.subscriptions[subscription_id] = payload payload_with_token = payload.copy() payload_with_token["token"] = self.session_token await ws.send(f"sub {subscription_id} {json.dumps(payload_with_token)}") return subscription_id async def unsubscribe(self, subscription_id): ws = await self._get_ws() logger.info(f"Unubscribing: {subscription_id}") await ws.send(f"unsub {subscription_id}") self.subscriptions.pop(subscription_id, None) self._previous_responses.pop(subscription_id, None) async def recv(self): ws = await self._get_ws() while True: response = await ws.recv() logger.debug(f"Received message: {response!r}") subscription_id = response[:response.find(" ")] code = response[response.find(" ") + 1: response.find(" ") + 2] payload_str = response[response.find(" ") + 2:].lstrip() if subscription_id not in self.subscriptions: if code != "C": logger.info(f"No active subscription for id {subscription_id}, dropping message") continue subscription = self.subscriptions[subscription_id] if code == 'A': self._previous_responses[subscription_id] = payload_str payload = json.loads(payload_str) if payload_str else {} return subscription_id, subscription, payload elif code == 'D': response = self._calculate_delta(subscription_id, payload_str) logger.debug(f"Payload is {response}") self._previous_responses[subscription_id] = response return subscription_id, subscription, json.loads(response) if code == 'C': self.subscriptions.pop(subscription_id, None) self._previous_responses.pop(subscription_id, None) continue elif code == 'E': logger.error(f"Received error message: {response!r}") await self.unsubscribe(subscription_id) payload = json.loads(payload_str) if payload_str else {} raise TradeRepublicError(subscription_id, subscription, payload) def _calculate_delta(self, subscription_id, delta_payload): previous_response = self._previous_responses[subscription_id] i, result = 0, [] for diff in delta_payload.split('\t'): sign = diff[0] if sign == '+': result.append(urllib.parse.unquote_plus(diff).strip()) elif sign == '-' or sign == '=': if sign == '=': result.append(previous_response[i:i + int(diff[1:])]) i += int(diff[1:]) return "".join(result) async def _recv_subscription(self, subscription_id): while True: response_subscription_id, _, response = await self.recv() if response_subscription_id == subscription_id: return response async def _receive_one(self, fut, timeout): subscription_id = await fut try: return await asyncio.wait_for(self._recv_subscription(subscription_id), timeout) finally: await self.unsubscribe(subscription_id) def run_blocking(self, fut, timeout=5.0): return get_or_create_eventloop().run_until_complete(self._receive_one(fut, timeout=timeout)) async def portfolio(self): return await self.subscribe({"type": "portfolio"}) async def watchlist(self): return await self.subscribe({"type": "watchlist"}) async def cash(self): return await self.subscribe({"type": "cash"}) async def available_cash_for_payout(self): return await self.subscribe({"type": "availableCashForPayout"}) async def portfolio_status(self): return await self.subscribe({"type": "portfolioStatus"}) async def portfolio_history(self, timeframe): return await self.subscribe({"type": "portfolioAggregateHistory", "range": timeframe}) async def instrument_details(self, isin): return await self.subscribe({"type": "instrument", "id": isin}) async def instrument_suitability(self, isin): return await self.subscribe({"type": "instrumentSuitability", "instrumentId": isin}) async def stock_details(self, isin): return await self.subscribe({"type": "stockDetails", "id": isin}) async def add_watchlist(self, isin): return await self.subscribe({"type": "addToWatchlist", "instrumentId": isin}) async def remove_watchlist(self, isin): return await self.subscribe({"type": "removeFromWatchlist", "instrumentId": isin}) async def ticker(self, isin, exchange="LSX"): return await self.subscribe({"type": "ticker", "id": f"{isin}.{exchange}"}) async def performance(self, isin, exchange="LSX"): return await self.subscribe({"type": "performance", "id": f"{isin}.{exchange}"}) async def performance_history(self, isin, timeframe, exchange="LSX", resolution=None): parameters = {"type": "aggregateHistory", "id": f"{isin}.{exchange}", "range": timeframe} if resolution: parameters["resolution"] = resolution return await self.subscribe(parameters) async def experience(self): return await self.subscribe({"type": "experience"}) async def motd(self): return await self.subscribe({"type": "messageOfTheDay"}) async def neon_cards(self): return await self.subscribe({"type": "neonCards"}) async def timeline(self, after=None): return await self.subscribe({"type": "timeline", "after": after}) async def timeline_detail(self, timeline_id): return await self.subscribe({"type": "timelineDetail", "id": timeline_id}) async def timeline_detail_order(self, order_id): return await self.subscribe({"type": "timelineDetail", "orderId": order_id}) async def timeline_detail_savings_plan(self, savings_plan_id): return await self.subscribe({"type": "timelineDetail", "savingsPlanId": savings_plan_id}) async def search_tags(self): return await self.subscribe({"type": "neonSearchTags"}) async def search_suggested_tags(self, query): return await self.subscribe({"type": "neonSearchSuggestedTags", "data": {"q": query}}) async def search(self, query, asset_type="stock", page=1, page_size=20, aggregate=False, only_savable=False, filter_index=None, filter_country=None, filter_sector=None, filter_region=None): search_parameters = { "q": query, "filter": [{'key': 'type', 'value': asset_type}], "page": page, "pageSize": page_size, } if only_savable: search_parameters["filter"].append({'key': 'attribute', 'value': 'savable'}) if filter_index: search_parameters["filter"].append({'key': 'index', 'value': filter_index}) if filter_country: search_parameters["filter"].append({'key': 'country', 'value': filter_country}) if filter_region: search_parameters["filter"].append({'key': 'region', 'value': filter_region}) if filter_sector: search_parameters["filter"].append({'key': 'sector', 'value': filter_sector}) search_type = "neonSearch" if not aggregate else "neonSearchAggregations" return await self.subscribe({"type": search_type, "data": search_parameters}) # product_type: [knockOutProduct, vanillaWarrant] # option_type: [call, put] async def search_derivative(self, underlying_isin, product_type="vanillaWarrant", option_type=None): return await self.subscribe( {"type": "derivatives", "underlying": underlying_isin, "productCategory": product_type, "optionType": option_type} ) async def order_overview(self): return await self.subscribe({"type": "orders"}) async def price_for_order(self, isin, exchange, order_type): return await self.subscribe( {"type": "priceForOrder", "parameters": {"exchangeId": exchange, "instrumentId": isin, "type": order_type}} ) async def cash_available_for_order(self): return await self.subscribe({"type": "availableCash"}) async def size_available_for_order(self, isin, exchange): return await self.subscribe( {"type": "availableSize", "parameters": {"exchangeId": exchange, "instrumentId": isin}} ) async def limit_order(self, isin, exchange, order_type, size, limit, expiry, expiry_date=None, warnings_shown=None): parameters = { "type": "simpleCreateOrder", "clientProcessId": str(uuid.uuid4()), "warningsShown": warnings_shown if warnings_shown else [], "parameters": { "instrumentId": isin, "exchangeId": exchange, "expiry": {"type": expiry}, "limit": limit, "mode": "limit", "size": size, "type": order_type, } } if expiry == "gtd" and expiry_date: parameters["parameters"]["expiry"]["value"] = expiry_date return await self.subscribe(parameters) async def market_order(self, isin, exchange, order_type, size, expiry, sell_fractions, expiry_date=None, warnings_shown=None): parameters = { "type": "simpleCreateOrder", "clientProcessId": str(uuid.uuid4()), "warningsShown": warnings_shown if warnings_shown else [], "parameters": { "instrumentId": isin, "exchangeId": exchange, "expiry": {"type": expiry}, "mode": "market", "sellFractions": sell_fractions, "size": size, "type": order_type, } } if expiry == "gtd" and expiry_date: parameters["parameters"]["expiry"]["value"] = expiry_date return await self.subscribe(parameters) async def stop_market_order(self, isin, exchange, order_type, size, stop, expiry, expiry_date=None, warnings_shown=None): parameters = { "type": "simpleCreateOrder", "clientProcessId": str(uuid.uuid4()), "warningsShown": warnings_shown if warnings_shown else [], "parameters": { "instrumentId": isin, "exchangeId": exchange, "expiry": {"type": expiry}, "mode": "stopMarket", "size": size, "stop": stop, "type": order_type, } } if expiry == "gtd" and expiry_date: parameters["parameters"]["expiry"]["value"] = expiry_date return await self.subscribe(parameters) async def cancel_order(self, order_id): return await self.subscribe({"type": "cancelOrder", "orderId": order_id}) async def savings_plan_overview(self): return await self.subscribe({"type": "savingsPlans"}) async def savings_plan_parameters(self, isin): return await self.subscribe({"type": "cancelSavingsPlan", "instrumentId": isin}) async def create_savings_plan(self, isin, amount, interval, start_date, start_date_type, start_date_value, warnings_shown=None): parameters = { "type": "createSavingsPlan", "warningsShown": warnings_shown if warnings_shown else [], "parameters": { "amount": amount, "instrumentId": isin, "interval": interval, "startDate": { 'nextExecutionDate': start_date, 'type': start_date_type, 'value': start_date_value } } } return await self.subscribe(parameters) async def change_savings_plan(self, savings_plan_id, isin, amount, interval, start_date, start_date_type, start_date_value, warnings_shown=None): parameters = { "id": savings_plan_id, "type": "createSavingsPlan", "warningsShown": warnings_shown if warnings_shown else [], "parameters": { "amount": amount, "instrumentId": isin, "interval": interval, "startDate": { 'nextExecutionDate': start_date, 'type': start_date_type, 'value': start_date_value } } } return await self.subscribe(parameters) async def cancel_savings_plan(self, savings_plan_id): return await self.subscribe({"type": "cancelSavingsPlan", "id": savings_plan_id}) async def price_alarm_overview(self): return await self.subscribe({"type": "priceAlarms"}) async def create_price_alarm(self, isin, price): return await self.subscribe({"type": "createPriceAlarm", "instrumentId": isin, "targetPrice": price}) async def cancel_price_alarm(self, price_alarm_id): return await self.subscribe({"type": "cancelPriceAlarm", "id": price_alarm_id}) async def news(self, isin): return await self.subscribe({"type": "neonNews", "isin": isin}) async def news_subscriptions(self): return await self.subscribe({"type": "newsSubscriptions"}) async def subscribe_news(self, isin): return await self.subscribe({"type": "subscribeNews", "instrumentId": isin}) async def unsubscribe_news(self, isin): return await self.subscribe({"type": "unsubscribeNews", "instrumentId": isin}) def payout(self, amount): return self._sign_request("/api/v1/payout", {"amount": amount}).json() def confirm_payout(self, process_id, code): r = self._sign_request(f"/api/v1/payout/{process_id}/code", {"code": code}) if r.status_code != 200: raise ValueError(f"Payout failed with response {r.text!r}") def settings(self): return self._sign_request("/api/v1/auth/account", method="GET").json() def order_cost(self, isin, exchange, order_mode, order_type, size, sell_fractions): url = f"/api/v1/user/costtransparency?instrumentId={isin}&exchangeId={exchange}" \ f"&mode={order_mode}&type={order_type}&size={size}&sellFractions={sell_fractions}" return self._sign_request(url, method="GET").text def savings_plan_cost(self, isin, amount, interval): url = f"/api/v1/user/savingsplancosttransparency?instrumentId={isin}&amount={amount}&interval={interval}" return self._sign_request(url, method="GET").text def __getattr__(self, name): if name[:9] == "blocking_": attr = object.__getattribute__(self, name[9:]) if hasattr(attr, '__call__'): return lambda *args, **kwargs: \ self.run_blocking(timeout=kwargs.pop("timeout", 5), fut=attr(*args, **kwargs)) return object.__getattribute__(self, name) class TradeRepublicError(ValueError): def __init__(self, subscription_id, subscription, error_message): self.subscription_id = subscription_id self.subscription = subscription self.error = error_message

39.737815

126

0.611487

import asyncio import base64 import hashlib import json import logging import time import urllib.parse import uuid import requests import websockets from ecdsa import NIST256p, SigningKey from ecdsa.util import sigencode_der logging.basicConfig( level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", ) logger = logging.getLogger(__name__) def get_or_create_eventloop(): try: return asyncio.get_event_loop() except RuntimeError as ex: if "There is no current event loop in thread" in str(ex): loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) return asyncio.get_event_loop() class TradeRepublicApi: _default_headers = {'User-Agent': 'TradeRepublic/Android 24/App Version 1.1.2875'} _host = "https://api.traderepublic.com" _refresh_token = None _session_token = None _session_token_expires_at = None credentials_file = "credentials" _ws = None get_or_create_eventloop() _lock = asyncio.Lock() _subscription_id_counter = 1 _previous_responses = {} subscriptions = {} @property def session_token(self): if not self._refresh_token: self.login() elif self._refresh_token and time.time() > self._session_token_expires_at: self.refresh_access_token() return self._session_token @session_token.setter def session_token(self, val): self._session_token_expires_at = time.time() + 290 self._session_token = val def __init__(self, phone_no=None, pin=None, keyfile=None, locale="de"): self._locale = locale if not (phone_no and pin): try: with open(self.credentials_file, 'r') as f: lines = f.readlines() self.phone_no = lines[0].strip() self.pin = lines[1].strip() except FileNotFoundError: raise ValueError(f"phone_no and pin must be specified explicitly or via credentials file") else: self.phone_no = phone_no self.pin = pin self.keyfile = keyfile if keyfile else "keyfile.pem" try: with open(self.keyfile, 'rb') as f: self.sk = SigningKey.from_pem(f.read(), hashfunc=hashlib.sha512) except FileNotFoundError as ex: logger.warn('File not found %s', self.keyfile) pass def interactive_device_reset(self): self.sk = SigningKey.generate(curve=NIST256p, hashfunc=hashlib.sha512) r = requests.post(f"{self._host}/api/v1/auth/account/reset/device", json={"phoneNumber": self.phone_no, "pin": self.pin}, headers=self._default_headers) if r.status_code == 200: process_id = r.json()['processId'] else: self.print_error_response(r) token = input("Please enter the sms code: ") self.pair_device(process_id, token) def initiate_device_reset(self): self.sk = SigningKey.generate(curve=NIST256p, hashfunc=hashlib.sha512) r = requests.post(f"{self._host}/api/v1/auth/account/reset/device", json={"phoneNumber": self.phone_no, "pin": self.pin}, headers=self._default_headers) if r.status_code == 200: # save processId with open(self.credentials_file, 'a') as f: f.write("\n") f.write(r.json()['processId']) logger.info("processId %s", r.json()['processId']) # save signature key with open(self.keyfile, 'wb') as f: f.write(self.sk.to_pem()) logger.info("writing to pem file") logger.info(self.sk.to_pem()) else: self.print_error_response(r) raise Exception(r.json()) def complete_device_reset(self, token): with open(self.credentials_file, 'r') as f: lines = f.readlines() process_id = lines[len(lines)-1].strip() # get the latest process_id with open(self.keyfile, 'rb') as f: self.sk = SigningKey.from_pem(f.read(), hashfunc=hashlib.sha512) if not process_id or not self.sk: raise ValueError("Initiate Device Reset first.") else: self.pair_device(process_id, token) def pair_device(self, process_id, token): logger.info("pairing device") pubkey_bytes = self.sk.get_verifying_key().to_string('uncompressed') pubkey_string = base64.b64encode(pubkey_bytes).decode('ascii') r = requests.post(f"{self._host}/api/v1/auth/account/reset/device/{process_id}/key", json={"code": token, "deviceKey": pubkey_string}, headers=self._default_headers) if r.status_code != 200: self.print_error_response(r) raise Exception(r.json()) @staticmethod def print_error_response(r): logger.error("%s %s", r.request.method, r.request.url) logger.error("%s %s", r.status_code, r.json()) def login(self): logging.info("Logging in") r = self._sign_request("/api/v1/auth/login", payload={"phoneNumber": self.phone_no, "pin": self.pin}) if r.status_code == 200: self._refresh_token = r.json()['refreshToken'] self.session_token = r.json()['sessionToken'] # else: # # the device lost the session, must be reseted # self.interactive_device_reset() # self.login() def refresh_access_token(self): logger.info("Refreshing access token") r = self._sign_request("/api/v1/auth/session", method="GET") self.session_token = r.json()['sessionToken'] def _sign_request(self, url_path, payload=None, method="POST"): ts = int(time.time() * 1000) payload_string = json.dumps(payload) if payload else "" signature_payload = f"{ts}.{payload_string}" signature = self.sk.sign(bytes(signature_payload, "utf-8"), hashfunc=hashlib.sha512, sigencode=sigencode_der) signature_string = base64.b64encode(signature).decode('ascii') headers = self._default_headers.copy() headers["X-Zeta-Timestamp"] = str(ts) headers["X-Zeta-Signature"] = signature_string headers['Content-Type'] = 'application/json' if url_path == "/api/v1/auth/login": pass elif url_path == "/api/v1/auth/session": headers['Authorization'] = f'Bearer {self._refresh_token}' elif self.session_token: headers['Authorization'] = f'Bearer {self.session_token}' return requests.request(method=method, url=f"{self._host}{url_path}", data=payload_string, headers=headers) async def _get_ws(self): if self._ws and self._ws.open: return self._ws logger.info(f"Connecting to websocket ...") self._ws = await websockets.connect("wss://api.traderepublic.com") connection_message = {'locale': self._locale} await self._ws.send(f"connect 21 {json.dumps(connection_message)}") response = await self._ws.recv() if not response == 'connected': raise ValueError(f"Connection Error: {response}") logger.info(f"Connected to websocket ...") return self._ws async def _next_subscription_id(self): async with self._lock: subscription_id = self._subscription_id_counter self._subscription_id_counter += 1 return str(subscription_id) async def subscribe(self, payload): subscription_id = await self._next_subscription_id() ws = await self._get_ws() logger.info(f"Subscribing: 'sub {subscription_id} {json.dumps(payload)}'") self.subscriptions[subscription_id] = payload payload_with_token = payload.copy() payload_with_token["token"] = self.session_token await ws.send(f"sub {subscription_id} {json.dumps(payload_with_token)}") return subscription_id async def unsubscribe(self, subscription_id): ws = await self._get_ws() logger.info(f"Unubscribing: {subscription_id}") await ws.send(f"unsub {subscription_id}") self.subscriptions.pop(subscription_id, None) self._previous_responses.pop(subscription_id, None) async def recv(self): ws = await self._get_ws() while True: response = await ws.recv() logger.debug(f"Received message: {response!r}") subscription_id = response[:response.find(" ")] code = response[response.find(" ") + 1: response.find(" ") + 2] payload_str = response[response.find(" ") + 2:].lstrip() if subscription_id not in self.subscriptions: if code != "C": logger.info(f"No active subscription for id {subscription_id}, dropping message") continue subscription = self.subscriptions[subscription_id] if code == 'A': self._previous_responses[subscription_id] = payload_str payload = json.loads(payload_str) if payload_str else {} return subscription_id, subscription, payload elif code == 'D': response = self._calculate_delta(subscription_id, payload_str) logger.debug(f"Payload is {response}") self._previous_responses[subscription_id] = response return subscription_id, subscription, json.loads(response) if code == 'C': self.subscriptions.pop(subscription_id, None) self._previous_responses.pop(subscription_id, None) continue elif code == 'E': logger.error(f"Received error message: {response!r}") await self.unsubscribe(subscription_id) payload = json.loads(payload_str) if payload_str else {} raise TradeRepublicError(subscription_id, subscription, payload) def _calculate_delta(self, subscription_id, delta_payload): previous_response = self._previous_responses[subscription_id] i, result = 0, [] for diff in delta_payload.split('\t'): sign = diff[0] if sign == '+': result.append(urllib.parse.unquote_plus(diff).strip()) elif sign == '-' or sign == '=': if sign == '=': result.append(previous_response[i:i + int(diff[1:])]) i += int(diff[1:]) return "".join(result) async def _recv_subscription(self, subscription_id): while True: response_subscription_id, _, response = await self.recv() if response_subscription_id == subscription_id: return response async def _receive_one(self, fut, timeout): subscription_id = await fut try: return await asyncio.wait_for(self._recv_subscription(subscription_id), timeout) finally: await self.unsubscribe(subscription_id) def run_blocking(self, fut, timeout=5.0): return get_or_create_eventloop().run_until_complete(self._receive_one(fut, timeout=timeout)) async def portfolio(self): return await self.subscribe({"type": "portfolio"}) async def watchlist(self): return await self.subscribe({"type": "watchlist"}) async def cash(self): return await self.subscribe({"type": "cash"}) async def available_cash_for_payout(self): return await self.subscribe({"type": "availableCashForPayout"}) async def portfolio_status(self): return await self.subscribe({"type": "portfolioStatus"}) async def portfolio_history(self, timeframe): return await self.subscribe({"type": "portfolioAggregateHistory", "range": timeframe}) async def instrument_details(self, isin): return await self.subscribe({"type": "instrument", "id": isin}) async def instrument_suitability(self, isin): return await self.subscribe({"type": "instrumentSuitability", "instrumentId": isin}) async def stock_details(self, isin): return await self.subscribe({"type": "stockDetails", "id": isin}) async def add_watchlist(self, isin): return await self.subscribe({"type": "addToWatchlist", "instrumentId": isin}) async def remove_watchlist(self, isin): return await self.subscribe({"type": "removeFromWatchlist", "instrumentId": isin}) async def ticker(self, isin, exchange="LSX"): return await self.subscribe({"type": "ticker", "id": f"{isin}.{exchange}"}) async def performance(self, isin, exchange="LSX"): return await self.subscribe({"type": "performance", "id": f"{isin}.{exchange}"}) async def performance_history(self, isin, timeframe, exchange="LSX", resolution=None): parameters = {"type": "aggregateHistory", "id": f"{isin}.{exchange}", "range": timeframe} if resolution: parameters["resolution"] = resolution return await self.subscribe(parameters) async def experience(self): return await self.subscribe({"type": "experience"}) async def motd(self): return await self.subscribe({"type": "messageOfTheDay"}) async def neon_cards(self): return await self.subscribe({"type": "neonCards"}) async def timeline(self, after=None): return await self.subscribe({"type": "timeline", "after": after}) async def timeline_detail(self, timeline_id): return await self.subscribe({"type": "timelineDetail", "id": timeline_id}) async def timeline_detail_order(self, order_id): return await self.subscribe({"type": "timelineDetail", "orderId": order_id}) async def timeline_detail_savings_plan(self, savings_plan_id): return await self.subscribe({"type": "timelineDetail", "savingsPlanId": savings_plan_id}) async def search_tags(self): return await self.subscribe({"type": "neonSearchTags"}) async def search_suggested_tags(self, query): return await self.subscribe({"type": "neonSearchSuggestedTags", "data": {"q": query}}) async def search(self, query, asset_type="stock", page=1, page_size=20, aggregate=False, only_savable=False, filter_index=None, filter_country=None, filter_sector=None, filter_region=None): search_parameters = { "q": query, "filter": [{'key': 'type', 'value': asset_type}], "page": page, "pageSize": page_size, } if only_savable: search_parameters["filter"].append({'key': 'attribute', 'value': 'savable'}) if filter_index: search_parameters["filter"].append({'key': 'index', 'value': filter_index}) if filter_country: search_parameters["filter"].append({'key': 'country', 'value': filter_country}) if filter_region: search_parameters["filter"].append({'key': 'region', 'value': filter_region}) if filter_sector: search_parameters["filter"].append({'key': 'sector', 'value': filter_sector}) search_type = "neonSearch" if not aggregate else "neonSearchAggregations" return await self.subscribe({"type": search_type, "data": search_parameters}) # product_type: [knockOutProduct, vanillaWarrant] # option_type: [call, put] async def search_derivative(self, underlying_isin, product_type="vanillaWarrant", option_type=None): return await self.subscribe( {"type": "derivatives", "underlying": underlying_isin, "productCategory": product_type, "optionType": option_type} ) async def order_overview(self): return await self.subscribe({"type": "orders"}) async def price_for_order(self, isin, exchange, order_type): return await self.subscribe( {"type": "priceForOrder", "parameters": {"exchangeId": exchange, "instrumentId": isin, "type": order_type}} ) async def cash_available_for_order(self): return await self.subscribe({"type": "availableCash"}) async def size_available_for_order(self, isin, exchange): return await self.subscribe( {"type": "availableSize", "parameters": {"exchangeId": exchange, "instrumentId": isin}} ) async def limit_order(self, isin, exchange, order_type, size, limit, expiry, expiry_date=None, warnings_shown=None): parameters = { "type": "simpleCreateOrder", "clientProcessId": str(uuid.uuid4()), "warningsShown": warnings_shown if warnings_shown else [], "parameters": { "instrumentId": isin, "exchangeId": exchange, "expiry": {"type": expiry}, "limit": limit, "mode": "limit", "size": size, "type": order_type, } } if expiry == "gtd" and expiry_date: parameters["parameters"]["expiry"]["value"] = expiry_date return await self.subscribe(parameters) async def market_order(self, isin, exchange, order_type, size, expiry, sell_fractions, expiry_date=None, warnings_shown=None): parameters = { "type": "simpleCreateOrder", "clientProcessId": str(uuid.uuid4()), "warningsShown": warnings_shown if warnings_shown else [], "parameters": { "instrumentId": isin, "exchangeId": exchange, "expiry": {"type": expiry}, "mode": "market", "sellFractions": sell_fractions, "size": size, "type": order_type, } } if expiry == "gtd" and expiry_date: parameters["parameters"]["expiry"]["value"] = expiry_date return await self.subscribe(parameters) async def stop_market_order(self, isin, exchange, order_type, size, stop, expiry, expiry_date=None, warnings_shown=None): parameters = { "type": "simpleCreateOrder", "clientProcessId": str(uuid.uuid4()), "warningsShown": warnings_shown if warnings_shown else [], "parameters": { "instrumentId": isin, "exchangeId": exchange, "expiry": {"type": expiry}, "mode": "stopMarket", "size": size, "stop": stop, "type": order_type, } } if expiry == "gtd" and expiry_date: parameters["parameters"]["expiry"]["value"] = expiry_date return await self.subscribe(parameters) async def cancel_order(self, order_id): return await self.subscribe({"type": "cancelOrder", "orderId": order_id}) async def savings_plan_overview(self): return await self.subscribe({"type": "savingsPlans"}) async def savings_plan_parameters(self, isin): return await self.subscribe({"type": "cancelSavingsPlan", "instrumentId": isin}) async def create_savings_plan(self, isin, amount, interval, start_date, start_date_type, start_date_value, warnings_shown=None): parameters = { "type": "createSavingsPlan", "warningsShown": warnings_shown if warnings_shown else [], "parameters": { "amount": amount, "instrumentId": isin, "interval": interval, "startDate": { 'nextExecutionDate': start_date, 'type': start_date_type, 'value': start_date_value } } } return await self.subscribe(parameters) async def change_savings_plan(self, savings_plan_id, isin, amount, interval, start_date, start_date_type, start_date_value, warnings_shown=None): parameters = { "id": savings_plan_id, "type": "createSavingsPlan", "warningsShown": warnings_shown if warnings_shown else [], "parameters": { "amount": amount, "instrumentId": isin, "interval": interval, "startDate": { 'nextExecutionDate': start_date, 'type': start_date_type, 'value': start_date_value } } } return await self.subscribe(parameters) async def cancel_savings_plan(self, savings_plan_id): return await self.subscribe({"type": "cancelSavingsPlan", "id": savings_plan_id}) async def price_alarm_overview(self): return await self.subscribe({"type": "priceAlarms"}) async def create_price_alarm(self, isin, price): return await self.subscribe({"type": "createPriceAlarm", "instrumentId": isin, "targetPrice": price}) async def cancel_price_alarm(self, price_alarm_id): return await self.subscribe({"type": "cancelPriceAlarm", "id": price_alarm_id}) async def news(self, isin): return await self.subscribe({"type": "neonNews", "isin": isin}) async def news_subscriptions(self): return await self.subscribe({"type": "newsSubscriptions"}) async def subscribe_news(self, isin): return await self.subscribe({"type": "subscribeNews", "instrumentId": isin}) async def unsubscribe_news(self, isin): return await self.subscribe({"type": "unsubscribeNews", "instrumentId": isin}) def payout(self, amount): return self._sign_request("/api/v1/payout", {"amount": amount}).json() def confirm_payout(self, process_id, code): r = self._sign_request(f"/api/v1/payout/{process_id}/code", {"code": code}) if r.status_code != 200: raise ValueError(f"Payout failed with response {r.text!r}") def settings(self): return self._sign_request("/api/v1/auth/account", method="GET").json() def order_cost(self, isin, exchange, order_mode, order_type, size, sell_fractions): url = f"/api/v1/user/costtransparency?instrumentId={isin}&exchangeId={exchange}" \ f"&mode={order_mode}&type={order_type}&size={size}&sellFractions={sell_fractions}" return self._sign_request(url, method="GET").text def savings_plan_cost(self, isin, amount, interval): url = f"/api/v1/user/savingsplancosttransparency?instrumentId={isin}&amount={amount}&interval={interval}" return self._sign_request(url, method="GET").text def __getattr__(self, name): if name[:9] == "blocking_": attr = object.__getattribute__(self, name[9:]) if hasattr(attr, '__call__'): return lambda *args, **kwargs: \ self.run_blocking(timeout=kwargs.pop("timeout", 5), fut=attr(*args, **kwargs)) return object.__getattribute__(self, name) class TradeRepublicError(ValueError): def __init__(self, subscription_id, subscription, error_message): self.subscription_id = subscription_id self.subscription = subscription self.error = error_message

true

f73653a564946bc4476618628286841d18811544

87

py

Python

URI/1 - INICIANTE/Python/1002 - AeraCirculo.py

william-james-pj/LogicaProgramacao

629f746e34da2e829dc7ea2e489ac36bb1b1fb13

[ "MIT" ]

1

2020-04-14T16:48:16.000Z

URI/1 - INICIANTE/Python/1002 - AeraCirculo.py

william-james-pj/LogicaProgramacao

629f746e34da2e829dc7ea2e489ac36bb1b1fb13

[ "MIT" ]

null

URI/1 - INICIANTE/Python/1002 - AeraCirculo.py

william-james-pj/LogicaProgramacao

629f746e34da2e829dc7ea2e489ac36bb1b1fb13

[ "MIT" ]

null

raio = float(input()) n = 3.14159 area = n * pow(raio,2) print('A={:.4f}'.format(area))

21.75

30

0.597701

raio = float(input()) n = 3.14159 area = n * pow(raio,2) print('A={:.4f}'.format(area))

true

f73653de4094f8605b644d6f1bb802bce0914670

2,100

py

Python

convert_ymls.py

geofabrik/openstreetmap-carto-vectortiles

80f8d70b17c2db4b24bfed94f128f3ef03e0ef16

[ "CC0-1.0" ]

50

2016-07-13T17:02:22.000Z

2021-12-21T14:00:27.000Z

convert_ymls.py

geofabrik/openstreetmap-carto-vectortiles

80f8d70b17c2db4b24bfed94f128f3ef03e0ef16

[ "CC0-1.0" ]

5

2016-12-22T14:40:57.000Z

2018-11-10T19:44:23.000Z

convert_ymls.py

geofabrik/openstreetmap-carto-vectortiles

80f8d70b17c2db4b24bfed94f128f3ef03e0ef16

[ "CC0-1.0" ]

6

2016-07-29T19:12:44.000Z

2022-03-31T14:41:33.000Z

import argparse, yaml, os, os.path parser = argparse.ArgumentParser() parser.add_argument("-i", "--input") parser.add_argument("-o", "--output") parser.add_argument("--tm2", "--tm", action="store_const", const="tm2", dest="action") parser.add_argument("--tm2source", "--tmsource", action="store_const", const="tm2source", dest="action") parser.add_argument("--zoom", default="14", help="Last zoom in the vector tile, default 14") parser.add_argument("--source", action="store_true", dest="source") parser.add_argument("--no-source", action="store_false", dest="source") parser.add_argument("--only-shapefiles", action="store_true") parser.add_argument("--only-postgis", action="store_true") args = parser.parse_args() cwd = os.getcwd() with open(args.input) as fp: projectfile = yaml.load(fp) if args.action == 'tm2': new_layers = [] for layer in projectfile['Layer']: new_layers.append({'id': layer['id']}) projectfile['Layer'] = new_layers if args.source: projectfile['source'] = "tmsource://{}/osm-carto.tm2source/".format(cwd) elif args.action == 'tm2source': del projectfile['source'] del projectfile['Stylesheet'] zoom = int(args.zoom) projectfile['maxzoom'] = zoom for layer in projectfile['Layer']: # If the maxzoom is less than the minzoom, don't do anything # This can happen for a generic land polygon shapefile, which has # maxzoom: 9. If you include a minzoom: 14 in that layer, then that # layer won't show up from 0-9, i.e. it won't show up at all. if layer['properties'].get('minzoom', 22) > zoom and layer['properties'].get('maxzoom', 22) >= zoom: layer['properties']['minzoom'] = zoom if args.only_shapefiles: projectfile['Layer'] = [l for l in projectfile['Layer'] if l['Datasource']['type'] == 'shape'] elif args.only_postgis: projectfile['Layer'] = [l for l in projectfile['Layer'] if l['Datasource']['type'] == 'postgis'] else: raise NotImplementedError() with open(args.output, 'w') as fp: yaml.safe_dump(projectfile, fp)

36.206897

108

0.661905

import argparse, yaml, os, os.path parser = argparse.ArgumentParser() parser.add_argument("-i", "--input") parser.add_argument("-o", "--output") parser.add_argument("--tm2", "--tm", action="store_const", const="tm2", dest="action") parser.add_argument("--tm2source", "--tmsource", action="store_const", const="tm2source", dest="action") parser.add_argument("--zoom", default="14", help="Last zoom in the vector tile, default 14") parser.add_argument("--source", action="store_true", dest="source") parser.add_argument("--no-source", action="store_false", dest="source") parser.add_argument("--only-shapefiles", action="store_true") parser.add_argument("--only-postgis", action="store_true") args = parser.parse_args() cwd = os.getcwd() with open(args.input) as fp: projectfile = yaml.load(fp) if args.action == 'tm2': new_layers = [] for layer in projectfile['Layer']: new_layers.append({'id': layer['id']}) projectfile['Layer'] = new_layers if args.source: projectfile['source'] = "tmsource://{}/osm-carto.tm2source/".format(cwd) elif args.action == 'tm2source': del projectfile['source'] del projectfile['Stylesheet'] zoom = int(args.zoom) projectfile['maxzoom'] = zoom for layer in projectfile['Layer']: # This can happen for a generic land polygon shapefile, which has # maxzoom: 9. If you include a minzoom: 14 in that layer, then that # layer won't show up from 0-9, i.e. it won't show up at all. if layer['properties'].get('minzoom', 22) > zoom and layer['properties'].get('maxzoom', 22) >= zoom: layer['properties']['minzoom'] = zoom if args.only_shapefiles: projectfile['Layer'] = [l for l in projectfile['Layer'] if l['Datasource']['type'] == 'shape'] elif args.only_postgis: projectfile['Layer'] = [l for l in projectfile['Layer'] if l['Datasource']['type'] == 'postgis'] else: raise NotImplementedError() with open(args.output, 'w') as fp: yaml.safe_dump(projectfile, fp)

true

f736544ee090ae0f772d827cff827a4f55b45858

3,479

py

Python

QUANTAXIS/QASetting/crontab.py

xiongyixiaoyang/QUANTAXIS

08441ce711e55385e2b01f80df17d34e7e89f564

[ "MIT" ]

2

2019-05-31T02:53:12.000Z

2020-03-09T03:21:55.000Z

QUANTAXIS/QASetting/crontab.py

xiongyixiaoyang/QUANTAXIS

08441ce711e55385e2b01f80df17d34e7e89f564

[ "MIT" ]

null

QUANTAXIS/QASetting/crontab.py

xiongyixiaoyang/QUANTAXIS

08441ce711e55385e2b01f80df17d34e7e89f564

[ "MIT" ]

3

2018-11-29T07:07:56.000Z

2021-02-09T17:24:56.000Z

# coding:utf-8 # # The MIT License (MIT) # # Copyright (c) 2016-2018 yutiansut/QUANTAXIS # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import md5 import time from .external.crontab import CronTab as _CronTab from QUANTAXIS.QASetting.cache import get_cache # Globals ###################################################################### DEBUG = False class CronTabItem(_CronTab): """A cron tab schedule. :param str cronschedule: The cron schedule. E.g. ``@daily``, ``0 0 * * */2 *``. `See the project page for more information <https://github.com/josiahcarlson/parse-crontab>`_. """ def __init__(self, cronschedule): self.__schedule = cronschedule super(CronTabItem, self).__init__(cronschedule) @property def schedule(self): return self.__schedule def next_time(self, asc=False): """Get the local time of the next schedule time this job will run. :param bool asc: Format the result with ``time.asctime()`` :returns: The epoch time or string representation of the epoch time that the job should be run next """ _time = time.localtime(time.time() + self.next()) if asc: return time.asctime(_time) return time.mktime(_time) class CronTab(object): """Represents a set of cron jobs, much like a crontab file. The jobs will be given an ID (md5 hash of description + command + schedule). If the job already exists in the cache, "last-run" and "last-run-result" will be read from the cache. If the job does not exist in the cache, it will be added. :param list jobs: A list of dictionaries representing a job """ def __init__(self, jobs): cache = get_cache() self.jobs = [] for job in jobs: m = md5.new() m.update(job["description"]) m.update(job["command"]) m.update(job["cron-job"]) job["id"] = m.hexdigest() job["cron-job"] = CronTabItem(job["cron-job"]) job["next-run"] = job["cron-job"].next_time() cached = cache.get(job["id"]) if cached: job["last-run"] = cached["last-run"] job["last-run-result"] = cached["last-run-result"] else: job["last-run"] = 0 job["last-run-result"] = 0 cache.add_job(job) self.jobs.append(job)

37.010638

80

0.634378

import md5 import time from .external.crontab import CronTab as _CronTab from QUANTAXIS.QASetting.cache import get_cache

true

f73654a1fc198e5bf51531dd3397e8f7e7919a6b

957

py

Python

stackl/helpers.py

ArtOfCode-/stackl

c66c025fa39c5fc5a6dbda7f0ea0628ee526b4b6

[ "MIT" ]

null

stackl/helpers.py

ArtOfCode-/stackl

c66c025fa39c5fc5a6dbda7f0ea0628ee526b4b6

[ "MIT" ]

null

stackl/helpers.py

ArtOfCode-/stackl

c66c025fa39c5fc5a6dbda7f0ea0628ee526b4b6

[ "MIT" ]

null

class Helpers: _cache = {} @classmethod def cached(cls, key, scope=None, func=None): if scope is not None: if scope not in cls._cache: cls._cache[scope] = {} if key in cls._cache[scope]: return cls._cache[scope][key] else: result = None if func is None else func() cls._cache[scope][key] = result return result else: if key in cls._cache: return cls._cache[key] else: result = None if func is None else func() cls._cache[key] = result return result @classmethod def cache(cls, key, scope=None, object=None): if scope is not None: if scope not in cls._cache: cls._cache[scope] = {} cls._cache[scope][key] = object else: cls._cache[key] = object

28.147059

57

0.487983

class Helpers: _cache = {} @classmethod def cached(cls, key, scope=None, func=None): if scope is not None: if scope not in cls._cache: cls._cache[scope] = {} if key in cls._cache[scope]: return cls._cache[scope][key] else: result = None if func is None else func() cls._cache[scope][key] = result return result else: if key in cls._cache: return cls._cache[key] else: result = None if func is None else func() cls._cache[key] = result return result @classmethod def cache(cls, key, scope=None, object=None): if scope is not None: if scope not in cls._cache: cls._cache[scope] = {} cls._cache[scope][key] = object else: cls._cache[key] = object

true

f736559162d2fb353e82402c79261dc665f58d3e

425

py

Python

pytorch_forecasting/__init__.py

KazukiNoto/pytorch-forecasting

8a1636388e091456f042f999892dd52733903dd6

[ "MIT" ]

1

2021-06-09T09:51:13.000Z

pytorch_forecasting/__init__.py

KazukiNoto/pytorch-forecasting

8a1636388e091456f042f999892dd52733903dd6

[ "MIT" ]

null

pytorch_forecasting/__init__.py

KazukiNoto/pytorch-forecasting

8a1636388e091456f042f999892dd52733903dd6

[ "MIT" ]

1

2021-06-15T11:31:44.000Z

""" PyTorch Forecasting package for timeseries forecasting with PyTorch. """ from pytorch_forecasting.data import EncoderNormalizer, GroupNormalizer, TimeSeriesDataSet from pytorch_forecasting.models import Baseline, NBeats, TemporalFusionTransformer __all__ = [ "TimeSeriesDataSet", "GroupNormalizer", "EncoderNormalizer", "TemporalFusionTransformer", "NBeats", "Baseline", ] __version__ = "0.0.0"

25

90

0.764706

from pytorch_forecasting.data import EncoderNormalizer, GroupNormalizer, TimeSeriesDataSet from pytorch_forecasting.models import Baseline, NBeats, TemporalFusionTransformer __all__ = [ "TimeSeriesDataSet", "GroupNormalizer", "EncoderNormalizer", "TemporalFusionTransformer", "NBeats", "Baseline", ] __version__ = "0.0.0"

true

f73655e939118e53abadbf7e66e1712cae7bc2c9

6,540

py

Python

tensorflow_toolkit/text_recognition/text_recognition/model.py

morkovka1337/openvino_training_extensions

846db45c264d6b061505213f51763520b9432ba9

[ "Apache-2.0" ]

3

2020-12-29T02:47:32.000Z

2021-11-12T08:12:51.000Z

tensorflow_toolkit/text_recognition/text_recognition/model.py

morkovka1337/openvino_training_extensions

846db45c264d6b061505213f51763520b9432ba9

[ "Apache-2.0" ]

28

2020-09-25T22:40:36.000Z

2022-03-12T00:37:36.000Z

tensorflow_toolkit/text_recognition/text_recognition/model.py

morkovka1337/openvino_training_extensions

846db45c264d6b061505213f51763520b9432ba9

[ "Apache-2.0" ]

1

2021-03-12T10:08:44.000Z

# Copyright (C) 2019 Intel Corporation # # 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. """ This module contains architecture of Text Recognition model.""" import tensorflow as tf from tensorflow.contrib import rnn import tensorflow.contrib.slim as slim class TextRecognition: """ Text recognition model definition. """ def __init__(self, is_training, num_classes, backbone_dropout=0.0): self.is_training = is_training self.lstm_dim = 256 self.num_classes = num_classes self.backbone_dropout = backbone_dropout def __call__(self, inputdata): with tf.variable_scope('shadow'): features = self.feature_extractor(inputdata=inputdata) logits = self.encoder_decoder(inputdata=tf.squeeze(features, axis=1)) return logits # pylint: disable=too-many-locals def feature_extractor(self, inputdata): """ Extracts features from input text image. """ with slim.arg_scope([slim.conv2d], padding='SAME', weights_initializer=tf.contrib.layers.variance_scaling_initializer(), weights_regularizer=slim.l2_regularizer(0.00025), biases_initializer=None, activation_fn=None): with slim.arg_scope([slim.batch_norm], updates_collections=None): bn0 = slim.batch_norm(inputdata, 0.9, scale=True, is_training=self.is_training, activation_fn=None) dropout1 = slim.dropout(bn0, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv1 = slim.conv2d(dropout1, num_outputs=64, kernel_size=3) bn1 = slim.batch_norm(conv1, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) pool1 = slim.max_pool2d(bn1, kernel_size=2, stride=2) dropout2 = slim.dropout(pool1, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv2 = slim.conv2d(dropout2, num_outputs=128, kernel_size=3) bn2 = slim.batch_norm(conv2, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) pool2 = slim.max_pool2d(bn2, kernel_size=2, stride=2) dropout3 = slim.dropout(pool2, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv3 = slim.conv2d(dropout3, num_outputs=256, kernel_size=3) bn3 = slim.batch_norm(conv3, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) dropout4 = slim.dropout(bn3, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv4 = slim.conv2d(dropout4, num_outputs=256, kernel_size=3) bn4 = slim.batch_norm(conv4, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) pool4 = slim.max_pool2d(bn4, kernel_size=[2, 1], stride=[2, 1]) dropout5 = slim.dropout(pool4, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv5 = slim.conv2d(dropout5, num_outputs=512, kernel_size=3) bn5 = slim.batch_norm(conv5, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) dropout6 = slim.dropout(bn5, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv6 = slim.conv2d(dropout6, num_outputs=512, kernel_size=3) bn6 = slim.batch_norm(conv6, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) pool6 = slim.max_pool2d(bn6, kernel_size=[2, 1], stride=[2, 1]) dropout7 = slim.dropout(pool6, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv7 = slim.conv2d(dropout7, num_outputs=512, kernel_size=2, stride=[2, 1]) bn7 = slim.batch_norm(conv7, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) return bn7 def encoder_decoder(self, inputdata): """ LSTM-based encoder-decoder module. """ with tf.variable_scope('LSTMLayers'): [batch_size, width, _] = inputdata.get_shape().as_list() with tf.variable_scope('encoder'): forward_cells = [] backward_cells = [] for _ in range(2): forward_cells.append(tf.nn.rnn_cell.LSTMCell(self.lstm_dim)) backward_cells.append(tf.nn.rnn_cell.LSTMCell(self.lstm_dim)) encoder_layer, _, _ = rnn.stack_bidirectional_dynamic_rnn( forward_cells, backward_cells, inputdata, dtype=tf.float32) with tf.variable_scope('decoder'): forward_cells = [] backward_cells = [] for _ in range(2): forward_cells.append(tf.nn.rnn_cell.LSTMCell(self.lstm_dim)) backward_cells.append(tf.nn.rnn_cell.LSTMCell(self.lstm_dim)) decoder_layer, _, _ = rnn.stack_bidirectional_dynamic_rnn( forward_cells, backward_cells, encoder_layer, dtype=tf.float32) rnn_reshaped = tf.reshape(decoder_layer, [batch_size * width, -1]) logits = slim.fully_connected(rnn_reshaped, self.num_classes, activation_fn=None) logits = tf.reshape(logits, [batch_size, width, self.num_classes]) rnn_out = tf.transpose(logits, (1, 0, 2)) return rnn_out

49.172932

97

0.595107

import tensorflow as tf from tensorflow.contrib import rnn import tensorflow.contrib.slim as slim class TextRecognition: def __init__(self, is_training, num_classes, backbone_dropout=0.0): self.is_training = is_training self.lstm_dim = 256 self.num_classes = num_classes self.backbone_dropout = backbone_dropout def __call__(self, inputdata): with tf.variable_scope('shadow'): features = self.feature_extractor(inputdata=inputdata) logits = self.encoder_decoder(inputdata=tf.squeeze(features, axis=1)) return logits def feature_extractor(self, inputdata): with slim.arg_scope([slim.conv2d], padding='SAME', weights_initializer=tf.contrib.layers.variance_scaling_initializer(), weights_regularizer=slim.l2_regularizer(0.00025), biases_initializer=None, activation_fn=None): with slim.arg_scope([slim.batch_norm], updates_collections=None): bn0 = slim.batch_norm(inputdata, 0.9, scale=True, is_training=self.is_training, activation_fn=None) dropout1 = slim.dropout(bn0, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv1 = slim.conv2d(dropout1, num_outputs=64, kernel_size=3) bn1 = slim.batch_norm(conv1, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) pool1 = slim.max_pool2d(bn1, kernel_size=2, stride=2) dropout2 = slim.dropout(pool1, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv2 = slim.conv2d(dropout2, num_outputs=128, kernel_size=3) bn2 = slim.batch_norm(conv2, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) pool2 = slim.max_pool2d(bn2, kernel_size=2, stride=2) dropout3 = slim.dropout(pool2, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv3 = slim.conv2d(dropout3, num_outputs=256, kernel_size=3) bn3 = slim.batch_norm(conv3, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) dropout4 = slim.dropout(bn3, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv4 = slim.conv2d(dropout4, num_outputs=256, kernel_size=3) bn4 = slim.batch_norm(conv4, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) pool4 = slim.max_pool2d(bn4, kernel_size=[2, 1], stride=[2, 1]) dropout5 = slim.dropout(pool4, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv5 = slim.conv2d(dropout5, num_outputs=512, kernel_size=3) bn5 = slim.batch_norm(conv5, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) dropout6 = slim.dropout(bn5, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv6 = slim.conv2d(dropout6, num_outputs=512, kernel_size=3) bn6 = slim.batch_norm(conv6, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) pool6 = slim.max_pool2d(bn6, kernel_size=[2, 1], stride=[2, 1]) dropout7 = slim.dropout(pool6, keep_prob=1.0 - self.backbone_dropout, is_training=self.is_training) conv7 = slim.conv2d(dropout7, num_outputs=512, kernel_size=2, stride=[2, 1]) bn7 = slim.batch_norm(conv7, 0.9, scale=True, is_training=self.is_training, activation_fn=tf.nn.relu) return bn7 def encoder_decoder(self, inputdata): with tf.variable_scope('LSTMLayers'): [batch_size, width, _] = inputdata.get_shape().as_list() with tf.variable_scope('encoder'): forward_cells = [] backward_cells = [] for _ in range(2): forward_cells.append(tf.nn.rnn_cell.LSTMCell(self.lstm_dim)) backward_cells.append(tf.nn.rnn_cell.LSTMCell(self.lstm_dim)) encoder_layer, _, _ = rnn.stack_bidirectional_dynamic_rnn( forward_cells, backward_cells, inputdata, dtype=tf.float32) with tf.variable_scope('decoder'): forward_cells = [] backward_cells = [] for _ in range(2): forward_cells.append(tf.nn.rnn_cell.LSTMCell(self.lstm_dim)) backward_cells.append(tf.nn.rnn_cell.LSTMCell(self.lstm_dim)) decoder_layer, _, _ = rnn.stack_bidirectional_dynamic_rnn( forward_cells, backward_cells, encoder_layer, dtype=tf.float32) rnn_reshaped = tf.reshape(decoder_layer, [batch_size * width, -1]) logits = slim.fully_connected(rnn_reshaped, self.num_classes, activation_fn=None) logits = tf.reshape(logits, [batch_size, width, self.num_classes]) rnn_out = tf.transpose(logits, (1, 0, 2)) return rnn_out

true

f7365729a08e2f23924988f567fa30354632418c

4,823

py

Python

docs/conf.py

michaelbilow/issho

ed0dd5487978b1753c14ce5d444d07448b13d168

[ "MIT" ]

1

2019-04-22T17:20:05.000Z

docs/conf.py

michaelbilow/smol

ed0dd5487978b1753c14ce5d444d07448b13d168

[ "MIT" ]

7

2019-03-28T14:26:46.000Z

2019-05-31T03:58:26.000Z

docs/conf.py

michaelbilow/smol

ed0dd5487978b1753c14ce5d444d07448b13d168

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # issho documentation build configuration file, created by # sphinx-quickstart on Fri Jun 9 13:47:02 2017. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. # If extensions (or modules to document with autodoc) are in another # directory, add these directories to sys.path here. If the directory is # relative to the documentation root, use os.path.abspath to make it # absolute, like shown here. # import os import sys sys.path.insert(0, os.path.abspath("..")) import issho # -- General configuration --------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ["sphinx.ext.autodoc", "sphinx.ext.viewcode"] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = ".rst" # The master toctree document. master_doc = "index" # General information about the project. project = u"issho" copyright = u"2019, Michael Bilow" author = u"Michael Bilow" # The version info for the project you're documenting, acts as replacement # for |version| and |release|, also used in various other places throughout # the built documents. # # The short X.Y version. version = issho.__version__ # The full version, including alpha/beta/rc tags. release = issho.__version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = "alabaster" # Theme options are theme-specific and customize the look and feel of a # theme further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # -- Options for HTMLHelp output --------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = "isshodoc" # -- Options for LaTeX output ------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass # [howto, manual, or own class]). latex_documents = [ (master_doc, "issho.tex", u"issho Documentation", u"Michael Bilow", "manual") ] # -- Options for manual page output ------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, "issho", u"issho Documentation", [author], 1)] # -- Options for Texinfo output ---------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( master_doc, "issho", u"issho Documentation", author, "issho", "Simple connections & command execution with a remote host.", "issho Documentation", ) ]

30.916667

81

0.681733

import os import sys sys.path.insert(0, os.path.abspath("..")) import issho extensions = ["sphinx.ext.autodoc", "sphinx.ext.viewcode"] templates_path = ["_templates"] source_suffix = ".rst" master_doc = "index" project = u"issho" copyright = u"2019, Michael Bilow" author = u"Michael Bilow" # for |version| and |release|, also used in various other places throughout # the built documents. # # The short X.Y version. version = issho.__version__ # The full version, including alpha/beta/rc tags. release = issho.__version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = "alabaster" # Theme options are theme-specific and customize the look and feel of a # theme further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # -- Options for HTMLHelp output --------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = "isshodoc" # -- Options for LaTeX output ------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass # [howto, manual, or own class]). latex_documents = [ (master_doc, "issho.tex", u"issho Documentation", u"Michael Bilow", "manual") ] # -- Options for manual page output ------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, "issho", u"issho Documentation", [author], 1)] # -- Options for Texinfo output ---------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( master_doc, "issho", u"issho Documentation", author, "issho", "Simple connections & command execution with a remote host.", "issho Documentation", ) ]

true

f7365971d36a0732ad199a569df15896294a5c5d

2,173

py

Python

app.py

liyunze-coding/Trigger-Me-Elmo-2

6950ffa4bfd264e213626f1ab3cff249fbab36da

[ "MIT" ]

1

2022-01-02T09:50:38.000Z

app.py

liyunze-coding/Trigger-Me-Elmo-2

6950ffa4bfd264e213626f1ab3cff249fbab36da

[ "MIT" ]

null

app.py

liyunze-coding/Trigger-Me-Elmo-2

6950ffa4bfd264e213626f1ab3cff249fbab36da

[ "MIT" ]

1

2022-01-10T19:59:15.000Z

from flask import Flask, render_template, request, jsonify import base64 import logging import numpy as np from deepface import DeepFace from PIL import Image from io import BytesIO import subprocess import os import cv2 import random import webbrowser app = Flask(__name__) log = logging.getLogger('werkzeug') log.setLevel(logging.ERROR) faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') error_path = {'race': {'asian': 0, 'indian': 0, 'black': 0, 'white': 0, 'middle eastern': 0, 'latino hispanic': 0}, 'dominant_race': '?'} directory = 'static/img' if 'img' not in os.listdir('static/'): os.mkdir(directory) for f in os.listdir(directory): os.remove(os.path.join(directory, f)) def generate_random_string(): numbers = '1234567890' res = ''.join(random.choice(numbers) for _ in range(10)) return f'{directory}/{res}.png' @app.route('/') def main(): return render_template('index.html') @app.route('/photocap') def photo_cap(): photo_base64 = request.args.get('photo') _, encoded = photo_base64.split(",", 1) binary_data = base64.b64decode(encoded) f = BytesIO() f.write(binary_data) f.seek(0) image = Image.open(f) image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) faces = faceCascade.detectMultiScale(gray, 1.3, 5) for (x, y, w, h) in faces: cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2) fn = generate_random_string() cv2.imwrite(fn, image) try: obj = DeepFace.analyze(image, actions=['race']) obj['filename'] = fn return jsonify(obj) except ValueError: other_json = error_path other_json['filename'] = fn return jsonify(other_json) except Exception as e: print(e) other_json = error_path other_json['filename'] = fn return jsonify(other_json) if __name__ == "__main__": # p = subprocess.Popen(['python -m SimpleHTTPServer'], shell=True) #Only for macOS webbrowser.open_new('http://127.0.0.1:8000/') app.run(host='localhost', port=8000, debug=True)

25.869048

88

0.658076

from flask import Flask, render_template, request, jsonify import base64 import logging import numpy as np from deepface import DeepFace from PIL import Image from io import BytesIO import subprocess import os import cv2 import random import webbrowser app = Flask(__name__) log = logging.getLogger('werkzeug') log.setLevel(logging.ERROR) faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') error_path = {'race': {'asian': 0, 'indian': 0, 'black': 0, 'white': 0, 'middle eastern': 0, 'latino hispanic': 0}, 'dominant_race': '?'} directory = 'static/img' if 'img' not in os.listdir('static/'): os.mkdir(directory) for f in os.listdir(directory): os.remove(os.path.join(directory, f)) def generate_random_string(): numbers = '1234567890' res = ''.join(random.choice(numbers) for _ in range(10)) return f'{directory}/{res}.png' @app.route('/') def main(): return render_template('index.html') @app.route('/photocap') def photo_cap(): photo_base64 = request.args.get('photo') _, encoded = photo_base64.split(",", 1) binary_data = base64.b64decode(encoded) f = BytesIO() f.write(binary_data) f.seek(0) image = Image.open(f) image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) faces = faceCascade.detectMultiScale(gray, 1.3, 5) for (x, y, w, h) in faces: cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2) fn = generate_random_string() cv2.imwrite(fn, image) try: obj = DeepFace.analyze(image, actions=['race']) obj['filename'] = fn return jsonify(obj) except ValueError: other_json = error_path other_json['filename'] = fn return jsonify(other_json) except Exception as e: print(e) other_json = error_path other_json['filename'] = fn return jsonify(other_json) if __name__ == "__main__": .open_new('http://127.0.0.1:8000/') app.run(host='localhost', port=8000, debug=True)

true

f7365980ef0f648a9b1148fdf6a7cf556501bab6

15,803

py

Python

solver.py

pyrito/SpeechSplit

ee70ee77e54d5b7cd1b39e7bef1cb96ae78f8beb

[ "MIT" ]

null

solver.py

pyrito/SpeechSplit

ee70ee77e54d5b7cd1b39e7bef1cb96ae78f8beb

[ "MIT" ]

null

solver.py

pyrito/SpeechSplit

ee70ee77e54d5b7cd1b39e7bef1cb96ae78f8beb

[ "MIT" ]

null

from torch.utils.tensorboard.summary import hparams from model import Generator_3 as Generator from model import InterpLnr import matplotlib.pyplot as plt import torch import torch.nn.functional as F import numpy as np import os import time import datetime import pickle from utils import pad_seq_to_2, quantize_f0_torch, quantize_f0_numpy # use demo data for simplicity # make your own validation set as needed validation_pt = pickle.load(open('assets/demo.pkl', "rb")) class Solver(object): """Solver for training""" def __init__(self, vcc_loader, config, hparams): """Initialize configurations.""" # Data loader. self.vcc_loader = vcc_loader self.hparams = hparams # Training configurations. self.num_iters = config.num_iters self.g_lr = config.g_lr self.beta1 = config.beta1 self.beta2 = config.beta2 self.resume_iters = config.resume_iters # Miscellaneous. self.use_tensorboard = config.use_tensorboard self.use_cuda = torch.cuda.is_available() self.device = torch.device('cuda:{}'.format(config.device_id) if self.use_cuda else 'cpu') # Directories. self.log_dir = config.log_dir self.sample_dir = config.sample_dir self.model_save_dir = config.model_save_dir # Step size. self.log_step = config.log_step self.sample_step = config.sample_step self.model_save_step = config.model_save_step # Build the model and tensorboard. self.build_model() if self.use_tensorboard: self.build_tensorboard() def build_model(self): self.G = Generator(self.hparams) self.Interp = InterpLnr(self.hparams) self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr, [self.beta1, self.beta2]) self.print_network(self.G, 'G') self.G.to(self.device) self.Interp.to(self.device) def print_network(self, model, name): """Print out the network information.""" num_params = 0 for p in model.parameters(): num_params += p.numel() print(model) print(name) print("The number of parameters: {}".format(num_params)) def print_optimizer(self, opt, name): print(opt) print(name) def restore_model(self, resume_iters): print('Loading the trained models from step {}...'.format(resume_iters)) G_path = os.path.join(self.model_save_dir, '{}-G.ckpt'.format(resume_iters)) g_checkpoint = torch.load(G_path, map_location=lambda storage, loc: storage) self.G.load_state_dict(g_checkpoint['model']) self.g_optimizer.load_state_dict(g_checkpoint['optimizer']) self.g_lr = self.g_optimizer.param_groups[0]['lr'] def build_tensorboard(self): """Build a tensorboard logger.""" from torch.utils.tensorboard import SummaryWriter self.writer = SummaryWriter(self.log_dir) def reset_grad(self): """Reset the gradient buffers.""" self.g_optimizer.zero_grad() def encode_context(self): # Set data loader. data_loader = self.vcc_loader # Fetch fixed inputs for debugging. data_iter = iter(data_loader) # Start encoding from scratch or resume from checkpoint. start_iters = 0 if self.resume_iters: print('Resuming ...') start_iters = self.resume_iters self.num_iters += self.resume_iters self.restore_model(self.resume_iters) # self.print_optimizer(self.g_optimizer, 'G_optimizer') # Print logs in specified order keys = ['G/loss_id'] # Start encoding. print('Start encoding...') start_time = time.time() encoded_audio = {} # May need this if looping doesn't work: # for i in max(range(start_iters, self.num_iters), len(self.vcc_loader)): print(len(self.vcc_loader)) count = 0 for i, (x_real_org, emb_org, f0_org, len_org, id_org) in enumerate(self.vcc_loader): # =================================================================================== # # 1. Send input data to device # # =================================================================================== # # x_real_org = x_real_org.to(self.device) # emb_org = emb_org.to(self.device) # len_org = len_org.to(self.device) # f0_org = f0_org.to(self.device) # =================================================================================== # # 2. Encode using the generator # # =================================================================================== # self.G = self.G.eval() pad = 8 - ((len_org[0] + 1) % 8) encode_length = len_org[0] + 1 + pad print(id_org) x_real_pad, _ = pad_seq_to_2(x_real_org, encode_length) # len_org = torch.tensor([val_sub[k][2]]).to(self.device) f0_org_pad, _ = pad_seq_to_2(f0_org, encode_length) # np.pad(f0_org, (0, 512-len_org[0]), 'constant', constant_values=(0, 0)) assert x_real_pad.shape[1] == f0_org_pad.shape[1] f0_quantized = quantize_f0_numpy(np.squeeze(f0_org_pad))[0] f0_onehot = f0_quantized[np.newaxis, :, :] f0_org_val = torch.from_numpy(f0_onehot).to(self.device) x_real_pad = torch.from_numpy(x_real_pad).to(self.device) x_f0 = torch.cat((x_real_pad, f0_org_val), dim=-1) code_content, code_pitch, code_rhythm, speaker_emb = self.G.forward_encode(x_f0, x_real_pad, emb_org) # code_content, code_pitch, code_rhythm, speaker_emb = self.G.forward_encode(x_f0_intrp_org, x_real_org, emb_org) # print(f'content: {code_content}') encoded_audio[id_org[0]] = code_content et = time.time() - start_time et = str(datetime.timedelta(seconds=et))[:-7] log = "Elapsed [{}], Audio file[{}/{}]".format(et, i+1, len(self.vcc_loader)) print(log) count += 1 if count % 100 == 0: with open(f'assets/encoded-{self.hparams.encode_mode}-{count}.pkl', 'wb') as f: pickle.dump(encoded_audio, f) del encoded_audio encoded_audio = {} #===================================================================================================================== def train(self): # Set data loader. data_loader = self.vcc_loader # Fetch fixed inputs for debugging. data_iter = iter(data_loader) # Start training from scratch or resume training. start_iters = 0 if self.resume_iters: print('Resuming ...') start_iters = self.resume_iters self.num_iters += self.resume_iters self.restore_model(self.resume_iters) self.print_optimizer(self.g_optimizer, 'G_optimizer') # Learning rate cache for decaying. g_lr = self.g_lr print ('Current learning rates, g_lr: {}.'.format(g_lr)) # Print logs in specified order keys = ['G/loss_id'] # Start training. print('Start training...') start_time = time.time() for i in range(start_iters, self.num_iters): # =================================================================================== # # 1. Preprocess input data # # =================================================================================== # # Fetch real images and labels. try: x_real_org, emb_org, f0_org, len_org = next(data_iter) except: data_iter = iter(data_loader) x_real_org, emb_org, f0_org, len_org = next(data_iter) x_real_org = x_real_org.to(self.device) emb_org = emb_org.to(self.device) len_org = len_org.to(self.device) f0_org = f0_org.to(self.device) # =================================================================================== # # 2. Train the generator # # =================================================================================== # self.G = self.G.train() # Identity mapping loss x_f0 = torch.cat((x_real_org, f0_org), dim=-1) x_f0_intrp = self.Interp(x_f0, len_org) f0_org_intrp = quantize_f0_torch(x_f0_intrp[:,:,-1])[0] x_f0_intrp_org = torch.cat((x_f0_intrp[:,:,:-1], f0_org_intrp), dim=-1) x_identic = self.G(x_f0_intrp_org, x_real_org, emb_org) g_loss_id = F.mse_loss(x_real_org, x_identic, reduction='mean') # Backward and optimize. g_loss = g_loss_id self.reset_grad() g_loss.backward() self.g_optimizer.step() # Logging. loss = {} loss['G/loss_id'] = g_loss_id.item() # =================================================================================== # # 4. Miscellaneous # # =================================================================================== # # Print out training information. if (i+1) % self.log_step == 0: et = time.time() - start_time et = str(datetime.timedelta(seconds=et))[:-7] log = "Elapsed [{}], Iteration [{}/{}]".format(et, i+1, self.num_iters) for tag in keys: log += ", {}: {:.8f}".format(tag, loss[tag]) print(log) if self.use_tensorboard: for tag, value in loss.items(): self.writer.add_scalar(tag, value, i+1) # Save model checkpoints. if (i+1) % self.model_save_step == 0: G_path = os.path.join(self.model_save_dir, '{}-G.ckpt'.format(i+1)) torch.save({'model': self.G.state_dict(), 'optimizer': self.g_optimizer.state_dict()}, G_path) print('Saved model checkpoints into {}...'.format(self.model_save_dir)) # Validation. if (i+1) % self.sample_step == 0: self.G = self.G.eval() with torch.no_grad(): loss_val = [] for val_sub in validation_pt: emb_org_val = torch.from_numpy(val_sub[1]).to(self.device) for k in range(2, 3): x_real_pad, _ = pad_seq_to_2(val_sub[k][0][np.newaxis,:,:], 192) len_org = torch.tensor([val_sub[k][2]]).to(self.device) f0_org = np.pad(val_sub[k][1], (0, 192-val_sub[k][2]), 'constant', constant_values=(0, 0)) f0_quantized = quantize_f0_numpy(f0_org)[0] f0_onehot = f0_quantized[np.newaxis, :, :] f0_org_val = torch.from_numpy(f0_onehot).to(self.device) x_real_pad = torch.from_numpy(x_real_pad).to(self.device) x_f0 = torch.cat((x_real_pad, f0_org_val), dim=-1) x_identic_val = self.G(x_f0, x_real_pad, emb_org_val) g_loss_val = F.mse_loss(x_real_pad, x_identic_val, reduction='sum') loss_val.append(g_loss_val.item()) val_loss = np.mean(loss_val) print('Validation loss: {}'.format(val_loss)) if self.use_tensorboard: self.writer.add_scalar('Validation_loss', val_loss, i+1) # plot test samples if (i+1) % self.sample_step == 0: self.G = self.G.eval() with torch.no_grad(): for val_sub in validation_pt: emb_org_val = torch.from_numpy(val_sub[1]).to(self.device) for k in range(2, 3): x_real_pad, _ = pad_seq_to_2(val_sub[k][0][np.newaxis,:,:], 192) len_org = torch.tensor([val_sub[k][2]]).to(self.device) f0_org = np.pad(val_sub[k][1], (0, 192-val_sub[k][2]), 'constant', constant_values=(0, 0)) f0_quantized = quantize_f0_numpy(f0_org)[0] f0_onehot = f0_quantized[np.newaxis, :, :] f0_org_val = torch.from_numpy(f0_onehot).to(self.device) x_real_pad = torch.from_numpy(x_real_pad).to(self.device) x_f0 = torch.cat((x_real_pad, f0_org_val), dim=-1) x_f0_F = torch.cat((x_real_pad, torch.zeros_like(f0_org_val)), dim=-1) x_f0_C = torch.cat((torch.zeros_like(x_real_pad), f0_org_val), dim=-1) x_identic_val = self.G(x_f0, x_real_pad, emb_org_val) x_identic_woF = self.G(x_f0_F, x_real_pad, emb_org_val) x_identic_woR = self.G(x_f0, torch.zeros_like(x_real_pad), emb_org_val) x_identic_woC = self.G(x_f0_C, x_real_pad, emb_org_val) melsp_gd_pad = x_real_pad[0].cpu().numpy().T melsp_out = x_identic_val[0].cpu().numpy().T melsp_woF = x_identic_woF[0].cpu().numpy().T melsp_woR = x_identic_woR[0].cpu().numpy().T melsp_woC = x_identic_woC[0].cpu().numpy().T min_value = np.min(np.hstack([melsp_gd_pad, melsp_out, melsp_woF, melsp_woR, melsp_woC])) max_value = np.max(np.hstack([melsp_gd_pad, melsp_out, melsp_woF, melsp_woR, melsp_woC])) # fig, (ax1,ax2,ax3,ax4,ax5) = plt.subplots(5, 1, sharex=True) # im1 = ax1.imshow(melsp_gd_pad, aspect='auto', vmin=min_value, vmax=max_value) # im2 = ax2.imshow(melsp_out, aspect='auto', vmin=min_value, vmax=max_value) # im3 = ax3.imshow(melsp_woC, aspect='auto', vmin=min_value, vmax=max_value) # im4 = ax4.imshow(melsp_woR, aspect='auto', vmin=min_value, vmax=max_value) # im5 = ax5.imshow(melsp_woF, aspect='auto', vmin=min_value, vmax=max_value) # plt.savefig(f'{self.sample_dir}/{i+1}_{val_sub[0]}_{k}.png', dpi=150) # plt.close(fig)

45.41092

137

0.485351

from torch.utils.tensorboard.summary import hparams from model import Generator_3 as Generator from model import InterpLnr import matplotlib.pyplot as plt import torch import torch.nn.functional as F import numpy as np import os import time import datetime import pickle from utils import pad_seq_to_2, quantize_f0_torch, quantize_f0_numpy validation_pt = pickle.load(open('assets/demo.pkl', "rb")) class Solver(object): def __init__(self, vcc_loader, config, hparams): self.vcc_loader = vcc_loader self.hparams = hparams self.num_iters = config.num_iters self.g_lr = config.g_lr self.beta1 = config.beta1 self.beta2 = config.beta2 self.resume_iters = config.resume_iters self.use_tensorboard = config.use_tensorboard self.use_cuda = torch.cuda.is_available() self.device = torch.device('cuda:{}'.format(config.device_id) if self.use_cuda else 'cpu') self.log_dir = config.log_dir self.sample_dir = config.sample_dir self.model_save_dir = config.model_save_dir self.log_step = config.log_step self.sample_step = config.sample_step self.model_save_step = config.model_save_step self.build_model() if self.use_tensorboard: self.build_tensorboard() def build_model(self): self.G = Generator(self.hparams) self.Interp = InterpLnr(self.hparams) self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr, [self.beta1, self.beta2]) self.print_network(self.G, 'G') self.G.to(self.device) self.Interp.to(self.device) def print_network(self, model, name): num_params = 0 for p in model.parameters(): num_params += p.numel() print(model) print(name) print("The number of parameters: {}".format(num_params)) def print_optimizer(self, opt, name): print(opt) print(name) def restore_model(self, resume_iters): print('Loading the trained models from step {}...'.format(resume_iters)) G_path = os.path.join(self.model_save_dir, '{}-G.ckpt'.format(resume_iters)) g_checkpoint = torch.load(G_path, map_location=lambda storage, loc: storage) self.G.load_state_dict(g_checkpoint['model']) self.g_optimizer.load_state_dict(g_checkpoint['optimizer']) self.g_lr = self.g_optimizer.param_groups[0]['lr'] def build_tensorboard(self): from torch.utils.tensorboard import SummaryWriter self.writer = SummaryWriter(self.log_dir) def reset_grad(self): self.g_optimizer.zero_grad() def encode_context(self): data_loader = self.vcc_loader data_iter = iter(data_loader) start_iters = 0 if self.resume_iters: print('Resuming ...') start_iters = self.resume_iters self.num_iters += self.resume_iters self.restore_model(self.resume_iters) keys = ['G/loss_id'] print('Start encoding...') start_time = time.time() encoded_audio = {} # for i in max(range(start_iters, self.num_iters), len(self.vcc_loader)): print(len(self.vcc_loader)) count = 0 for i, (x_real_org, emb_org, f0_org, len_org, id_org) in enumerate(self.vcc_loader): # =================================================================================== # # 1. Send input data to device # # =================================================================================== # # x_real_org = x_real_org.to(self.device) # emb_org = emb_org.to(self.device) # len_org = len_org.to(self.device) # f0_org = f0_org.to(self.device) # =================================================================================== # # 2. Encode using the generator # # =================================================================================== # self.G = self.G.eval() pad = 8 - ((len_org[0] + 1) % 8) encode_length = len_org[0] + 1 + pad print(id_org) x_real_pad, _ = pad_seq_to_2(x_real_org, encode_length) # len_org = torch.tensor([val_sub[k][2]]).to(self.device) f0_org_pad, _ = pad_seq_to_2(f0_org, encode_length) # np.pad(f0_org, (0, 512-len_org[0]), 'constant', constant_values=(0, 0)) assert x_real_pad.shape[1] == f0_org_pad.shape[1] f0_quantized = quantize_f0_numpy(np.squeeze(f0_org_pad))[0] f0_onehot = f0_quantized[np.newaxis, :, :] f0_org_val = torch.from_numpy(f0_onehot).to(self.device) x_real_pad = torch.from_numpy(x_real_pad).to(self.device) x_f0 = torch.cat((x_real_pad, f0_org_val), dim=-1) code_content, code_pitch, code_rhythm, speaker_emb = self.G.forward_encode(x_f0, x_real_pad, emb_org) # code_content, code_pitch, code_rhythm, speaker_emb = self.G.forward_encode(x_f0_intrp_org, x_real_org, emb_org) # print(f'content: {code_content}') encoded_audio[id_org[0]] = code_content et = time.time() - start_time et = str(datetime.timedelta(seconds=et))[:-7] log = "Elapsed [{}], Audio file[{}/{}]".format(et, i+1, len(self.vcc_loader)) print(log) count += 1 if count % 100 == 0: with open(f'assets/encoded-{self.hparams.encode_mode}-{count}.pkl', 'wb') as f: pickle.dump(encoded_audio, f) del encoded_audio encoded_audio = {} #===================================================================================================================== def train(self): # Set data loader. data_loader = self.vcc_loader # Fetch fixed inputs for debugging. data_iter = iter(data_loader) # Start training from scratch or resume training. start_iters = 0 if self.resume_iters: print('Resuming ...') start_iters = self.resume_iters self.num_iters += self.resume_iters self.restore_model(self.resume_iters) self.print_optimizer(self.g_optimizer, 'G_optimizer') # Learning rate cache for decaying. g_lr = self.g_lr print ('Current learning rates, g_lr: {}.'.format(g_lr)) # Print logs in specified order keys = ['G/loss_id'] # Start training. print('Start training...') start_time = time.time() for i in range(start_iters, self.num_iters): # =================================================================================== # # 1. Preprocess input data # # =================================================================================== # # Fetch real images and labels. try: x_real_org, emb_org, f0_org, len_org = next(data_iter) except: data_iter = iter(data_loader) x_real_org, emb_org, f0_org, len_org = next(data_iter) x_real_org = x_real_org.to(self.device) emb_org = emb_org.to(self.device) len_org = len_org.to(self.device) f0_org = f0_org.to(self.device) # =================================================================================== # # 2. Train the generator # # =================================================================================== # self.G = self.G.train() # Identity mapping loss x_f0 = torch.cat((x_real_org, f0_org), dim=-1) x_f0_intrp = self.Interp(x_f0, len_org) f0_org_intrp = quantize_f0_torch(x_f0_intrp[:,:,-1])[0] x_f0_intrp_org = torch.cat((x_f0_intrp[:,:,:-1], f0_org_intrp), dim=-1) x_identic = self.G(x_f0_intrp_org, x_real_org, emb_org) g_loss_id = F.mse_loss(x_real_org, x_identic, reduction='mean') # Backward and optimize. g_loss = g_loss_id self.reset_grad() g_loss.backward() self.g_optimizer.step() # Logging. loss = {} loss['G/loss_id'] = g_loss_id.item() # =================================================================================== # # 4. Miscellaneous # # =================================================================================== # # Print out training information. if (i+1) % self.log_step == 0: et = time.time() - start_time et = str(datetime.timedelta(seconds=et))[:-7] log = "Elapsed [{}], Iteration [{}/{}]".format(et, i+1, self.num_iters) for tag in keys: log += ", {}: {:.8f}".format(tag, loss[tag]) print(log) if self.use_tensorboard: for tag, value in loss.items(): self.writer.add_scalar(tag, value, i+1) # Save model checkpoints. if (i+1) % self.model_save_step == 0: G_path = os.path.join(self.model_save_dir, '{}-G.ckpt'.format(i+1)) torch.save({'model': self.G.state_dict(), 'optimizer': self.g_optimizer.state_dict()}, G_path) print('Saved model checkpoints into {}...'.format(self.model_save_dir)) # Validation. if (i+1) % self.sample_step == 0: self.G = self.G.eval() with torch.no_grad(): loss_val = [] for val_sub in validation_pt: emb_org_val = torch.from_numpy(val_sub[1]).to(self.device) for k in range(2, 3): x_real_pad, _ = pad_seq_to_2(val_sub[k][0][np.newaxis,:,:], 192) len_org = torch.tensor([val_sub[k][2]]).to(self.device) f0_org = np.pad(val_sub[k][1], (0, 192-val_sub[k][2]), 'constant', constant_values=(0, 0)) f0_quantized = quantize_f0_numpy(f0_org)[0] f0_onehot = f0_quantized[np.newaxis, :, :] f0_org_val = torch.from_numpy(f0_onehot).to(self.device) x_real_pad = torch.from_numpy(x_real_pad).to(self.device) x_f0 = torch.cat((x_real_pad, f0_org_val), dim=-1) x_identic_val = self.G(x_f0, x_real_pad, emb_org_val) g_loss_val = F.mse_loss(x_real_pad, x_identic_val, reduction='sum') loss_val.append(g_loss_val.item()) val_loss = np.mean(loss_val) print('Validation loss: {}'.format(val_loss)) if self.use_tensorboard: self.writer.add_scalar('Validation_loss', val_loss, i+1) # plot test samples if (i+1) % self.sample_step == 0: self.G = self.G.eval() with torch.no_grad(): for val_sub in validation_pt: emb_org_val = torch.from_numpy(val_sub[1]).to(self.device) for k in range(2, 3): x_real_pad, _ = pad_seq_to_2(val_sub[k][0][np.newaxis,:,:], 192) len_org = torch.tensor([val_sub[k][2]]).to(self.device) f0_org = np.pad(val_sub[k][1], (0, 192-val_sub[k][2]), 'constant', constant_values=(0, 0)) f0_quantized = quantize_f0_numpy(f0_org)[0] f0_onehot = f0_quantized[np.newaxis, :, :] f0_org_val = torch.from_numpy(f0_onehot).to(self.device) x_real_pad = torch.from_numpy(x_real_pad).to(self.device) x_f0 = torch.cat((x_real_pad, f0_org_val), dim=-1) x_f0_F = torch.cat((x_real_pad, torch.zeros_like(f0_org_val)), dim=-1) x_f0_C = torch.cat((torch.zeros_like(x_real_pad), f0_org_val), dim=-1) x_identic_val = self.G(x_f0, x_real_pad, emb_org_val) x_identic_woF = self.G(x_f0_F, x_real_pad, emb_org_val) x_identic_woR = self.G(x_f0, torch.zeros_like(x_real_pad), emb_org_val) x_identic_woC = self.G(x_f0_C, x_real_pad, emb_org_val) melsp_gd_pad = x_real_pad[0].cpu().numpy().T melsp_out = x_identic_val[0].cpu().numpy().T melsp_woF = x_identic_woF[0].cpu().numpy().T melsp_woR = x_identic_woR[0].cpu().numpy().T melsp_woC = x_identic_woC[0].cpu().numpy().T min_value = np.min(np.hstack([melsp_gd_pad, melsp_out, melsp_woF, melsp_woR, melsp_woC])) max_value = np.max(np.hstack([melsp_gd_pad, melsp_out, melsp_woF, melsp_woR, melsp_woC])) # fig, (ax1,ax2,ax3,ax4,ax5) = plt.subplots(5, 1, sharex=True) # im1 = ax1.imshow(melsp_gd_pad, aspect='auto', vmin=min_value, vmax=max_value) # im2 = ax2.imshow(melsp_out, aspect='auto', vmin=min_value, vmax=max_value) # im3 = ax3.imshow(melsp_woC, aspect='auto', vmin=min_value, vmax=max_value) # im4 = ax4.imshow(melsp_woR, aspect='auto', vmin=min_value, vmax=max_value) # im5 = ax5.imshow(melsp_woF, aspect='auto', vmin=min_value, vmax=max_value) # plt.savefig(f'{self.sample_dir}/{i+1}_{val_sub[0]}_{k}.png', dpi=150) # plt.close(fig)

true

f7365a1da28790199225cb69fa9ccb4430d35ff0

1,133

py

Python

cms/test_utils/project/placeholderapp/admin.py

foobacca/django-cms

d15586b399b18de49208bca74495a771cdbd494f

[ "BSD-3-Clause" ]

1

2021-04-08T13:49:04.000Z

cms/test_utils/project/placeholderapp/admin.py

foobacca/django-cms

d15586b399b18de49208bca74495a771cdbd494f

[ "BSD-3-Clause" ]

8

2021-06-08T23:36:57.000Z

2022-03-12T00:49:20.000Z

cms/test_utils/project/placeholderapp/admin.py

foobacca/django-cms

d15586b399b18de49208bca74495a771cdbd494f

[ "BSD-3-Clause" ]

null

from cms.admin.placeholderadmin import PlaceholderAdmin from cms.test_utils.project.placeholderapp.models import (Example1, MultilingualExample1, TwoPlaceholderExample) from django.contrib import admin from hvad.admin import TranslatableAdmin class MixinAdmin(admin.ModelAdmin): def formfield_for_dbfield(self, db_field, **kwargs): """ Hook for specifying the form Field instance for a given database Field instance. If kwargs are given, they're passed to the form Field's constructor. """ # silly test that placeholderadmin doesn't fuck stuff up request = kwargs.pop('request', None) return super(MixinAdmin, self).formfield_for_dbfield(db_field, request=request, **kwargs) class ExampleAdmin(PlaceholderAdmin, MixinAdmin): pass class TwoPlaceholderExampleAdmin(PlaceholderAdmin, MixinAdmin): pass class MultilingualAdmin(TranslatableAdmin, PlaceholderAdmin): pass admin.site.register(Example1, ExampleAdmin) admin.site.register(TwoPlaceholderExample, TwoPlaceholderExampleAdmin) admin.site.register(MultilingualExample1, MultilingualAdmin)

32.371429

112

0.772286

from cms.admin.placeholderadmin import PlaceholderAdmin from cms.test_utils.project.placeholderapp.models import (Example1, MultilingualExample1, TwoPlaceholderExample) from django.contrib import admin from hvad.admin import TranslatableAdmin class MixinAdmin(admin.ModelAdmin): def formfield_for_dbfield(self, db_field, **kwargs): request = kwargs.pop('request', None) return super(MixinAdmin, self).formfield_for_dbfield(db_field, request=request, **kwargs) class ExampleAdmin(PlaceholderAdmin, MixinAdmin): pass class TwoPlaceholderExampleAdmin(PlaceholderAdmin, MixinAdmin): pass class MultilingualAdmin(TranslatableAdmin, PlaceholderAdmin): pass admin.site.register(Example1, ExampleAdmin) admin.site.register(TwoPlaceholderExample, TwoPlaceholderExampleAdmin) admin.site.register(MultilingualExample1, MultilingualAdmin)

true

f7365a4261bf5528b341b9ad8439969db155368a

591

py

Python

setup.py

Lanseuo/alasan

198a340b74429ccab2f4095c39b245936c60ea4c

[ "MIT" ]

null

setup.py

Lanseuo/alasan

198a340b74429ccab2f4095c39b245936c60ea4c

[ "MIT" ]

5

2020-05-10T12:03:03.000Z

2020-05-10T12:04:40.000Z

setup.py

Lanseuo/alasan

198a340b74429ccab2f4095c39b245936c60ea4c

[ "MIT" ]

null

from setuptools import setup, find_packages with open("README.md", "r") as f: long_description = f.read() setup( name="alasan", version="0.0.1", author="Lucas Hild", author_email="contact@lucas-hild.de", description="Alasan helps you build Alexa skills on AWS Lambda using Python.", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/Lanseuo/alasan", packages=find_packages(), install_requires="click", entry_points=''' [console_scripts] alasan=alasan.cli:cli ''' )

26.863636

82

0.680203

from setuptools import setup, find_packages with open("README.md", "r") as f: long_description = f.read() setup( name="alasan", version="0.0.1", author="Lucas Hild", author_email="contact@lucas-hild.de", description="Alasan helps you build Alexa skills on AWS Lambda using Python.", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/Lanseuo/alasan", packages=find_packages(), install_requires="click", entry_points=''' [console_scripts] alasan=alasan.cli:cli ''' )

true

f7365b4272faa183cad3252dfec1668c82b1770d

599

py

Python

var/spack/repos/builtin/packages/perl-math-cdf/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

null

var/spack/repos/builtin/packages/perl-math-cdf/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

8

2021-11-09T20:28:40.000Z

2022-03-15T03:26:33.000Z

var/spack/repos/builtin/packages/perl-math-cdf/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2

2019-02-08T20:37:20.000Z

2019-03-31T15:19:26.000Z

# Copyright 2013-2022 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack.package import * class PerlMathCdf(PerlPackage): """Generate probabilities and quantiles from several statistical probability functions""" homepage = "https://metacpan.org/pod/Math::CDF" url = "http://search.cpan.org/CPAN/authors/id/C/CA/CALLAHAN/Math-CDF-0.1.tar.gz" version('0.1', sha256='7896bf250835ce47dcc813cb8cf9dc576c5455de42e822dcd7d8d3fef2125565')

35.235294

93

0.749583

from spack.package import * class PerlMathCdf(PerlPackage): homepage = "https://metacpan.org/pod/Math::CDF" url = "http://search.cpan.org/CPAN/authors/id/C/CA/CALLAHAN/Math-CDF-0.1.tar.gz" version('0.1', sha256='7896bf250835ce47dcc813cb8cf9dc576c5455de42e822dcd7d8d3fef2125565')

true

f7365b43e013e39a7186f6896627308675c2098b

15,826

py

Python

vyper/context/types/bases.py

erdnaag/vyper

22bef3a4b4161db18c7831041e20b917984cff83

[ "Apache-2.0" ]

1

2020-07-04T01:47:26.000Z

vyper/context/types/bases.py

erdnaag/vyper

22bef3a4b4161db18c7831041e20b917984cff83

[ "Apache-2.0" ]

null

vyper/context/types/bases.py

erdnaag/vyper

22bef3a4b4161db18c7831041e20b917984cff83

[ "Apache-2.0" ]

null

import copy from collections import OrderedDict from enum import Enum from typing import Any, Dict, Optional, Tuple, Type, Union from vyper import ast as vy_ast from vyper.context.types.abstract import AbstractDataType from vyper.exceptions import ( CompilerPanic, ImmutableViolation, InvalidLiteral, InvalidOperation, NamespaceCollision, StructureException, UnexpectedNodeType, UnexpectedValue, UnknownAttribute, ) class DataLocation(Enum): UNSET = 0 MEMORY = 1 STORAGE = 2 CALLDATA = 3 class BasePrimitive: """ Base class for primitive type classes. Primitives are objects that are invoked when applying a type to a variable. They must contain a `from_annotation` (and optionally `from_literal`) method that returns their equivalent `BaseTypeDefinition` object. Attributes ---------- _id : str The name of the type. _type : BaseTypeDefinition The related `BaseTypeDefinition` class generated from this primitive _as_array: bool, optional If `True`, this type can be used as the base member for an array. _valid_literal : Tuple A tuple of Vyper ast classes that may be assigned this type. """ _id: str _type: Type["BaseTypeDefinition"] _valid_literal: Tuple @classmethod def from_annotation( cls, node: Union[vy_ast.Name, vy_ast.Call], location: DataLocation = DataLocation.UNSET, is_immutable: bool = False, is_public: bool = False, ) -> "BaseTypeDefinition": """ Generate a `BaseTypeDefinition` instance of this type from `AnnAssign.annotation` Arguments --------- node : VyperNode Vyper ast node from the `annotation` member of an `AnnAssign` node. Returns ------- BaseTypeDefinition BaseTypeDefinition related to the primitive that the method was called on. """ if not isinstance(node, vy_ast.Name): raise StructureException("Invalid type assignment", node) if node.id != cls._id: raise UnexpectedValue("Node id does not match type name") return cls._type(location, is_immutable, is_public) @classmethod def from_literal(cls, node: vy_ast.Constant) -> "BaseTypeDefinition": """ Generate a `BaseTypeDefinition` instance of this type from a literal constant. This method is called on every primitive class in order to determine potential types for a `Constant` AST node. Types that may be assigned from literals should include a `_valid_literal` attribute, containing a list of AST node classes that may be valid for this type. If the `_valid_literal` attribute is not included, the type cannot be assigned to a literal. Arguments --------- node : VyperNode `Constant` Vyper ast node, or a list or tuple of constants. Returns ------- BaseTypeDefinition BaseTypeDefinition related to the primitive that the method was called on. """ if not isinstance(node, vy_ast.Constant): raise UnexpectedNodeType(f"Attempted to validate a '{node.ast_type}' node.") if not isinstance(node, cls._valid_literal): raise InvalidLiteral(f"Invalid literal type for {cls.__name__}", node) return cls._type() @classmethod def compare_type( cls, other: Union["BaseTypeDefinition", "BasePrimitive", AbstractDataType] ) -> bool: """ Compare this type object against another type object. Failed comparisons must return `False`, not raise an exception. This method is not intended to be called directly. Type comparisons are handled by methods in `vyper.context.validation.utils` Arguments --------- other : BaseTypeDefinition Another type object to be compared against this one. Returns ------- bool Indicates if the types are equivalent. """ return isinstance(other, cls._type) @classmethod def fetch_call_return(self, node: vy_ast.Call) -> "BaseTypeDefinition": """ Validate a call to this type and return the result. This method must raise if the type is not callable, or the call arguments are not valid. Arguments --------- node : Call Vyper ast node of call action to validate. Returns ------- BaseTypeDefinition, optional Type generated as a result of the call. """ raise StructureException("Type is not callable", node) @classmethod def get_index_type(self, node: vy_ast.Index) -> None: # always raises - do not implement in inherited classes raise StructureException("Types cannot be indexed", node) @classmethod def get_member(cls, key: str, node: vy_ast.Attribute) -> None: # always raises - do not implement in inherited classes raise StructureException("Types do not have members", node) @classmethod def validate_modification(cls, node: Union[vy_ast.Assign, vy_ast.AugAssign]) -> None: # always raises - do not implement in inherited classes raise InvalidOperation("Cannot assign to a type", node) class BaseTypeDefinition: """ Base class for type definition classes. Type definitions are objects that represent the type of a specific object within a contract. They are usually derived from a `BasePrimitive` counterpart. Class Attributes ----------------- _id : str The name of the type. _is_callable : bool, optional If `True`, attempts to assign this value without calling it will raise a more expressive error message recommending that the user performs a function call. Object Attributes ----------------- is_immutable : bool, optional If `True`, the value of this object cannot be modified after assignment. """ def __init__( self, location: DataLocation = DataLocation.UNSET, is_immutable: bool = False, is_public: bool = False, ) -> None: self.location = location self.is_immutable = is_immutable self.is_public = is_public def from_annotation(self, node: vy_ast.VyperNode, **kwargs: Any) -> None: # always raises, user should have used a primitive raise StructureException("Value is not a type", node) def compare_type( self, other: Union["BaseTypeDefinition", BasePrimitive, AbstractDataType] ) -> bool: """ Compare this type object against another type object. Failed comparisons must return `False`, not raise an exception. This method is not intended to be called directly. Type comparisons are handled by methods in `vyper.context.validation.utils` Arguments --------- other : BaseTypeDefinition Another type object to be compared against this one. Returns ------- bool Indicates if the types are equivalent. """ return isinstance(other, type(self)) def validate_numeric_op( self, node: Union[vy_ast.UnaryOp, vy_ast.BinOp, vy_ast.AugAssign] ) -> None: """ Validate a numeric operation for this type. Arguments --------- node : UnaryOp | BinOp | AugAssign Vyper ast node of the numeric operation to be validated. Returns ------- None. A failed validation must raise an exception. """ raise InvalidOperation(f"Cannot perform {node.op.description} on {self}", node) def validate_boolean_op(self, node: vy_ast.BoolOp) -> None: """ Validate a boolean operation for this type. Arguments --------- node : BoolOp Vyper ast node of the boolean operation to be validated. Returns ------- None. A failed validation must raise an exception. """ raise InvalidOperation(f"Invalid type for operand: {self}", node) def validate_comparator(self, node: vy_ast.Compare) -> None: """ Validate a comparator for this type. Arguments --------- node : Compare Vyper ast node of the comparator to be validated. Returns ------- None. A failed validation must raise an exception. """ if not isinstance(node.op, (vy_ast.Eq, vy_ast.NotEq)): raise InvalidOperation( f"Cannot perform {node.op.description} comparison on {self}", node ) def validate_implements(self, node: vy_ast.AnnAssign) -> None: """ Validate an implements statement. This method is unique to user-defined interfaces. It should not be included in other types. Arguments --------- node : AnnAssign Vyper ast node of the implements statement being validated. Returns ------- None. A failed validation must raise an exception. """ raise StructureException("Value is not an interface", node) def fetch_call_return(self, node: vy_ast.Call) -> Union["BaseTypeDefinition", None]: """ Validate a call to this value and return the result. This method must raise if the value is not callable, or the call arguments are not valid. Arguments --------- node : Call Vyper ast node of call action to validate. Returns ------- BaseTypeDefinition, optional Type generated as a result of the call. """ raise StructureException("Value is not callable", node) def get_index_type(self, node: vy_ast.Index) -> "BaseTypeDefinition": """ Validate an index reference and return the given type at the index. Arguments --------- node : Index Vyper ast node from the `slice` member of a Subscript node. Returns ------- BaseTypeDefinition Type object for value at the given index. """ raise StructureException(f"Type '{self}' does not support indexing", node) def get_member(self, key: str, node: vy_ast.Attribute) -> "BaseTypeDefinition": """ Validate an attribute reference and return the given type for the member. Arguments --------- key : str Name of the member being accessed. node: Attribute Vyper ast Attribute node representing the member being accessed. Returns ------- BaseTypeDefinition A type object for the value of the given member. Raises if the member does not exist for the given type. """ raise StructureException(f"Type '{self}' does not support members", node) def validate_modification(self, node: Union[vy_ast.Assign, vy_ast.AugAssign]) -> None: """ Validate an attempt to modify this value. Raises if the value is a constant or involves an invalid operation. Arguments --------- node : Assign | AugAssign Vyper ast node of the modifying action. """ if self.location == DataLocation.CALLDATA: raise ImmutableViolation("Cannot write to calldata", node) if self.is_immutable: raise ImmutableViolation("Immutable value cannot be written to", node) if isinstance(node, vy_ast.AugAssign): self.validate_numeric_op(node) def get_signature(self) -> Tuple[Tuple, Optional["BaseTypeDefinition"]]: raise CompilerPanic("Method must be implemented by the inherited class") def compare_signature(self, other: "BaseTypeDefinition") -> bool: """ Compare the signature of this type with another type. Used when determining if an interface has been implemented. This method should not be directly implemented by any inherited classes. """ if not self.is_public: return False arguments, return_type = self.get_signature() other_arguments, other_return_type = other.get_signature() if len(arguments) != len(other_arguments): return False for a, b in zip(arguments, other_arguments): if not a.compare_type(b): return False if return_type and not return_type.compare_type(other_return_type): # type: ignore return False return True class ValueTypeDefinition(BaseTypeDefinition): """ Base class for types representing a single value. Class attributes ---------------- _valid_literal: VyperNode | Tuple A vyper ast class or tuple of ast classes that can represent valid literals for the given type. Including this attribute will allow literal values to be assigned this type. """ def __repr__(self): return self._id def get_signature(self): return (), self class MemberTypeDefinition(ValueTypeDefinition): """ Base class for types that have accessible members. Class attributes ---------------- _type_members : Dict[str, BaseType] Dictionary of members common to all values of this type. Object attributes ----------------- members : OrderedDict[str, BaseType] Dictionary of members for the given type. """ _type_members: Dict def __init__( self, location: DataLocation = DataLocation.UNSET, is_immutable: bool = False, is_public: bool = False, ) -> None: super().__init__(location, is_immutable, is_public) self.members: OrderedDict = OrderedDict() def add_member(self, name: str, type_: BaseTypeDefinition) -> None: if name in self.members: raise NamespaceCollision(f"Member {name} already exists in {self}") if name in getattr(self, "_type_members", []): raise NamespaceCollision(f"Member {name} already exists in {self}") self.members[name] = type_ def get_member(self, key: str, node: vy_ast.VyperNode) -> BaseTypeDefinition: if key in self.members: return self.members[key] elif key in getattr(self, "_type_members", []): type_ = copy.deepcopy(self._type_members[key]) type_.location = self.location type_.is_immutable = self.is_immutable return type_ raise UnknownAttribute(f"{self} has no member '{key}'", node) def __repr__(self): return f"{self._id}" class IndexableTypeDefinition(BaseTypeDefinition): """ Base class for indexable types such as arrays and mappings. Attributes ---------- key_type: BaseType Type representing the index value for this object. value_type : BaseType Type representing the value(s) contained in this object. _id : str Name of the type. """ def __init__( self, value_type: BaseTypeDefinition, key_type: BaseTypeDefinition, _id: str, location: DataLocation = DataLocation.UNSET, is_immutable: bool = False, is_public: bool = False, ) -> None: super().__init__(location, is_immutable, is_public) self.value_type = value_type self.key_type = key_type self._id = _id def get_signature(self) -> Tuple[Tuple, Optional[BaseTypeDefinition]]: new_args, return_type = self.value_type.get_signature() return (self.key_type,) + new_args, return_type

32.166667

91

0.625174

import copy from collections import OrderedDict from enum import Enum from typing import Any, Dict, Optional, Tuple, Type, Union from vyper import ast as vy_ast from vyper.context.types.abstract import AbstractDataType from vyper.exceptions import ( CompilerPanic, ImmutableViolation, InvalidLiteral, InvalidOperation, NamespaceCollision, StructureException, UnexpectedNodeType, UnexpectedValue, UnknownAttribute, ) class DataLocation(Enum): UNSET = 0 MEMORY = 1 STORAGE = 2 CALLDATA = 3 class BasePrimitive: _id: str _type: Type["BaseTypeDefinition"] _valid_literal: Tuple @classmethod def from_annotation( cls, node: Union[vy_ast.Name, vy_ast.Call], location: DataLocation = DataLocation.UNSET, is_immutable: bool = False, is_public: bool = False, ) -> "BaseTypeDefinition": if not isinstance(node, vy_ast.Name): raise StructureException("Invalid type assignment", node) if node.id != cls._id: raise UnexpectedValue("Node id does not match type name") return cls._type(location, is_immutable, is_public) @classmethod def from_literal(cls, node: vy_ast.Constant) -> "BaseTypeDefinition": if not isinstance(node, vy_ast.Constant): raise UnexpectedNodeType(f"Attempted to validate a '{node.ast_type}' node.") if not isinstance(node, cls._valid_literal): raise InvalidLiteral(f"Invalid literal type for {cls.__name__}", node) return cls._type() @classmethod def compare_type( cls, other: Union["BaseTypeDefinition", "BasePrimitive", AbstractDataType] ) -> bool: return isinstance(other, cls._type) @classmethod def fetch_call_return(self, node: vy_ast.Call) -> "BaseTypeDefinition": raise StructureException("Type is not callable", node) @classmethod def get_index_type(self, node: vy_ast.Index) -> None: raise StructureException("Types cannot be indexed", node) @classmethod def get_member(cls, key: str, node: vy_ast.Attribute) -> None: raise StructureException("Types do not have members", node) @classmethod def validate_modification(cls, node: Union[vy_ast.Assign, vy_ast.AugAssign]) -> None: raise InvalidOperation("Cannot assign to a type", node) class BaseTypeDefinition: def __init__( self, location: DataLocation = DataLocation.UNSET, is_immutable: bool = False, is_public: bool = False, ) -> None: self.location = location self.is_immutable = is_immutable self.is_public = is_public def from_annotation(self, node: vy_ast.VyperNode, **kwargs: Any) -> None: raise StructureException("Value is not a type", node) def compare_type( self, other: Union["BaseTypeDefinition", BasePrimitive, AbstractDataType] ) -> bool: return isinstance(other, type(self)) def validate_numeric_op( self, node: Union[vy_ast.UnaryOp, vy_ast.BinOp, vy_ast.AugAssign] ) -> None: raise InvalidOperation(f"Cannot perform {node.op.description} on {self}", node) def validate_boolean_op(self, node: vy_ast.BoolOp) -> None: raise InvalidOperation(f"Invalid type for operand: {self}", node) def validate_comparator(self, node: vy_ast.Compare) -> None: if not isinstance(node.op, (vy_ast.Eq, vy_ast.NotEq)): raise InvalidOperation( f"Cannot perform {node.op.description} comparison on {self}", node ) def validate_implements(self, node: vy_ast.AnnAssign) -> None: raise StructureException("Value is not an interface", node) def fetch_call_return(self, node: vy_ast.Call) -> Union["BaseTypeDefinition", None]: raise StructureException("Value is not callable", node) def get_index_type(self, node: vy_ast.Index) -> "BaseTypeDefinition": raise StructureException(f"Type '{self}' does not support indexing", node) def get_member(self, key: str, node: vy_ast.Attribute) -> "BaseTypeDefinition": raise StructureException(f"Type '{self}' does not support members", node) def validate_modification(self, node: Union[vy_ast.Assign, vy_ast.AugAssign]) -> None: if self.location == DataLocation.CALLDATA: raise ImmutableViolation("Cannot write to calldata", node) if self.is_immutable: raise ImmutableViolation("Immutable value cannot be written to", node) if isinstance(node, vy_ast.AugAssign): self.validate_numeric_op(node) def get_signature(self) -> Tuple[Tuple, Optional["BaseTypeDefinition"]]: raise CompilerPanic("Method must be implemented by the inherited class") def compare_signature(self, other: "BaseTypeDefinition") -> bool: if not self.is_public: return False arguments, return_type = self.get_signature() other_arguments, other_return_type = other.get_signature() if len(arguments) != len(other_arguments): return False for a, b in zip(arguments, other_arguments): if not a.compare_type(b): return False if return_type and not return_type.compare_type(other_return_type): return False return True class ValueTypeDefinition(BaseTypeDefinition): def __repr__(self): return self._id def get_signature(self): return (), self class MemberTypeDefinition(ValueTypeDefinition): _type_members: Dict def __init__( self, location: DataLocation = DataLocation.UNSET, is_immutable: bool = False, is_public: bool = False, ) -> None: super().__init__(location, is_immutable, is_public) self.members: OrderedDict = OrderedDict() def add_member(self, name: str, type_: BaseTypeDefinition) -> None: if name in self.members: raise NamespaceCollision(f"Member {name} already exists in {self}") if name in getattr(self, "_type_members", []): raise NamespaceCollision(f"Member {name} already exists in {self}") self.members[name] = type_ def get_member(self, key: str, node: vy_ast.VyperNode) -> BaseTypeDefinition: if key in self.members: return self.members[key] elif key in getattr(self, "_type_members", []): type_ = copy.deepcopy(self._type_members[key]) type_.location = self.location type_.is_immutable = self.is_immutable return type_ raise UnknownAttribute(f"{self} has no member '{key}'", node) def __repr__(self): return f"{self._id}" class IndexableTypeDefinition(BaseTypeDefinition): def __init__( self, value_type: BaseTypeDefinition, key_type: BaseTypeDefinition, _id: str, location: DataLocation = DataLocation.UNSET, is_immutable: bool = False, is_public: bool = False, ) -> None: super().__init__(location, is_immutable, is_public) self.value_type = value_type self.key_type = key_type self._id = _id def get_signature(self) -> Tuple[Tuple, Optional[BaseTypeDefinition]]: new_args, return_type = self.value_type.get_signature() return (self.key_type,) + new_args, return_type

true

f7365b6614a2b2f2a73eeb08494f8f98110af4db

9,560

py

Python

mars/dataframe/datastore/tests/test_datastore_execute.py

snsnlou/mars

6b8eec162eccc8bb980a98ca2cf1e6a4b866d302

[ "Apache-2.0" ]

1

2021-11-30T12:07:21.000Z

mars/dataframe/datastore/tests/test_datastore_execute.py

snsnlou/mars

6b8eec162eccc8bb980a98ca2cf1e6a4b866d302

[ "Apache-2.0" ]

null

mars/dataframe/datastore/tests/test_datastore_execute.py

snsnlou/mars

6b8eec162eccc8bb980a98ca2cf1e6a4b866d302

[ "Apache-2.0" ]

null

# Copyright 1999-2020 Alibaba Group Holding Ltd. # # 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. import os import tempfile import unittest import numpy as np import pandas as pd import mars.dataframe as md from mars.config import option_context from mars.dataframe import DataFrame from mars.deploy.local.core import new_cluster from mars.session import new_session from mars.tests.core import TestBase, flaky try: import vineyard except ImportError: vineyard = None try: import sqlalchemy except ImportError: sqlalchemy = None try: import pyarrow as pa except ImportError: pa = None try: import fastparquet except ImportError: fastparquet = None _exec_timeout = 120 if 'CI' in os.environ else -1 class Test(TestBase): def setUp(self): super().setUp() self.ctx, self.executor = self._create_test_context() def testToCSVExecution(self): index = pd.RangeIndex(100, 0, -1, name='index') raw = pd.DataFrame({ 'col1': np.random.rand(100), 'col2': np.random.choice(['a', 'b', 'c'], (100,)), 'col3': np.arange(100) }, index=index) df = DataFrame(raw, chunk_size=33) with tempfile.TemporaryDirectory() as base_path: # DATAFRAME TESTS # test one file with dataframe path = os.path.join(base_path, 'out.csv') r = df.to_csv(path) self.executor.execute_dataframe(r) result = pd.read_csv(path, dtype=raw.dtypes.to_dict()) result.set_index('index', inplace=True) pd.testing.assert_frame_equal(result, raw) # test multi files with dataframe path = os.path.join(base_path, 'out-*.csv') r = df.to_csv(path) self.executor.execute_dataframe(r) dfs = [pd.read_csv(os.path.join(base_path, f'out-{i}.csv'), dtype=raw.dtypes.to_dict()) for i in range(4)] result = pd.concat(dfs, axis=0) result.set_index('index', inplace=True) pd.testing.assert_frame_equal(result, raw) pd.testing.assert_frame_equal(dfs[1].set_index('index'), raw.iloc[33: 66]) with self.ctx: # test df with unknown shape df2 = DataFrame(raw, chunk_size=(50, 2)) df2 = df2[df2['col1'] < 1] path2 = os.path.join(base_path, 'out2.csv') r = df2.to_csv(path2) self.executor.execute_dataframes([r]) result = pd.read_csv(path2, dtype=raw.dtypes.to_dict()) result.set_index('index', inplace=True) pd.testing.assert_frame_equal(result, raw) # SERIES TESTS series = md.Series(raw.col1, chunk_size=33) # test one file with series path = os.path.join(base_path, 'out.csv') r = series.to_csv(path) self.executor.execute_dataframe(r) result = pd.read_csv(path, dtype=raw.dtypes.to_dict()) result.set_index('index', inplace=True) pd.testing.assert_frame_equal(result, raw.col1.to_frame()) # test multi files with series path = os.path.join(base_path, 'out-*.csv') r = series.to_csv(path) self.executor.execute_dataframe(r) dfs = [pd.read_csv(os.path.join(base_path, f'out-{i}.csv'), dtype=raw.dtypes.to_dict()) for i in range(4)] result = pd.concat(dfs, axis=0) result.set_index('index', inplace=True) pd.testing.assert_frame_equal(result, raw.col1.to_frame()) pd.testing.assert_frame_equal(dfs[1].set_index('index'), raw.col1.to_frame().iloc[33: 66]) @unittest.skipIf(sqlalchemy is None, 'sqlalchemy not installed') def testToSQL(self): index = pd.RangeIndex(100, 0, -1, name='index') raw = pd.DataFrame({ 'col1': np.random.rand(100), 'col2': np.random.choice(['a', 'b', 'c'], (100,)), 'col3': np.arange(100).astype('int64'), }, index=index) with tempfile.TemporaryDirectory() as d: table_name1 = 'test_table' table_name2 = 'test_table2' uri = 'sqlite:///' + os.path.join(d, 'test.db') engine = sqlalchemy.create_engine(uri) # test write dataframe df = DataFrame(raw, chunk_size=33) r = df.to_sql(table_name1, con=engine) self.executor.execute_dataframe(r) written = pd.read_sql(table_name1, con=engine, index_col='index') \ .sort_index(ascending=False) pd.testing.assert_frame_equal(raw, written) # test write with existing table with self.assertRaises(ValueError): df.to_sql(table_name1, con=uri).execute() # test write series series = md.Series(raw.col1, chunk_size=33) with engine.connect() as conn: r = series.to_sql(table_name2, con=conn) self.executor.execute_dataframe(r) written = pd.read_sql(table_name2, con=engine, index_col='index') \ .sort_index(ascending=False) pd.testing.assert_frame_equal(raw.col1.to_frame(), written) @unittest.skipIf(vineyard is None, 'vineyard not installed') @flaky(max_runs=3) def testToVineyard(self): def run_with_given_session(session, **kw): ipc_socket = os.environ.get('VINEYARD_IPC_SOCKET', '/tmp/vineyard/vineyard.sock') with option_context({'vineyard.socket': ipc_socket}): df1 = DataFrame(pd.DataFrame(np.arange(12).reshape(3, 4), columns=['a', 'b', 'c', 'd']), chunk_size=2) object_id = df1.to_vineyard().execute(session=session, **kw).fetch(session=session) df2 = md.from_vineyard(object_id) df1_value = df1.execute(session=session, **kw).fetch(session=session) df2_value = df2.execute(session=session, **kw).fetch(session=session) pd.testing.assert_frame_equal( df1_value.reset_index(drop=True), df2_value.reset_index(drop=True)) with new_session().as_default() as session: run_with_given_session(session) with new_cluster(scheduler_n_process=2, worker_n_process=2, shared_memory='20M', web=False) as cluster: with new_session(cluster.endpoint).as_default() as session: run_with_given_session(session, timeout=_exec_timeout) @unittest.skipIf(pa is None, 'pyarrow not installed') def testToParquetArrowExecution(self): raw = pd.DataFrame({ 'col1': np.random.rand(100), 'col2': np.arange(100), 'col3': np.random.choice(['a', 'b', 'c'], (100,)), }) df = DataFrame(raw, chunk_size=33) with tempfile.TemporaryDirectory() as base_path: # DATAFRAME TESTS path = os.path.join(base_path, 'out-*.parquet') r = df.to_parquet(path) self.executor.execute_dataframe(r) read_df = md.read_parquet(path) result = self.executor.execute_dataframe(read_df, concat=True)[0] result = result.sort_index() pd.testing.assert_frame_equal(result, raw) read_df = md.read_parquet(path) result = self.executor.execute_dataframe(read_df, concat=True)[0] result = result.sort_index() pd.testing.assert_frame_equal(result, raw) # test read_parquet then to_parquet read_df = md.read_parquet(path) r = read_df.to_parquet(path) self.executor.execute_dataframes([r]) # test partition_cols path = os.path.join(base_path, 'out-partitioned') r = df.to_parquet(path, partition_cols=['col3']) self.executor.execute_dataframe(r) read_df = md.read_parquet(path) result = self.executor.execute_dataframe(read_df, concat=True)[0] result['col3'] = result['col3'].astype('object') pd.testing.assert_frame_equal(result.sort_values('col1').reset_index(drop=True), raw.sort_values('col1').reset_index(drop=True)) @unittest.skipIf(fastparquet is None, 'fastparquet not installed') def testToParquetFastParquetExecution(self): raw = pd.DataFrame({ 'col1': np.random.rand(100), 'col2': np.arange(100), 'col3': np.random.choice(['a', 'b', 'c'], (100,)), }) df = DataFrame(raw, chunk_size=33) with tempfile.TemporaryDirectory() as base_path: # test fastparquet path = os.path.join(base_path, 'out-fastparquet-*.parquet') r = df.to_parquet(path, engine='fastparquet', compression='gzip') self.executor.execute_dataframe(r)

39.341564

104

0.598954

import os import tempfile import unittest import numpy as np import pandas as pd import mars.dataframe as md from mars.config import option_context from mars.dataframe import DataFrame from mars.deploy.local.core import new_cluster from mars.session import new_session from mars.tests.core import TestBase, flaky try: import vineyard except ImportError: vineyard = None try: import sqlalchemy except ImportError: sqlalchemy = None try: import pyarrow as pa except ImportError: pa = None try: import fastparquet except ImportError: fastparquet = None _exec_timeout = 120 if 'CI' in os.environ else -1 class Test(TestBase): def setUp(self): super().setUp() self.ctx, self.executor = self._create_test_context() def testToCSVExecution(self): index = pd.RangeIndex(100, 0, -1, name='index') raw = pd.DataFrame({ 'col1': np.random.rand(100), 'col2': np.random.choice(['a', 'b', 'c'], (100,)), 'col3': np.arange(100) }, index=index) df = DataFrame(raw, chunk_size=33) with tempfile.TemporaryDirectory() as base_path: path = os.path.join(base_path, 'out.csv') r = df.to_csv(path) self.executor.execute_dataframe(r) result = pd.read_csv(path, dtype=raw.dtypes.to_dict()) result.set_index('index', inplace=True) pd.testing.assert_frame_equal(result, raw) path = os.path.join(base_path, 'out-*.csv') r = df.to_csv(path) self.executor.execute_dataframe(r) dfs = [pd.read_csv(os.path.join(base_path, f'out-{i}.csv'), dtype=raw.dtypes.to_dict()) for i in range(4)] result = pd.concat(dfs, axis=0) result.set_index('index', inplace=True) pd.testing.assert_frame_equal(result, raw) pd.testing.assert_frame_equal(dfs[1].set_index('index'), raw.iloc[33: 66]) with self.ctx: df2 = DataFrame(raw, chunk_size=(50, 2)) df2 = df2[df2['col1'] < 1] path2 = os.path.join(base_path, 'out2.csv') r = df2.to_csv(path2) self.executor.execute_dataframes([r]) result = pd.read_csv(path2, dtype=raw.dtypes.to_dict()) result.set_index('index', inplace=True) pd.testing.assert_frame_equal(result, raw) series = md.Series(raw.col1, chunk_size=33) path = os.path.join(base_path, 'out.csv') r = series.to_csv(path) self.executor.execute_dataframe(r) result = pd.read_csv(path, dtype=raw.dtypes.to_dict()) result.set_index('index', inplace=True) pd.testing.assert_frame_equal(result, raw.col1.to_frame()) path = os.path.join(base_path, 'out-*.csv') r = series.to_csv(path) self.executor.execute_dataframe(r) dfs = [pd.read_csv(os.path.join(base_path, f'out-{i}.csv'), dtype=raw.dtypes.to_dict()) for i in range(4)] result = pd.concat(dfs, axis=0) result.set_index('index', inplace=True) pd.testing.assert_frame_equal(result, raw.col1.to_frame()) pd.testing.assert_frame_equal(dfs[1].set_index('index'), raw.col1.to_frame().iloc[33: 66]) @unittest.skipIf(sqlalchemy is None, 'sqlalchemy not installed') def testToSQL(self): index = pd.RangeIndex(100, 0, -1, name='index') raw = pd.DataFrame({ 'col1': np.random.rand(100), 'col2': np.random.choice(['a', 'b', 'c'], (100,)), 'col3': np.arange(100).astype('int64'), }, index=index) with tempfile.TemporaryDirectory() as d: table_name1 = 'test_table' table_name2 = 'test_table2' uri = 'sqlite:///' + os.path.join(d, 'test.db') engine = sqlalchemy.create_engine(uri) df = DataFrame(raw, chunk_size=33) r = df.to_sql(table_name1, con=engine) self.executor.execute_dataframe(r) written = pd.read_sql(table_name1, con=engine, index_col='index') \ .sort_index(ascending=False) pd.testing.assert_frame_equal(raw, written) with self.assertRaises(ValueError): df.to_sql(table_name1, con=uri).execute() series = md.Series(raw.col1, chunk_size=33) with engine.connect() as conn: r = series.to_sql(table_name2, con=conn) self.executor.execute_dataframe(r) written = pd.read_sql(table_name2, con=engine, index_col='index') \ .sort_index(ascending=False) pd.testing.assert_frame_equal(raw.col1.to_frame(), written) @unittest.skipIf(vineyard is None, 'vineyard not installed') @flaky(max_runs=3) def testToVineyard(self): def run_with_given_session(session, **kw): ipc_socket = os.environ.get('VINEYARD_IPC_SOCKET', '/tmp/vineyard/vineyard.sock') with option_context({'vineyard.socket': ipc_socket}): df1 = DataFrame(pd.DataFrame(np.arange(12).reshape(3, 4), columns=['a', 'b', 'c', 'd']), chunk_size=2) object_id = df1.to_vineyard().execute(session=session, **kw).fetch(session=session) df2 = md.from_vineyard(object_id) df1_value = df1.execute(session=session, **kw).fetch(session=session) df2_value = df2.execute(session=session, **kw).fetch(session=session) pd.testing.assert_frame_equal( df1_value.reset_index(drop=True), df2_value.reset_index(drop=True)) with new_session().as_default() as session: run_with_given_session(session) with new_cluster(scheduler_n_process=2, worker_n_process=2, shared_memory='20M', web=False) as cluster: with new_session(cluster.endpoint).as_default() as session: run_with_given_session(session, timeout=_exec_timeout) @unittest.skipIf(pa is None, 'pyarrow not installed') def testToParquetArrowExecution(self): raw = pd.DataFrame({ 'col1': np.random.rand(100), 'col2': np.arange(100), 'col3': np.random.choice(['a', 'b', 'c'], (100,)), }) df = DataFrame(raw, chunk_size=33) with tempfile.TemporaryDirectory() as base_path: path = os.path.join(base_path, 'out-*.parquet') r = df.to_parquet(path) self.executor.execute_dataframe(r) read_df = md.read_parquet(path) result = self.executor.execute_dataframe(read_df, concat=True)[0] result = result.sort_index() pd.testing.assert_frame_equal(result, raw) read_df = md.read_parquet(path) result = self.executor.execute_dataframe(read_df, concat=True)[0] result = result.sort_index() pd.testing.assert_frame_equal(result, raw) read_df = md.read_parquet(path) r = read_df.to_parquet(path) self.executor.execute_dataframes([r]) path = os.path.join(base_path, 'out-partitioned') r = df.to_parquet(path, partition_cols=['col3']) self.executor.execute_dataframe(r) read_df = md.read_parquet(path) result = self.executor.execute_dataframe(read_df, concat=True)[0] result['col3'] = result['col3'].astype('object') pd.testing.assert_frame_equal(result.sort_values('col1').reset_index(drop=True), raw.sort_values('col1').reset_index(drop=True)) @unittest.skipIf(fastparquet is None, 'fastparquet not installed') def testToParquetFastParquetExecution(self): raw = pd.DataFrame({ 'col1': np.random.rand(100), 'col2': np.arange(100), 'col3': np.random.choice(['a', 'b', 'c'], (100,)), }) df = DataFrame(raw, chunk_size=33) with tempfile.TemporaryDirectory() as base_path: path = os.path.join(base_path, 'out-fastparquet-*.parquet') r = df.to_parquet(path, engine='fastparquet', compression='gzip') self.executor.execute_dataframe(r)

true

f7365c6d507efee3fdb9f656365455527ac25a4f

5,172

py

Python

setup.py

Eothred/pymad

f560239325452adbb1d37b6408f275ac03674069

[ "ECL-2.0", "Apache-2.0" ]

3

2015-01-21T07:36:11.000Z

2020-05-29T12:18:33.000Z

setup.py

Eothred/pymad

f560239325452adbb1d37b6408f275ac03674069

[ "ECL-2.0", "Apache-2.0" ]

2

2015-03-22T02:24:57.000Z

2015-10-15T15:50:51.000Z

setup.py

Eothred/pymad

f560239325452adbb1d37b6408f275ac03674069

[ "ECL-2.0", "Apache-2.0" ]

1

2017-02-08T16:55:01.000Z

#------------------------------------------------------------------------------- # This file is part of PyMad. # # Copyright (c) 2011, CERN. 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. #------------------------------------------------------------------------------- # Make sure setuptools is available. NOTE: the try/except hack is required to # make installation work with pip: If an older version of setuptools is # already imported, `use_setuptools()` will just exit the current process. try: import pkg_resources except ImportError: from ez_setup import use_setuptools use_setuptools() from setuptools import setup, Extension from distutils.util import get_platform import sys from os import path # Version of pymad (major,minor): PYMADVERSION=(0, 9) # setuptools.Extension automatically converts all '.pyx' extensions to '.c' # extensions if detecting that neither Cython nor Pyrex is available. Early # versions of setuptools don't know about Cython. Since we don't use Pyrex # in this module, this leads to problems in the two cases where Cython is # available and Pyrex is not or vice versa. Therefore, setuptools.Extension # needs to be patched to match our needs: try: # Use Cython if available: from Cython.Build import cythonize except ImportError: # Otherwise, always use the distributed .c instead of the .pyx file: def cythonize(extensions): def pyx_to_c(source): return source[:-4]+'.c' if source.endswith('.pyx') else source for ext in extensions: ext.sources = list(map(pyx_to_c, ext.sources)) missing_sources = [s for s in ext.sources if not path.exists(s)] if missing_sources: raise OSError(('Missing source file: {0[0]!r}. ' 'Install Cython to resolve this problem.') .format(missing_sources)) return extensions else: orig_Extension = Extension class Extension(orig_Extension): """Extension that *never* replaces '.pyx' by '.c' (using Cython).""" def __init__(self, name, sources, *args, **kwargs): orig_Extension.__init__(self, name, sources, *args, **kwargs) self.sources = sources # Let's just use the default system headers: include_dirs = [] library_dirs = [] # Parse command line option: --madxdir=/path/to/madxinstallation. We could # use build_ext.user_options instead, but then the --madxdir argument can # be passed only to the 'build_ext' command, not to 'build' or 'install', # which is a minor nuisance. for arg in sys.argv[:]: if arg.startswith('--madxdir='): sys.argv.remove(arg) prefix = path.expanduser(arg.split('=', 1)[1]) lib_path_candidates = [path.join(prefix, 'lib'), path.join(prefix, 'lib64')] include_dirs += [path.join(prefix, 'include')] library_dirs += list(filter(path.isdir, lib_path_candidates)) # required libraries if get_platform() == "win32" or get_platform() == "win-amd64": libraries = ['madx', 'stdc++', 'ptc', 'gfortran', 'msvcrt'] else: libraries = ['madx', 'stdc++', 'c'] # Common arguments for the Cython extensions: extension_args = dict( define_macros=[('MAJOR_VERSION', PYMADVERSION[0]), ('MINOR_VERSION', PYMADVERSION[1])], libraries=libraries, include_dirs=include_dirs, library_dirs=library_dirs, runtime_library_dirs=library_dirs, extra_compile_args=['-std=c99'], ) # Compose a long description for PyPI: long_description = None try: long_description = open('README.rst').read() long_description += '\n' + open('COPYING.rst').read() long_description += '\n' + open('CHANGES.rst').read() except IOError: pass setup( name='cern-cpymad', version='.'.join(map(str, PYMADVERSION)), description='Cython binding to MAD-X', long_description=long_description, url='http://pymad.github.io/cpymad', package_dir={ '': 'src' # look for packages in src/ subfolder }, ext_modules = cythonize([ Extension('cern.cpymad.libmadx', sources=["src/cern/cpymad/libmadx.pyx"], **extension_args), ]), namespace_packages=[ 'cern' ], packages = [ "cern", "cern.resource", "cern.cpymad", ], include_package_data=True, # include files matched by MANIFEST.in author='PyMAD developers', author_email='pymad@cern.ch', setup_requires=[ ], install_requires=[ 'setuptools', 'numpy', 'PyYAML', ], license = 'CERN Standard Copyright License' )

35.424658

80

0.642691

try: import pkg_resources except ImportError: from ez_setup import use_setuptools use_setuptools() from setuptools import setup, Extension from distutils.util import get_platform import sys from os import path PYMADVERSION=(0, 9) try: from Cython.Build import cythonize except ImportError: def cythonize(extensions): def pyx_to_c(source): return source[:-4]+'.c' if source.endswith('.pyx') else source for ext in extensions: ext.sources = list(map(pyx_to_c, ext.sources)) missing_sources = [s for s in ext.sources if not path.exists(s)] if missing_sources: raise OSError(('Missing source file: {0[0]!r}. ' 'Install Cython to resolve this problem.') .format(missing_sources)) return extensions else: orig_Extension = Extension class Extension(orig_Extension): """Extension that *never* replaces '.pyx' by '.c' (using Cython).""" def __init__(self, name, sources, *args, **kwargs): orig_Extension.__init__(self, name, sources, *args, **kwargs) self.sources = sources include_dirs = [] library_dirs = [] # Parse command line option: --madxdir=/path/to/madxinstallation. We could # use build_ext.user_options instead, but then the --madxdir argument can # be passed only to the 'build_ext' command, not to 'build' or 'install', # which is a minor nuisance. for arg in sys.argv[:]: if arg.startswith('--madxdir='): sys.argv.remove(arg) prefix = path.expanduser(arg.split('=', 1)[1]) lib_path_candidates = [path.join(prefix, 'lib'), path.join(prefix, 'lib64')] include_dirs += [path.join(prefix, 'include')] library_dirs += list(filter(path.isdir, lib_path_candidates)) # required libraries if get_platform() == "win32" or get_platform() == "win-amd64": libraries = ['madx', 'stdc++', 'ptc', 'gfortran', 'msvcrt'] else: libraries = ['madx', 'stdc++', 'c'] # Common arguments for the Cython extensions: extension_args = dict( define_macros=[('MAJOR_VERSION', PYMADVERSION[0]), ('MINOR_VERSION', PYMADVERSION[1])], libraries=libraries, include_dirs=include_dirs, library_dirs=library_dirs, runtime_library_dirs=library_dirs, extra_compile_args=['-std=c99'], ) # Compose a long description for PyPI: long_description = None try: long_description = open('README.rst').read() long_description += '\n' + open('COPYING.rst').read() long_description += '\n' + open('CHANGES.rst').read() except IOError: pass setup( name='cern-cpymad', version='.'.join(map(str, PYMADVERSION)), description='Cython binding to MAD-X', long_description=long_description, url='http://pymad.github.io/cpymad', package_dir={ '': 'src' # look for packages in src/ subfolder }, ext_modules = cythonize([ Extension('cern.cpymad.libmadx', sources=["src/cern/cpymad/libmadx.pyx"], **extension_args), ]), namespace_packages=[ 'cern' ], packages = [ "cern", "cern.resource", "cern.cpymad", ], include_package_data=True, # include files matched by MANIFEST.in author='PyMAD developers', author_email='pymad@cern.ch', setup_requires=[ ], install_requires=[ 'setuptools', 'numpy', 'PyYAML', ], license = 'CERN Standard Copyright License' )

true

f7365c9b7c7c285ab21ee7e4bac00b72277fcf50

122

py

Python

pypro/modulos/context_processors.py

wosubtil/curso-django

4673e71fbbec062c74dfaf866ff13b81383e3fcc

[ "MIT" ]

null

pypro/modulos/context_processors.py

wosubtil/curso-django

4673e71fbbec062c74dfaf866ff13b81383e3fcc

[ "MIT" ]

806

2020-09-18T11:26:48.000Z

2022-03-31T00:43:46.000Z

pypro/modulos/context_processors.py

taniodev/curso-django

aa5b0edd6ca55d2ea7f73220644d5c64a96c60df

[ "MIT" ]

1

2020-08-06T19:50:33.000Z

from pypro.modulos import facade def listar_modulos(request): return {'MODULOS': facade.listar_modulos_ordenados()}

20.333333

57

0.778689

from pypro.modulos import facade def listar_modulos(request): return {'MODULOS': facade.listar_modulos_ordenados()}

true

f7365cf7cf02c8871fff6ab4ece9ab8717635813

7,703

py

Python

roms/tests/joy/spadtest-nes-0.01/tools/pilbmp2nes.py

MrKOSMOS/ANESE

8ae814d615479b1496c98033a1f5bc4da5921c6f

[ "MIT" ]

349

2017-11-15T22:51:00.000Z

2022-03-21T13:43:57.000Z

tools/pilbmp2nes.py

pubby/Ralph-4

dcda1d3e5bce25ca22b54dfee82eb64625d64d25

[ "CC-BY-4.0" ]

12

2018-08-28T21:38:29.000Z

2021-12-11T16:24:36.000Z

tools/pilbmp2nes.py

pubby/Ralph-4

dcda1d3e5bce25ca22b54dfee82eb64625d64d25

[ "CC-BY-4.0" ]

28

2018-06-10T07:31:13.000Z

2022-03-21T10:54:26.000Z

#!/usr/bin/env python # # Bitmap to multi-console CHR converter using PIL or Pillow # # Copyright 2014 Damian Yerrick # Copying and distribution of this file, with or without # modification, are permitted in any medium without royalty # provided the copyright notice and this notice are preserved. # This file is offered as-is, without any warranty. # from __future__ import with_statement, print_function, unicode_literals from PIL import Image from time import sleep import array # python 2/3 cross compatibility fixes try: xrange except NameError: xrange = range try: raw_input except NameError: raw_input = input try: next except NameError: next = lambda x: x.next() def blank_byte_array(): try: return array.array('B') except TypeError: return array.array(b'B') def formatTilePlanar(tile, planemap, hflip=False, little=False): """Turn a tile into bitplanes. Planemap opcodes: 10 -- bit 1 then bit 0 of each tile 0,1 -- planar interleaved by rows 0;1 -- planar interlaved by planes 0,1;2,3 -- SNES/PCE format """ hflip = 7 if hflip else 0 if (tile.size != (8, 8)): return None pixels = list(tile.getdata()) pixelrows = [pixels[i:i + 8] for i in xrange(0, 64, 8)] if hflip: for row in pixelrows: row.reverse() out = blank_byte_array() planemap = [[[int(c) for c in row] for row in plane.split(',')] for plane in planemap.split(';')] # format: [tile-plane number][plane-within-row number][bit number] # we have five (!) nested loops # outermost: separate planes # within separate planes: pixel rows # within pixel rows: row planes # within row planes: pixels # within pixels: bits for plane in planemap: for pxrow in pixelrows: for rowplane in plane: rowbits = 1 thisrow = blank_byte_array() for px in pxrow: for bitnum in rowplane: rowbits = (rowbits << 1) | ((px >> bitnum) & 1) if rowbits >= 0x100: thisrow.append(rowbits & 0xFF) rowbits = 1 if little: thisrow.reverse() out.extend(thisrow) return out.tostring() def pilbmp2chr(im, tileWidth=8, tileHeight=8, formatTile=lambda im: formatTilePlanar(im, "0;1")): """Convert a bitmap image into a list of byte strings representing tiles.""" im.load() (w, h) = im.size outdata = [] for mt_y in range(0, h, tileHeight): for mt_x in range(0, w, tileWidth): metatile = im.crop((mt_x, mt_y, mt_x + tileWidth, mt_y + tileHeight)) for tile_y in range(0, tileHeight, 8): for tile_x in range(0, tileWidth, 8): tile = metatile.crop((tile_x, tile_y, tile_x + 8, tile_y + 8)) data = formatTile(tile) outdata.append(data) return outdata def parse_argv(argv): from optparse import OptionParser parser = OptionParser(usage="usage: %prog [options] [-i] INFILE [-o] OUTFILE") parser.add_option("-i", "--image", dest="infilename", help="read image from INFILE", metavar="INFILE") parser.add_option("-o", "--output", dest="outfilename", help="write CHR data to OUTFILE", metavar="OUTFILE") parser.add_option("-W", "--tile-width", dest="tileWidth", help="set width of metatiles", metavar="HEIGHT", type="int", default=8) parser.add_option("--packbits", dest="packbits", help="use PackBits RLE compression", action="store_true", default=False) parser.add_option("-H", "--tile-height", dest="tileHeight", help="set height of metatiles", metavar="HEIGHT", type="int", default=8) parser.add_option("-1", dest="planes", help="set 1bpp mode (default: 2bpp NES)", action="store_const", const="0", default="0;1") parser.add_option("--planes", dest="planes", help="set the plane map (1bpp: 0) (NES: 0;1) (GB: 0,1) (SMS:0,1,2,3) (TG16/SNES: 0,1;2,3) (MD: 3210)") parser.add_option("--hflip", dest="hflip", help="horizontally flip all tiles (most significant pixel on right)", action="store_true", default=False) parser.add_option("--little", dest="little", help="reverse the bytes within each row-plane (needed for GBA and a few others)", action="store_true", default=False) (options, args) = parser.parse_args(argv[1:]) tileWidth = int(options.tileWidth) if tileWidth <= 0: raise ValueError("tile width '%d' must be positive" % tileWidth) tileHeight = int(options.tileHeight) if tileHeight <= 0: raise ValueError("tile height '%d' must be positive" % tileHeight) # Fill unfilled roles with positional arguments argsreader = iter(args) try: infilename = options.infilename if infilename is None: infilename = next(argsreader) except StopIteration: raise ValueError("not enough filenames") outfilename = options.outfilename if outfilename is None: try: outfilename = next(argsreader) except StopIteration: outfilename = '-' if outfilename == '-': import sys if sys.stdout.isatty(): raise ValueError("cannot write CHR to terminal") return (infilename, outfilename, tileWidth, tileHeight, options.packbits, options.planes, options.hflip, options.little) argvTestingMode = True def make_stdout_binary(): """Ensure that sys.stdout is in binary mode, with no newline translation.""" # Recipe from # http://code.activestate.com/recipes/65443-sending-binary-data-to-stdout-under-windows/ # via http://stackoverflow.com/a/2374507/2738262 if sys.platform == "win32": import os, msvcrt msvcrt.setmode(sys.stdout.fileno(), os.O_BINARY) def main(argv=None): import sys if argv is None: argv = sys.argv if (argvTestingMode and len(argv) < 2 and sys.stdin.isatty() and sys.stdout.isatty()): argv.extend(raw_input('args:').split()) try: (infilename, outfilename, tileWidth, tileHeight, usePackBits, planes, hflip, little) = parse_argv(argv) except Exception as e: sys.stderr.write("%s: %s\n" % (argv[0], str(e))) sys.exit(1) im = Image.open(infilename) outdata = pilbmp2chr(im, tileWidth, tileHeight, lambda im: formatTilePlanar(im, planes, hflip, little)) outdata = b''.join(outdata) if usePackBits: from packbits import PackBits sz = len(outdata) % 0x10000 outdata = PackBits(outdata).flush().tostring() outdata = b''.join([chr(sz >> 8), chr(sz & 0xFF), outdata]) # Read input file outfp = None try: if outfilename != '-': outfp = open(outfilename, 'wb') else: outfp = sys.stdout make_stdout_binary() outfp.write(outdata) finally: if outfp and outfilename != '-': outfp.close() if __name__=='__main__': main() ## main(['pilbmp2nes.py', '../tilesets/char_pinocchio.png', 'char_pinocchio.chr']) ## main(['pilbmp2nes.py', '--packbits', '../tilesets/char_pinocchio.png', 'char_pinocchio.pkb'])

36.164319

124

0.589251

from __future__ import with_statement, print_function, unicode_literals from PIL import Image from time import sleep import array try: xrange except NameError: xrange = range try: raw_input except NameError: raw_input = input try: next except NameError: next = lambda x: x.next() def blank_byte_array(): try: return array.array('B') except TypeError: return array.array(b'B') def formatTilePlanar(tile, planemap, hflip=False, little=False): hflip = 7 if hflip else 0 if (tile.size != (8, 8)): return None pixels = list(tile.getdata()) pixelrows = [pixels[i:i + 8] for i in xrange(0, 64, 8)] if hflip: for row in pixelrows: row.reverse() out = blank_byte_array() planemap = [[[int(c) for c in row] for row in plane.split(',')] for plane in planemap.split(';')] for plane in planemap: for pxrow in pixelrows: for rowplane in plane: rowbits = 1 thisrow = blank_byte_array() for px in pxrow: for bitnum in rowplane: rowbits = (rowbits << 1) | ((px >> bitnum) & 1) if rowbits >= 0x100: thisrow.append(rowbits & 0xFF) rowbits = 1 if little: thisrow.reverse() out.extend(thisrow) return out.tostring() def pilbmp2chr(im, tileWidth=8, tileHeight=8, formatTile=lambda im: formatTilePlanar(im, "0;1")): im.load() (w, h) = im.size outdata = [] for mt_y in range(0, h, tileHeight): for mt_x in range(0, w, tileWidth): metatile = im.crop((mt_x, mt_y, mt_x + tileWidth, mt_y + tileHeight)) for tile_y in range(0, tileHeight, 8): for tile_x in range(0, tileWidth, 8): tile = metatile.crop((tile_x, tile_y, tile_x + 8, tile_y + 8)) data = formatTile(tile) outdata.append(data) return outdata def parse_argv(argv): from optparse import OptionParser parser = OptionParser(usage="usage: %prog [options] [-i] INFILE [-o] OUTFILE") parser.add_option("-i", "--image", dest="infilename", help="read image from INFILE", metavar="INFILE") parser.add_option("-o", "--output", dest="outfilename", help="write CHR data to OUTFILE", metavar="OUTFILE") parser.add_option("-W", "--tile-width", dest="tileWidth", help="set width of metatiles", metavar="HEIGHT", type="int", default=8) parser.add_option("--packbits", dest="packbits", help="use PackBits RLE compression", action="store_true", default=False) parser.add_option("-H", "--tile-height", dest="tileHeight", help="set height of metatiles", metavar="HEIGHT", type="int", default=8) parser.add_option("-1", dest="planes", help="set 1bpp mode (default: 2bpp NES)", action="store_const", const="0", default="0;1") parser.add_option("--planes", dest="planes", help="set the plane map (1bpp: 0) (NES: 0;1) (GB: 0,1) (SMS:0,1,2,3) (TG16/SNES: 0,1;2,3) (MD: 3210)") parser.add_option("--hflip", dest="hflip", help="horizontally flip all tiles (most significant pixel on right)", action="store_true", default=False) parser.add_option("--little", dest="little", help="reverse the bytes within each row-plane (needed for GBA and a few others)", action="store_true", default=False) (options, args) = parser.parse_args(argv[1:]) tileWidth = int(options.tileWidth) if tileWidth <= 0: raise ValueError("tile width '%d' must be positive" % tileWidth) tileHeight = int(options.tileHeight) if tileHeight <= 0: raise ValueError("tile height '%d' must be positive" % tileHeight) argsreader = iter(args) try: infilename = options.infilename if infilename is None: infilename = next(argsreader) except StopIteration: raise ValueError("not enough filenames") outfilename = options.outfilename if outfilename is None: try: outfilename = next(argsreader) except StopIteration: outfilename = '-' if outfilename == '-': import sys if sys.stdout.isatty(): raise ValueError("cannot write CHR to terminal") return (infilename, outfilename, tileWidth, tileHeight, options.packbits, options.planes, options.hflip, options.little) argvTestingMode = True def make_stdout_binary(): if sys.platform == "win32": import os, msvcrt msvcrt.setmode(sys.stdout.fileno(), os.O_BINARY) def main(argv=None): import sys if argv is None: argv = sys.argv if (argvTestingMode and len(argv) < 2 and sys.stdin.isatty() and sys.stdout.isatty()): argv.extend(raw_input('args:').split()) try: (infilename, outfilename, tileWidth, tileHeight, usePackBits, planes, hflip, little) = parse_argv(argv) except Exception as e: sys.stderr.write("%s: %s\n" % (argv[0], str(e))) sys.exit(1) im = Image.open(infilename) outdata = pilbmp2chr(im, tileWidth, tileHeight, lambda im: formatTilePlanar(im, planes, hflip, little)) outdata = b''.join(outdata) if usePackBits: from packbits import PackBits sz = len(outdata) % 0x10000 outdata = PackBits(outdata).flush().tostring() outdata = b''.join([chr(sz >> 8), chr(sz & 0xFF), outdata]) outfp = None try: if outfilename != '-': outfp = open(outfilename, 'wb') else: outfp = sys.stdout make_stdout_binary() outfp.write(outdata) finally: if outfp and outfilename != '-': outfp.close() if __name__=='__main__': main()

true

f7365d1f3a6d608c0c9d0eddaeb0a2bb1cb6cb4f

34,363

py

Python

src/azure-cli/azure/cli/command_modules/appservice/tests/latest/test_staticapp_commands_thru_mock.py

YuanyuanNi/azure-cli

63844964374858bfacd209bfe1b69eb456bd64ca

[ "MIT" ]

1

2021-09-07T18:51:21.000Z

src/azure-cli/azure/cli/command_modules/appservice/tests/latest/test_staticapp_commands_thru_mock.py

YuanyuanNi/azure-cli

63844964374858bfacd209bfe1b69eb456bd64ca

[ "MIT" ]

1

2020-08-08T03:56:56.000Z

src/azure-cli/azure/cli/command_modules/appservice/tests/latest/test_staticapp_commands_thru_mock.py

YuanyuanNi/azure-cli

63844964374858bfacd209bfe1b69eb456bd64ca

[ "MIT" ]

1

2022-02-16T18:23:11.000Z

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- import unittest from unittest import mock from azure.cli.command_modules.appservice.static_sites import \ list_staticsites, show_staticsite, delete_staticsite, create_staticsites, CLIError, disconnect_staticsite, \ reconnect_staticsite, list_staticsite_environments, show_staticsite_environment, list_staticsite_domains, \ set_staticsite_domain, delete_staticsite_domain, list_staticsite_functions, list_staticsite_app_settings, \ set_staticsite_app_settings, delete_staticsite_app_settings, list_staticsite_users, \ invite_staticsite_users, update_staticsite_users, update_staticsite, list_staticsite_secrets, \ reset_staticsite_api_key, delete_staticsite_environment, link_user_function, unlink_user_function, get_user_function, \ assign_identity, remove_identity, show_identity from azure.core.exceptions import ResourceNotFoundError class TestStaticAppCommands(unittest.TestCase): def setUp(self): _set_up_client_mock(self) _set_up_fake_apps(self) def test_list_empty_staticapp(self): self.staticapp_client.list.return_value = [] response = list_staticsites(self.mock_cmd) self.assertEqual(len(response), 0) def test_list_staticapp_with_resourcegroup(self): self.staticapp_client.get_static_sites_by_resource_group.return_value = [self.app1] response = list_staticsites(self.mock_cmd, self.rg1) self.staticapp_client.get_static_sites_by_resource_group.assert_called_once_with(self.rg1) self.assertEqual(len(response), 1) self.assertIn(self.app1, response) def test_list_staticapp_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] response = list_staticsites(self.mock_cmd) self.assertEqual(len(response), 2) self.assertIn(self.app1, response) self.assertIn(self.app2, response) def test_show_staticapp_with_resourcegroup(self): self.staticapp_client.get_static_site.return_value = self.app1 response = show_staticsite(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.get_static_site.assert_called_once_with(self.rg1, self.name1) self.assertEqual(self.app1, response) def test_show_staticapp_without_resourcegroup(self): self.staticapp_client.get_static_site.return_value = self.app1 self.staticapp_client.list.return_value = [self.app1, self.app2] response = show_staticsite(self.mock_cmd, self.name1) self.staticapp_client.get_static_site.assert_called_once_with(self.rg1, self.name1) self.assertEqual(self.app1, response) def test_show_staticapp_not_exist(self): self.staticapp_client.get_static_site.return_value = self.app1 self.staticapp_client.list.return_value = [self.app1, self.app2] with self.assertRaises(CLIError): show_staticsite(self.mock_cmd, self.name1_not_exist) def test_delete_staticapp_with_resourcegroup(self): delete_staticsite(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.begin_delete_static_site.assert_called_once_with(resource_group_name=self.rg1, name=self.name1) def test_delete_staticapp_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] delete_staticsite(self.mock_cmd, self.name1) self.staticapp_client.begin_delete_static_site.assert_called_once_with(resource_group_name=self.rg1, name=self.name1) def test_delete_staticapp_not_exist(self): with self.assertRaises(CLIError): delete_staticsite(self.mock_cmd, self.name1_not_exist) def test_create_staticapp(self): from azure.mgmt.web.models import StaticSiteARMResource, StaticSiteBuildProperties, SkuDescription self.mock_cmd.get_models.return_value = StaticSiteARMResource, StaticSiteBuildProperties, SkuDescription app_location = './src' api_location = './api/' output_location = '/.git/' tags = {'key1': 'value1'} with mock.patch("azure.cli.command_modules.appservice.static_sites.show_staticsite", side_effect=ResourceNotFoundError("msg")): create_staticsites( self.mock_cmd, self.rg1, self.name1, self.location1, self.source1, self.branch1, self.token1, app_location=app_location, api_location=api_location, output_location=output_location, tags=tags) self.staticapp_client.begin_create_or_update_static_site.assert_called_once() arg_list = self.staticapp_client.begin_create_or_update_static_site.call_args[1] self.assertEqual(self.name1, arg_list["name"]) self.assertEqual(self.rg1, arg_list["resource_group_name"]) self.assertEqual(self.location1, arg_list["static_site_envelope"].location) self.assertEqual(self.source1, arg_list["static_site_envelope"].repository_url) self.assertEqual(self.branch1, arg_list["static_site_envelope"].branch) self.assertEqual(tags, arg_list["static_site_envelope"].tags) self.assertEqual('Free', arg_list["static_site_envelope"].sku.name) self.assertEqual(app_location, arg_list["static_site_envelope"].build_properties.app_location) self.assertEqual(api_location, arg_list["static_site_envelope"].build_properties.api_location) self.assertEqual(output_location, arg_list["static_site_envelope"].build_properties.app_artifact_location) # assert that a duplicate create call doesn't raise an error or call client create method again create_staticsites( self.mock_cmd, self.rg1, self.name1, self.location1, self.source1, self.branch1, self.token1, app_location=app_location, api_location=api_location, output_location=output_location, tags=tags) self.staticapp_client.begin_create_or_update_static_site.assert_called_once() def test_create_staticapp_with_standard_sku(self): from azure.mgmt.web.models import StaticSiteARMResource, StaticSiteBuildProperties, SkuDescription self.mock_cmd.get_models.return_value = StaticSiteARMResource, StaticSiteBuildProperties, SkuDescription with mock.patch("azure.cli.command_modules.appservice.static_sites.show_staticsite", side_effect=ResourceNotFoundError("msg")): create_staticsites( self.mock_cmd, self.rg1, self.name1, self.location1, self.source1, self.branch1, self.token1, sku='standard') self.staticapp_client.begin_create_or_update_static_site.assert_called_once() arg_list = self.staticapp_client.begin_create_or_update_static_site.call_args[1] self.assertEqual('Standard', arg_list["static_site_envelope"].sku.name) def test_create_staticapp_missing_token(self): app_location = './src' api_location = './api/' output_location = '/.git/' tags = {'key1': 'value1'} with self.assertRaises(CLIError): with mock.patch("azure.cli.command_modules.appservice.static_sites.show_staticsite", side_effect=ResourceNotFoundError("msg")): create_staticsites( self.mock_cmd, self.rg1, self.name1, self.location1, self.source1, self.branch1, app_location=app_location, api_location=api_location, output_location=output_location, tags=tags) def test_update_staticapp(self): from azure.mgmt.web.models import StaticSiteARMResource, SkuDescription self.mock_cmd.get_models.return_value = StaticSiteARMResource, SkuDescription self.staticapp_client.get_static_site.return_value = self.app1 self.staticapp_client.list.return_value = [self.app1, self.app2] tags = {'key1': 'value1'} sku = 'Standard' update_staticsite(self.mock_cmd, self.name1, self.source2, self.branch2, self.token2, tags=tags, sku=sku) self.staticapp_client.update_static_site.assert_called_once() arg_list = self.staticapp_client.update_static_site.call_args[1] self.assertEqual(self.name1, arg_list["name"]) self.assertEqual(self.source2, arg_list["static_site_envelope"].repository_url) self.assertEqual(self.branch2, arg_list["static_site_envelope"].branch) self.assertEqual(self.token2, arg_list["static_site_envelope"].repository_token) self.assertEqual(tags, arg_list["static_site_envelope"].tags) self.assertEqual(sku, arg_list["static_site_envelope"].sku.name) def test_update_staticapp_with_no_values_passed_in(self): from azure.mgmt.web.models import StaticSiteARMResource, SkuDescription self.mock_cmd.get_models.return_value = StaticSiteARMResource, SkuDescription self.staticapp_client.get_static_site.return_value = self.app1 self.staticapp_client.list.return_value = [self.app1, self.app2] update_staticsite(self.mock_cmd, self.name1) self.staticapp_client.update_static_site.assert_called_once() arg_list = self.staticapp_client.update_static_site.call_args[1] self.assertEqual(self.name1, arg_list["name"]) self.assertEqual(self.source1, arg_list["static_site_envelope"].repository_url) self.assertEqual(self.branch1, arg_list["static_site_envelope"].branch) self.assertEqual(self.token1, arg_list["static_site_envelope"].repository_token) self.assertEqual(self.app1.tags, arg_list["static_site_envelope"].tags) self.assertEqual('Free', arg_list["static_site_envelope"].sku.name) def test_update_staticapp_not_exist(self): from azure.mgmt.web.models import StaticSiteARMResource, SkuDescription self.mock_cmd.get_models.return_value = StaticSiteARMResource, SkuDescription self.staticapp_client.get_static_site.return_value = self.app1 self.staticapp_client.list.return_value = [self.app1, self.app2] with self.assertRaises(CLIError): update_staticsite(self.mock_cmd, self.name1_not_exist) def test_disconnect_staticapp_with_resourcegroup(self): disconnect_staticsite(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.begin_detach_static_site.assert_called_once_with(resource_group_name=self.rg1, name=self.name1) def test_disconnect_staticapp_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] disconnect_staticsite(self.mock_cmd, self.name1) self.staticapp_client.begin_detach_static_site.assert_called_once_with(resource_group_name=self.rg1, name=self.name1) @mock.patch('azure.cli.command_modules.appservice.static_sites.create_staticsites', autospec=True) def test_reconnect_staticapp_with_resourcegroup(self, create_staticsites_mock): self.staticapp_client.list.return_value = [self.app1, self.app2] reconnect_staticsite(self.mock_cmd, self.name1, self.source1, self.branch1, self.token1, resource_group_name=self.rg1) create_staticsites_mock.assert_called_once_with(self.mock_cmd, self.rg1, self.name1, self.location1, self.source1, self.branch1, self.token1, login_with_github=False, no_wait=False) @mock.patch('azure.cli.command_modules.appservice.static_sites.create_staticsites', autospec=True) def test_reconnect_staticapp_without_resourcegroup(self, create_staticsites_mock): self.staticapp_client.list.return_value = [self.app1, self.app2] reconnect_staticsite(self.mock_cmd, self.name1, self.source1, self.branch1, self.token1) create_staticsites_mock.assert_called_once_with(self.mock_cmd, self.rg1, self.name1, self.location1, self.source1, self.branch1, self.token1, login_with_github=False, no_wait=False) def test_list_staticsite_environments_with_resourcegroup(self): list_staticsite_environments(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.get_static_site_builds.assert_called_once_with(self.rg1, self.name1) def test_list_staticsite_environments_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] list_staticsite_environments(self.mock_cmd, self.name1) self.staticapp_client.get_static_site_builds.assert_called_once_with(self.rg1, self.name1) def test_show_staticsite_environment_with_resourcegroup(self): show_staticsite_environment(self.mock_cmd, self.name1, self.environment1, self.rg1) self.staticapp_client.get_static_site_build.assert_called_once_with(self.rg1, self.name1, self.environment1) def test_show_staticsite_environment_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] show_staticsite_environment(self.mock_cmd, self.name1, self.environment1) self.staticapp_client.get_static_site_build.assert_called_once_with(self.rg1, self.name1, self.environment1) def test_set_staticsite_domain_with_resourcegroup(self): set_staticsite_domain(self.mock_cmd, self.name1, self.hostname1, self.rg1) self.staticapp_client.begin_validate_custom_domain_can_be_added_to_static_site.assert_called_once_with( self.rg1, self.name1, self.hostname1, self.hostname1_validation) self.staticapp_client.begin_create_or_update_static_site_custom_domain.assert_called_once_with( resource_group_name=self.rg1, name=self.name1, domain_name=self.hostname1, static_site_custom_domain_request_properties_envelope=self.hostname1_validation) def test_set_staticsite_domain_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] set_staticsite_domain(self.mock_cmd, self.name1, self.hostname1) self.staticapp_client.begin_validate_custom_domain_can_be_added_to_static_site.assert_called_once_with( self.rg1, self.name1, self.hostname1, self.hostname1_validation) self.staticapp_client.begin_create_or_update_static_site_custom_domain.assert_called_once_with( resource_group_name=self.rg1, name=self.name1, domain_name=self.hostname1, static_site_custom_domain_request_properties_envelope=self.hostname1_validation) def test_delete_staticsite_domain_with_resourcegroup(self): delete_staticsite_domain(self.mock_cmd, self.name1, self.hostname1, self.rg1) self.staticapp_client.begin_delete_static_site_custom_domain.assert_called_once_with( resource_group_name=self.rg1, name=self.name1, domain_name=self.hostname1) def test_delete_staticsite_domain_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] delete_staticsite_domain(self.mock_cmd, self.name1, self.hostname1) self.staticapp_client.begin_delete_static_site_custom_domain.assert_called_once_with( resource_group_name=self.rg1, name=self.name1, domain_name=self.hostname1) def test_delete_staticsite_environment_with_resourcegroup(self): delete_staticsite_environment(self.mock_cmd, self.name1, self.environment1, self.rg1) self.staticapp_client.begin_delete_static_site_build.assert_called_once_with(self.rg1, self.name1, self.environment1) def test_delete_staticsite_environment_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] delete_staticsite_environment(self.mock_cmd, self.name1, self.environment1) self.staticapp_client.begin_delete_static_site_build.assert_called_once_with(self.rg1, self.name1, self.environment1) def test_list_staticsite_functions_with_resourcegroup(self): list_staticsite_functions(self.mock_cmd, self.name1, self.rg1, self.environment1) self.staticapp_client.list_static_site_build_functions.assert_called_once_with( self.rg1, self.name1, self.environment1) def test_list_staticsite_functions_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] list_staticsite_functions(self.mock_cmd, self.name1, environment_name=self.environment1) self.staticapp_client.list_static_site_build_functions.assert_called_once_with( self.rg1, self.name1, self.environment1) def test_list_staticsite_app_settings_with_resourcegroup(self): list_staticsite_app_settings(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.list_static_site_app_settings.assert_called_once_with( self.rg1, self.name1) def test_list_staticsite_app_settings_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] list_staticsite_app_settings(self.mock_cmd, self.name1) self.staticapp_client.list_static_site_app_settings.assert_called_once_with( self.rg1, self.name1) def test_set_staticsite_app_settings_with_resourcegroup(self): from azure.mgmt.web.models import StringDictionary app_settings1_input = ['key1=val1', 'key2=val2==', 'key3=val3='] self.staticapp_client.list_static_site_app_settings.return_value = StringDictionary(properties={}) set_staticsite_app_settings(self.mock_cmd, self.name1, app_settings1_input, self.rg1) self.staticapp_client.create_or_update_static_site_app_settings.assert_called_once() def test_set_staticsite_app_settings_without_resourcegroup(self): from azure.mgmt.web.models import StringDictionary app_settings1_input = ['key1=val1', 'key2=val2==', 'key3=val3='] self.staticapp_client.list.return_value = [self.app1, self.app2] self.staticapp_client.list_static_site_app_settings.return_value = StringDictionary(properties={}) set_staticsite_app_settings(self.mock_cmd, self.name1, app_settings1_input) self.staticapp_client.create_or_update_static_site_app_settings.assert_called_once() def test_delete_staticsite_app_settings_with_resourcegroup(self): # setup current_app_settings = {'key1': 'val1', 'key2': 'val2'} app_settings_keys_to_delete = ['key1'] class AppSettings: properties = current_app_settings self.staticapp_client.list_static_site_app_settings.return_value = AppSettings # action delete_staticsite_app_settings(self.mock_cmd, self.name1, app_settings_keys_to_delete, self.rg1) # validate self.staticapp_client.create_or_update_static_site_app_settings.assert_called_once() def test_delete_staticsite_app_settings_without_resourcegroup(self): # setup current_app_settings = {'key1': 'val1', 'key2': 'val2'} app_settings_keys_to_delete = ['key1'] class AppSettings: properties = current_app_settings self.staticapp_client.list_static_site_app_settings.return_value = AppSettings self.staticapp_client.list.return_value = [self.app1, self.app2] # action delete_staticsite_app_settings(self.mock_cmd, self.name1, app_settings_keys_to_delete) # validate self.staticapp_client.create_or_update_static_site_app_settings.assert_called_once() def test_list_staticsite_users_with_resourcegroup(self): authentication_provider = 'GitHub' list_staticsite_users(self.mock_cmd, self.name1, self.rg1, authentication_provider=authentication_provider) self.staticapp_client.list_static_site_users.assert_called_once_with( self.rg1, self.name1, authentication_provider) def test_list_staticsite_users_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] authentication_provider = 'GitHub' list_staticsite_users(self.mock_cmd, self.name1, authentication_provider=authentication_provider) self.staticapp_client.list_static_site_users.assert_called_once_with( self.rg1, self.name1, authentication_provider) def test_invite_staticsite_users_with_resourcegroup(self): authentication_provider = 'GitHub' user_details = 'JohnDoe' roles = 'Contributor,Reviewer' invitation_expiration_in_hours = 2 from azure.mgmt.web.models import StaticSiteUserInvitationRequestResource self.mock_cmd.get_models.return_value = StaticSiteUserInvitationRequestResource invite_staticsite_users(self.mock_cmd, self.name1, authentication_provider, user_details, self.hostname1, roles, invitation_expiration_in_hours, self.rg1) arg_list = self.staticapp_client.create_user_roles_invitation_link.call_args[0] self.assertEqual(self.rg1, arg_list[0]) self.assertEqual(self.name1, arg_list[1]) self.assertEqual(self.hostname1, arg_list[2].domain) self.assertEqual(authentication_provider, arg_list[2].provider) self.assertEqual(user_details, arg_list[2].user_details) self.assertEqual(invitation_expiration_in_hours, arg_list[2].num_hours_to_expiration) def test_invite_staticsite_users_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] authentication_provider = 'GitHub' user_details = 'JohnDoe' roles = 'Contributor,Reviewer' invitation_expiration_in_hours = 2 from azure.mgmt.web.models import StaticSiteUserInvitationRequestResource self.mock_cmd.get_models.return_value = StaticSiteUserInvitationRequestResource invite_staticsite_users(self.mock_cmd, self.name1, authentication_provider, user_details, self.hostname1, roles, invitation_expiration_in_hours) arg_list = self.staticapp_client.create_user_roles_invitation_link.call_args[0] self.assertEqual(self.rg1, arg_list[0]) self.assertEqual(self.name1, arg_list[1]) self.assertEqual(self.hostname1, arg_list[2].domain) self.assertEqual(authentication_provider, arg_list[2].provider) self.assertEqual(user_details, arg_list[2].user_details) self.assertEqual(invitation_expiration_in_hours, arg_list[2].num_hours_to_expiration) def test_update_staticsite_users_with_resourcegroup_with_all_args(self): roles = 'Contributor,Reviewer' authentication_provider = 'GitHub' user_details = 'JohnDoe' user_id = 100 update_staticsite_users(self.mock_cmd, self.name1, roles, authentication_provider=authentication_provider, user_details=user_details, user_id=user_id, resource_group_name=self.rg1) self.staticapp_client.update_static_site_user.assert_called_once_with( self.rg1, self.name1, authentication_provider, user_id, roles=roles) def test_update_staticsite_users_with_resourcegroup_without_auth_provider(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' authentication_provider = 'GitHub' user_id = '100' _mock_list_users_for_without_auth_provider(self, user_id, authentication_provider, user_details) update_staticsite_users(self.mock_cmd, self.name1, roles, user_details=user_details, user_id=user_id, resource_group_name=self.rg1) self.staticapp_client.update_static_site_user.assert_called_once_with( self.rg1, self.name1, authentication_provider, user_id, roles=roles) def test_update_staticsite_users_with_resourcegroup_without_auth_provider_user_not_found(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' user_id = '100' _mock_list_users_for_without_auth_provider(self, 'other_user_id', 'dummy_authentication_provider', 'dummy_user_details') with self.assertRaises(CLIError): update_staticsite_users(self.mock_cmd, self.name1, roles, user_details=user_details, user_id=user_id, resource_group_name=self.rg1) def test_update_staticsite_users_with_resourcegroup_without_user_id_without_auth_provider(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' authentication_provider = 'GitHub' user_id = '100' _mock_list_users_for_without_auth_provider(self, user_id, authentication_provider, user_details) update_staticsite_users(self.mock_cmd, self.name1, roles, user_details=user_details, resource_group_name=self.rg1) self.staticapp_client.update_static_site_user.assert_called_once_with( self.rg1, self.name1, authentication_provider, user_id, roles=roles) def test_update_staticsite_users_with_resourcegroup_without_user_id_without_auth_provider_user_not_found(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' _mock_list_users_for_without_auth_provider(self, 'dummy_user_id', 'dummy_authentication_provider', 'other_user_details') with self.assertRaises(CLIError): update_staticsite_users(self.mock_cmd, self.name1, roles, user_details=user_details, resource_group_name=self.rg1) def test_update_staticsite_users_with_resourcegroup_without_user_id(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' authentication_provider = 'GitHub' user_id = '100' _mock_list_users_for_without_auth_provider(self, user_id, authentication_provider, user_details) update_staticsite_users(self.mock_cmd, self.name1, roles, authentication_provider=authentication_provider, user_details=user_details, resource_group_name=self.rg1) self.staticapp_client.update_static_site_user.assert_called_once_with( self.rg1, self.name1, authentication_provider, user_id, roles=roles) def test_update_staticsite_users_with_resourcegroup_without_user_id_user_not_found(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' authentication_provider = 'GitHub' _mock_list_users_for_without_auth_provider(self, 'dummy_user_id', 'dummy_authentication_provider', 'other_user_details') with self.assertRaises(CLIError): update_staticsite_users(self.mock_cmd, self.name1, roles, authentication_provider=authentication_provider, user_details=user_details, resource_group_name=self.rg1) def test_update_staticsite_users_with_resourcegroup_without_user_id_without_user_details(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' authentication_provider = 'GitHub' user_id = '100' _mock_list_users_for_without_auth_provider(self, user_id, authentication_provider, user_details) with self.assertRaises(CLIError): update_staticsite_users(self.mock_cmd, self.name1, roles, authentication_provider=authentication_provider, resource_group_name=self.rg1) def test_list_staticsite_secrets(self): from azure.mgmt.web.models import StringDictionary self.staticapp_client.list_static_site_secrets.return_value = StringDictionary(properties={"apiKey": "key"}) secret = list_staticsite_secrets(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.list_static_site_secrets.assert_called_once_with(resource_group_name=self.rg1, name=self.name1) from ast import literal_eval self.assertEqual(literal_eval(secret.__str__())["properties"]["apiKey"], "key") def test_staticsite_identity_assign(self): from azure.mgmt.web.models import ManagedServiceIdentity, ManagedServiceIdentityType self.mock_cmd.get_models.return_value = ManagedServiceIdentity, ManagedServiceIdentityType assign_identity(self.mock_cmd, self.rg1, self.name1) self.staticapp_client.begin_create_or_update_static_site.assert_called_once() def test_staticsite_identity_remove(self): from azure.mgmt.web.models import ManagedServiceIdentityType, Components1Jq1T4ISchemasManagedserviceidentityPropertiesUserassignedidentitiesAdditionalproperties get_models = lambda s: ManagedServiceIdentityType if s == "ManagedServiceIdentityType" else Components1Jq1T4ISchemasManagedserviceidentityPropertiesUserassignedidentitiesAdditionalproperties self.mock_cmd.get_models.side_effect = get_models remove_identity(self.mock_cmd, self.rg1, self.name1) self.staticapp_client.begin_create_or_update_static_site.assert_called_once() def test_staticsite_identity_show(self): mock_site = mock.MagicMock() mock_site.identity = "identity" self.staticapp_client.get_static_site.return_value = mock_site self.assertEqual(show_identity(self.mock_cmd, self.rg1, self.name1), "identity") def test_reset_staticsite_api_key(self): from azure.mgmt.web.models import StringDictionary, StaticSiteResetPropertiesARMResource self.staticapp_client.get_static_site.return_value = self.app1 self.staticapp_client.reset_static_site_api_key.return_value = StringDictionary(properties={"apiKey": "new_key"}) self.mock_cmd.get_models.return_value = StaticSiteResetPropertiesARMResource secret = reset_staticsite_api_key(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.get_static_site.assert_called_once_with(self.rg1, self.name1) self.mock_cmd.get_models.assert_called_once_with('StaticSiteResetPropertiesARMResource') self.staticapp_client.reset_static_site_api_key.assert_called_once() from ast import literal_eval reset_envelope = literal_eval(self.staticapp_client.reset_static_site_api_key.call_args[1]["reset_properties_envelope"].__str__()) self.assertEqual(reset_envelope["repository_token"], self.token1) @mock.patch("azure.cli.command_modules.appservice.static_sites.show_functionapp") def test_functions_link(self, *args, **kwargs): functionapp_name = "functionapp" functionapp_resource_id = "/subscriptions/sub/resourceGroups/{}/providers/Microsoft.Web/sites/{}".format( self.rg1, functionapp_name ) link_user_function(self.mock_cmd, self.name1, self.rg1, functionapp_resource_id) self.staticapp_client.begin_register_user_provided_function_app_with_static_site.assert_called_once() @mock.patch("azure.cli.command_modules.appservice.static_sites.get_user_function", return_value=[mock.MagicMock()]) def test_functions_unlink(self, *args, **kwargs): unlink_user_function(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.detach_user_provided_function_app_from_static_site.assert_called_once() def test_functions_show(self, *args, **kwargs): get_user_function(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.get_user_provided_function_apps_for_static_site.assert_called_once() def _set_up_client_mock(self): self.mock_cmd = mock.MagicMock() self.mock_cmd.cli_ctx = mock.MagicMock() self.staticapp_client = mock.MagicMock() client_factory_patcher = mock.patch( 'azure.cli.command_modules.appservice.static_sites._get_staticsites_client_factory', autospec=True) self.addCleanup(client_factory_patcher.stop) self.mock_static_site_client_factory = client_factory_patcher.start() self.mock_static_site_client_factory.return_value = self.staticapp_client def _set_up_fake_apps(self): from azure.mgmt.web.models import StaticSiteCustomDomainRequestPropertiesARMResource self.rg1 = 'rg1' self.name1 = 'name1' self.name1_not_exist = 'name1_not_exist' self.location1 = 'location1' self.source1 = 'https://github.com/Contoso/My-First-Static-App' self.branch1 = 'dev' self.token1 = 'TOKEN_1' self.environment1 = 'default' self.hostname1 = 'www.app1.com' self.hostname1_validation = StaticSiteCustomDomainRequestPropertiesARMResource(validation_method="cname-delegation") self.app1 = _contruct_static_site_object( self.rg1, self.name1, self.location1, self.source1, self.branch1, self.token1) self.rg2 = 'rg2' self.name2 = 'name2' self.location2 = 'location2' self.source2 = 'https://github.com/Contoso/My-Second-Static-App' self.branch2 = 'master' self.token2 = 'TOKEN_2' self.environment1 = 'prod' self.hostname1 = 'www.app2.com' self.app2 = _contruct_static_site_object( self.rg2, self.name2, self.location2, self.source2, self.branch2, self.token2) def _contruct_static_site_object(rg, app_name, location, source, branch, token): from azure.mgmt.web.models import StaticSiteARMResource, SkuDescription app = StaticSiteARMResource( location=location, repository_url=source, branch=branch, repository_token=token, sku=SkuDescription(name='Free', tier='Free')) app.name = app_name app.id = \ "/subscriptions/sub/resourceGroups/{}/providers/Microsoft.Web/staticSites/{}".format(rg, app_name) return app def _mock_list_users_for_without_auth_provider(self, user_id, authentication_provider, user_details): class User: def __init__(self, name, provider, display_name): self.name = name self.provider = provider self.display_name = display_name user1 = User(user_id, authentication_provider, user_details) user2 = User(user_id + '2', authentication_provider + '2', user_details + '2') self.staticapp_client.list_static_site_users.return_value = [user1, user2]

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import unittest from unittest import mock from azure.cli.command_modules.appservice.static_sites import \ list_staticsites, show_staticsite, delete_staticsite, create_staticsites, CLIError, disconnect_staticsite, \ reconnect_staticsite, list_staticsite_environments, show_staticsite_environment, list_staticsite_domains, \ set_staticsite_domain, delete_staticsite_domain, list_staticsite_functions, list_staticsite_app_settings, \ set_staticsite_app_settings, delete_staticsite_app_settings, list_staticsite_users, \ invite_staticsite_users, update_staticsite_users, update_staticsite, list_staticsite_secrets, \ reset_staticsite_api_key, delete_staticsite_environment, link_user_function, unlink_user_function, get_user_function, \ assign_identity, remove_identity, show_identity from azure.core.exceptions import ResourceNotFoundError class TestStaticAppCommands(unittest.TestCase): def setUp(self): _set_up_client_mock(self) _set_up_fake_apps(self) def test_list_empty_staticapp(self): self.staticapp_client.list.return_value = [] response = list_staticsites(self.mock_cmd) self.assertEqual(len(response), 0) def test_list_staticapp_with_resourcegroup(self): self.staticapp_client.get_static_sites_by_resource_group.return_value = [self.app1] response = list_staticsites(self.mock_cmd, self.rg1) self.staticapp_client.get_static_sites_by_resource_group.assert_called_once_with(self.rg1) self.assertEqual(len(response), 1) self.assertIn(self.app1, response) def test_list_staticapp_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] response = list_staticsites(self.mock_cmd) self.assertEqual(len(response), 2) self.assertIn(self.app1, response) self.assertIn(self.app2, response) def test_show_staticapp_with_resourcegroup(self): self.staticapp_client.get_static_site.return_value = self.app1 response = show_staticsite(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.get_static_site.assert_called_once_with(self.rg1, self.name1) self.assertEqual(self.app1, response) def test_show_staticapp_without_resourcegroup(self): self.staticapp_client.get_static_site.return_value = self.app1 self.staticapp_client.list.return_value = [self.app1, self.app2] response = show_staticsite(self.mock_cmd, self.name1) self.staticapp_client.get_static_site.assert_called_once_with(self.rg1, self.name1) self.assertEqual(self.app1, response) def test_show_staticapp_not_exist(self): self.staticapp_client.get_static_site.return_value = self.app1 self.staticapp_client.list.return_value = [self.app1, self.app2] with self.assertRaises(CLIError): show_staticsite(self.mock_cmd, self.name1_not_exist) def test_delete_staticapp_with_resourcegroup(self): delete_staticsite(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.begin_delete_static_site.assert_called_once_with(resource_group_name=self.rg1, name=self.name1) def test_delete_staticapp_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] delete_staticsite(self.mock_cmd, self.name1) self.staticapp_client.begin_delete_static_site.assert_called_once_with(resource_group_name=self.rg1, name=self.name1) def test_delete_staticapp_not_exist(self): with self.assertRaises(CLIError): delete_staticsite(self.mock_cmd, self.name1_not_exist) def test_create_staticapp(self): from azure.mgmt.web.models import StaticSiteARMResource, StaticSiteBuildProperties, SkuDescription self.mock_cmd.get_models.return_value = StaticSiteARMResource, StaticSiteBuildProperties, SkuDescription app_location = './src' api_location = './api/' output_location = '/.git/' tags = {'key1': 'value1'} with mock.patch("azure.cli.command_modules.appservice.static_sites.show_staticsite", side_effect=ResourceNotFoundError("msg")): create_staticsites( self.mock_cmd, self.rg1, self.name1, self.location1, self.source1, self.branch1, self.token1, app_location=app_location, api_location=api_location, output_location=output_location, tags=tags) self.staticapp_client.begin_create_or_update_static_site.assert_called_once() arg_list = self.staticapp_client.begin_create_or_update_static_site.call_args[1] self.assertEqual(self.name1, arg_list["name"]) self.assertEqual(self.rg1, arg_list["resource_group_name"]) self.assertEqual(self.location1, arg_list["static_site_envelope"].location) self.assertEqual(self.source1, arg_list["static_site_envelope"].repository_url) self.assertEqual(self.branch1, arg_list["static_site_envelope"].branch) self.assertEqual(tags, arg_list["static_site_envelope"].tags) self.assertEqual('Free', arg_list["static_site_envelope"].sku.name) self.assertEqual(app_location, arg_list["static_site_envelope"].build_properties.app_location) self.assertEqual(api_location, arg_list["static_site_envelope"].build_properties.api_location) self.assertEqual(output_location, arg_list["static_site_envelope"].build_properties.app_artifact_location) create_staticsites( self.mock_cmd, self.rg1, self.name1, self.location1, self.source1, self.branch1, self.token1, app_location=app_location, api_location=api_location, output_location=output_location, tags=tags) self.staticapp_client.begin_create_or_update_static_site.assert_called_once() def test_create_staticapp_with_standard_sku(self): from azure.mgmt.web.models import StaticSiteARMResource, StaticSiteBuildProperties, SkuDescription self.mock_cmd.get_models.return_value = StaticSiteARMResource, StaticSiteBuildProperties, SkuDescription with mock.patch("azure.cli.command_modules.appservice.static_sites.show_staticsite", side_effect=ResourceNotFoundError("msg")): create_staticsites( self.mock_cmd, self.rg1, self.name1, self.location1, self.source1, self.branch1, self.token1, sku='standard') self.staticapp_client.begin_create_or_update_static_site.assert_called_once() arg_list = self.staticapp_client.begin_create_or_update_static_site.call_args[1] self.assertEqual('Standard', arg_list["static_site_envelope"].sku.name) def test_create_staticapp_missing_token(self): app_location = './src' api_location = './api/' output_location = '/.git/' tags = {'key1': 'value1'} with self.assertRaises(CLIError): with mock.patch("azure.cli.command_modules.appservice.static_sites.show_staticsite", side_effect=ResourceNotFoundError("msg")): create_staticsites( self.mock_cmd, self.rg1, self.name1, self.location1, self.source1, self.branch1, app_location=app_location, api_location=api_location, output_location=output_location, tags=tags) def test_update_staticapp(self): from azure.mgmt.web.models import StaticSiteARMResource, SkuDescription self.mock_cmd.get_models.return_value = StaticSiteARMResource, SkuDescription self.staticapp_client.get_static_site.return_value = self.app1 self.staticapp_client.list.return_value = [self.app1, self.app2] tags = {'key1': 'value1'} sku = 'Standard' update_staticsite(self.mock_cmd, self.name1, self.source2, self.branch2, self.token2, tags=tags, sku=sku) self.staticapp_client.update_static_site.assert_called_once() arg_list = self.staticapp_client.update_static_site.call_args[1] self.assertEqual(self.name1, arg_list["name"]) self.assertEqual(self.source2, arg_list["static_site_envelope"].repository_url) self.assertEqual(self.branch2, arg_list["static_site_envelope"].branch) self.assertEqual(self.token2, arg_list["static_site_envelope"].repository_token) self.assertEqual(tags, arg_list["static_site_envelope"].tags) self.assertEqual(sku, arg_list["static_site_envelope"].sku.name) def test_update_staticapp_with_no_values_passed_in(self): from azure.mgmt.web.models import StaticSiteARMResource, SkuDescription self.mock_cmd.get_models.return_value = StaticSiteARMResource, SkuDescription self.staticapp_client.get_static_site.return_value = self.app1 self.staticapp_client.list.return_value = [self.app1, self.app2] update_staticsite(self.mock_cmd, self.name1) self.staticapp_client.update_static_site.assert_called_once() arg_list = self.staticapp_client.update_static_site.call_args[1] self.assertEqual(self.name1, arg_list["name"]) self.assertEqual(self.source1, arg_list["static_site_envelope"].repository_url) self.assertEqual(self.branch1, arg_list["static_site_envelope"].branch) self.assertEqual(self.token1, arg_list["static_site_envelope"].repository_token) self.assertEqual(self.app1.tags, arg_list["static_site_envelope"].tags) self.assertEqual('Free', arg_list["static_site_envelope"].sku.name) def test_update_staticapp_not_exist(self): from azure.mgmt.web.models import StaticSiteARMResource, SkuDescription self.mock_cmd.get_models.return_value = StaticSiteARMResource, SkuDescription self.staticapp_client.get_static_site.return_value = self.app1 self.staticapp_client.list.return_value = [self.app1, self.app2] with self.assertRaises(CLIError): update_staticsite(self.mock_cmd, self.name1_not_exist) def test_disconnect_staticapp_with_resourcegroup(self): disconnect_staticsite(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.begin_detach_static_site.assert_called_once_with(resource_group_name=self.rg1, name=self.name1) def test_disconnect_staticapp_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] disconnect_staticsite(self.mock_cmd, self.name1) self.staticapp_client.begin_detach_static_site.assert_called_once_with(resource_group_name=self.rg1, name=self.name1) @mock.patch('azure.cli.command_modules.appservice.static_sites.create_staticsites', autospec=True) def test_reconnect_staticapp_with_resourcegroup(self, create_staticsites_mock): self.staticapp_client.list.return_value = [self.app1, self.app2] reconnect_staticsite(self.mock_cmd, self.name1, self.source1, self.branch1, self.token1, resource_group_name=self.rg1) create_staticsites_mock.assert_called_once_with(self.mock_cmd, self.rg1, self.name1, self.location1, self.source1, self.branch1, self.token1, login_with_github=False, no_wait=False) @mock.patch('azure.cli.command_modules.appservice.static_sites.create_staticsites', autospec=True) def test_reconnect_staticapp_without_resourcegroup(self, create_staticsites_mock): self.staticapp_client.list.return_value = [self.app1, self.app2] reconnect_staticsite(self.mock_cmd, self.name1, self.source1, self.branch1, self.token1) create_staticsites_mock.assert_called_once_with(self.mock_cmd, self.rg1, self.name1, self.location1, self.source1, self.branch1, self.token1, login_with_github=False, no_wait=False) def test_list_staticsite_environments_with_resourcegroup(self): list_staticsite_environments(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.get_static_site_builds.assert_called_once_with(self.rg1, self.name1) def test_list_staticsite_environments_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] list_staticsite_environments(self.mock_cmd, self.name1) self.staticapp_client.get_static_site_builds.assert_called_once_with(self.rg1, self.name1) def test_show_staticsite_environment_with_resourcegroup(self): show_staticsite_environment(self.mock_cmd, self.name1, self.environment1, self.rg1) self.staticapp_client.get_static_site_build.assert_called_once_with(self.rg1, self.name1, self.environment1) def test_show_staticsite_environment_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] show_staticsite_environment(self.mock_cmd, self.name1, self.environment1) self.staticapp_client.get_static_site_build.assert_called_once_with(self.rg1, self.name1, self.environment1) def test_set_staticsite_domain_with_resourcegroup(self): set_staticsite_domain(self.mock_cmd, self.name1, self.hostname1, self.rg1) self.staticapp_client.begin_validate_custom_domain_can_be_added_to_static_site.assert_called_once_with( self.rg1, self.name1, self.hostname1, self.hostname1_validation) self.staticapp_client.begin_create_or_update_static_site_custom_domain.assert_called_once_with( resource_group_name=self.rg1, name=self.name1, domain_name=self.hostname1, static_site_custom_domain_request_properties_envelope=self.hostname1_validation) def test_set_staticsite_domain_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] set_staticsite_domain(self.mock_cmd, self.name1, self.hostname1) self.staticapp_client.begin_validate_custom_domain_can_be_added_to_static_site.assert_called_once_with( self.rg1, self.name1, self.hostname1, self.hostname1_validation) self.staticapp_client.begin_create_or_update_static_site_custom_domain.assert_called_once_with( resource_group_name=self.rg1, name=self.name1, domain_name=self.hostname1, static_site_custom_domain_request_properties_envelope=self.hostname1_validation) def test_delete_staticsite_domain_with_resourcegroup(self): delete_staticsite_domain(self.mock_cmd, self.name1, self.hostname1, self.rg1) self.staticapp_client.begin_delete_static_site_custom_domain.assert_called_once_with( resource_group_name=self.rg1, name=self.name1, domain_name=self.hostname1) def test_delete_staticsite_domain_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] delete_staticsite_domain(self.mock_cmd, self.name1, self.hostname1) self.staticapp_client.begin_delete_static_site_custom_domain.assert_called_once_with( resource_group_name=self.rg1, name=self.name1, domain_name=self.hostname1) def test_delete_staticsite_environment_with_resourcegroup(self): delete_staticsite_environment(self.mock_cmd, self.name1, self.environment1, self.rg1) self.staticapp_client.begin_delete_static_site_build.assert_called_once_with(self.rg1, self.name1, self.environment1) def test_delete_staticsite_environment_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] delete_staticsite_environment(self.mock_cmd, self.name1, self.environment1) self.staticapp_client.begin_delete_static_site_build.assert_called_once_with(self.rg1, self.name1, self.environment1) def test_list_staticsite_functions_with_resourcegroup(self): list_staticsite_functions(self.mock_cmd, self.name1, self.rg1, self.environment1) self.staticapp_client.list_static_site_build_functions.assert_called_once_with( self.rg1, self.name1, self.environment1) def test_list_staticsite_functions_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] list_staticsite_functions(self.mock_cmd, self.name1, environment_name=self.environment1) self.staticapp_client.list_static_site_build_functions.assert_called_once_with( self.rg1, self.name1, self.environment1) def test_list_staticsite_app_settings_with_resourcegroup(self): list_staticsite_app_settings(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.list_static_site_app_settings.assert_called_once_with( self.rg1, self.name1) def test_list_staticsite_app_settings_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] list_staticsite_app_settings(self.mock_cmd, self.name1) self.staticapp_client.list_static_site_app_settings.assert_called_once_with( self.rg1, self.name1) def test_set_staticsite_app_settings_with_resourcegroup(self): from azure.mgmt.web.models import StringDictionary app_settings1_input = ['key1=val1', 'key2=val2==', 'key3=val3='] self.staticapp_client.list_static_site_app_settings.return_value = StringDictionary(properties={}) set_staticsite_app_settings(self.mock_cmd, self.name1, app_settings1_input, self.rg1) self.staticapp_client.create_or_update_static_site_app_settings.assert_called_once() def test_set_staticsite_app_settings_without_resourcegroup(self): from azure.mgmt.web.models import StringDictionary app_settings1_input = ['key1=val1', 'key2=val2==', 'key3=val3='] self.staticapp_client.list.return_value = [self.app1, self.app2] self.staticapp_client.list_static_site_app_settings.return_value = StringDictionary(properties={}) set_staticsite_app_settings(self.mock_cmd, self.name1, app_settings1_input) self.staticapp_client.create_or_update_static_site_app_settings.assert_called_once() def test_delete_staticsite_app_settings_with_resourcegroup(self): # setup current_app_settings = {'key1': 'val1', 'key2': 'val2'} app_settings_keys_to_delete = ['key1'] class AppSettings: properties = current_app_settings self.staticapp_client.list_static_site_app_settings.return_value = AppSettings # action delete_staticsite_app_settings(self.mock_cmd, self.name1, app_settings_keys_to_delete, self.rg1) # validate self.staticapp_client.create_or_update_static_site_app_settings.assert_called_once() def test_delete_staticsite_app_settings_without_resourcegroup(self): # setup current_app_settings = {'key1': 'val1', 'key2': 'val2'} app_settings_keys_to_delete = ['key1'] class AppSettings: properties = current_app_settings self.staticapp_client.list_static_site_app_settings.return_value = AppSettings self.staticapp_client.list.return_value = [self.app1, self.app2] # action delete_staticsite_app_settings(self.mock_cmd, self.name1, app_settings_keys_to_delete) # validate self.staticapp_client.create_or_update_static_site_app_settings.assert_called_once() def test_list_staticsite_users_with_resourcegroup(self): authentication_provider = 'GitHub' list_staticsite_users(self.mock_cmd, self.name1, self.rg1, authentication_provider=authentication_provider) self.staticapp_client.list_static_site_users.assert_called_once_with( self.rg1, self.name1, authentication_provider) def test_list_staticsite_users_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] authentication_provider = 'GitHub' list_staticsite_users(self.mock_cmd, self.name1, authentication_provider=authentication_provider) self.staticapp_client.list_static_site_users.assert_called_once_with( self.rg1, self.name1, authentication_provider) def test_invite_staticsite_users_with_resourcegroup(self): authentication_provider = 'GitHub' user_details = 'JohnDoe' roles = 'Contributor,Reviewer' invitation_expiration_in_hours = 2 from azure.mgmt.web.models import StaticSiteUserInvitationRequestResource self.mock_cmd.get_models.return_value = StaticSiteUserInvitationRequestResource invite_staticsite_users(self.mock_cmd, self.name1, authentication_provider, user_details, self.hostname1, roles, invitation_expiration_in_hours, self.rg1) arg_list = self.staticapp_client.create_user_roles_invitation_link.call_args[0] self.assertEqual(self.rg1, arg_list[0]) self.assertEqual(self.name1, arg_list[1]) self.assertEqual(self.hostname1, arg_list[2].domain) self.assertEqual(authentication_provider, arg_list[2].provider) self.assertEqual(user_details, arg_list[2].user_details) self.assertEqual(invitation_expiration_in_hours, arg_list[2].num_hours_to_expiration) def test_invite_staticsite_users_without_resourcegroup(self): self.staticapp_client.list.return_value = [self.app1, self.app2] authentication_provider = 'GitHub' user_details = 'JohnDoe' roles = 'Contributor,Reviewer' invitation_expiration_in_hours = 2 from azure.mgmt.web.models import StaticSiteUserInvitationRequestResource self.mock_cmd.get_models.return_value = StaticSiteUserInvitationRequestResource invite_staticsite_users(self.mock_cmd, self.name1, authentication_provider, user_details, self.hostname1, roles, invitation_expiration_in_hours) arg_list = self.staticapp_client.create_user_roles_invitation_link.call_args[0] self.assertEqual(self.rg1, arg_list[0]) self.assertEqual(self.name1, arg_list[1]) self.assertEqual(self.hostname1, arg_list[2].domain) self.assertEqual(authentication_provider, arg_list[2].provider) self.assertEqual(user_details, arg_list[2].user_details) self.assertEqual(invitation_expiration_in_hours, arg_list[2].num_hours_to_expiration) def test_update_staticsite_users_with_resourcegroup_with_all_args(self): roles = 'Contributor,Reviewer' authentication_provider = 'GitHub' user_details = 'JohnDoe' user_id = 100 update_staticsite_users(self.mock_cmd, self.name1, roles, authentication_provider=authentication_provider, user_details=user_details, user_id=user_id, resource_group_name=self.rg1) self.staticapp_client.update_static_site_user.assert_called_once_with( self.rg1, self.name1, authentication_provider, user_id, roles=roles) def test_update_staticsite_users_with_resourcegroup_without_auth_provider(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' authentication_provider = 'GitHub' user_id = '100' _mock_list_users_for_without_auth_provider(self, user_id, authentication_provider, user_details) update_staticsite_users(self.mock_cmd, self.name1, roles, user_details=user_details, user_id=user_id, resource_group_name=self.rg1) self.staticapp_client.update_static_site_user.assert_called_once_with( self.rg1, self.name1, authentication_provider, user_id, roles=roles) def test_update_staticsite_users_with_resourcegroup_without_auth_provider_user_not_found(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' user_id = '100' _mock_list_users_for_without_auth_provider(self, 'other_user_id', 'dummy_authentication_provider', 'dummy_user_details') with self.assertRaises(CLIError): update_staticsite_users(self.mock_cmd, self.name1, roles, user_details=user_details, user_id=user_id, resource_group_name=self.rg1) def test_update_staticsite_users_with_resourcegroup_without_user_id_without_auth_provider(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' authentication_provider = 'GitHub' user_id = '100' _mock_list_users_for_without_auth_provider(self, user_id, authentication_provider, user_details) update_staticsite_users(self.mock_cmd, self.name1, roles, user_details=user_details, resource_group_name=self.rg1) self.staticapp_client.update_static_site_user.assert_called_once_with( self.rg1, self.name1, authentication_provider, user_id, roles=roles) def test_update_staticsite_users_with_resourcegroup_without_user_id_without_auth_provider_user_not_found(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' _mock_list_users_for_without_auth_provider(self, 'dummy_user_id', 'dummy_authentication_provider', 'other_user_details') with self.assertRaises(CLIError): update_staticsite_users(self.mock_cmd, self.name1, roles, user_details=user_details, resource_group_name=self.rg1) def test_update_staticsite_users_with_resourcegroup_without_user_id(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' authentication_provider = 'GitHub' user_id = '100' _mock_list_users_for_without_auth_provider(self, user_id, authentication_provider, user_details) update_staticsite_users(self.mock_cmd, self.name1, roles, authentication_provider=authentication_provider, user_details=user_details, resource_group_name=self.rg1) self.staticapp_client.update_static_site_user.assert_called_once_with( self.rg1, self.name1, authentication_provider, user_id, roles=roles) def test_update_staticsite_users_with_resourcegroup_without_user_id_user_not_found(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' authentication_provider = 'GitHub' _mock_list_users_for_without_auth_provider(self, 'dummy_user_id', 'dummy_authentication_provider', 'other_user_details') with self.assertRaises(CLIError): update_staticsite_users(self.mock_cmd, self.name1, roles, authentication_provider=authentication_provider, user_details=user_details, resource_group_name=self.rg1) def test_update_staticsite_users_with_resourcegroup_without_user_id_without_user_details(self): roles = 'Contributor,Reviewer' user_details = 'JohnDoe' authentication_provider = 'GitHub' user_id = '100' _mock_list_users_for_without_auth_provider(self, user_id, authentication_provider, user_details) with self.assertRaises(CLIError): update_staticsite_users(self.mock_cmd, self.name1, roles, authentication_provider=authentication_provider, resource_group_name=self.rg1) def test_list_staticsite_secrets(self): from azure.mgmt.web.models import StringDictionary self.staticapp_client.list_static_site_secrets.return_value = StringDictionary(properties={"apiKey": "key"}) secret = list_staticsite_secrets(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.list_static_site_secrets.assert_called_once_with(resource_group_name=self.rg1, name=self.name1) from ast import literal_eval self.assertEqual(literal_eval(secret.__str__())["properties"]["apiKey"], "key") def test_staticsite_identity_assign(self): from azure.mgmt.web.models import ManagedServiceIdentity, ManagedServiceIdentityType self.mock_cmd.get_models.return_value = ManagedServiceIdentity, ManagedServiceIdentityType assign_identity(self.mock_cmd, self.rg1, self.name1) self.staticapp_client.begin_create_or_update_static_site.assert_called_once() def test_staticsite_identity_remove(self): from azure.mgmt.web.models import ManagedServiceIdentityType, Components1Jq1T4ISchemasManagedserviceidentityPropertiesUserassignedidentitiesAdditionalproperties get_models = lambda s: ManagedServiceIdentityType if s == "ManagedServiceIdentityType" else Components1Jq1T4ISchemasManagedserviceidentityPropertiesUserassignedidentitiesAdditionalproperties self.mock_cmd.get_models.side_effect = get_models remove_identity(self.mock_cmd, self.rg1, self.name1) self.staticapp_client.begin_create_or_update_static_site.assert_called_once() def test_staticsite_identity_show(self): mock_site = mock.MagicMock() mock_site.identity = "identity" self.staticapp_client.get_static_site.return_value = mock_site self.assertEqual(show_identity(self.mock_cmd, self.rg1, self.name1), "identity") def test_reset_staticsite_api_key(self): from azure.mgmt.web.models import StringDictionary, StaticSiteResetPropertiesARMResource self.staticapp_client.get_static_site.return_value = self.app1 self.staticapp_client.reset_static_site_api_key.return_value = StringDictionary(properties={"apiKey": "new_key"}) self.mock_cmd.get_models.return_value = StaticSiteResetPropertiesARMResource secret = reset_staticsite_api_key(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.get_static_site.assert_called_once_with(self.rg1, self.name1) self.mock_cmd.get_models.assert_called_once_with('StaticSiteResetPropertiesARMResource') self.staticapp_client.reset_static_site_api_key.assert_called_once() from ast import literal_eval reset_envelope = literal_eval(self.staticapp_client.reset_static_site_api_key.call_args[1]["reset_properties_envelope"].__str__()) self.assertEqual(reset_envelope["repository_token"], self.token1) @mock.patch("azure.cli.command_modules.appservice.static_sites.show_functionapp") def test_functions_link(self, *args, **kwargs): functionapp_name = "functionapp" functionapp_resource_id = "/subscriptions/sub/resourceGroups/{}/providers/Microsoft.Web/sites/{}".format( self.rg1, functionapp_name ) link_user_function(self.mock_cmd, self.name1, self.rg1, functionapp_resource_id) self.staticapp_client.begin_register_user_provided_function_app_with_static_site.assert_called_once() @mock.patch("azure.cli.command_modules.appservice.static_sites.get_user_function", return_value=[mock.MagicMock()]) def test_functions_unlink(self, *args, **kwargs): unlink_user_function(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.detach_user_provided_function_app_from_static_site.assert_called_once() def test_functions_show(self, *args, **kwargs): get_user_function(self.mock_cmd, self.name1, self.rg1) self.staticapp_client.get_user_provided_function_apps_for_static_site.assert_called_once() def _set_up_client_mock(self): self.mock_cmd = mock.MagicMock() self.mock_cmd.cli_ctx = mock.MagicMock() self.staticapp_client = mock.MagicMock() client_factory_patcher = mock.patch( 'azure.cli.command_modules.appservice.static_sites._get_staticsites_client_factory', autospec=True) self.addCleanup(client_factory_patcher.stop) self.mock_static_site_client_factory = client_factory_patcher.start() self.mock_static_site_client_factory.return_value = self.staticapp_client def _set_up_fake_apps(self): from azure.mgmt.web.models import StaticSiteCustomDomainRequestPropertiesARMResource self.rg1 = 'rg1' self.name1 = 'name1' self.name1_not_exist = 'name1_not_exist' self.location1 = 'location1' self.source1 = 'https://github.com/Contoso/My-First-Static-App' self.branch1 = 'dev' self.token1 = 'TOKEN_1' self.environment1 = 'default' self.hostname1 = 'www.app1.com' self.hostname1_validation = StaticSiteCustomDomainRequestPropertiesARMResource(validation_method="cname-delegation") self.app1 = _contruct_static_site_object( self.rg1, self.name1, self.location1, self.source1, self.branch1, self.token1) self.rg2 = 'rg2' self.name2 = 'name2' self.location2 = 'location2' self.source2 = 'https://github.com/Contoso/My-Second-Static-App' self.branch2 = 'master' self.token2 = 'TOKEN_2' self.environment1 = 'prod' self.hostname1 = 'www.app2.com' self.app2 = _contruct_static_site_object( self.rg2, self.name2, self.location2, self.source2, self.branch2, self.token2) def _contruct_static_site_object(rg, app_name, location, source, branch, token): from azure.mgmt.web.models import StaticSiteARMResource, SkuDescription app = StaticSiteARMResource( location=location, repository_url=source, branch=branch, repository_token=token, sku=SkuDescription(name='Free', tier='Free')) app.name = app_name app.id = \ "/subscriptions/sub/resourceGroups/{}/providers/Microsoft.Web/staticSites/{}".format(rg, app_name) return app def _mock_list_users_for_without_auth_provider(self, user_id, authentication_provider, user_details): class User: def __init__(self, name, provider, display_name): self.name = name self.provider = provider self.display_name = display_name user1 = User(user_id, authentication_provider, user_details) user2 = User(user_id + '2', authentication_provider + '2', user_details + '2') self.staticapp_client.list_static_site_users.return_value = [user1, user2]

true

f7365d53fe98443b87d6023ffdb580ca656c6f9e

11,556

py

Python

pybind/nos/v7_1_0/interface/hundredgigabitethernet/switchport/trunk/native_vlan_classification/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/nos/v7_1_0/interface/hundredgigabitethernet/switchport/trunk/native_vlan_classification/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

null

pybind/nos/v7_1_0/interface/hundredgigabitethernet/switchport/trunk/native_vlan_classification/__init__.py

shivharis/pybind

4e1c6d54b9fd722ccec25546ba2413d79ce337e6

[ "Apache-2.0" ]

1

2021-11-05T22:15:42.000Z

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ class native_vlan_classification(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-interface - based on the path /interface/hundredgigabitethernet/switchport/trunk/native-vlan-classification. Each member element of the container is represented as a class variable - with a specific YANG type. """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__native_vlan_id','__native_vlan_ctag_id',) _yang_name = 'native-vlan-classification' _rest_name = '' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__native_vlan_id = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'2..8191']}), is_leaf=True, yang_name="native-vlan-id", rest_name="native-vlan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Set the native VLAN characteristics of the \nLayer2 trunk interface for classifying untagged\ntraffic', u'alt-name': u'native-vlan', u'cli-full-no': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='native-vlan-type', is_config=True) self.__native_vlan_ctag_id = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..4094']}), is_leaf=True, yang_name="native-vlan-ctag-id", rest_name="ctag", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Associate a Ctag.', u'alt-name': u'ctag'}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='dot1q-vlan-type', is_config=True) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'interface', u'hundredgigabitethernet', u'switchport', u'trunk', u'native-vlan-classification'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'interface', u'HundredGigabitEthernet', u'switchport', u'trunk'] def _get_native_vlan_id(self): """ Getter method for native_vlan_id, mapped from YANG variable /interface/hundredgigabitethernet/switchport/trunk/native_vlan_classification/native_vlan_id (native-vlan-type) YANG Description: The native vlan for an interface. """ return self.__native_vlan_id def _set_native_vlan_id(self, v, load=False): """ Setter method for native_vlan_id, mapped from YANG variable /interface/hundredgigabitethernet/switchport/trunk/native_vlan_classification/native_vlan_id (native-vlan-type) If this variable is read-only (config: false) in the source YANG file, then _set_native_vlan_id is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_native_vlan_id() directly. YANG Description: The native vlan for an interface. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'2..8191']}), is_leaf=True, yang_name="native-vlan-id", rest_name="native-vlan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Set the native VLAN characteristics of the \nLayer2 trunk interface for classifying untagged\ntraffic', u'alt-name': u'native-vlan', u'cli-full-no': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='native-vlan-type', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """native_vlan_id must be of a type compatible with native-vlan-type""", 'defined-type': "brocade-interface:native-vlan-type", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'2..8191']}), is_leaf=True, yang_name="native-vlan-id", rest_name="native-vlan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Set the native VLAN characteristics of the \nLayer2 trunk interface for classifying untagged\ntraffic', u'alt-name': u'native-vlan', u'cli-full-no': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='native-vlan-type', is_config=True)""", }) self.__native_vlan_id = t if hasattr(self, '_set'): self._set() def _unset_native_vlan_id(self): self.__native_vlan_id = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'2..8191']}), is_leaf=True, yang_name="native-vlan-id", rest_name="native-vlan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Set the native VLAN characteristics of the \nLayer2 trunk interface for classifying untagged\ntraffic', u'alt-name': u'native-vlan', u'cli-full-no': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='native-vlan-type', is_config=True) def _get_native_vlan_ctag_id(self): """ Getter method for native_vlan_ctag_id, mapped from YANG variable /interface/hundredgigabitethernet/switchport/trunk/native_vlan_classification/native_vlan_ctag_id (dot1q-vlan-type) YANG Description: Associate a Ctag. """ return self.__native_vlan_ctag_id def _set_native_vlan_ctag_id(self, v, load=False): """ Setter method for native_vlan_ctag_id, mapped from YANG variable /interface/hundredgigabitethernet/switchport/trunk/native_vlan_classification/native_vlan_ctag_id (dot1q-vlan-type) If this variable is read-only (config: false) in the source YANG file, then _set_native_vlan_ctag_id is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_native_vlan_ctag_id() directly. YANG Description: Associate a Ctag. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..4094']}), is_leaf=True, yang_name="native-vlan-ctag-id", rest_name="ctag", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Associate a Ctag.', u'alt-name': u'ctag'}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='dot1q-vlan-type', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """native_vlan_ctag_id must be of a type compatible with dot1q-vlan-type""", 'defined-type': "brocade-interface:dot1q-vlan-type", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..4094']}), is_leaf=True, yang_name="native-vlan-ctag-id", rest_name="ctag", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Associate a Ctag.', u'alt-name': u'ctag'}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='dot1q-vlan-type', is_config=True)""", }) self.__native_vlan_ctag_id = t if hasattr(self, '_set'): self._set() def _unset_native_vlan_ctag_id(self): self.__native_vlan_ctag_id = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..4094']}), is_leaf=True, yang_name="native-vlan-ctag-id", rest_name="ctag", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Associate a Ctag.', u'alt-name': u'ctag'}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='dot1q-vlan-type', is_config=True) native_vlan_id = __builtin__.property(_get_native_vlan_id, _set_native_vlan_id) native_vlan_ctag_id = __builtin__.property(_get_native_vlan_ctag_id, _set_native_vlan_ctag_id) _pyangbind_elements = {'native_vlan_id': native_vlan_id, 'native_vlan_ctag_id': native_vlan_ctag_id, }

69.614458

765

0.74325

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ class native_vlan_classification(PybindBase): __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__native_vlan_id','__native_vlan_ctag_id',) _yang_name = 'native-vlan-classification' _rest_name = '' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__native_vlan_id = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'2..8191']}), is_leaf=True, yang_name="native-vlan-id", rest_name="native-vlan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Set the native VLAN characteristics of the \nLayer2 trunk interface for classifying untagged\ntraffic', u'alt-name': u'native-vlan', u'cli-full-no': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='native-vlan-type', is_config=True) self.__native_vlan_ctag_id = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..4094']}), is_leaf=True, yang_name="native-vlan-ctag-id", rest_name="ctag", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Associate a Ctag.', u'alt-name': u'ctag'}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='dot1q-vlan-type', is_config=True) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'interface', u'hundredgigabitethernet', u'switchport', u'trunk', u'native-vlan-classification'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'interface', u'HundredGigabitEthernet', u'switchport', u'trunk'] def _get_native_vlan_id(self): return self.__native_vlan_id def _set_native_vlan_id(self, v, load=False): if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'2..8191']}), is_leaf=True, yang_name="native-vlan-id", rest_name="native-vlan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Set the native VLAN characteristics of the \nLayer2 trunk interface for classifying untagged\ntraffic', u'alt-name': u'native-vlan', u'cli-full-no': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='native-vlan-type', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """native_vlan_id must be of a type compatible with native-vlan-type""", 'defined-type': "brocade-interface:native-vlan-type", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'2..8191']}), is_leaf=True, yang_name="native-vlan-id", rest_name="native-vlan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Set the native VLAN characteristics of the \nLayer2 trunk interface for classifying untagged\ntraffic', u'alt-name': u'native-vlan', u'cli-full-no': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='native-vlan-type', is_config=True)""", }) self.__native_vlan_id = t if hasattr(self, '_set'): self._set() def _unset_native_vlan_id(self): self.__native_vlan_id = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'2..8191']}), is_leaf=True, yang_name="native-vlan-id", rest_name="native-vlan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Set the native VLAN characteristics of the \nLayer2 trunk interface for classifying untagged\ntraffic', u'alt-name': u'native-vlan', u'cli-full-no': None}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='native-vlan-type', is_config=True) def _get_native_vlan_ctag_id(self): return self.__native_vlan_ctag_id def _set_native_vlan_ctag_id(self, v, load=False): if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..4094']}), is_leaf=True, yang_name="native-vlan-ctag-id", rest_name="ctag", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Associate a Ctag.', u'alt-name': u'ctag'}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='dot1q-vlan-type', is_config=True) except (TypeError, ValueError): raise ValueError({ 'error-string': """native_vlan_ctag_id must be of a type compatible with dot1q-vlan-type""", 'defined-type': "brocade-interface:dot1q-vlan-type", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..4094']}), is_leaf=True, yang_name="native-vlan-ctag-id", rest_name="ctag", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Associate a Ctag.', u'alt-name': u'ctag'}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='dot1q-vlan-type', is_config=True)""", }) self.__native_vlan_ctag_id = t if hasattr(self, '_set'): self._set() def _unset_native_vlan_ctag_id(self): self.__native_vlan_ctag_id = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..4094']}), is_leaf=True, yang_name="native-vlan-ctag-id", rest_name="ctag", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'native_vlan_phy_interface_conf', u'info': u'Associate a Ctag.', u'alt-name': u'ctag'}}, namespace='urn:brocade.com:mgmt:brocade-interface', defining_module='brocade-interface', yang_type='dot1q-vlan-type', is_config=True) native_vlan_id = __builtin__.property(_get_native_vlan_id, _set_native_vlan_id) native_vlan_ctag_id = __builtin__.property(_get_native_vlan_ctag_id, _set_native_vlan_ctag_id) _pyangbind_elements = {'native_vlan_id': native_vlan_id, 'native_vlan_ctag_id': native_vlan_ctag_id, }

true

f7365e5d99eac8285580b730f2f811951fa7ba87

1,861

py

Python

test_runner.py

tmatsuo/appengine-blobstoremigrator-python

c59c84c1a91cdab47489fc4d32ddcf35c579ba6a

[ "Apache-2.0" ]

12

2015-06-10T19:48:45.000Z

2021-04-15T06:11:01.000Z

test_runner.py

tmatsuo/appengine-blobstoremigrator-python

c59c84c1a91cdab47489fc4d32ddcf35c579ba6a

[ "Apache-2.0" ]

1

2021-03-21T10:33:24.000Z

test_runner.py

tmatsuo/appengine-blobstoremigrator-python

c59c84c1a91cdab47489fc4d32ddcf35c579ba6a

[ "Apache-2.0" ]

6

2016-07-12T02:11:43.000Z

2021-10-03T00:54:03.000Z

#!/usr/bin/python # Copyright 2015 Google Inc. 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. import os import sys import unittest CUR_DIR = os.path.dirname(__file__) SRC_DIR = os.path.join(CUR_DIR, 'src') TEST_DIR = os.path.join(CUR_DIR, 'test') LIB_DIR = os.path.join(SRC_DIR, 'lib') def _fix_path(): """ Finds the google_appengine directory and fixes Python imports to use it. (Mostly) copied from Pipelines API. """ if SRC_DIR not in sys.path: sys.path.append(SRC_DIR) if LIB_DIR not in sys.path: sys.path.append(LIB_DIR) all_paths = os.environ.get('PYTHONPATH').split(os.pathsep) for path_dir in all_paths: dev_appserver_path = os.path.join(path_dir, 'dev_appserver.py') if os.path.exists(dev_appserver_path): google_appengine = os.path.dirname(os.path.realpath(dev_appserver_path)) sys.path.append(google_appengine) # Use the next import will fix up sys.path even further to bring in # any dependent lib directories that the SDK needs. dev_appserver = __import__('dev_appserver') sys.path.extend(dev_appserver.EXTRA_PATHS) return _fix_path() def run_tests(): """Run all unit tests.""" suite = unittest.TestLoader().discover(TEST_DIR, pattern='*_test.py') unittest.TextTestRunner(verbosity=1).run(suite) if __name__ == '__main__': run_tests()

31.016667

78

0.73079

import os import sys import unittest CUR_DIR = os.path.dirname(__file__) SRC_DIR = os.path.join(CUR_DIR, 'src') TEST_DIR = os.path.join(CUR_DIR, 'test') LIB_DIR = os.path.join(SRC_DIR, 'lib') def _fix_path(): if SRC_DIR not in sys.path: sys.path.append(SRC_DIR) if LIB_DIR not in sys.path: sys.path.append(LIB_DIR) all_paths = os.environ.get('PYTHONPATH').split(os.pathsep) for path_dir in all_paths: dev_appserver_path = os.path.join(path_dir, 'dev_appserver.py') if os.path.exists(dev_appserver_path): google_appengine = os.path.dirname(os.path.realpath(dev_appserver_path)) sys.path.append(google_appengine) dev_appserver = __import__('dev_appserver') sys.path.extend(dev_appserver.EXTRA_PATHS) return _fix_path() def run_tests(): suite = unittest.TestLoader().discover(TEST_DIR, pattern='*_test.py') unittest.TextTestRunner(verbosity=1).run(suite) if __name__ == '__main__': run_tests()

true

f7365eb934ee15b523ebb1bbcdf7881c563276d4

18,861

py

Python

stratipy/filtering_diffusion.py

candleinwindsteve/Stratipy

ea505df1e4830141c590922d654edfbde498b924

[ "BSD-3-Clause" ]

1

2021-03-08T22:52:19.000Z

stratipy/filtering_diffusion.py

candleinwindsteve/Stratipy

ea505df1e4830141c590922d654edfbde498b924

[ "BSD-3-Clause" ]

null

stratipy/filtering_diffusion.py

candleinwindsteve/Stratipy

ea505df1e4830141c590922d654edfbde498b924

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # coding: utf-8 import sys import numpy as np import scipy.sparse as sp from scipy.sparse.linalg import norm from scipy.io import loadmat, savemat from nbs_class import Ppi, Patient from subprocess import call # import h5py import os import glob import time import datetime # NOTE mutationProfileDiffusion -> propagation # mutationProfile -> M, PPIAdjacencyMatrix -> adj, dataFolder -> result_folder # PPI_influence_min -> ppi_influence_min, PPI_influence_max-> ppi_influence_max # PPI_influence()-> calcul_ppi_influence(), PPI_influence -> ppi_influence # influenceDistance->influence_distance # influenceMat -> ppi_influence, PPIneighboorsMax -> ngh_max, # bestInfluencers -> best_influencers # filteredGenes -> deg0, keepSingletons -> keep_singletons # mutationsMin -> min_mutation, mutationsMax -> mutationsMax # newnet -> ppi_ngh, netFinal -> ppi_final, mutFinal -> mut_final # filteredPatients -> filtered_patients # @profile def propagation(M, adj, alpha=0.7, tol=10e-6): # TODO equation, M, alpha """Network propagation iterative process Iterative algorithm for apply propagation using random walk on a network: Initialize:: X1 = M Repeat:: X2 = alpha * X1.A + (1-alpha) * M X1 = X2 Until:: norm(X2-X1) < tol Where:: A : degree-normalized adjacency matrix Parameters ---------- M : sparse matrix Data matrix to be diffused. adj : sparse matrix Adjacency matrice. alpha : float, default: 0.7 Diffusion/propagation factor with 0 <= alpha <= 1. For alpha = 0 : no diffusion. For alpha = 1 : tol : float, default: 10e-6 Convergence threshold. Returns ------- X2 : sparse matrix Smoothed matrix. """ print(' ==== propagation ==== ') n = adj.shape[0] # diagonal = 1 -> degree # TODO to set diagonal = 0 before applying eye adj = adj+sp.eye(n, dtype=np.float32) d = sp.dia_matrix((np.array(adj.sum(axis=0))**-1, [0]), shape=(n, n), dtype=np.float32) A = adj.dot(d) X1 = M.astype(np.float32) X2 = alpha * X1.dot(A) + (1-alpha) * M i = 0 while norm(X2-X1) > tol: X1 = X2 X2 = alpha * X1.dot(A) + (1-alpha) * M i += 1 print('Propagation iteration = {} ----- {}'.format( i, datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) return X2 # @profile def compare_ij_ji(ppi, out_min=True, out_max=True): """Helper function for calcul_ppi_influence In most cases the influence (propagation) is not symmetric. We have to compare weight (a_ij) and (a_ji) for all pairs in order to obtain symmetric matrix/matrices. 2 choices available: minimum or maximum weight. a = min [(a_ij),(a_ji)] a = max [(a_ij),(a_ji)] Minimum weight is chosen to avoid Hubs phenomenon. Parameters ---------- ppi : sparse matrix Matrice to apply comparison. out_min, out_max : boolean, default: True Minimum and/or maximum weight is chosen. Returns ------- ppi_min, ppi_max : sparse matrix Symmertric matrix with minimum and/or maximum weight. """ # TODO matrice type of ppi n = ppi.shape[0] ppi = ppi.tolil() # need "lil_matrix" for reshape # transpose to compare ppi(ij) and ppi(ji) ppi_transp = sp.lil_matrix.transpose(ppi) # reshape to 1D matrix ppi_1d = ppi.reshape((1, n**2)) ppi_1d_transp = ppi_transp.reshape((1, n**2)) # reshapeto original size matrix after comparison (min/max) if out_min and out_max: ppi_min = (sp.coo_matrix.tolil( sp.coo_matrix.min(sp.vstack([ppi_1d, ppi_1d_transp]), axis=0)) ).reshape((n, n)).astype(np.float32) ppi_max = (sp.coo_matrix.tolil( sp.coo_matrix.max(sp.vstack([ppi_1d, ppi_1d_transp]), axis=0)) ).reshape((n, n)).astype(np.float32) print('ppi_min', type(ppi_min), ppi_min.dtype, ppi_min.shape) print('ppi_max', type(ppi_max), ppi_max.dtype, ppi_max.shape) return ppi_min, ppi_max elif out_min: ppi_min = (sp.coo_matrix.tolil( sp.coo_matrix.min(sp.vstack([ppi_1d, ppi_1d_transp]), axis=0, dtype=np.float32))).reshape((n, n)) return ppi_min elif out_max: ppi_max = (sp.coo_matrix.tolil( sp.coo_matrix.max(sp.vstack([ppi_1d, ppi_1d_transp]), axis=0, dtype=np.float32))).reshape((n, n)) return ppi_max else: print('You have to choice Min or Max') # TODO change error message # @profile def calcul_final_influence(M, adj, result_folder, influence_weight='min', simplification=True, compute=False, overwrite=False, alpha=0.7, tol=10e-6): """Compute network influence score Network propagation iterative process is applied on PPI. (1) The network influence distance matrix and (2) influence matrices based on minimum / maximum weight are saved as MATLAB-style files (.mat). - (1) : 'influence_distance_alpha={}_tol={}.mat' in 'influence_distance' directory - (2) : 'ppi_influence_alpha={}_tol={}.mat' in 'ppi_influence' directory Where {} are parameter values. The directories will be automatically created if not exist. If compute=False, the latest data of directory will be taken into account: - latest data with same parameters (alpha and tol) - if not exist, latest data of directory but with differents parameters Parameters ---------- M : sparse matrix Data matrix to be diffused. adj : sparse matrix Adjacency matrice. result_folder : str Path to create a new directory for save new files. If you want to creat in current directory, enter '/directory_name'. Absolute path is also supported. influence_weight : simplification : boolean, default: True compute : boolean, default: False If True, new network influence score will be computed. If False, the latest network influence score will be taken into account. overwrite : boolean, default: False If True, new network influence score will be computed even if the file which same parameters already exists in the directory. alpha : float, default: 0.7 Diffusion (propagation) factor with 0 <= alpha <= 1. For alpha = 0 : no diffusion. For alpha = 1 : tol : float, default: 10e-6 Convergence threshold. Returns ------- final_influence : sparse matrix Smoothed PPI influence matrices based on minimum / maximum weight. """ influence_distance_directory = result_folder + 'influence_distance/' influence_distance_file = ( influence_distance_directory + 'influence_distance_alpha={}_tol={}.mat'.format(alpha, tol)) ####### final_influence_directory = result_folder + 'final_influence/' final_influence_file = ( final_influence_directory + 'final_influence_simp={}_alpha={}_tol={}.mat'.format( simplification, alpha, tol)) ####### existance_same_param = os.path.exists(final_influence_file) # TODO overwrite condition # check if same parameters file exists in directory if existance_same_param: final_influence_data = loadmat(final_influence_file) if influence_weight == 'min': final_influence = final_influence_data['final_influence_min'] else: final_influence = final_influence_data['final_influence_max'] print('final influence matrix', type(final_influence), final_influence.shape) print('***** Same parameters file of FINAL INFLUENCE already exists ***** {}' .format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) else: if compute: start = time.time() # check if influence distance file exists existance_same_influence = os.path.exists(influence_distance_file) if existance_same_influence: influence_data = loadmat(influence_distance_file) influence = influence_data['influence_distance'] print('***** Same parameters file of INFLUENCE DISTANCE already exists ***** {}' .format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) else: influence = propagation(M, adj, alpha, tol) print('influence', type(influence), influence.dtype) # save influence distance before simplification with parameters' values in filename os.makedirs(influence_distance_directory, exist_ok=True) # NOTE For Python ≥ 3.2 print(' ==== Start to save INFLUENCE DISTANCE ==== {}' .format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) start_save = time.time() savemat(influence_distance_file, {'influence_distance': influence, 'alpha': alpha}, do_compression=True) end_save = time.time() print("---------- save time = {} ---------- {}" .format(datetime.timedelta(seconds=end_save - start_save), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) # simplification: multiply by PPI adjacency matrix if simplification: influence = influence.multiply(sp.lil_matrix(adj)) # -> influence as csr_matrix else: print("---------- No simplification ----------") pass # compare influence[i,j] and influence[j,i] => min/max => final influence start_ij = time.time() final_influence_min, final_influence_max = compare_ij_ji( influence, out_min=True, out_max=True) end_ij = time.time() print("---------- compare ij/ji = {} ---------- {}" .format(datetime.timedelta(seconds=end_ij - start_ij), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) # save final influence with parameters' values in filename os.makedirs(final_influence_directory, exist_ok=True) print(' ==== Start to save FINAL INFLUENCE ==== {}' .format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) start_save = time.time() savemat(final_influence_file, {'final_influence_min': final_influence_min, 'final_influence_max': final_influence_max, 'alpha': alpha}, do_compression=True) end_save = time.time() print("---------- save time = {} ---------- {}" .format(datetime.timedelta(seconds=end_save - start_save), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) if influence_weight == 'min': final_influence = final_influence_min else: final_influence = final_influence_max end = time.time() print("---------- Influence = {} ---------- {}" .format(datetime.timedelta(seconds=end-start), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) # take most recent file else: for x in final_influence_file, influence_distance_directory: print(x) newest_file = max(glob.iglob(x + '*.mat'), key=os.path.getctime) final_influence_data = loadmat(newest_file) if x == final_influence_directory: if influence_weight == 'min': final_influence = final_influence_data['final_influence_min'] else: final_influence = final_influence_data['final_influence_max'] return final_influence # @profile def best_neighboors(ppi_filt, final_influence, ngh_max): """Helper function for filter_ppi_patients Keeps only the connections with the best influencers. Parameters ---------- ppi_filt : sparse matrix Filtration from ppi_total : only genes in PPI are considered. final_influence : Smoothed PPI influence matrices based on minimum or maximum weight. ngh_max : int Number of best influencers in PPI. Returns ------- ppi_ngh : sparse matrix PPI with only best influencers. """ ngh_max = ngh_max + 1 # central protein included final_influence = final_influence.todense() print(type(final_influence)) ppi_filt = ppi_filt.todense() ppi_ngh = np.zeros(ppi_filt.shape, dtype=np.float32) print('ppi_ngh', ppi_ngh.shape) for i in range(ppi_filt.shape[0]): best_influencers = np.argpartition(-final_influence[i, :], ngh_max)[:ngh_max] #NOTE different result if same value exists several times # best_influencers2 = np.argpartition(final_influence[i, :], -ngh_max)[-ngh_max:] ppi_ngh[i, best_influencers] = ppi_filt[i, best_influencers] ppi_ngh = np.max(np.dstack((ppi_ngh, ppi_ngh.T)), axis=2) print('ppi_ngh ', ppi_ngh.dtype) # too stringent if np.min return sp.csc_matrix(ppi_ngh) # @profile def filter_ppi_patients(ppi_total, mut_total, ppi_filt, final_influence, ngh_max, keep_singletons=False, min_mutation=10, max_mutation=2000): """Keeping only the connections with the best influencers and Filtering some patients based on mutation number 'the 11 most influential neighbors of each gene in the network as determined by network influence distance were used' 'Only mutation data generated using the Illumina GAIIx platform were retained for subsequent analy- sis, and patients with fewer than 10 mutations were discarded.' Parameters ---------- ppi_total : sparse matrix Built from all sparse sub-matrices (AA, ... , CC). mut_total : sparse matrix Patients' mutation profiles of all genes (rows: patients, columns: genes of AA, BB and CC). ppi_filt : sparse matrix Filtration from ppi_total : only genes in PPI are considered. final_influence : Smoothed PPI influence matrices based on minimum or maximum weight. ngh_max : int Number of best influencers in PPI. keep_singletons : boolean, default: False If True, proteins not annotated in PPI (genes founded only in patients' mutation profiles) will be also considered. If False, only annotated proteins in PPI will be considered. min_mutation, max_mutation : int Numbers of lowest mutations and highest mutations per patient. Returns ------- ppi_final, mut_final : sparse matrix PPI and mutation profiles after filtering. """ # n = final_influence.shape[0] # final_influence = index_to_sym_matrix(n, final_influence) ppi_ngh = best_neighboors(ppi_filt, final_influence, ngh_max) print('ppi_ngh ', ppi_ngh.dtype) deg0 = Ppi(ppi_total).deg == 0 # True if protein degree = 0 if keep_singletons: ppi_final = sp.bmat([ [ppi_ngh, sp.csc_matrix((ppi_ngh.shape[0], sum(deg0)))], [sp.csc_matrix((sum(deg0), ppi_ngh.shape[0])), sp.csc_matrix((sum(deg0), sum(deg0)))] ]) # -> COO matrix # mut_final=sp.bmat([[mut_total[:,deg0==False],mut_total[:,deg0==True]]]) mut_final = mut_total else: ppi_final = ppi_ngh mut_final = mut_total[:, Ppi(ppi_total).deg > 0] # filtered_patients = np.array([k < min_mutation or k > max_mutation for k in Patient(mut_final).mut_per_patient]) # mut_final = mut_final[filtered_patients == False, :] # to avoid worse comparison '== False' mut_final = mut_final[np.array([min_mutation < k < max_mutation for k in Patient(mut_final).mut_per_patient])] print("Removing %i patients with less than %i or more than %i mutations" % (mut_total.shape[0]-mut_final.shape[0], min_mutation, max_mutation)) print("New adjacency matrix:", ppi_final.shape) print("New mutation profile matrix:", mut_final.shape) return ppi_final, mut_final # @profile def quantile_norm_mean(anarray): """Helper function for propagation_profile Forces the observations/variables to have identical intensity distribution. Parameters ---------- Returns ------- """ A = np.squeeze(np.asarray(anarray.T)) AA = np.zeros_like(A) I = np.argsort(A, axis=0) AA[I, np.arange(A.shape[1])] = np.mean(A[I, np.arange(A.shape[1])], axis=1)[:, np.newaxis] return AA.T # @profile def quantile_norm_median(anarray): A = np.squeeze(np.asarray(anarray.T)) AA = np.zeros_like(A) I = np.argsort(A, axis=0) AA[I, np.arange(A.shape[1])] = np.median(A[I, np.arange(A.shape[1])], axis=1)[:, np.newaxis] return AA.T # @profile def propagation_profile(mut_raw, adj, alpha, tol, qn): # TODO error messages start = time.time() if alpha > 0: # TODO verification of same parameter file mut_propag = propagation(mut_raw, adj, alpha, tol).todense() mut_propag[np.isnan(mut_propag)] = 0 if qn == 'mean': mut_type = 'mean_qn' mut_propag = quantile_norm_mean(mut_propag) elif qn == 'median': mut_type = 'median_qn' mut_propag = quantile_norm_median(mut_propag) else: mut_type = 'diff' end = time.time() print("---------- Propagation on {} mutation profile = {} ---------- {}" .format(mut_type, datetime.timedelta(seconds=end-start), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) return mut_type, mut_propag else: mut_type = 'raw' mut_raw = mut_raw.todense() end = time.time() print("---------- Propagation on {} mutation profile = {} ---------- {}" .format(mut_type, datetime.timedelta(seconds=end-start), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) return mut_type, mut_raw

37.05501

118

0.600286

import sys import numpy as np import scipy.sparse as sp from scipy.sparse.linalg import norm from scipy.io import loadmat, savemat from nbs_class import Ppi, Patient from subprocess import call import os import glob import time import datetime def propagation(M, adj, alpha=0.7, tol=10e-6): print(' ==== propagation ==== ') n = adj.shape[0] adj = adj+sp.eye(n, dtype=np.float32) d = sp.dia_matrix((np.array(adj.sum(axis=0))**-1, [0]), shape=(n, n), dtype=np.float32) A = adj.dot(d) X1 = M.astype(np.float32) X2 = alpha * X1.dot(A) + (1-alpha) * M i = 0 while norm(X2-X1) > tol: X1 = X2 X2 = alpha * X1.dot(A) + (1-alpha) * M i += 1 print('Propagation iteration = {} ----- {}'.format( i, datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) return X2 def compare_ij_ji(ppi, out_min=True, out_max=True): n = ppi.shape[0] ppi = ppi.tolil() ppi_transp = sp.lil_matrix.transpose(ppi) ppi_1d = ppi.reshape((1, n**2)) ppi_1d_transp = ppi_transp.reshape((1, n**2)) if out_min and out_max: ppi_min = (sp.coo_matrix.tolil( sp.coo_matrix.min(sp.vstack([ppi_1d, ppi_1d_transp]), axis=0)) ).reshape((n, n)).astype(np.float32) ppi_max = (sp.coo_matrix.tolil( sp.coo_matrix.max(sp.vstack([ppi_1d, ppi_1d_transp]), axis=0)) ).reshape((n, n)).astype(np.float32) print('ppi_min', type(ppi_min), ppi_min.dtype, ppi_min.shape) print('ppi_max', type(ppi_max), ppi_max.dtype, ppi_max.shape) return ppi_min, ppi_max elif out_min: ppi_min = (sp.coo_matrix.tolil( sp.coo_matrix.min(sp.vstack([ppi_1d, ppi_1d_transp]), axis=0, dtype=np.float32))).reshape((n, n)) return ppi_min elif out_max: ppi_max = (sp.coo_matrix.tolil( sp.coo_matrix.max(sp.vstack([ppi_1d, ppi_1d_transp]), axis=0, dtype=np.float32))).reshape((n, n)) return ppi_max else: print('You have to choice Min or Max') def calcul_final_influence(M, adj, result_folder, influence_weight='min', simplification=True, compute=False, overwrite=False, alpha=0.7, tol=10e-6): influence_distance_directory = result_folder + 'influence_distance/' influence_distance_file = ( influence_distance_directory + 'influence_distance_alpha={}_tol={}.mat'.format(alpha, tol)) directory = result_folder + 'final_influence/' final_influence_file = ( final_influence_directory + 'final_influence_simp={}_alpha={}_tol={}.mat'.format( simplification, alpha, tol)) param = os.path.exists(final_influence_file) if existance_same_param: final_influence_data = loadmat(final_influence_file) if influence_weight == 'min': final_influence = final_influence_data['final_influence_min'] else: final_influence = final_influence_data['final_influence_max'] print('final influence matrix', type(final_influence), final_influence.shape) print('***** Same parameters file of FINAL INFLUENCE already exists ***** {}' .format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) else: if compute: start = time.time() existance_same_influence = os.path.exists(influence_distance_file) if existance_same_influence: influence_data = loadmat(influence_distance_file) influence = influence_data['influence_distance'] print('***** Same parameters file of INFLUENCE DISTANCE already exists ***** {}' .format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) else: influence = propagation(M, adj, alpha, tol) print('influence', type(influence), influence.dtype) os.makedirs(influence_distance_directory, exist_ok=True) # NOTE For Python ≥ 3.2 print(' ==== Start to save INFLUENCE DISTANCE ==== {}' .format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) start_save = time.time() savemat(influence_distance_file, {'influence_distance': influence, 'alpha': alpha}, do_compression=True) end_save = time.time() print("---------- save time = {} ---------- {}" .format(datetime.timedelta(seconds=end_save - start_save), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) # simplification: multiply by PPI adjacency matrix if simplification: influence = influence.multiply(sp.lil_matrix(adj)) # -> influence as csr_matrix else: print("---------- No simplification ----------") pass # compare influence[i,j] and influence[j,i] => min/max => final influence start_ij = time.time() final_influence_min, final_influence_max = compare_ij_ji( influence, out_min=True, out_max=True) end_ij = time.time() print("---------- compare ij/ji = {} ---------- {}" .format(datetime.timedelta(seconds=end_ij - start_ij), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) # save final influence with parameters' values in filename os.makedirs(final_influence_directory, exist_ok=True) print(' ==== Start to save FINAL INFLUENCE ==== {}' .format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) start_save = time.time() savemat(final_influence_file, {'final_influence_min': final_influence_min, 'final_influence_max': final_influence_max, 'alpha': alpha}, do_compression=True) end_save = time.time() print("---------- save time = {} ---------- {}" .format(datetime.timedelta(seconds=end_save - start_save), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) if influence_weight == 'min': final_influence = final_influence_min else: final_influence = final_influence_max end = time.time() print("---------- Influence = {} ---------- {}" .format(datetime.timedelta(seconds=end-start), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) else: for x in final_influence_file, influence_distance_directory: print(x) newest_file = max(glob.iglob(x + '*.mat'), key=os.path.getctime) final_influence_data = loadmat(newest_file) if x == final_influence_directory: if influence_weight == 'min': final_influence = final_influence_data['final_influence_min'] else: final_influence = final_influence_data['final_influence_max'] return final_influence def best_neighboors(ppi_filt, final_influence, ngh_max): ngh_max = ngh_max + 1 final_influence = final_influence.todense() print(type(final_influence)) ppi_filt = ppi_filt.todense() ppi_ngh = np.zeros(ppi_filt.shape, dtype=np.float32) print('ppi_ngh', ppi_ngh.shape) for i in range(ppi_filt.shape[0]): best_influencers = np.argpartition(-final_influence[i, :], ngh_max)[:ngh_max] ppi_ngh[i, best_influencers] = ppi_filt[i, best_influencers] ppi_ngh = np.max(np.dstack((ppi_ngh, ppi_ngh.T)), axis=2) print('ppi_ngh ', ppi_ngh.dtype) return sp.csc_matrix(ppi_ngh) def filter_ppi_patients(ppi_total, mut_total, ppi_filt, final_influence, ngh_max, keep_singletons=False, min_mutation=10, max_mutation=2000): ppi_ngh = best_neighboors(ppi_filt, final_influence, ngh_max) print('ppi_ngh ', ppi_ngh.dtype) deg0 = Ppi(ppi_total).deg == 0 if keep_singletons: ppi_final = sp.bmat([ [ppi_ngh, sp.csc_matrix((ppi_ngh.shape[0], sum(deg0)))], [sp.csc_matrix((sum(deg0), ppi_ngh.shape[0])), sp.csc_matrix((sum(deg0), sum(deg0)))] ]) mut_final = mut_total else: ppi_final = ppi_ngh mut_final = mut_total[:, Ppi(ppi_total).deg > 0] mut_final = mut_final[np.array([min_mutation < k < max_mutation for k in Patient(mut_final).mut_per_patient])] print("Removing %i patients with less than %i or more than %i mutations" % (mut_total.shape[0]-mut_final.shape[0], min_mutation, max_mutation)) print("New adjacency matrix:", ppi_final.shape) print("New mutation profile matrix:", mut_final.shape) return ppi_final, mut_final def quantile_norm_mean(anarray): A = np.squeeze(np.asarray(anarray.T)) AA = np.zeros_like(A) I = np.argsort(A, axis=0) AA[I, np.arange(A.shape[1])] = np.mean(A[I, np.arange(A.shape[1])], axis=1)[:, np.newaxis] return AA.T def quantile_norm_median(anarray): A = np.squeeze(np.asarray(anarray.T)) AA = np.zeros_like(A) I = np.argsort(A, axis=0) AA[I, np.arange(A.shape[1])] = np.median(A[I, np.arange(A.shape[1])], axis=1)[:, np.newaxis] return AA.T def propagation_profile(mut_raw, adj, alpha, tol, qn): start = time.time() if alpha > 0: mut_propag = propagation(mut_raw, adj, alpha, tol).todense() mut_propag[np.isnan(mut_propag)] = 0 if qn == 'mean': mut_type = 'mean_qn' mut_propag = quantile_norm_mean(mut_propag) elif qn == 'median': mut_type = 'median_qn' mut_propag = quantile_norm_median(mut_propag) else: mut_type = 'diff' end = time.time() print("---------- Propagation on {} mutation profile = {} ---------- {}" .format(mut_type, datetime.timedelta(seconds=end-start), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) return mut_type, mut_propag else: mut_type = 'raw' mut_raw = mut_raw.todense() end = time.time() print("---------- Propagation on {} mutation profile = {} ---------- {}" .format(mut_type, datetime.timedelta(seconds=end-start), datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) return mut_type, mut_raw

true

f73661c09ab447a073f32a96f89585dfb6482f20

5,509

py

Python

src/lib/utils/video.py

HLJ1997/human-action-recognition

550fcc3d1a2d8b338535ab12cb2124cf2e9c0be0

[ "MIT" ]

1

2021-07-14T09:15:09.000Z

src/lib/utils/video.py

HLJ1997/human-action-recognition

550fcc3d1a2d8b338535ab12cb2124cf2e9c0be0

[ "MIT" ]

null

src/lib/utils/video.py

HLJ1997/human-action-recognition

550fcc3d1a2d8b338535ab12cb2124cf2e9c0be0

[ "MIT" ]

null

""" Credit to original code https://github.com/tryolabs/norfair/blob/master/norfair/video.py modified to get output file path as user provided filename suffix and changed constructor of Video class """ import os import os.path as osp import time from typing import List, Optional, Union, Tuple import cv2 import numpy as np from rich import print from rich.progress import BarColumn, Progress, ProgressColumn, TimeRemainingColumn def get_terminal_size(default: Tuple[int, int] = (80, 24)) -> Tuple[int, int]: columns, lines = default for fd in range(0, 3): # First in order 0=Std In, 1=Std Out, 2=Std Error try: columns, lines = os.get_terminal_size(fd) except OSError: continue break return columns, lines class Video: def __init__(self, source: str): self.source = source is_webcam = lambda x: isinstance(x, int) self.display = 'webcam' if is_webcam(source) \ else osp.basename(source) # Read Input Video self.video_capture = cv2.VideoCapture(source) if not self.video_capture.isOpened: self._fail( f"[bold red]Error:[/bold red] '{self.source}' does not seem to be a video file supported by OpenCV. If the video file is not the problem, please check that your OpenCV installation is working correctly." ) self.total_frames = 0 if is_webcam(source) \ else int(self.video_capture.get(cv2.CAP_PROP_FRAME_COUNT)) self.frame_cnt = 0 description = 'Run' # Setup progressbar if self.display: description += f" | {self.display}" progress_bar_fields: List[Union[str, ProgressColumn]] = [ "[progress.description]{task.description}", BarColumn(), "[yellow]{task.fields[process_fps]:.2f}fps[/yellow]", ] progress_bar_fields.insert( 2, "[progress.percentage]{task.percentage:>3.0f}%" ) progress_bar_fields.insert( 3, TimeRemainingColumn(), ) self.progress_bar = Progress( *progress_bar_fields, auto_refresh=False, redirect_stdout=False, redirect_stderr=False, ) self.task = self.progress_bar.add_task( self.abbreviate_description(description), total=self.total_frames, start=self.source, process_fps=0, ) # This is a generator, note the yield keyword below. def __iter__(self): with self.progress_bar as progress_bar: start = time.time() # Iterate over video while True: self.frame_cnt += 1 ret, frame = self.video_capture.read() if ret is False or frame is None: break self.fps = self.frame_cnt / (time.time() - start) progress_bar.update( self.task, advance=1, refresh=True, process_fps=self.fps ) yield frame self.stop() def stop(self): self.video_capture.release() cv2.destroyAllWindows() def _fail(self, msg: str): print(msg) exit() def show(self, frame: np.array, winname: str='show',downsample_ratio: float = 1.0) -> int: # Resize to lower resolution for faster streaming over slow connections if self.frame_cnt == 1: cv2.namedWindow(winname, cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) cv2.resizeWindow(winname, 640, 480) cv2.moveWindow(winname, 20, 20) if downsample_ratio != 1.0: frame = cv2.resize( frame, ( frame.shape[1] // downsample_ratio, frame.shape[0] // downsample_ratio, ), ) cv2.imshow(winname, frame) return cv2.waitKey(1) def get_writer(self, frame: np.array, output_path: str, fps: Optional[int]=20) -> int: fourcc = cv2.VideoWriter_fourcc(*"XVID") output_size = (frame.shape[1], frame.shape[0]) # OpenCV format is (width, height) writer = cv2.VideoWriter(output_path, fourcc, fps, output_size) return writer def get_output_file_path(self, output_folder, suffix: List=[]) -> str: os.makedirs(output_folder, exist_ok=True) filename = '{}_' * (len(suffix)+1) filename = filename.format( 'webcam' if isinstance(self.source, int) else osp.splitext(self.display)[0], *iter(suffix) ) output_path = osp.join(output_folder, f'{filename[:-1]}.avi') return output_path def abbreviate_description(self, description: str) -> str: """Conditionally abbreviate description so that progress bar fits in small terminals""" terminal_columns, _ = get_terminal_size() space_for_description = ( int(terminal_columns) - 25 ) # Leave 25 space for progressbar if len(description) < space_for_description: return description else: return "{} ... {}".format( description[: space_for_description // 2 - 3], description[-space_for_description // 2 + 3 :], ) if __name__ == '__main__': path = '/home/zmh/hdd/Test_Videos/Tracking/aung_la_fight_cut_1.mp4' video = Video(path) for i in video: video.show(i, 'debug')

36.243421

219

0.592304

import os import os.path as osp import time from typing import List, Optional, Union, Tuple import cv2 import numpy as np from rich import print from rich.progress import BarColumn, Progress, ProgressColumn, TimeRemainingColumn def get_terminal_size(default: Tuple[int, int] = (80, 24)) -> Tuple[int, int]: columns, lines = default for fd in range(0, 3): try: columns, lines = os.get_terminal_size(fd) except OSError: continue break return columns, lines class Video: def __init__(self, source: str): self.source = source is_webcam = lambda x: isinstance(x, int) self.display = 'webcam' if is_webcam(source) \ else osp.basename(source) self.video_capture = cv2.VideoCapture(source) if not self.video_capture.isOpened: self._fail( f"[bold red]Error:[/bold red] '{self.source}' does not seem to be a video file supported by OpenCV. If the video file is not the problem, please check that your OpenCV installation is working correctly." ) self.total_frames = 0 if is_webcam(source) \ else int(self.video_capture.get(cv2.CAP_PROP_FRAME_COUNT)) self.frame_cnt = 0 description = 'Run' if self.display: description += f" | {self.display}" progress_bar_fields: List[Union[str, ProgressColumn]] = [ "[progress.description]{task.description}", BarColumn(), "[yellow]{task.fields[process_fps]:.2f}fps[/yellow]", ] progress_bar_fields.insert( 2, "[progress.percentage]{task.percentage:>3.0f}%" ) progress_bar_fields.insert( 3, TimeRemainingColumn(), ) self.progress_bar = Progress( *progress_bar_fields, auto_refresh=False, redirect_stdout=False, redirect_stderr=False, ) self.task = self.progress_bar.add_task( self.abbreviate_description(description), total=self.total_frames, start=self.source, process_fps=0, ) def __iter__(self): with self.progress_bar as progress_bar: start = time.time() while True: self.frame_cnt += 1 ret, frame = self.video_capture.read() if ret is False or frame is None: break self.fps = self.frame_cnt / (time.time() - start) progress_bar.update( self.task, advance=1, refresh=True, process_fps=self.fps ) yield frame self.stop() def stop(self): self.video_capture.release() cv2.destroyAllWindows() def _fail(self, msg: str): print(msg) exit() def show(self, frame: np.array, winname: str='show',downsample_ratio: float = 1.0) -> int: if self.frame_cnt == 1: cv2.namedWindow(winname, cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) cv2.resizeWindow(winname, 640, 480) cv2.moveWindow(winname, 20, 20) if downsample_ratio != 1.0: frame = cv2.resize( frame, ( frame.shape[1] // downsample_ratio, frame.shape[0] // downsample_ratio, ), ) cv2.imshow(winname, frame) return cv2.waitKey(1) def get_writer(self, frame: np.array, output_path: str, fps: Optional[int]=20) -> int: fourcc = cv2.VideoWriter_fourcc(*"XVID") output_size = (frame.shape[1], frame.shape[0]) writer = cv2.VideoWriter(output_path, fourcc, fps, output_size) return writer def get_output_file_path(self, output_folder, suffix: List=[]) -> str: os.makedirs(output_folder, exist_ok=True) filename = '{}_' * (len(suffix)+1) filename = filename.format( 'webcam' if isinstance(self.source, int) else osp.splitext(self.display)[0], *iter(suffix) ) output_path = osp.join(output_folder, f'{filename[:-1]}.avi') return output_path def abbreviate_description(self, description: str) -> str: terminal_columns, _ = get_terminal_size() space_for_description = ( int(terminal_columns) - 25 ) if len(description) < space_for_description: return description else: return "{} ... {}".format( description[: space_for_description // 2 - 3], description[-space_for_description // 2 + 3 :], ) if __name__ == '__main__': path = '/home/zmh/hdd/Test_Videos/Tracking/aung_la_fight_cut_1.mp4' video = Video(path) for i in video: video.show(i, 'debug')

true

f7366281db5c2cbc331075eeeac4051f393c83a9

4,294

py

Python

Self Driving Car/Python with Tensorflow/driveSDC.py

MohammadWasil/Self-Driving-Car

9ef5b77e1268623c11e4c39d5c8e1e990caee273

[ "MIT" ]

7

2018-12-19T18:44:02.000Z

2022-03-18T08:22:43.000Z

Self Driving Car/Python with Tensorflow/driveSDC.py

MohammadWasil/Self-Driving-Car

9ef5b77e1268623c11e4c39d5c8e1e990caee273

[ "MIT" ]

null

Self Driving Car/Python with Tensorflow/driveSDC.py

MohammadWasil/Self-Driving-Car

9ef5b77e1268623c11e4c39d5c8e1e990caee273

[ "MIT" ]

4

2019-03-29T14:12:49.000Z

2021-01-01T15:08:01.000Z

import socket from tensorflow.keras.models import load_model from PIL import ImageGrab import numpy as np import cv2 import os #Load the model. model = load_model(r"D:\Unity Game\Self Driving Car\SDCProgram\Best Models\data-003.h5") # Directory to load the model # Socket Tcp Connection. host = "127.0.0.1" port = 25001 # Port number #data = "1,1,11" # Data to be send sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # TCP connection print("starting connection") try: sock.connect((host, port)) #To connect ot the given port. print("Connected") except: print("Might happen socket is closed!") ####### def send_data(steering_angle, throttle): data_01 = str(steering_angle) data_02 = str(throttle) data = data_01 + ',' + data_02 sock.sendall(data.encode("utf-8")) # To send the data steeringAngleList = [] velocityList = [] throttleList = [] steeringAngle = 0 velocity = 0 throttle = 0 arr1=[] arr2=[] arr3=[] splitted_data = [] reply=[] def socketConnection(): global globalsteeringAngle global velocity global throttle try: #data = "1,0" reply = sock.recv(2048).decode("utf-8") # To receive the data #######send_data(reply) #print("Actual data received is: ", reply) splitted_data = reply.split(',') #print("after splitting the data: ", splitted_data) arr1.append(splitted_data[0]) arr2.append(splitted_data[1]) arr3.append(splitted_data[2]) steeringAngle = float(splitted_data[0]) velocity = float(splitted_data[1]) throttle = float(splitted_data[2]) except: print("Exception") steeringAngleList = np.array(arr1) velocityList = np.array(arr2) throttleList = np.array(arr3) return steeringAngleList, velocityList, throttleList, steeringAngle, velocity, throttle filename = r"D:\ML\Unity-ML\Drive SDC.csv" #Directory to save your current Data in a csv file. def csv_file(steer_Angle, velocity, throttle): #print("Writing to csv file!") f = open(filename, "w") f.write("{},{},{}\n".format("Steerring Angle", "Current Velocity", "Throttle")) for x in zip( steer_Angle, velocity, throttle): f.write("{},{},{}\n".format(x[0], x[1], x[2])) f.close() ############################# MAX_SPEED = 25 MIN_SPEED = 10 speed_limit = MAX_SPEED def preprocess(image): return cv2.resize(image, (200, 66), cv2.INTER_AREA) def drive(image, steering_angle, velocity, throttle): try: image = np.asarray(image) # from PIL image to numpy array image = preprocess(image) # apply the preprocessing image = np.array([image]) # the model expects 4D array steering_angle = float(model.predict(image, batch_size=1)) steering_angle = (steering_angle/10) global speed_limit if velocity > speed_limit: speed_limit = MIN_SPEED # slow down else: speed_limit = MAX_SPEED throttle = 1.0 - steering_angle**2 - (velocity/speed_limit)**2 print('{} {} {}'.format(steering_angle, throttle, velocity)) steering_angle = (steering_angle*10) send_data(steering_angle, throttle) except Exception as e: print("Exception Occured", e) num = 0 path = r"D:\ML\Unity-ML\Drive SDC" # Destination/path to which all the current images will be saved while (True): num = num + 1 imageName = 'Wasil'+ str(num) + '.png' # Name of the images. #collecting current data strAngl, vlcty, thrttl, steeringAngle, velocity, throttle = socketConnection() image = np.array(ImageGrab.grab(bbox=(0, 120, 750, 540))) # Taking the screebshot and adding in the array csv_file(strAngl, vlcty, thrttl) cv2.imwrite(os.path.join(path, imageName), image) # Trying to save the image in the exact same directory. drive(image, steeringAngle, velocity, throttle) """ ### NOTE: divide steering angle by 10. """

30.453901

148

0.6034

import socket from tensorflow.keras.models import load_model from PIL import ImageGrab import numpy as np import cv2 import os model = load_model(r"D:\Unity Game\Self Driving Car\SDCProgram\Best Models\data-003.h5") host = "127.0.0.1" port = 25001 ket(socket.AF_INET, socket.SOCK_STREAM) print("starting connection") try: sock.connect((host, port)) print("Connected") except: print("Might happen socket is closed!") ngle, throttle): data_01 = str(steering_angle) data_02 = str(throttle) data = data_01 + ',' + data_02 sock.sendall(data.encode("utf-8")) steeringAngleList = [] velocityList = [] throttleList = [] steeringAngle = 0 velocity = 0 throttle = 0 arr1=[] arr2=[] arr3=[] splitted_data = [] reply=[] def socketConnection(): global globalsteeringAngle global velocity global throttle try: reply = sock.recv(2048).decode("utf-8") ppend(splitted_data[1]) arr3.append(splitted_data[2]) steeringAngle = float(splitted_data[0]) velocity = float(splitted_data[1]) throttle = float(splitted_data[2]) except: print("Exception") steeringAngleList = np.array(arr1) velocityList = np.array(arr2) throttleList = np.array(arr3) return steeringAngleList, velocityList, throttleList, steeringAngle, velocity, throttle filename = r"D:\ML\Unity-ML\Drive SDC.csv" def csv_file(steer_Angle, velocity, throttle): f = open(filename, "w") f.write("{},{},{}\n".format("Steerring Angle", "Current Velocity", "Throttle")) for x in zip( steer_Angle, velocity, throttle): f.write("{},{},{}\n".format(x[0], x[1], x[2])) f.close() it: speed_limit = MIN_SPEED else: speed_limit = MAX_SPEED throttle = 1.0 - steering_angle**2 - (velocity/speed_limit)**2 print('{} {} {}'.format(steering_angle, throttle, velocity)) steering_angle = (steering_angle*10) send_data(steering_angle, throttle) except Exception as e: print("Exception Occured", e) num = 0 path = r"D:\ML\Unity-ML\Drive SDC" while (True): num = num + 1 imageName = 'Wasil'+ str(num) + '.png' strAngl, vlcty, thrttl, steeringAngle, velocity, throttle = socketConnection() image = np.array(ImageGrab.grab(bbox=(0, 120, 750, 540))) csv_file(strAngl, vlcty, thrttl) cv2.imwrite(os.path.join(path, imageName), image) drive(image, steeringAngle, velocity, throttle)

true

f736631eac1f687cc72652145a76d0e53fa16fc2

4,127

py

Python

fiasco/element.py

fluxtransport/fiasco

9d70d8bdb03197be1ddfd433e1392e214a1468e8

[ "BSD-3-Clause" ]

null

fiasco/element.py

fluxtransport/fiasco

9d70d8bdb03197be1ddfd433e1392e214a1468e8

[ "BSD-3-Clause" ]

null

fiasco/element.py

fluxtransport/fiasco

9d70d8bdb03197be1ddfd433e1392e214a1468e8

[ "BSD-3-Clause" ]

null

""" Classes and functions for element-level operations """ import numpy as np import astropy.units as u import plasmapy import fiasco __all__ = ['Element'] class Element(fiasco.IonCollection): """ Collection of all ions for a particular element. The `Element` object provides a way to logically group together ions of the same element. This provides an easy way to compute element-level derived quantities such as the ionization fraction as a function of temperature. Parameters ---------- element_name : `str`, `int` Symbol, atomic number, or full name of the element temperature : `~astropy.units.Quantity` See Also -------- fiasco.Ion : All the same keyword arguments can also be passed here. """ @u.quantity_input def __init__(self, element_name, temperature: u.K, **kwargs): if type(element_name) is str: element_name = element_name.capitalize() Z = plasmapy.atomic.atomic_number(element_name) ion_list = [] for i in range(Z + 1): ion = fiasco.Ion(f'{Z} {i+1}', temperature, **kwargs) ion_list.append(ion) super().__init__(*ion_list) @property def atomic_symbol(self): return self[0].atomic_symbol @property def atomic_number(self): return self[0].atomic_number @property def element_name(self): return self[0].element_name @property def abundance(self): return self[0].abundance def _rate_matrix(self): rate_matrix = np.zeros(self.temperature.shape+(self.atomic_number+1, self.atomic_number+1)) rate_unit = self[0].ionization_rate().unit rate_matrix = rate_matrix * rate_unit for i in range(1, self.atomic_number): rate_matrix[:, i, i] = -(self[i].ionization_rate() + self[i].recombination_rate()) rate_matrix[:, i, i-1] = self[i-1].ionization_rate() rate_matrix[:, i, i+1] = self[i+1].recombination_rate() rate_matrix[:, 0, 0] = -(self[0].ionization_rate() + self[0].recombination_rate()) rate_matrix[:, 0, 1] = self[1].recombination_rate() rate_matrix[:, -1, -1] = -(self[-1].ionization_rate() + self[-1].recombination_rate()) rate_matrix[:, -1, -2] = self[-2].ionization_rate() return rate_matrix def equilibrium_ionization(self, **kwargs): """ Calculate the ionization fraction, in equilibrium, for all ions of the element. Calculate the population fractions for every ion of this element as a function of temperature, assuming ionization equilibrium. Parameters ---------- rate_matrix : `~astropy.units.Quantity`, optional :math:`Z+1` by :math:`Z+1` matrix of ionization and recombination rates. If not given, this will be computed automatically. See Also -------- fiasco.Ion.ionization_rate fiasco.Ion.recombination_rate """ rate_matrix = kwargs.get('rate_matrix', None) if rate_matrix is None: rate_matrix = self._rate_matrix() # Solve system of equations using singular value decomposition _, _, V = np.linalg.svd(rate_matrix.value) # Select columns of V with smallest eigenvalues (returned in descending order) # NOTE: must take the absolute value as the SVD solution is only accurate up # to the sign. We require that the solutions must be positive. ioneq = np.fabs(V[:, -1, :]) ioneq /= ioneq.sum(axis=1)[:, np.newaxis] return u.Quantity(ioneq) def __getitem__(self, value): if type(value) is str: el, ion = value.split() if '+' in ion: value = int(ion.strip('+')) else: value = int(ion) - 1 return super().__getitem__(value) def __repr__(self): ion_list = '\n'.join([i.ion_name for i in self._ion_list]) return f"""Element ------- {self.atomic_symbol} ({self.atomic_number}) -- {self.element_name} Available Ions -------------- {ion_list}"""

33.282258

99

0.620305

import numpy as np import astropy.units as u import plasmapy import fiasco __all__ = ['Element'] class Element(fiasco.IonCollection): @u.quantity_input def __init__(self, element_name, temperature: u.K, **kwargs): if type(element_name) is str: element_name = element_name.capitalize() Z = plasmapy.atomic.atomic_number(element_name) ion_list = [] for i in range(Z + 1): ion = fiasco.Ion(f'{Z} {i+1}', temperature, **kwargs) ion_list.append(ion) super().__init__(*ion_list) @property def atomic_symbol(self): return self[0].atomic_symbol @property def atomic_number(self): return self[0].atomic_number @property def element_name(self): return self[0].element_name @property def abundance(self): return self[0].abundance def _rate_matrix(self): rate_matrix = np.zeros(self.temperature.shape+(self.atomic_number+1, self.atomic_number+1)) rate_unit = self[0].ionization_rate().unit rate_matrix = rate_matrix * rate_unit for i in range(1, self.atomic_number): rate_matrix[:, i, i] = -(self[i].ionization_rate() + self[i].recombination_rate()) rate_matrix[:, i, i-1] = self[i-1].ionization_rate() rate_matrix[:, i, i+1] = self[i+1].recombination_rate() rate_matrix[:, 0, 0] = -(self[0].ionization_rate() + self[0].recombination_rate()) rate_matrix[:, 0, 1] = self[1].recombination_rate() rate_matrix[:, -1, -1] = -(self[-1].ionization_rate() + self[-1].recombination_rate()) rate_matrix[:, -1, -2] = self[-2].ionization_rate() return rate_matrix def equilibrium_ionization(self, **kwargs): rate_matrix = kwargs.get('rate_matrix', None) if rate_matrix is None: rate_matrix = self._rate_matrix() _, _, V = np.linalg.svd(rate_matrix.value) ioneq = np.fabs(V[:, -1, :]) ioneq /= ioneq.sum(axis=1)[:, np.newaxis] return u.Quantity(ioneq) def __getitem__(self, value): if type(value) is str: el, ion = value.split() if '+' in ion: value = int(ion.strip('+')) else: value = int(ion) - 1 return super().__getitem__(value) def __repr__(self): ion_list = '\n'.join([i.ion_name for i in self._ion_list]) return f"""Element ------- {self.atomic_symbol} ({self.atomic_number}) -- {self.element_name} Available Ions -------------- {ion_list}"""

true

f73663f3d443c0c444771b600f5712a2e9d729ec

983

py

Python

whoahqa/tests/models/test_reporting_period_factory.py

onaio/who-adolescent-hqa

108a7e60b025d0723247f5f02eab2c4d41f5a02a

[ "Apache-2.0" ]

null

whoahqa/tests/models/test_reporting_period_factory.py

onaio/who-adolescent-hqa

108a7e60b025d0723247f5f02eab2c4d41f5a02a

[ "Apache-2.0" ]

2

2018-01-09T08:58:11.000Z

2019-01-18T09:20:14.000Z

whoahqa/tests/models/test_reporting_period_factory.py

onaio/who-adolescent-hqa

108a7e60b025d0723247f5f02eab2c4d41f5a02a

[ "Apache-2.0" ]

null

import datetime from pyramid import testing from whoahqa.models import ( DBSession, ReportingPeriodFactory, ReportingPeriod,) from whoahqa.tests.test_base import TestBase class TestReportingPeriodFactory(TestBase): def test_get_item_retrieves_by_id(self): period = ReportingPeriod( title="2014/2015", start_date=datetime.date(2014, 2, 1), end_date=datetime.date(2015, 2, 1)) DBSession.add(period) DBSession.flush() id = period.id factory = ReportingPeriodFactory( testing.DummyRequest()) period = factory.__getitem__(id) self.assertEqual(period.id, id) self.assertEqual(period.__parent__, factory) self.assertEqual(period.__name__, id) def test_get_item_throws_key_error_if_non_existent_id(self): factory = ReportingPeriodFactory( testing.DummyRequest()) self.assertRaises(KeyError, factory.__getitem__, '0')

29.787879

64

0.677518

import datetime from pyramid import testing from whoahqa.models import ( DBSession, ReportingPeriodFactory, ReportingPeriod,) from whoahqa.tests.test_base import TestBase class TestReportingPeriodFactory(TestBase): def test_get_item_retrieves_by_id(self): period = ReportingPeriod( title="2014/2015", start_date=datetime.date(2014, 2, 1), end_date=datetime.date(2015, 2, 1)) DBSession.add(period) DBSession.flush() id = period.id factory = ReportingPeriodFactory( testing.DummyRequest()) period = factory.__getitem__(id) self.assertEqual(period.id, id) self.assertEqual(period.__parent__, factory) self.assertEqual(period.__name__, id) def test_get_item_throws_key_error_if_non_existent_id(self): factory = ReportingPeriodFactory( testing.DummyRequest()) self.assertRaises(KeyError, factory.__getitem__, '0')

true

f7366410d3402dde7686f8ca4b30c6c1c2234403

52,914

py

Python

models/new/sencebgan.py

yigitozgumus/Polimi_Thesis

711c1edcf1fdb92fc6c15bf5ab1be141c13995c3

[ "MIT" ]

3

2019-07-27T14:00:42.000Z

2020-01-17T17:07:51.000Z

models/new/sencebgan.py

yigitozgumus/Polimi_Thesis

711c1edcf1fdb92fc6c15bf5ab1be141c13995c3

[ "MIT" ]

null

models/new/sencebgan.py

yigitozgumus/Polimi_Thesis

711c1edcf1fdb92fc6c15bf5ab1be141c13995c3

[ "MIT" ]

4

2019-10-22T02:58:26.000Z

2020-10-06T09:59:26.000Z

import tensorflow as tf from base.base_model import BaseModel from utils.alad_utils import get_getter import utils.alad_utils as sn class SENCEBGAN(BaseModel): def __init__(self, config): super(SENCEBGAN, self).__init__(config) self.build_model() self.init_saver() def build_model(self): ############################################################################################ # INIT ############################################################################################ # Kernel initialization for the convolutions if self.config.trainer.init_type == "normal": self.init_kernel = tf.random_normal_initializer(mean=0.0, stddev=0.02) elif self.config.trainer.init_type == "xavier": self.init_kernel = tf.contrib.layers.xavier_initializer( uniform=False, seed=None, dtype=tf.float32 ) # Placeholders self.is_training_gen = tf.placeholder(tf.bool) self.is_training_dis = tf.placeholder(tf.bool) self.is_training_enc_g = tf.placeholder(tf.bool) self.is_training_enc_r = tf.placeholder(tf.bool) self.feature_match1 = tf.placeholder(tf.float32) self.feature_match2 = tf.placeholder(tf.float32) self.image_input = tf.placeholder( tf.float32, shape=[None] + self.config.trainer.image_dims, name="x" ) self.noise_tensor = tf.placeholder( tf.float32, shape=[None, self.config.trainer.noise_dim], name="noise" ) ############################################################################################ # MODEL ############################################################################################ self.logger.info("Building training graph...") with tf.variable_scope("SENCEBGAN"): # First training part # G(z) ==> x' with tf.variable_scope("Generator_Model"): self.image_gen = self.generator(self.noise_tensor) # Discriminator outputs with tf.variable_scope("Discriminator_Model"): self.embedding_real, self.decoded_real = self.discriminator( self.image_input, do_spectral_norm=self.config.trainer.do_spectral_norm ) self.embedding_fake, self.decoded_fake = self.discriminator( self.image_gen, do_spectral_norm=self.config.trainer.do_spectral_norm ) # Second training part # E(x) ==> z' with tf.variable_scope("Encoder_G_Model"): self.image_encoded = self.encoder_g(self.image_input) # G(z') ==> G(E(x)) ==> x'' with tf.variable_scope("Generator_Model"): self.image_gen_enc = self.generator(self.image_encoded) # Discriminator outputs with tf.variable_scope("Discriminator_Model"): self.embedding_enc_fake, self.decoded_enc_fake = self.discriminator( self.image_gen_enc, do_spectral_norm=self.config.trainer.do_spectral_norm ) self.embedding_enc_real, self.decoded_enc_real = self.discriminator( self.image_input, do_spectral_norm=self.config.trainer.do_spectral_norm ) with tf.variable_scope("Discriminator_Model_XX"): self.im_logit_real, self.im_f_real = self.discriminator_xx( self.image_input, self.image_input, do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.im_logit_fake, self.im_f_fake = self.discriminator_xx( self.image_input, self.image_gen_enc, do_spectral_norm=self.config.trainer.do_spectral_norm, ) # Third training part with tf.variable_scope("Encoder_G_Model"): self.image_encoded_r = self.encoder_g(self.image_input) with tf.variable_scope("Generator_Model"): self.image_gen_enc_r = self.generator(self.image_encoded_r) with tf.variable_scope("Encoder_R_Model"): self.image_ege = self.encoder_r(self.image_gen_enc_r) with tf.variable_scope("Discriminator_Model_ZZ"): self.z_logit_real, self.z_f_real = self.discriminator_zz( self.image_encoded_r, self.image_encoded_r, do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.z_logit_fake, self.z_f_fake = self.discriminator_zz( self.image_encoded_r, self.image_ege, do_spectral_norm=self.config.trainer.do_spectral_norm, ) ############################################################################################ # LOSS FUNCTIONS ############################################################################################ with tf.name_scope("Loss_Functions"): with tf.name_scope("Generator_Discriminator"): # Discriminator Loss if self.config.trainer.mse_mode == "norm": self.disc_loss_real = tf.reduce_mean( self.mse_loss( self.decoded_real, self.image_input, mode="norm", order=self.config.trainer.order, ) ) self.disc_loss_fake = tf.reduce_mean( self.mse_loss( self.decoded_fake, self.image_gen, mode="norm", order=self.config.trainer.order, ) ) elif self.config.trainer.mse_mode == "mse": self.disc_loss_real = self.mse_loss( self.decoded_real, self.image_input, mode="mse", order=self.config.trainer.order, ) self.disc_loss_fake = self.mse_loss( self.decoded_fake, self.image_gen, mode="mse", order=self.config.trainer.order, ) self.loss_discriminator = ( tf.math.maximum(self.config.trainer.disc_margin - self.disc_loss_fake, 0) + self.disc_loss_real ) # Generator Loss pt_loss = 0 if self.config.trainer.pullaway: pt_loss = self.pullaway_loss(self.embedding_fake) self.loss_generator = self.disc_loss_fake + self.config.trainer.pt_weight * pt_loss # New addition to enforce visual similarity delta_noise = self.embedding_real - self.embedding_fake delta_flat = tf.layers.Flatten()(delta_noise) loss_noise_gen = tf.reduce_mean(tf.norm(delta_flat, ord=2, axis=1, keepdims=False)) self.loss_generator += 0.1 * loss_noise_gen with tf.name_scope("Encoder_G"): if self.config.trainer.mse_mode == "norm": self.loss_enc_rec = tf.reduce_mean( self.mse_loss( self.image_gen_enc, self.image_input, mode="norm", order=self.config.trainer.order, ) ) self.loss_enc_f = tf.reduce_mean( self.mse_loss( self.decoded_enc_real, self.decoded_enc_fake, mode="norm", order=self.config.trainer.order, ) ) elif self.config.trainer.mse_mode == "mse": self.loss_enc_rec = tf.reduce_mean( self.mse_loss( self.image_gen_enc, self.image_input, mode="mse", order=self.config.trainer.order, ) ) self.loss_enc_f = tf.reduce_mean( self.mse_loss( self.embedding_enc_real, self.embedding_enc_fake, mode="mse", order=self.config.trainer.order, ) ) self.loss_encoder_g = ( self.loss_enc_rec + self.config.trainer.encoder_f_factor * self.loss_enc_f ) if self.config.trainer.enable_disc_xx: self.enc_xx_real = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.im_logit_real, labels=tf.zeros_like(self.im_logit_real) ) self.enc_xx_fake = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.im_logit_fake, labels=tf.ones_like(self.im_logit_fake) ) self.enc_loss_xx = tf.reduce_mean(self.enc_xx_real + self.enc_xx_fake) self.loss_encoder_g += self.enc_loss_xx with tf.name_scope("Encoder_R"): if self.config.trainer.mse_mode == "norm": self.loss_encoder_r = tf.reduce_mean( self.mse_loss( self.image_ege, self.image_encoded_r, mode="norm", order=self.config.trainer.order, ) ) elif self.config.trainer.mse_mode == "mse": self.loss_encoder_r = tf.reduce_mean( self.mse_loss( self.image_ege, self.image_encoded_r, mode="mse", order=self.config.trainer.order, ) ) if self.config.trainer.enable_disc_zz: self.enc_zz_real = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.z_logit_real, labels=tf.zeros_like(self.z_logit_real) ) self.enc_zz_fake = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.z_logit_fake, labels=tf.ones_like(self.z_logit_fake) ) self.enc_loss_zz = tf.reduce_mean(self.enc_zz_real + self.enc_zz_fake) self.loss_encoder_r += self.enc_loss_zz if self.config.trainer.enable_disc_xx: with tf.name_scope("Discriminator_XX"): self.loss_xx_real = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.im_logit_real, labels=tf.ones_like(self.im_logit_real) ) self.loss_xx_fake = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.im_logit_fake, labels=tf.zeros_like(self.im_logit_fake) ) self.dis_loss_xx = tf.reduce_mean(self.loss_xx_real + self.loss_xx_fake) if self.config.trainer.enable_disc_zz: with tf.name_scope("Discriminator_ZZ"): self.loss_zz_real = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.z_logit_real, labels=tf.ones_like(self.z_logit_real) ) self.loss_zz_fake = tf.nn.sigmoid_cross_entropy_with_logits( logits=self.z_logit_fake, labels=tf.zeros_like(self.z_logit_fake) ) self.dis_loss_zz = tf.reduce_mean(self.loss_zz_real + self.loss_zz_fake) ############################################################################################ # OPTIMIZERS ############################################################################################ with tf.name_scope("Optimizers"): self.generator_optimizer = tf.train.AdamOptimizer( self.config.trainer.standard_lr_gen, beta1=self.config.trainer.optimizer_adam_beta1, beta2=self.config.trainer.optimizer_adam_beta2, ) self.encoder_g_optimizer = tf.train.AdamOptimizer( self.config.trainer.standard_lr_enc, beta1=self.config.trainer.optimizer_adam_beta1, beta2=self.config.trainer.optimizer_adam_beta2, ) self.encoder_r_optimizer = tf.train.AdamOptimizer( self.config.trainer.standard_lr_enc, beta1=self.config.trainer.optimizer_adam_beta1, beta2=self.config.trainer.optimizer_adam_beta2, ) self.discriminator_optimizer = tf.train.AdamOptimizer( self.config.trainer.standard_lr_dis, beta1=self.config.trainer.optimizer_adam_beta1, beta2=self.config.trainer.optimizer_adam_beta2, ) # Collect all the variables all_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) # Generator Network Variables self.generator_vars = [ v for v in all_variables if v.name.startswith("SENCEBGAN/Generator_Model") ] # Discriminator Network Variables self.discriminator_vars = [ v for v in all_variables if v.name.startswith("SENCEBGAN/Discriminator_Model") ] # Discriminator Network Variables self.encoder_g_vars = [ v for v in all_variables if v.name.startswith("SENCEBGAN/Encoder_G_Model") ] self.encoder_r_vars = [ v for v in all_variables if v.name.startswith("SENCEBGAN/Encoder_R_Model") ] self.dxxvars = [ v for v in all_variables if v.name.startswith("SENCEBGAN/Discriminator_Model_XX") ] self.dzzvars = [ v for v in all_variables if v.name.startswith("SENCEBGAN/Discriminator_Model_ZZ") ] # Generator Network Operations self.gen_update_ops = tf.get_collection( tf.GraphKeys.UPDATE_OPS, scope="SENCEBGAN/Generator_Model" ) # Discriminator Network Operations self.disc_update_ops = tf.get_collection( tf.GraphKeys.UPDATE_OPS, scope="SENCEBGAN/Discriminator_Model" ) self.encg_update_ops = tf.get_collection( tf.GraphKeys.UPDATE_OPS, scope="SENCEBGAN/Encoder_G_Model" ) self.encr_update_ops = tf.get_collection( tf.GraphKeys.UPDATE_OPS, scope="SENCEBGAN/Encoder_R_Model" ) self.update_ops_dis_xx = tf.get_collection( tf.GraphKeys.UPDATE_OPS, scope="SENCEBGAN/Discriminator_Model_XX" ) self.update_ops_dis_zz = tf.get_collection( tf.GraphKeys.UPDATE_OPS, scope="SENCEBGAN/Discriminator_Model_ZZ" ) with tf.control_dependencies(self.gen_update_ops): self.gen_op = self.generator_optimizer.minimize( self.loss_generator, var_list=self.generator_vars, global_step=self.global_step_tensor, ) with tf.control_dependencies(self.disc_update_ops): self.disc_op = self.discriminator_optimizer.minimize( self.loss_discriminator, var_list=self.discriminator_vars ) with tf.control_dependencies(self.encg_update_ops): self.encg_op = self.encoder_g_optimizer.minimize( self.loss_encoder_g, var_list=self.encoder_g_vars, global_step=self.global_step_tensor, ) with tf.control_dependencies(self.encr_update_ops): self.encr_op = self.encoder_r_optimizer.minimize( self.loss_encoder_r, var_list=self.encoder_r_vars, global_step=self.global_step_tensor, ) if self.config.trainer.enable_disc_xx: with tf.control_dependencies(self.update_ops_dis_xx): self.disc_op_xx = self.discriminator_optimizer.minimize( self.dis_loss_xx, var_list=self.dxxvars ) if self.config.trainer.enable_disc_zz: with tf.control_dependencies(self.update_ops_dis_zz): self.disc_op_zz = self.discriminator_optimizer.minimize( self.dis_loss_zz, var_list=self.dzzvars ) # Exponential Moving Average for Estimation self.dis_ema = tf.train.ExponentialMovingAverage(decay=self.config.trainer.ema_decay) maintain_averages_op_dis = self.dis_ema.apply(self.discriminator_vars) self.gen_ema = tf.train.ExponentialMovingAverage(decay=self.config.trainer.ema_decay) maintain_averages_op_gen = self.gen_ema.apply(self.generator_vars) self.encg_ema = tf.train.ExponentialMovingAverage(decay=self.config.trainer.ema_decay) maintain_averages_op_encg = self.encg_ema.apply(self.encoder_g_vars) self.encr_ema = tf.train.ExponentialMovingAverage(decay=self.config.trainer.ema_decay) maintain_averages_op_encr = self.encr_ema.apply(self.encoder_r_vars) if self.config.trainer.enable_disc_xx: self.dis_xx_ema = tf.train.ExponentialMovingAverage( decay=self.config.trainer.ema_decay ) maintain_averages_op_dis_xx = self.dis_xx_ema.apply(self.dxxvars) if self.config.trainer.enable_disc_zz: self.dis_zz_ema = tf.train.ExponentialMovingAverage( decay=self.config.trainer.ema_decay ) maintain_averages_op_dis_zz = self.dis_zz_ema.apply(self.dzzvars) with tf.control_dependencies([self.disc_op]): self.train_dis_op = tf.group(maintain_averages_op_dis) with tf.control_dependencies([self.gen_op]): self.train_gen_op = tf.group(maintain_averages_op_gen) with tf.control_dependencies([self.encg_op]): self.train_enc_g_op = tf.group(maintain_averages_op_encg) with tf.control_dependencies([self.encr_op]): self.train_enc_r_op = tf.group(maintain_averages_op_encr) if self.config.trainer.enable_disc_xx: with tf.control_dependencies([self.disc_op_xx]): self.train_dis_op_xx = tf.group(maintain_averages_op_dis_xx) if self.config.trainer.enable_disc_zz: with tf.control_dependencies([self.disc_op_zz]): self.train_dis_op_zz = tf.group(maintain_averages_op_dis_zz) ############################################################################################ # TESTING ############################################################################################ self.logger.info("Building Testing Graph...") with tf.variable_scope("SENCEBGAN"): with tf.variable_scope("Discriminator_Model"): self.embedding_q_ema, self.decoded_q_ema = self.discriminator( self.image_input, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) with tf.variable_scope("Generator_Model"): self.image_gen_ema = self.generator( self.embedding_q_ema, getter=get_getter(self.gen_ema) ) with tf.variable_scope("Discriminator_Model"): self.embedding_rec_ema, self.decoded_rec_ema = self.discriminator( self.image_gen_ema, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) # Second Training Part with tf.variable_scope("Encoder_G_Model"): self.image_encoded_ema = self.encoder_g( self.image_input, getter=get_getter(self.encg_ema) ) with tf.variable_scope("Generator_Model"): self.image_gen_enc_ema = self.generator( self.image_encoded_ema, getter=get_getter(self.gen_ema) ) with tf.variable_scope("Discriminator_Model"): self.embedding_enc_fake_ema, self.decoded_enc_fake_ema = self.discriminator( self.image_gen_enc_ema, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.embedding_enc_real_ema, self.decoded_enc_real_ema = self.discriminator( self.image_input, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) if self.config.trainer.enable_disc_xx: with tf.variable_scope("Discriminator_Model_XX"): self.im_logit_real_ema, self.im_f_real_ema = self.discriminator_xx( self.image_input, self.image_input, getter=get_getter(self.dis_xx_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.im_logit_fake_ema, self.im_f_fake_ema = self.discriminator_xx( self.image_input, self.image_gen_enc_ema, getter=get_getter(self.dis_xx_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) # Third training part with tf.variable_scope("Encoder_G_Model"): self.image_encoded_r_ema = self.encoder_g(self.image_input) with tf.variable_scope("Generator_Model"): self.image_gen_enc_r_ema = self.generator(self.image_encoded_r_ema) with tf.variable_scope("Encoder_R_Model"): self.image_ege_ema = self.encoder_r(self.image_gen_enc_r_ema) with tf.variable_scope("Discriminator_Model"): self.embedding_encr_fake_ema, self.decoded_encr_fake_ema = self.discriminator( self.image_gen_enc_r_ema, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.embedding_encr_real_ema, self.decoded_encr_real_ema = self.discriminator( self.image_input, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) if self.config.trainer.enable_disc_zz: with tf.variable_scope("Discriminator_Model_ZZ"): self.z_logit_real_ema, self.z_f_real_ema = self.discriminator_zz( self.image_encoded_r_ema, self.image_encoded_r_ema, getter=get_getter(self.dis_zz_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.z_logit_fake_ema, self.z_f_fake_ema = self.discriminator_zz( self.image_encoded_r_ema, self.image_ege_ema, getter=get_getter(self.dis_zz_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) with tf.name_scope("Testing"): with tf.name_scope("Image_Based"): delta = self.image_input - self.image_gen_enc_ema self.rec_residual = -delta delta_flat = tf.layers.Flatten()(delta) img_score_l1 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="img_loss__1" ) self.img_score_l1 = tf.squeeze(img_score_l1) delta = self.decoded_enc_fake_ema - self.decoded_enc_real_ema delta_flat = tf.layers.Flatten()(delta) img_score_l2 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="img_loss__2" ) self.img_score_l2 = tf.squeeze(img_score_l2) with tf.name_scope("Noise_Based"): delta = self.image_encoded_r_ema - self.image_ege_ema delta_flat = tf.layers.Flatten()(delta) final_score_1 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="final_score_1" ) self.final_score_1 = tf.squeeze(final_score_1) self.score_comb_im = ( 1 * self.img_score_l1 + self.feature_match1 * self.final_score_1 ) delta = self.image_encoded_r_ema - self.embedding_enc_fake_ema delta_flat = tf.layers.Flatten()(delta) final_score_2 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="final_score_2" ) self.final_score_2 = tf.squeeze(final_score_2) delta = self.embedding_encr_real_ema - self.embedding_encr_fake_ema delta_flat = tf.layers.Flatten()(delta) final_score_3 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="final_score_3" ) self.final_score_3 = tf.squeeze(final_score_3) # Combo 1 self.score_comb_z = ( (1 - self.feature_match2) * self.final_score_2 + self.feature_match2 * self.final_score_3 ) # Combo 2 if self.config.trainer.enable_disc_xx: delta = self.im_f_real_ema - self.im_f_fake_ema delta_flat = tf.layers.Flatten()(delta) final_score_4 = tf.norm( delta_flat, ord=1, axis=1, keepdims=False, name="final_score_4" ) self.final_score_4 = tf.squeeze(final_score_4) delta = self.z_f_real_ema - self.z_f_fake_ema delta_flat = tf.layers.Flatten()(delta) final_score_6 = tf.norm( delta_flat, ord=1, axis=1, keepdims=False, name="final_score_6" ) self.final_score_6 = tf.squeeze(final_score_6) ############################################################################################ # TENSORBOARD ############################################################################################ if self.config.log.enable_summary: with tf.name_scope("train_summary"): with tf.name_scope("dis_summary"): tf.summary.scalar("loss_disc", self.loss_discriminator, ["dis"]) tf.summary.scalar("loss_disc_real", self.disc_loss_real, ["dis"]) tf.summary.scalar("loss_disc_fake", self.disc_loss_fake, ["dis"]) if self.config.trainer.enable_disc_xx: tf.summary.scalar("loss_dis_xx", self.dis_loss_xx, ["enc_g"]) if self.config.trainer.enable_disc_zz: tf.summary.scalar("loss_dis_zz", self.dis_loss_zz, ["enc_r"]) with tf.name_scope("gen_summary"): tf.summary.scalar("loss_generator", self.loss_generator, ["gen"]) with tf.name_scope("enc_summary"): tf.summary.scalar("loss_encoder_g", self.loss_encoder_g, ["enc_g"]) tf.summary.scalar("loss_encoder_r", self.loss_encoder_r, ["enc_r"]) with tf.name_scope("img_summary"): tf.summary.image("input_image", self.image_input, 1, ["img_1"]) tf.summary.image("reconstructed", self.image_gen, 1, ["img_1"]) # From discriminator in part 1 tf.summary.image("decoded_real", self.decoded_real, 1, ["img_1"]) tf.summary.image("decoded_fake", self.decoded_fake, 1, ["img_1"]) # Second Stage of Training tf.summary.image("input_enc", self.image_input, 1, ["img_2"]) tf.summary.image("reconstructed", self.image_gen_enc, 1, ["img_2"]) # From discriminator in part 2 tf.summary.image("decoded_enc_real", self.decoded_enc_real, 1, ["img_2"]) tf.summary.image("decoded_enc_fake", self.decoded_enc_fake, 1, ["img_2"]) # Testing tf.summary.image("input_image", self.image_input, 1, ["test"]) tf.summary.image("reconstructed", self.image_gen_enc_r_ema, 1, ["test"]) tf.summary.image("residual", self.rec_residual, 1, ["test"]) self.sum_op_dis = tf.summary.merge_all("dis") self.sum_op_gen = tf.summary.merge_all("gen") self.sum_op_enc_g = tf.summary.merge_all("enc_g") self.sum_op_enc_r = tf.summary.merge_all("enc_r") self.sum_op_im_1 = tf.summary.merge_all("img_1") self.sum_op_im_2 = tf.summary.merge_all("img_2") self.sum_op_im_test = tf.summary.merge_all("test") self.sum_op = tf.summary.merge([self.sum_op_dis, self.sum_op_gen]) ############################################################################################### # MODULES ############################################################################################### def generator(self, noise_input, getter=None): with tf.variable_scope("Generator", custom_getter=getter, reuse=tf.AUTO_REUSE): net_name = "Layer_1" with tf.variable_scope(net_name): x_g = tf.layers.Dense( units=2 * 2 * 256, kernel_initializer=self.init_kernel, name="fc" )(noise_input) x_g = tf.layers.batch_normalization( x_g, momentum=self.config.trainer.batch_momentum, training=self.is_training_gen, name="batch_normalization", ) x_g = tf.nn.leaky_relu( features=x_g, alpha=self.config.trainer.leakyReLU_alpha, name="relu" ) x_g = tf.reshape(x_g, [-1, 2, 2, 256]) net_name = "Layer_2" with tf.variable_scope(net_name): x_g = tf.layers.Conv2DTranspose( filters=128, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2t", )(x_g) x_g = tf.layers.batch_normalization( x_g, momentum=self.config.trainer.batch_momentum, training=self.is_training_gen, name="batch_normalization", ) x_g = tf.nn.leaky_relu( features=x_g, alpha=self.config.trainer.leakyReLU_alpha, name="relu" ) net_name = "Layer_3" with tf.variable_scope(net_name): x_g = tf.layers.Conv2DTranspose( filters=64, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2t", )(x_g) x_g = tf.layers.batch_normalization( x_g, momentum=self.config.trainer.batch_momentum, training=self.is_training_gen, name="batch_normalization", ) x_g = tf.nn.leaky_relu( features=x_g, alpha=self.config.trainer.leakyReLU_alpha, name="relu" ) net_name = "Layer_4" with tf.variable_scope(net_name): x_g = tf.layers.Conv2DTranspose( filters=32, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2t", )(x_g) x_g = tf.layers.batch_normalization( x_g, momentum=self.config.trainer.batch_momentum, training=self.is_training_gen, name="batch_normalization", ) x_g = tf.nn.leaky_relu( features=x_g, alpha=self.config.trainer.leakyReLU_alpha, name="relu" ) net_name = "Layer_5" with tf.variable_scope(net_name): x_g = tf.layers.Conv2DTranspose( filters=1, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2t", )(x_g) x_g = tf.tanh(x_g, name="tanh") return x_g def discriminator(self, image_input, getter=None, do_spectral_norm=False): layers = sn if do_spectral_norm else tf.layers with tf.variable_scope("Discriminator", custom_getter=getter, reuse=tf.AUTO_REUSE): with tf.variable_scope("Encoder"): x_e = tf.reshape( image_input, [-1, self.config.data_loader.image_size, self.config.data_loader.image_size, 1], ) net_name = "Layer_1" with tf.variable_scope(net_name): x_e = layers.conv2d( x_e, filters=32, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv", ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) # 14 x 14 x 64 net_name = "Layer_2" with tf.variable_scope(net_name): x_e = layers.conv2d( x_e, filters=64, kernel_size=5, padding="same", strides=2, kernel_initializer=self.init_kernel, name="conv", ) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) # 7 x 7 x 128 net_name = "Layer_3" with tf.variable_scope(net_name): x_e = layers.conv2d( x_e, filters=128, kernel_size=5, padding="same", strides=2, kernel_initializer=self.init_kernel, name="conv", ) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) # 4 x 4 x 256 x_e = tf.layers.Flatten()(x_e) net_name = "Layer_4" with tf.variable_scope(net_name): x_e = layers.dense( x_e, units=self.config.trainer.noise_dim, kernel_initializer=self.init_kernel, name="fc", ) embedding = x_e with tf.variable_scope("Decoder"): net = tf.reshape(embedding, [-1, 1, 1, self.config.trainer.noise_dim]) net_name = "layer_1" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=256, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv1", )(net) net = tf.layers.batch_normalization( inputs=net, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, name="tconv1/bn", ) net = tf.nn.relu(features=net, name="tconv1/relu") net_name = "layer_2" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=128, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv2", )(net) net = tf.layers.batch_normalization( inputs=net, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, name="tconv2/bn", ) net = tf.nn.relu(features=net, name="tconv2/relu") net_name = "layer_3" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=64, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv3", )(net) net = tf.layers.batch_normalization( inputs=net, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, name="tconv3/bn", ) net = tf.nn.relu(features=net, name="tconv3/relu") net_name = "layer_4" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=32, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv4", )(net) net = tf.layers.batch_normalization( inputs=net, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, name="tconv4/bn", ) net = tf.nn.relu(features=net, name="tconv4/relu") net_name = "layer_5" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=1, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv5", )(net) decoded = tf.nn.tanh(net, name="tconv5/tanh") return embedding, decoded def encoder_g(self, image_input, getter=None): with tf.variable_scope("Encoder_G", custom_getter=getter, reuse=tf.AUTO_REUSE): x_e = tf.reshape( image_input, [-1, self.config.data_loader.image_size, self.config.data_loader.image_size, 1], ) net_name = "Layer_1" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=64, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_g, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) net_name = "Layer_2" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=128, kernel_size=5, padding="same", strides=(2, 2), kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_g, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) net_name = "Layer_3" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=256, kernel_size=5, padding="same", strides=(2, 2), kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_g, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) x_e = tf.layers.Flatten()(x_e) net_name = "Layer_4" with tf.variable_scope(net_name): x_e = tf.layers.Dense( units=self.config.trainer.noise_dim, kernel_initializer=self.init_kernel, name="fc", )(x_e) return x_e def encoder_r(self, image_input, getter=None): with tf.variable_scope("Encoder_R", custom_getter=getter, reuse=tf.AUTO_REUSE): x_e = tf.reshape( image_input, [-1, self.config.data_loader.image_size, self.config.data_loader.image_size, 1], ) net_name = "Layer_1" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=64, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_r, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) net_name = "Layer_2" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=128, kernel_size=5, padding="same", strides=(2, 2), kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_r, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) net_name = "Layer_3" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=256, kernel_size=5, padding="same", strides=(2, 2), kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_r, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) x_e = tf.layers.Flatten()(x_e) net_name = "Layer_4" with tf.variable_scope(net_name): x_e = tf.layers.Dense( units=self.config.trainer.noise_dim, kernel_initializer=self.init_kernel, name="fc", )(x_e) return x_e # Regularizer discriminator for the Generator Encoder def discriminator_xx(self, img_tensor, recreated_img, getter=None, do_spectral_norm=False): """ Discriminator architecture in tensorflow Discriminates between (x, x) and (x, rec_x) Args: img_tensor: recreated_img: getter: for exponential moving average during inference reuse: sharing variables or not do_spectral_norm: """ layers = sn if do_spectral_norm else tf.layers with tf.variable_scope("Discriminator_xx", reuse=tf.AUTO_REUSE, custom_getter=getter): net = tf.concat([img_tensor, recreated_img], axis=1) net_name = "layer_1" with tf.variable_scope(net_name): net = layers.conv2d( net, filters=64, kernel_size=4, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv1", ) net = tf.nn.leaky_relu( features=net, alpha=self.config.trainer.leakyReLU_alpha, name="conv2/leaky_relu" ) net = tf.layers.dropout( net, rate=self.config.trainer.dropout_rate, training=self.is_training_enc_g, name="dropout", ) with tf.variable_scope(net_name, reuse=True): weights = tf.get_variable("conv1/kernel") net_name = "layer_2" with tf.variable_scope(net_name): net = layers.conv2d( net, filters=128, kernel_size=4, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2", ) net = tf.nn.leaky_relu( features=net, alpha=self.config.trainer.leakyReLU_alpha, name="conv2/leaky_relu" ) net = tf.layers.dropout( net, rate=self.config.trainer.dropout_rate, training=self.is_training_enc_g, name="dropout", ) net = tf.layers.Flatten()(net) intermediate_layer = net net_name = "layer_3" with tf.variable_scope(net_name): net = tf.layers.dense(net, units=1, kernel_initializer=self.init_kernel, name="fc") logits = tf.squeeze(net) return logits, intermediate_layer # Regularizer discriminator for the Reconstruction Encoder def discriminator_zz(self, noise_tensor, recreated_noise, getter=None, do_spectral_norm=False): """ Discriminator architecture in tensorflow Discriminates between (z, z) and (z, rec_z) Args: noise_tensor: recreated_noise: getter: for exponential moving average during inference reuse: sharing variables or not do_spectral_norm: """ layers = sn if do_spectral_norm else tf.layers with tf.variable_scope("Discriminator_zz", reuse=tf.AUTO_REUSE, custom_getter=getter): y = tf.concat([noise_tensor, recreated_noise], axis=-1) net_name = "y_layer_1" with tf.variable_scope(net_name): y = layers.dense(y, units=64, kernel_initializer=self.init_kernel, name="fc") y = tf.nn.leaky_relu(features=y, alpha=self.config.trainer.leakyReLU_alpha) y = tf.layers.dropout( y, rate=self.config.trainer.dropout_rate, training=self.is_training_enc_r, name="dropout", ) net_name = "y_layer_2" with tf.variable_scope(net_name): y = layers.dense(y, units=32, kernel_initializer=self.init_kernel, name="fc") y = tf.nn.leaky_relu(features=y, alpha=self.config.trainer.leakyReLU_alpha) y = tf.layers.dropout( y, rate=self.config.trainer.dropout_rate, training=self.is_training_enc_r, name="dropout", ) intermediate_layer = y net_name = "y_layer_3" with tf.variable_scope(net_name): y = layers.dense(y, units=1, kernel_initializer=self.init_kernel, name="fc") logits = tf.squeeze(y) return logits, intermediate_layer ############################################################################################### # CUSTOM LOSSES ############################################################################################### def mse_loss(self, pred, data, mode="norm", order=2): if mode == "norm": delta = pred - data delta = tf.layers.Flatten()(delta) loss_val = tf.norm(delta, ord=order, axis=1, keepdims=False) elif mode == "mse": loss_val = tf.reduce_mean(tf.squared_difference(pred, data)) return loss_val def pullaway_loss(self, embeddings): norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True)) normalized_embeddings = embeddings / norm similarity = tf.matmul(normalized_embeddings, normalized_embeddings, transpose_b=True) batch_size = tf.cast(tf.shape(embeddings)[0], tf.float32) pt_loss = (tf.reduce_sum(similarity) - batch_size) / (batch_size * (batch_size - 1)) return pt_loss def init_saver(self): self.saver = tf.train.Saver(max_to_keep=self.config.log.max_to_keep)

46.785146

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import tensorflow as tf from base.base_model import BaseModel from utils.alad_utils import get_getter import utils.alad_utils as sn class SENCEBGAN(BaseModel): def __init__(self, config): super(SENCEBGAN, self).__init__(config) self.build_model() self.init_saver() def build_model(self): y(self.discriminator_vars) self.gen_ema = tf.train.ExponentialMovingAverage(decay=self.config.trainer.ema_decay) maintain_averages_op_gen = self.gen_ema.apply(self.generator_vars) self.encg_ema = tf.train.ExponentialMovingAverage(decay=self.config.trainer.ema_decay) maintain_averages_op_encg = self.encg_ema.apply(self.encoder_g_vars) self.encr_ema = tf.train.ExponentialMovingAverage(decay=self.config.trainer.ema_decay) maintain_averages_op_encr = self.encr_ema.apply(self.encoder_r_vars) if self.config.trainer.enable_disc_xx: self.dis_xx_ema = tf.train.ExponentialMovingAverage( decay=self.config.trainer.ema_decay ) maintain_averages_op_dis_xx = self.dis_xx_ema.apply(self.dxxvars) if self.config.trainer.enable_disc_zz: self.dis_zz_ema = tf.train.ExponentialMovingAverage( decay=self.config.trainer.ema_decay ) maintain_averages_op_dis_zz = self.dis_zz_ema.apply(self.dzzvars) with tf.control_dependencies([self.disc_op]): self.train_dis_op = tf.group(maintain_averages_op_dis) with tf.control_dependencies([self.gen_op]): self.train_gen_op = tf.group(maintain_averages_op_gen) with tf.control_dependencies([self.encg_op]): self.train_enc_g_op = tf.group(maintain_averages_op_encg) with tf.control_dependencies([self.encr_op]): self.train_enc_r_op = tf.group(maintain_averages_op_encr) if self.config.trainer.enable_disc_xx: with tf.control_dependencies([self.disc_op_xx]): self.train_dis_op_xx = tf.group(maintain_averages_op_dis_xx) if self.config.trainer.enable_disc_zz: with tf.control_dependencies([self.disc_op_zz]): self.train_dis_op_zz = tf.group(maintain_averages_op_dis_zz) ############################################################################################ # TESTING ############################################################################################ self.logger.info("Building Testing Graph...") with tf.variable_scope("SENCEBGAN"): with tf.variable_scope("Discriminator_Model"): self.embedding_q_ema, self.decoded_q_ema = self.discriminator( self.image_input, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) with tf.variable_scope("Generator_Model"): self.image_gen_ema = self.generator( self.embedding_q_ema, getter=get_getter(self.gen_ema) ) with tf.variable_scope("Discriminator_Model"): self.embedding_rec_ema, self.decoded_rec_ema = self.discriminator( self.image_gen_ema, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) # Second Training Part with tf.variable_scope("Encoder_G_Model"): self.image_encoded_ema = self.encoder_g( self.image_input, getter=get_getter(self.encg_ema) ) with tf.variable_scope("Generator_Model"): self.image_gen_enc_ema = self.generator( self.image_encoded_ema, getter=get_getter(self.gen_ema) ) with tf.variable_scope("Discriminator_Model"): self.embedding_enc_fake_ema, self.decoded_enc_fake_ema = self.discriminator( self.image_gen_enc_ema, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.embedding_enc_real_ema, self.decoded_enc_real_ema = self.discriminator( self.image_input, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) if self.config.trainer.enable_disc_xx: with tf.variable_scope("Discriminator_Model_XX"): self.im_logit_real_ema, self.im_f_real_ema = self.discriminator_xx( self.image_input, self.image_input, getter=get_getter(self.dis_xx_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.im_logit_fake_ema, self.im_f_fake_ema = self.discriminator_xx( self.image_input, self.image_gen_enc_ema, getter=get_getter(self.dis_xx_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) # Third training part with tf.variable_scope("Encoder_G_Model"): self.image_encoded_r_ema = self.encoder_g(self.image_input) with tf.variable_scope("Generator_Model"): self.image_gen_enc_r_ema = self.generator(self.image_encoded_r_ema) with tf.variable_scope("Encoder_R_Model"): self.image_ege_ema = self.encoder_r(self.image_gen_enc_r_ema) with tf.variable_scope("Discriminator_Model"): self.embedding_encr_fake_ema, self.decoded_encr_fake_ema = self.discriminator( self.image_gen_enc_r_ema, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.embedding_encr_real_ema, self.decoded_encr_real_ema = self.discriminator( self.image_input, getter=get_getter(self.dis_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) if self.config.trainer.enable_disc_zz: with tf.variable_scope("Discriminator_Model_ZZ"): self.z_logit_real_ema, self.z_f_real_ema = self.discriminator_zz( self.image_encoded_r_ema, self.image_encoded_r_ema, getter=get_getter(self.dis_zz_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) self.z_logit_fake_ema, self.z_f_fake_ema = self.discriminator_zz( self.image_encoded_r_ema, self.image_ege_ema, getter=get_getter(self.dis_zz_ema), do_spectral_norm=self.config.trainer.do_spectral_norm, ) with tf.name_scope("Testing"): with tf.name_scope("Image_Based"): delta = self.image_input - self.image_gen_enc_ema self.rec_residual = -delta delta_flat = tf.layers.Flatten()(delta) img_score_l1 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="img_loss__1" ) self.img_score_l1 = tf.squeeze(img_score_l1) delta = self.decoded_enc_fake_ema - self.decoded_enc_real_ema delta_flat = tf.layers.Flatten()(delta) img_score_l2 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="img_loss__2" ) self.img_score_l2 = tf.squeeze(img_score_l2) with tf.name_scope("Noise_Based"): delta = self.image_encoded_r_ema - self.image_ege_ema delta_flat = tf.layers.Flatten()(delta) final_score_1 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="final_score_1" ) self.final_score_1 = tf.squeeze(final_score_1) self.score_comb_im = ( 1 * self.img_score_l1 + self.feature_match1 * self.final_score_1 ) delta = self.image_encoded_r_ema - self.embedding_enc_fake_ema delta_flat = tf.layers.Flatten()(delta) final_score_2 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="final_score_2" ) self.final_score_2 = tf.squeeze(final_score_2) delta = self.embedding_encr_real_ema - self.embedding_encr_fake_ema delta_flat = tf.layers.Flatten()(delta) final_score_3 = tf.norm( delta_flat, ord=2, axis=1, keepdims=False, name="final_score_3" ) self.final_score_3 = tf.squeeze(final_score_3) # Combo 1 self.score_comb_z = ( (1 - self.feature_match2) * self.final_score_2 + self.feature_match2 * self.final_score_3 ) # Combo 2 if self.config.trainer.enable_disc_xx: delta = self.im_f_real_ema - self.im_f_fake_ema delta_flat = tf.layers.Flatten()(delta) final_score_4 = tf.norm( delta_flat, ord=1, axis=1, keepdims=False, name="final_score_4" ) self.final_score_4 = tf.squeeze(final_score_4) delta = self.z_f_real_ema - self.z_f_fake_ema delta_flat = tf.layers.Flatten()(delta) final_score_6 = tf.norm( delta_flat, ord=1, axis=1, keepdims=False, name="final_score_6" ) self.final_score_6 = tf.squeeze(final_score_6) ############################################################################################ # TENSORBOARD ############################################################################################ if self.config.log.enable_summary: with tf.name_scope("train_summary"): with tf.name_scope("dis_summary"): tf.summary.scalar("loss_disc", self.loss_discriminator, ["dis"]) tf.summary.scalar("loss_disc_real", self.disc_loss_real, ["dis"]) tf.summary.scalar("loss_disc_fake", self.disc_loss_fake, ["dis"]) if self.config.trainer.enable_disc_xx: tf.summary.scalar("loss_dis_xx", self.dis_loss_xx, ["enc_g"]) if self.config.trainer.enable_disc_zz: tf.summary.scalar("loss_dis_zz", self.dis_loss_zz, ["enc_r"]) with tf.name_scope("gen_summary"): tf.summary.scalar("loss_generator", self.loss_generator, ["gen"]) with tf.name_scope("enc_summary"): tf.summary.scalar("loss_encoder_g", self.loss_encoder_g, ["enc_g"]) tf.summary.scalar("loss_encoder_r", self.loss_encoder_r, ["enc_r"]) with tf.name_scope("img_summary"): tf.summary.image("input_image", self.image_input, 1, ["img_1"]) tf.summary.image("reconstructed", self.image_gen, 1, ["img_1"]) # From discriminator in part 1 tf.summary.image("decoded_real", self.decoded_real, 1, ["img_1"]) tf.summary.image("decoded_fake", self.decoded_fake, 1, ["img_1"]) # Second Stage of Training tf.summary.image("input_enc", self.image_input, 1, ["img_2"]) tf.summary.image("reconstructed", self.image_gen_enc, 1, ["img_2"]) # From discriminator in part 2 tf.summary.image("decoded_enc_real", self.decoded_enc_real, 1, ["img_2"]) tf.summary.image("decoded_enc_fake", self.decoded_enc_fake, 1, ["img_2"]) # Testing tf.summary.image("input_image", self.image_input, 1, ["test"]) tf.summary.image("reconstructed", self.image_gen_enc_r_ema, 1, ["test"]) tf.summary.image("residual", self.rec_residual, 1, ["test"]) self.sum_op_dis = tf.summary.merge_all("dis") self.sum_op_gen = tf.summary.merge_all("gen") self.sum_op_enc_g = tf.summary.merge_all("enc_g") self.sum_op_enc_r = tf.summary.merge_all("enc_r") self.sum_op_im_1 = tf.summary.merge_all("img_1") self.sum_op_im_2 = tf.summary.merge_all("img_2") self.sum_op_im_test = tf.summary.merge_all("test") self.sum_op = tf.summary.merge([self.sum_op_dis, self.sum_op_gen]) ############################################################################################### # MODULES ############################################################################################### def generator(self, noise_input, getter=None): with tf.variable_scope("Generator", custom_getter=getter, reuse=tf.AUTO_REUSE): net_name = "Layer_1" with tf.variable_scope(net_name): x_g = tf.layers.Dense( units=2 * 2 * 256, kernel_initializer=self.init_kernel, name="fc" )(noise_input) x_g = tf.layers.batch_normalization( x_g, momentum=self.config.trainer.batch_momentum, training=self.is_training_gen, name="batch_normalization", ) x_g = tf.nn.leaky_relu( features=x_g, alpha=self.config.trainer.leakyReLU_alpha, name="relu" ) x_g = tf.reshape(x_g, [-1, 2, 2, 256]) net_name = "Layer_2" with tf.variable_scope(net_name): x_g = tf.layers.Conv2DTranspose( filters=128, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2t", )(x_g) x_g = tf.layers.batch_normalization( x_g, momentum=self.config.trainer.batch_momentum, training=self.is_training_gen, name="batch_normalization", ) x_g = tf.nn.leaky_relu( features=x_g, alpha=self.config.trainer.leakyReLU_alpha, name="relu" ) net_name = "Layer_3" with tf.variable_scope(net_name): x_g = tf.layers.Conv2DTranspose( filters=64, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2t", )(x_g) x_g = tf.layers.batch_normalization( x_g, momentum=self.config.trainer.batch_momentum, training=self.is_training_gen, name="batch_normalization", ) x_g = tf.nn.leaky_relu( features=x_g, alpha=self.config.trainer.leakyReLU_alpha, name="relu" ) net_name = "Layer_4" with tf.variable_scope(net_name): x_g = tf.layers.Conv2DTranspose( filters=32, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2t", )(x_g) x_g = tf.layers.batch_normalization( x_g, momentum=self.config.trainer.batch_momentum, training=self.is_training_gen, name="batch_normalization", ) x_g = tf.nn.leaky_relu( features=x_g, alpha=self.config.trainer.leakyReLU_alpha, name="relu" ) net_name = "Layer_5" with tf.variable_scope(net_name): x_g = tf.layers.Conv2DTranspose( filters=1, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2t", )(x_g) x_g = tf.tanh(x_g, name="tanh") return x_g def discriminator(self, image_input, getter=None, do_spectral_norm=False): layers = sn if do_spectral_norm else tf.layers with tf.variable_scope("Discriminator", custom_getter=getter, reuse=tf.AUTO_REUSE): with tf.variable_scope("Encoder"): x_e = tf.reshape( image_input, [-1, self.config.data_loader.image_size, self.config.data_loader.image_size, 1], ) net_name = "Layer_1" with tf.variable_scope(net_name): x_e = layers.conv2d( x_e, filters=32, kernel_size=5, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv", ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) # 14 x 14 x 64 net_name = "Layer_2" with tf.variable_scope(net_name): x_e = layers.conv2d( x_e, filters=64, kernel_size=5, padding="same", strides=2, kernel_initializer=self.init_kernel, name="conv", ) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) # 7 x 7 x 128 net_name = "Layer_3" with tf.variable_scope(net_name): x_e = layers.conv2d( x_e, filters=128, kernel_size=5, padding="same", strides=2, kernel_initializer=self.init_kernel, name="conv", ) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) # 4 x 4 x 256 x_e = tf.layers.Flatten()(x_e) net_name = "Layer_4" with tf.variable_scope(net_name): x_e = layers.dense( x_e, units=self.config.trainer.noise_dim, kernel_initializer=self.init_kernel, name="fc", ) embedding = x_e with tf.variable_scope("Decoder"): net = tf.reshape(embedding, [-1, 1, 1, self.config.trainer.noise_dim]) net_name = "layer_1" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=256, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv1", )(net) net = tf.layers.batch_normalization( inputs=net, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, name="tconv1/bn", ) net = tf.nn.relu(features=net, name="tconv1/relu") net_name = "layer_2" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=128, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv2", )(net) net = tf.layers.batch_normalization( inputs=net, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, name="tconv2/bn", ) net = tf.nn.relu(features=net, name="tconv2/relu") net_name = "layer_3" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=64, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv3", )(net) net = tf.layers.batch_normalization( inputs=net, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, name="tconv3/bn", ) net = tf.nn.relu(features=net, name="tconv3/relu") net_name = "layer_4" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=32, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv4", )(net) net = tf.layers.batch_normalization( inputs=net, momentum=self.config.trainer.batch_momentum, training=self.is_training_dis, name="tconv4/bn", ) net = tf.nn.relu(features=net, name="tconv4/relu") net_name = "layer_5" with tf.variable_scope(net_name): net = tf.layers.Conv2DTranspose( filters=1, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="tconv5", )(net) decoded = tf.nn.tanh(net, name="tconv5/tanh") return embedding, decoded def encoder_g(self, image_input, getter=None): with tf.variable_scope("Encoder_G", custom_getter=getter, reuse=tf.AUTO_REUSE): x_e = tf.reshape( image_input, [-1, self.config.data_loader.image_size, self.config.data_loader.image_size, 1], ) net_name = "Layer_1" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=64, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_g, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) net_name = "Layer_2" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=128, kernel_size=5, padding="same", strides=(2, 2), kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_g, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) net_name = "Layer_3" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=256, kernel_size=5, padding="same", strides=(2, 2), kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_g, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) x_e = tf.layers.Flatten()(x_e) net_name = "Layer_4" with tf.variable_scope(net_name): x_e = tf.layers.Dense( units=self.config.trainer.noise_dim, kernel_initializer=self.init_kernel, name="fc", )(x_e) return x_e def encoder_r(self, image_input, getter=None): with tf.variable_scope("Encoder_R", custom_getter=getter, reuse=tf.AUTO_REUSE): x_e = tf.reshape( image_input, [-1, self.config.data_loader.image_size, self.config.data_loader.image_size, 1], ) net_name = "Layer_1" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=64, kernel_size=5, strides=(2, 2), padding="same", kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_r, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) net_name = "Layer_2" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=128, kernel_size=5, padding="same", strides=(2, 2), kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_r, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) net_name = "Layer_3" with tf.variable_scope(net_name): x_e = tf.layers.Conv2D( filters=256, kernel_size=5, padding="same", strides=(2, 2), kernel_initializer=self.init_kernel, name="conv", )(x_e) x_e = tf.layers.batch_normalization( x_e, momentum=self.config.trainer.batch_momentum, training=self.is_training_enc_r, ) x_e = tf.nn.leaky_relu( features=x_e, alpha=self.config.trainer.leakyReLU_alpha, name="leaky_relu" ) x_e = tf.layers.Flatten()(x_e) net_name = "Layer_4" with tf.variable_scope(net_name): x_e = tf.layers.Dense( units=self.config.trainer.noise_dim, kernel_initializer=self.init_kernel, name="fc", )(x_e) return x_e # Regularizer discriminator for the Generator Encoder def discriminator_xx(self, img_tensor, recreated_img, getter=None, do_spectral_norm=False): layers = sn if do_spectral_norm else tf.layers with tf.variable_scope("Discriminator_xx", reuse=tf.AUTO_REUSE, custom_getter=getter): net = tf.concat([img_tensor, recreated_img], axis=1) net_name = "layer_1" with tf.variable_scope(net_name): net = layers.conv2d( net, filters=64, kernel_size=4, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv1", ) net = tf.nn.leaky_relu( features=net, alpha=self.config.trainer.leakyReLU_alpha, name="conv2/leaky_relu" ) net = tf.layers.dropout( net, rate=self.config.trainer.dropout_rate, training=self.is_training_enc_g, name="dropout", ) with tf.variable_scope(net_name, reuse=True): weights = tf.get_variable("conv1/kernel") net_name = "layer_2" with tf.variable_scope(net_name): net = layers.conv2d( net, filters=128, kernel_size=4, strides=2, padding="same", kernel_initializer=self.init_kernel, name="conv2", ) net = tf.nn.leaky_relu( features=net, alpha=self.config.trainer.leakyReLU_alpha, name="conv2/leaky_relu" ) net = tf.layers.dropout( net, rate=self.config.trainer.dropout_rate, training=self.is_training_enc_g, name="dropout", ) net = tf.layers.Flatten()(net) intermediate_layer = net net_name = "layer_3" with tf.variable_scope(net_name): net = tf.layers.dense(net, units=1, kernel_initializer=self.init_kernel, name="fc") logits = tf.squeeze(net) return logits, intermediate_layer # Regularizer discriminator for the Reconstruction Encoder def discriminator_zz(self, noise_tensor, recreated_noise, getter=None, do_spectral_norm=False): layers = sn if do_spectral_norm else tf.layers with tf.variable_scope("Discriminator_zz", reuse=tf.AUTO_REUSE, custom_getter=getter): y = tf.concat([noise_tensor, recreated_noise], axis=-1) net_name = "y_layer_1" with tf.variable_scope(net_name): y = layers.dense(y, units=64, kernel_initializer=self.init_kernel, name="fc") y = tf.nn.leaky_relu(features=y, alpha=self.config.trainer.leakyReLU_alpha) y = tf.layers.dropout( y, rate=self.config.trainer.dropout_rate, training=self.is_training_enc_r, name="dropout", ) net_name = "y_layer_2" with tf.variable_scope(net_name): y = layers.dense(y, units=32, kernel_initializer=self.init_kernel, name="fc") y = tf.nn.leaky_relu(features=y, alpha=self.config.trainer.leakyReLU_alpha) y = tf.layers.dropout( y, rate=self.config.trainer.dropout_rate, training=self.is_training_enc_r, name="dropout", ) intermediate_layer = y net_name = "y_layer_3" with tf.variable_scope(net_name): y = layers.dense(y, units=1, kernel_initializer=self.init_kernel, name="fc") logits = tf.squeeze(y) return logits, intermediate_layer ############################################################################################### # CUSTOM LOSSES ############################################################################################### def mse_loss(self, pred, data, mode="norm", order=2): if mode == "norm": delta = pred - data delta = tf.layers.Flatten()(delta) loss_val = tf.norm(delta, ord=order, axis=1, keepdims=False) elif mode == "mse": loss_val = tf.reduce_mean(tf.squared_difference(pred, data)) return loss_val def pullaway_loss(self, embeddings): norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True)) normalized_embeddings = embeddings / norm similarity = tf.matmul(normalized_embeddings, normalized_embeddings, transpose_b=True) batch_size = tf.cast(tf.shape(embeddings)[0], tf.float32) pt_loss = (tf.reduce_sum(similarity) - batch_size) / (batch_size * (batch_size - 1)) return pt_loss def init_saver(self): self.saver = tf.train.Saver(max_to_keep=self.config.log.max_to_keep)

true

f7366460f1bafd203830c13ee2d0e7ade440377d

643

py

Python

lfd/environment/settings.py

def670/lfd

9fef15f556cba49dd4b42c0c29505a4137f95fc5

[ "BSD-2-Clause" ]

36

2015-05-22T14:47:18.000Z

2021-07-27T15:30:36.000Z

lfd/environment/settings.py

jeffmahler/lfd

ecc6b934db098c0b1af9946454917b6dc911cb74

[ "BSD-2-Clause" ]

null

lfd/environment/settings.py

jeffmahler/lfd

ecc6b934db098c0b1af9946454917b6dc911cb74

[ "BSD-2-Clause" ]

13

2015-05-22T15:38:07.000Z

2021-07-28T03:20:35.000Z

#: GRIPPER_OPEN_CLOSE_THRESH = 0.04 # .06 for fig8 (thick rope), 0.04 for thin rope (overhand) #: ROPE_RADIUS = .005 #: ROPE_ANG_STIFFNESS = .1 #: ROPE_ANG_DAMPING = 1 #: ROPE_LIN_DAMPING = .75 #: ROPE_ANG_LIMIT = .4 #: ROPE_LIN_STOP_ERP = .2 #: ROPE_MASS = 1.0 #: ROPE_RADIUS_THICK = .008 #: window properties for the viewer's window WINDOW_PROP = [0,0,1500,1500] #: transposed homogeneous matrix for the viewer's camera CAMERA_MATRIX = [[ 0, 1, 0, 0], [ -1, 0, 0.5, 0], [ 0.5, 0, 1, 0], [ 2.25, 0, 4.5, 1]] try: from lfd_settings.environment.settings import * except ImportError: pass

19.484848

91

0.623639

GRIPPER_OPEN_CLOSE_THRESH = 0.04 ROPE_RADIUS = .005 ROPE_ANG_STIFFNESS = .1 ROPE_ANG_DAMPING = 1 ROPE_LIN_DAMPING = .75 ROPE_ANG_LIMIT = .4 ROPE_LIN_STOP_ERP = .2 ROPE_MASS = 1.0 ROPE_RADIUS_THICK = .008 WINDOW_PROP = [0,0,1500,1500] #: transposed homogeneous matrix for the viewer's camera CAMERA_MATRIX = [[ 0, 1, 0, 0], [ -1, 0, 0.5, 0], [ 0.5, 0, 1, 0], [ 2.25, 0, 4.5, 1]] try: from lfd_settings.environment.settings import * except ImportError: pass

true

f73666fc6d19b1f19d47b390ab17540bd4d53368

579

py

Python

setup.py

hanhha/console_ui

4fd3d37367bc23316620d5ad76666981762fb344

[ "MIT" ]

1

2022-02-04T03:20:31.000Z

setup.py

alimogh/console_ui

4fd3d37367bc23316620d5ad76666981762fb344

[ "MIT" ]

null

setup.py

alimogh/console_ui

4fd3d37367bc23316620d5ad76666981762fb344

[ "MIT" ]

1

2022-02-04T03:22:03.000Z

#!/usr/bin/env python3 from setuptools import setup import sys if sys.version_info < (3,0): sys.exit ("Python version < 3.0 is not supported") elif sys.version_info < (3,6): print ("Using Python version < 3.6 would cause issue relevant to the order of windows generated") setup (name = 'console_ui', version = '1.0', py_modules=['wingen', 'console_utils'], description = 'A quick UI generator using Curses', author = 'Hanh Ha', author_email = 'tranhanh.haminh@gmail.com', license = 'MIT', url = 'https://github.com/hanhha/console_ui', )

28.95

98

0.668394

from setuptools import setup import sys if sys.version_info < (3,0): sys.exit ("Python version < 3.0 is not supported") elif sys.version_info < (3,6): print ("Using Python version < 3.6 would cause issue relevant to the order of windows generated") setup (name = 'console_ui', version = '1.0', py_modules=['wingen', 'console_utils'], description = 'A quick UI generator using Curses', author = 'Hanh Ha', author_email = 'tranhanh.haminh@gmail.com', license = 'MIT', url = 'https://github.com/hanhha/console_ui', )

true

f736674bc693ddff36470a5c55535e91fbce24e8

1,355

py

Python

src/spn/experiments/AQP/leaves/static/InferenceRange.py

tkrons/SPFlow_topdownrules

6227fc973f4f36da7fbe25fa500d656eb7273033

[ "Apache-2.0" ]

199

2018-11-13T10:37:45.000Z

2022-02-06T17:07:28.000Z

src/spn/experiments/AQP/leaves/static/InferenceRange.py

minimrbanana/SPFlow

32233bf29d107c62f0f727b0e64aaa74b37cfe1e

[ "Apache-2.0" ]

46

2018-11-30T13:40:38.000Z

2022-01-22T21:05:07.000Z

src/spn/experiments/AQP/leaves/static/InferenceRange.py

minimrbanana/SPFlow

32233bf29d107c62f0f727b0e64aaa74b37cfe1e

[ "Apache-2.0" ]

78

2018-11-13T10:37:48.000Z

2022-03-14T21:34:13.000Z

""" Created on June 21, 2018 @author: Moritz """ import numpy as np from spn.algorithms.Inference import add_node_likelihood from spn.experiments.AQP.leaves.static.StaticNumeric import StaticNumeric def static_likelihood_range(node, ranges, dtype=np.float64, **kwargs): assert len(node.scope) == 1, node.scope probs = np.ones((ranges.shape[0], 1), dtype=dtype) ranges = ranges[:, node.scope[0]] for i, rang in enumerate(ranges): # Skip if no range is specified aka use a log-probability of 0 for that instance if rang is None: continue # Skip if no values for the range are provided if rang.is_impossible(): probs[i] = 0 # Compute the sum of the probability of all possible values probs[i] = sum([_compute_probability_for_range(node, interval) for interval in rang.get_ranges()]) return probs def _compute_probability_for_range(node, interval): if len(interval) == 1: if node.val == interval[0]: return 1 else: return 0 else: lower = interval[0] higher = interval[1] if lower <= node.val and node.val <= higher: return 1 else: return 0 def add_static_inference_range_support(): add_node_likelihood(StaticNumeric, static_likelihood_range)

25.092593

106

0.647232

import numpy as np from spn.algorithms.Inference import add_node_likelihood from spn.experiments.AQP.leaves.static.StaticNumeric import StaticNumeric def static_likelihood_range(node, ranges, dtype=np.float64, **kwargs): assert len(node.scope) == 1, node.scope probs = np.ones((ranges.shape[0], 1), dtype=dtype) ranges = ranges[:, node.scope[0]] for i, rang in enumerate(ranges): if rang is None: continue if rang.is_impossible(): probs[i] = 0 probs[i] = sum([_compute_probability_for_range(node, interval) for interval in rang.get_ranges()]) return probs def _compute_probability_for_range(node, interval): if len(interval) == 1: if node.val == interval[0]: return 1 else: return 0 else: lower = interval[0] higher = interval[1] if lower <= node.val and node.val <= higher: return 1 else: return 0 def add_static_inference_range_support(): add_node_likelihood(StaticNumeric, static_likelihood_range)

true

f7366767fdec0eacbd4d0d9376ef997a8cdd4826

35,093

py

Python

sphinx/writers/html.py

PeerHerholz/smobsc

db34d2bb96b80579bd4a3f4c198a6b524c5a134a

[ "BSD-2-Clause" ]

3

2019-06-11T09:42:08.000Z

2020-03-10T15:57:09.000Z

sphinx/writers/html.py

PeerHerholz/smobsc

db34d2bb96b80579bd4a3f4c198a6b524c5a134a

[ "BSD-2-Clause" ]

12

2019-01-09T15:43:57.000Z

2020-01-21T10:46:30.000Z

sphinx/writers/html.py

PeerHerholz/smobsc

db34d2bb96b80579bd4a3f4c198a6b524c5a134a

[ "BSD-2-Clause" ]

10

2019-02-04T11:49:35.000Z

2020-03-21T13:32:20.000Z

""" sphinx.writers.html ~~~~~~~~~~~~~~~~~~~ docutils writers handling Sphinx' custom nodes. :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS. :license: BSD, see LICENSE for details. """ import copy import os import posixpath import sys import warnings from typing import Iterable, cast from docutils import nodes from docutils.writers.html4css1 import Writer, HTMLTranslator as BaseTranslator from sphinx import addnodes from sphinx.builders import Builder from sphinx.deprecation import RemovedInSphinx30Warning, RemovedInSphinx40Warning from sphinx.locale import admonitionlabels, _, __ from sphinx.util import logging from sphinx.util.docutils import SphinxTranslator from sphinx.util.images import get_image_size if False: # For type annotation from typing import Any # NOQA from sphinx.builders.html import StandaloneHTMLBuilder # NOQA logger = logging.getLogger(__name__) # A good overview of the purpose behind these classes can be found here: # http://www.arnebrodowski.de/blog/write-your-own-restructuredtext-writer.html class HTMLWriter(Writer): # override embed-stylesheet default value to 0. settings_spec = copy.deepcopy(Writer.settings_spec) for _setting in settings_spec[2]: if '--embed-stylesheet' in _setting[1]: _setting[2]['default'] = 0 def __init__(self, builder): # type: (StandaloneHTMLBuilder) -> None super().__init__() self.builder = builder def translate(self): # type: () -> None # sadly, this is mostly copied from parent class visitor = self.builder.create_translator(self.document, self.builder) self.visitor = cast(HTMLTranslator, visitor) self.document.walkabout(visitor) self.output = self.visitor.astext() for attr in ('head_prefix', 'stylesheet', 'head', 'body_prefix', 'body_pre_docinfo', 'docinfo', 'body', 'fragment', 'body_suffix', 'meta', 'title', 'subtitle', 'header', 'footer', 'html_prolog', 'html_head', 'html_title', 'html_subtitle', 'html_body', ): setattr(self, attr, getattr(visitor, attr, None)) self.clean_meta = ''.join(self.visitor.meta[2:]) class HTMLTranslator(SphinxTranslator, BaseTranslator): """ Our custom HTML translator. """ builder = None # type: StandaloneHTMLBuilder def __init__(self, *args): # type: (Any) -> None if isinstance(args[0], nodes.document) and isinstance(args[1], Builder): document, builder = args else: warnings.warn('The order of arguments for HTMLTranslator has been changed. ' 'Please give "document" as 1st and "builder" as 2nd.', RemovedInSphinx40Warning, stacklevel=2) builder, document = args super().__init__(document, builder) self.highlighter = self.builder.highlighter self.docnames = [self.builder.current_docname] # for singlehtml builder self.manpages_url = self.config.manpages_url self.protect_literal_text = 0 self.permalink_text = self.config.html_add_permalinks # support backwards-compatible setting to a bool if not isinstance(self.permalink_text, str): self.permalink_text = self.permalink_text and '\u00B6' or '' self.permalink_text = self.encode(self.permalink_text) self.secnumber_suffix = self.config.html_secnumber_suffix self.param_separator = '' self.optional_param_level = 0 self._table_row_index = 0 self._fieldlist_row_index = 0 self.required_params_left = 0 def visit_start_of_file(self, node): # type: (nodes.Element) -> None # only occurs in the single-file builder self.docnames.append(node['docname']) self.body.append('<span id="document-%s"></span>' % node['docname']) def depart_start_of_file(self, node): # type: (nodes.Element) -> None self.docnames.pop() def visit_desc(self, node): # type: (nodes.Element) -> None self.body.append(self.starttag(node, 'dl', CLASS=node['objtype'])) def depart_desc(self, node): # type: (nodes.Element) -> None self.body.append('</dl>\n\n') def visit_desc_signature(self, node): # type: (nodes.Element) -> None # the id is set automatically self.body.append(self.starttag(node, 'dt')) # anchor for per-desc interactive data if node.parent['objtype'] != 'describe' \ and node['ids'] and node['first']: self.body.append('' % node['ids'][0]) def depart_desc_signature(self, node): # type: (nodes.Element) -> None if not node.get('is_multiline'): self.add_permalink_ref(node, _('Permalink to this definition')) self.body.append('</dt>\n') def visit_desc_signature_line(self, node): # type: (nodes.Element) -> None pass def depart_desc_signature_line(self, node): # type: (nodes.Element) -> None if node.get('add_permalink'): # the permalink info is on the parent desc_signature node self.add_permalink_ref(node.parent, _('Permalink to this definition')) self.body.append('<br />') def visit_desc_addname(self, node): # type: (nodes.Element) -> None self.body.append(self.starttag(node, 'code', '', CLASS='descclassname')) def depart_desc_addname(self, node): # type: (nodes.Element) -> None self.body.append('</code>') def visit_desc_type(self, node): # type: (nodes.Element) -> None pass def depart_desc_type(self, node): # type: (nodes.Element) -> None pass def visit_desc_returns(self, node): # type: (nodes.Element) -> None self.body.append(' → ') def depart_desc_returns(self, node): # type: (nodes.Element) -> None pass def visit_desc_name(self, node): # type: (nodes.Element) -> None self.body.append(self.starttag(node, 'code', '', CLASS='descname')) def depart_desc_name(self, node): # type: (nodes.Element) -> None self.body.append('</code>') def visit_desc_parameterlist(self, node): # type: (nodes.Element) -> None self.body.append('<span class="sig-paren">(</span>') self.first_param = 1 self.optional_param_level = 0 # How many required parameters are left. self.required_params_left = sum([isinstance(c, addnodes.desc_parameter) for c in node.children]) self.param_separator = node.child_text_separator def depart_desc_parameterlist(self, node): # type: (nodes.Element) -> None self.body.append('<span class="sig-paren">)</span>') # If required parameters are still to come, then put the comma after # the parameter. Otherwise, put the comma before. This ensures that # signatures like the following render correctly (see issue #1001): # # foo([a, ]b, c[, d]) # def visit_desc_parameter(self, node): # type: (nodes.Element) -> None if self.first_param: self.first_param = 0 elif not self.required_params_left: self.body.append(self.param_separator) if self.optional_param_level == 0: self.required_params_left -= 1 if not node.hasattr('noemph'): self.body.append('<em>') def depart_desc_parameter(self, node): # type: (nodes.Element) -> None if not node.hasattr('noemph'): self.body.append('</em>') if self.required_params_left: self.body.append(self.param_separator) def visit_desc_optional(self, node): # type: (nodes.Element) -> None self.optional_param_level += 1 self.body.append('<span class="optional">[</span>') def depart_desc_optional(self, node): # type: (nodes.Element) -> None self.optional_param_level -= 1 self.body.append('<span class="optional">]</span>') def visit_desc_annotation(self, node): # type: (nodes.Element) -> None self.body.append(self.starttag(node, 'em', '', CLASS='property')) def depart_desc_annotation(self, node): # type: (nodes.Element) -> None self.body.append('</em>') def visit_desc_content(self, node): # type: (nodes.Element) -> None self.body.append(self.starttag(node, 'dd', '')) def depart_desc_content(self, node): # type: (nodes.Element) -> None self.body.append('</dd>') def visit_versionmodified(self, node): # type: (nodes.Element) -> None self.body.append(self.starttag(node, 'div', CLASS=node['type'])) def depart_versionmodified(self, node): # type: (nodes.Element) -> None self.body.append('</div>\n') # overwritten def visit_reference(self, node): # type: (nodes.Element) -> None atts = {'class': 'reference'} if node.get('internal') or 'refuri' not in node: atts['class'] += ' internal' else: atts['class'] += ' external' if 'refuri' in node: atts['href'] = node['refuri'] or '#' if self.settings.cloak_email_addresses and atts['href'].startswith('mailto:'): atts['href'] = self.cloak_mailto(atts['href']) self.in_mailto = True else: assert 'refid' in node, \ 'References must have "refuri" or "refid" attribute.' atts['href'] = '#' + node['refid'] if not isinstance(node.parent, nodes.TextElement): assert len(node) == 1 and isinstance(node[0], nodes.image) atts['class'] += ' image-reference' if 'reftitle' in node: atts['title'] = node['reftitle'] if 'target' in node: atts['target'] = node['target'] self.body.append(self.starttag(node, 'a', '', **atts)) if node.get('secnumber'): self.body.append(('%s' + self.secnumber_suffix) % '.'.join(map(str, node['secnumber']))) def visit_number_reference(self, node): # type: (nodes.Element) -> None self.visit_reference(node) def depart_number_reference(self, node): # type: (nodes.Element) -> None self.depart_reference(node) # overwritten -- we don't want source comments to show up in the HTML def visit_comment(self, node): # type: ignore # type: (nodes.Element) -> None raise nodes.SkipNode # overwritten def visit_admonition(self, node, name=''): # type: (nodes.Element, str) -> None self.body.append(self.starttag( node, 'div', CLASS=('admonition ' + name))) if name: node.insert(0, nodes.title(name, admonitionlabels[name])) self.set_first_last(node) def visit_seealso(self, node): # type: (nodes.Element) -> None self.visit_admonition(node, 'seealso') def depart_seealso(self, node): # type: (nodes.Element) -> None self.depart_admonition(node) def add_secnumber(self, node): # type: (nodes.Element) -> None if node.get('secnumber'): self.body.append('.'.join(map(str, node['secnumber'])) + self.secnumber_suffix) elif isinstance(node.parent, nodes.section): if self.builder.name == 'singlehtml': docname = self.docnames[-1] anchorname = "%s/#%s" % (docname, node.parent['ids'][0]) if anchorname not in self.builder.secnumbers: anchorname = "%s/" % docname # try first heading which has no anchor else: anchorname = '#' + node.parent['ids'][0] if anchorname not in self.builder.secnumbers: anchorname = '' # try first heading which has no anchor if self.builder.secnumbers.get(anchorname): numbers = self.builder.secnumbers[anchorname] self.body.append('.'.join(map(str, numbers)) + self.secnumber_suffix) def add_fignumber(self, node): # type: (nodes.Element) -> None def append_fignumber(figtype, figure_id): # type: (str, str) -> None if self.builder.name == 'singlehtml': key = "%s/%s" % (self.docnames[-1], figtype) else: key = figtype if figure_id in self.builder.fignumbers.get(key, {}): self.body.append('<span class="caption-number">') prefix = self.builder.config.numfig_format.get(figtype) if prefix is None: msg = __('numfig_format is not defined for %s') % figtype logger.warning(msg) else: numbers = self.builder.fignumbers[key][figure_id] self.body.append(prefix % '.'.join(map(str, numbers)) + ' ') self.body.append('</span>') figtype = self.builder.env.domains['std'].get_enumerable_node_type(node) if figtype: if len(node['ids']) == 0: msg = __('Any IDs not assigned for %s node') % node.tagname logger.warning(msg, location=node) else: append_fignumber(figtype, node['ids'][0]) def add_permalink_ref(self, node, title): # type: (nodes.Element, str) -> None if node['ids'] and self.permalink_text and self.builder.add_permalinks: format = '<a class="headerlink" href="#%s" title="%s">%s</a>' self.body.append(format % (node['ids'][0], title, self.permalink_text)) def generate_targets_for_listing(self, node): # type: (nodes.Element) -> None """Generate hyperlink targets for listings. Original visit_bullet_list(), visit_definition_list() and visit_enumerated_list() generates hyperlink targets inside listing tags (<ul>, <ol> and <dl>) if multiple IDs are assigned to listings. That is invalid DOM structure. (This is a bug of docutils <= 0.12) This exports hyperlink targets before listings to make valid DOM structure. """ for id in node['ids'][1:]: self.body.append('<span id="%s"></span>' % id) node['ids'].remove(id) # overwritten def visit_bullet_list(self, node): # type: (nodes.Element) -> None if len(node) == 1 and isinstance(node[0], addnodes.toctree): # avoid emitting empty <ul></ul> raise nodes.SkipNode self.generate_targets_for_listing(node) super().visit_bullet_list(node) # overwritten def visit_enumerated_list(self, node): # type: (nodes.Element) -> None self.generate_targets_for_listing(node) super().visit_enumerated_list(node) # overwritten def visit_title(self, node): # type: (nodes.Element) -> None super().visit_title(node) self.add_secnumber(node) self.add_fignumber(node.parent) if isinstance(node.parent, nodes.table): self.body.append('<span class="caption-text">') def depart_title(self, node): # type: (nodes.Element) -> None close_tag = self.context[-1] if (self.permalink_text and self.builder.add_permalinks and node.parent.hasattr('ids') and node.parent['ids']): # add permalink anchor if close_tag.startswith('</h'): self.add_permalink_ref(node.parent, _('Permalink to this headline')) elif close_tag.startswith('</a></h'): self.body.append('</a><a class="headerlink" href="#%s" ' % node.parent['ids'][0] + 'title="%s">%s' % ( _('Permalink to this headline'), self.permalink_text)) elif isinstance(node.parent, nodes.table): self.body.append('</span>') self.add_permalink_ref(node.parent, _('Permalink to this table')) elif isinstance(node.parent, nodes.table): self.body.append('</span>') super().depart_title(node) # overwritten def visit_literal_block(self, node): # type: (nodes.Element) -> None if node.rawsource != node.astext(): # most probably a parsed-literal block -- don't highlight return super().visit_literal_block(node) lang = node.get('language', 'default') linenos = node.get('linenos', False) highlight_args = node.get('highlight_args', {}) highlight_args['force'] = node.get('force_highlighting', False) if lang is self.builder.config.highlight_language: # only pass highlighter options for original language opts = self.builder.config.highlight_options else: opts = {} highlighted = self.highlighter.highlight_block( node.rawsource, lang, opts=opts, linenos=linenos, location=(self.builder.current_docname, node.line), **highlight_args ) starttag = self.starttag(node, 'div', suffix='', CLASS='highlight-%s notranslate' % lang) self.body.append(starttag + highlighted + '</div>\n') raise nodes.SkipNode def visit_caption(self, node): # type: (nodes.Element) -> None if isinstance(node.parent, nodes.container) and node.parent.get('literal_block'): self.body.append('<div class="code-block-caption">') else: super().visit_caption(node) self.add_fignumber(node.parent) self.body.append(self.starttag(node, 'span', '', CLASS='caption-text')) def depart_caption(self, node): # type: (nodes.Element) -> None self.body.append('</span>') # append permalink if available if isinstance(node.parent, nodes.container) and node.parent.get('literal_block'): self.add_permalink_ref(node.parent, _('Permalink to this code')) elif isinstance(node.parent, nodes.figure): image_nodes = node.parent.traverse(nodes.image) target_node = image_nodes and image_nodes[0] or node.parent self.add_permalink_ref(target_node, _('Permalink to this image')) elif node.parent.get('toctree'): self.add_permalink_ref(node.parent.parent, _('Permalink to this toctree')) if isinstance(node.parent, nodes.container) and node.parent.get('literal_block'): self.body.append('</div>\n') else: super().depart_caption(node) def visit_doctest_block(self, node): # type: (nodes.Element) -> None self.visit_literal_block(node) # overwritten to add the <div> (for XHTML compliance) def visit_block_quote(self, node): # type: (nodes.Element) -> None self.body.append(self.starttag(node, 'blockquote') + '<div>') def depart_block_quote(self, node): # type: (nodes.Element) -> None self.body.append('</div></blockquote>\n') # overwritten def visit_literal(self, node): # type: (nodes.Element) -> None if 'kbd' in node['classes']: self.body.append(self.starttag(node, 'kbd', '', CLASS='docutils literal notranslate')) else: self.body.append(self.starttag(node, 'code', '', CLASS='docutils literal notranslate')) self.protect_literal_text += 1 def depart_literal(self, node): # type: (nodes.Element) -> None if 'kbd' in node['classes']: self.body.append('</kbd>') else: self.protect_literal_text -= 1 self.body.append('</code>') def visit_productionlist(self, node): # type: (nodes.Element) -> None self.body.append(self.starttag(node, 'pre')) names = [] productionlist = cast(Iterable[addnodes.production], node) for production in productionlist: names.append(production['tokenname']) maxlen = max(len(name) for name in names) lastname = None for production in productionlist: if production['tokenname']: lastname = production['tokenname'].ljust(maxlen) self.body.append(self.starttag(production, 'strong', '')) self.body.append(lastname + '</strong> ::= ') elif lastname is not None: self.body.append('%s ' % (' ' * len(lastname))) production.walkabout(self) self.body.append('\n') self.body.append('</pre>\n') raise nodes.SkipNode def depart_productionlist(self, node): # type: (nodes.Element) -> None pass def visit_production(self, node): # type: (nodes.Element) -> None pass def depart_production(self, node): # type: (nodes.Element) -> None pass def visit_centered(self, node): # type: (nodes.Element) -> None self.body.append(self.starttag(node, 'p', CLASS="centered") + '<strong>') def depart_centered(self, node): # type: (nodes.Element) -> None self.body.append('</strong></p>') # overwritten def should_be_compact_paragraph(self, node): # type: (nodes.Node) -> bool """Determine if the <p> tags around paragraph can be omitted.""" if isinstance(node.parent, addnodes.desc_content): # Never compact desc_content items. return False if isinstance(node.parent, addnodes.versionmodified): # Never compact versionmodified nodes. return False return super().should_be_compact_paragraph(node) def visit_compact_paragraph(self, node): # type: (nodes.Element) -> None pass def depart_compact_paragraph(self, node): # type: (nodes.Element) -> None pass def visit_download_reference(self, node): # type: (nodes.Element) -> None atts = {'class': 'reference download', 'download': ''} if not self.builder.download_support: self.context.append('') elif 'refuri' in node: atts['class'] += ' external' atts['href'] = node['refuri'] self.body.append(self.starttag(node, 'a', '', **atts)) self.context.append('</a>') elif 'filename' in node: atts['class'] += ' internal' atts['href'] = posixpath.join(self.builder.dlpath, node['filename']) self.body.append(self.starttag(node, 'a', '', **atts)) self.context.append('</a>') else: self.context.append('') def depart_download_reference(self, node): # type: (nodes.Element) -> None self.body.append(self.context.pop()) # overwritten def visit_image(self, node): # type: (nodes.Element) -> None olduri = node['uri'] # rewrite the URI if the environment knows about it if olduri in self.builder.images: node['uri'] = posixpath.join(self.builder.imgpath, self.builder.images[olduri]) uri = node['uri'] if uri.lower().endswith(('svg', 'svgz')): atts = {'src': uri} if 'width' in node: atts['width'] = node['width'] if 'height' in node: atts['height'] = node['height'] atts['alt'] = node.get('alt', uri) if 'align' in node: self.body.append('<div align="%s" class="align-%s">' % (node['align'], node['align'])) self.context.append('</div>\n') else: self.context.append('') self.body.append(self.emptytag(node, 'img', '', **atts)) return if 'scale' in node: # Try to figure out image height and width. Docutils does that too, # but it tries the final file name, which does not necessarily exist # yet at the time the HTML file is written. if not ('width' in node and 'height' in node): size = get_image_size(os.path.join(self.builder.srcdir, olduri)) if size is None: logger.warning(__('Could not obtain image size. :scale: option is ignored.'), # NOQA location=node) else: if 'width' not in node: node['width'] = str(size[0]) if 'height' not in node: node['height'] = str(size[1]) super().visit_image(node) # overwritten def depart_image(self, node): # type: (nodes.Element) -> None if node['uri'].lower().endswith(('svg', 'svgz')): self.body.append(self.context.pop()) else: super().depart_image(node) def visit_toctree(self, node): # type: (nodes.Element) -> None # this only happens when formatting a toc from env.tocs -- in this # case we don't want to include the subtree raise nodes.SkipNode def visit_index(self, node): # type: (nodes.Element) -> None raise nodes.SkipNode def visit_tabular_col_spec(self, node): # type: (nodes.Element) -> None raise nodes.SkipNode def visit_glossary(self, node): # type: (nodes.Element) -> None pass def depart_glossary(self, node): # type: (nodes.Element) -> None pass def visit_acks(self, node): # type: (nodes.Element) -> None pass def depart_acks(self, node): # type: (nodes.Element) -> None pass def visit_hlist(self, node): # type: (nodes.Element) -> None self.body.append('<table class="hlist"><tr>') def depart_hlist(self, node): # type: (nodes.Element) -> None self.body.append('</tr></table>\n') def visit_hlistcol(self, node): # type: (nodes.Element) -> None self.body.append('<td>') def depart_hlistcol(self, node): # type: (nodes.Element) -> None self.body.append('</td>') def visit_option_group(self, node): # type: (nodes.Element) -> None super().visit_option_group(node) self.context[-2] = self.context[-2].replace(' ', ' ') # overwritten def visit_Text(self, node): # type: (nodes.Text) -> None text = node.astext() encoded = self.encode(text) if self.protect_literal_text: # moved here from base class's visit_literal to support # more formatting in literal nodes for token in self.words_and_spaces.findall(encoded): if token.strip(): # protect literal text from line wrapping self.body.append('<span class="pre">%s</span>' % token) elif token in ' \n': # allow breaks at whitespace self.body.append(token) else: # protect runs of multiple spaces; the last one can wrap self.body.append(' ' * (len(token) - 1) + ' ') else: if self.in_mailto and self.settings.cloak_email_addresses: encoded = self.cloak_email(encoded) self.body.append(encoded) def visit_note(self, node): # type: (nodes.Element) -> None self.visit_admonition(node, 'note') def depart_note(self, node): # type: (nodes.Element) -> None self.depart_admonition(node) def visit_warning(self, node): # type: (nodes.Element) -> None self.visit_admonition(node, 'warning') def depart_warning(self, node): # type: (nodes.Element) -> None self.depart_admonition(node) def visit_attention(self, node): # type: (nodes.Element) -> None self.visit_admonition(node, 'attention') def depart_attention(self, node): # type: (nodes.Element) -> None self.depart_admonition(node) def visit_caution(self, node): # type: (nodes.Element) -> None self.visit_admonition(node, 'caution') def depart_caution(self, node): # type: (nodes.Element) -> None self.depart_admonition(node) def visit_danger(self, node): # type: (nodes.Element) -> None self.visit_admonition(node, 'danger') def depart_danger(self, node): # type: (nodes.Element) -> None self.depart_admonition(node) def visit_error(self, node): # type: (nodes.Element) -> None self.visit_admonition(node, 'error') def depart_error(self, node): # type: (nodes.Element) -> None self.depart_admonition(node) def visit_hint(self, node): # type: (nodes.Element) -> None self.visit_admonition(node, 'hint') def depart_hint(self, node): # type: (nodes.Element) -> None self.depart_admonition(node) def visit_important(self, node): # type: (nodes.Element) -> None self.visit_admonition(node, 'important') def depart_important(self, node): # type: (nodes.Element) -> None self.depart_admonition(node) def visit_tip(self, node): # type: (nodes.Element) -> None self.visit_admonition(node, 'tip') def depart_tip(self, node): # type: (nodes.Element) -> None self.depart_admonition(node) def visit_literal_emphasis(self, node): # type: (nodes.Element) -> None return self.visit_emphasis(node) def depart_literal_emphasis(self, node): # type: (nodes.Element) -> None return self.depart_emphasis(node) def visit_literal_strong(self, node): # type: (nodes.Element) -> None return self.visit_strong(node) def depart_literal_strong(self, node): # type: (nodes.Element) -> None return self.depart_strong(node) def visit_abbreviation(self, node): # type: (nodes.Element) -> None attrs = {} if node.hasattr('explanation'): attrs['title'] = node['explanation'] self.body.append(self.starttag(node, 'abbr', '', **attrs)) def depart_abbreviation(self, node): # type: (nodes.Element) -> None self.body.append('</abbr>') def visit_manpage(self, node): # type: (nodes.Element) -> None self.visit_literal_emphasis(node) if self.manpages_url: node['refuri'] = self.manpages_url.format(**node.attributes) self.visit_reference(node) def depart_manpage(self, node): # type: (nodes.Element) -> None if self.manpages_url: self.depart_reference(node) self.depart_literal_emphasis(node) # overwritten to add even/odd classes def visit_table(self, node): # type: (nodes.Element) -> None self._table_row_index = 0 return super().visit_table(node) def visit_row(self, node): # type: (nodes.Element) -> None self._table_row_index += 1 if self._table_row_index % 2 == 0: node['classes'].append('row-even') else: node['classes'].append('row-odd') self.body.append(self.starttag(node, 'tr', '')) node.column = 0 # type: ignore def visit_entry(self, node): # type: (nodes.Element) -> None super().visit_entry(node) if self.body[-1] == ' ': self.body[-1] = ' ' def visit_field_list(self, node): # type: (nodes.Element) -> None self._fieldlist_row_index = 0 return super().visit_field_list(node) def visit_field(self, node): # type: (nodes.Element) -> None self._fieldlist_row_index += 1 if self._fieldlist_row_index % 2 == 0: node['classes'].append('field-even') else: node['classes'].append('field-odd') self.body.append(self.starttag(node, 'tr', '', CLASS='field')) def visit_field_name(self, node): # type: (nodes.Element) -> None context_count = len(self.context) super().visit_field_name(node) if context_count != len(self.context): self.context[-1] = self.context[-1].replace(' ', ' ') def visit_math(self, node, math_env=''): # type: (nodes.Element, str) -> None name = self.builder.math_renderer_name visit, _ = self.builder.app.registry.html_inline_math_renderers[name] visit(self, node) def depart_math(self, node, math_env=''): # type: (nodes.Element, str) -> None name = self.builder.math_renderer_name _, depart = self.builder.app.registry.html_inline_math_renderers[name] if depart: depart(self, node) def visit_math_block(self, node, math_env=''): # type: (nodes.Element, str) -> None name = self.builder.math_renderer_name visit, _ = self.builder.app.registry.html_block_math_renderers[name] visit(self, node) def depart_math_block(self, node, math_env=''): # type: (nodes.Element, str) -> None name = self.builder.math_renderer_name _, depart = self.builder.app.registry.html_block_math_renderers[name] if depart: depart(self, node) def unknown_visit(self, node): # type: (nodes.Node) -> None raise NotImplementedError('Unknown node: ' + node.__class__.__name__) # --------- METHODS FOR COMPATIBILITY -------------------------------------- @property def highlightlang(self): # type: () -> str warnings.warn('HTMLTranslator.highlightlang is deprecated.', RemovedInSphinx30Warning, stacklevel=2) return self.builder.config.highlight_language @property def highlightlang_base(self): # type: () -> str warnings.warn('HTMLTranslator.highlightlang_base is deprecated.', RemovedInSphinx30Warning) return self.builder.config.highlight_language @property def highlightopts(self): # type: () -> str warnings.warn('HTMLTranslator.highlightopts is deprecated.', RemovedInSphinx30Warning, stacklevel=2) return self.builder.config.highlight_options @property def highlightlinenothreshold(self): # type: () -> int warnings.warn('HTMLTranslator.highlightlinenothreshold is deprecated.', RemovedInSphinx30Warning, stacklevel=2) return sys.maxsize

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import copy import os import posixpath import sys import warnings from typing import Iterable, cast from docutils import nodes from docutils.writers.html4css1 import Writer, HTMLTranslator as BaseTranslator from sphinx import addnodes from sphinx.builders import Builder from sphinx.deprecation import RemovedInSphinx30Warning, RemovedInSphinx40Warning from sphinx.locale import admonitionlabels, _, __ from sphinx.util import logging from sphinx.util.docutils import SphinxTranslator from sphinx.util.images import get_image_size if False: from typing import Any from sphinx.builders.html import StandaloneHTMLBuilder logger = logging.getLogger(__name__) class HTMLWriter(Writer): settings_spec = copy.deepcopy(Writer.settings_spec) for _setting in settings_spec[2]: if '--embed-stylesheet' in _setting[1]: _setting[2]['default'] = 0 def __init__(self, builder): super().__init__() self.builder = builder def translate(self): visitor = self.builder.create_translator(self.document, self.builder) self.visitor = cast(HTMLTranslator, visitor) self.document.walkabout(visitor) self.output = self.visitor.astext() for attr in ('head_prefix', 'stylesheet', 'head', 'body_prefix', 'body_pre_docinfo', 'docinfo', 'body', 'fragment', 'body_suffix', 'meta', 'title', 'subtitle', 'header', 'footer', 'html_prolog', 'html_head', 'html_title', 'html_subtitle', 'html_body', ): setattr(self, attr, getattr(visitor, attr, None)) self.clean_meta = ''.join(self.visitor.meta[2:]) class HTMLTranslator(SphinxTranslator, BaseTranslator): builder = None def __init__(self, *args): if isinstance(args[0], nodes.document) and isinstance(args[1], Builder): document, builder = args else: warnings.warn('The order of arguments for HTMLTranslator has been changed. ' 'Please give "document" as 1st and "builder" as 2nd.', RemovedInSphinx40Warning, stacklevel=2) builder, document = args super().__init__(document, builder) self.highlighter = self.builder.highlighter self.docnames = [self.builder.current_docname] self.manpages_url = self.config.manpages_url self.protect_literal_text = 0 self.permalink_text = self.config.html_add_permalinks if not isinstance(self.permalink_text, str): self.permalink_text = self.permalink_text and '\u00B6' or '' self.permalink_text = self.encode(self.permalink_text) self.secnumber_suffix = self.config.html_secnumber_suffix self.param_separator = '' self.optional_param_level = 0 self._table_row_index = 0 self._fieldlist_row_index = 0 self.required_params_left = 0 def visit_start_of_file(self, node): self.docnames.append(node['docname']) self.body.append('<span id="document-%s"></span>' % node['docname']) def depart_start_of_file(self, node): self.docnames.pop() def visit_desc(self, node): self.body.append(self.starttag(node, 'dl', CLASS=node['objtype'])) def depart_desc(self, node): self.body.append('</dl>\n\n') def visit_desc_signature(self, node): self.body.append(self.starttag(node, 'dt')) if node.parent['objtype'] != 'describe' \ and node['ids'] and node['first']: self.body.append('' % node['ids'][0]) def depart_desc_signature(self, node): if not node.get('is_multiline'): self.add_permalink_ref(node, _('Permalink to this definition')) self.body.append('</dt>\n') def visit_desc_signature_line(self, node): pass def depart_desc_signature_line(self, node): if node.get('add_permalink'): self.add_permalink_ref(node.parent, _('Permalink to this definition')) self.body.append('<br />') def visit_desc_addname(self, node): self.body.append(self.starttag(node, 'code', '', CLASS='descclassname')) def depart_desc_addname(self, node): self.body.append('</code>') def visit_desc_type(self, node): pass def depart_desc_type(self, node): pass def visit_desc_returns(self, node): self.body.append(' → ') def depart_desc_returns(self, node): pass def visit_desc_name(self, node): self.body.append(self.starttag(node, 'code', '', CLASS='descname')) def depart_desc_name(self, node): self.body.append('</code>') def visit_desc_parameterlist(self, node): self.body.append('<span class="sig-paren">(</span>') self.first_param = 1 self.optional_param_level = 0 self.required_params_left = sum([isinstance(c, addnodes.desc_parameter) for c in node.children]) self.param_separator = node.child_text_separator def depart_desc_parameterlist(self, node): self.body.append('<span class="sig-paren">)</span>') def visit_desc_parameter(self, node): if self.first_param: self.first_param = 0 elif not self.required_params_left: self.body.append(self.param_separator) if self.optional_param_level == 0: self.required_params_left -= 1 if not node.hasattr('noemph'): self.body.append('<em>') def depart_desc_parameter(self, node): if not node.hasattr('noemph'): self.body.append('</em>') if self.required_params_left: self.body.append(self.param_separator) def visit_desc_optional(self, node): self.optional_param_level += 1 self.body.append('<span class="optional">[</span>') def depart_desc_optional(self, node): self.optional_param_level -= 1 self.body.append('<span class="optional">]</span>') def visit_desc_annotation(self, node): self.body.append(self.starttag(node, 'em', '', CLASS='property')) def depart_desc_annotation(self, node): self.body.append('</em>') def visit_desc_content(self, node): self.body.append(self.starttag(node, 'dd', '')) def depart_desc_content(self, node): self.body.append('</dd>') def visit_versionmodified(self, node): self.body.append(self.starttag(node, 'div', CLASS=node['type'])) def depart_versionmodified(self, node): self.body.append('</div>\n') def visit_reference(self, node): atts = {'class': 'reference'} if node.get('internal') or 'refuri' not in node: atts['class'] += ' internal' else: atts['class'] += ' external' if 'refuri' in node: atts['href'] = node['refuri'] or '#' if self.settings.cloak_email_addresses and atts['href'].startswith('mailto:'): atts['href'] = self.cloak_mailto(atts['href']) self.in_mailto = True else: assert 'refid' in node, \ 'References must have "refuri" or "refid" attribute.' atts['href'] = '#' + node['refid'] if not isinstance(node.parent, nodes.TextElement): assert len(node) == 1 and isinstance(node[0], nodes.image) atts['class'] += ' image-reference' if 'reftitle' in node: atts['title'] = node['reftitle'] if 'target' in node: atts['target'] = node['target'] self.body.append(self.starttag(node, 'a', '', **atts)) if node.get('secnumber'): self.body.append(('%s' + self.secnumber_suffix) % '.'.join(map(str, node['secnumber']))) def visit_number_reference(self, node): self.visit_reference(node) def depart_number_reference(self, node): self.depart_reference(node) def visit_comment(self, node): # type: ignore # type: (nodes.Element) -> None raise nodes.SkipNode # overwritten def visit_admonition(self, node, name=''): # type: (nodes.Element, str) -> None self.body.append(self.starttag( node, 'div', CLASS=('admonition ' + name))) if name: node.insert(0, nodes.title(name, admonitionlabels[name])) self.set_first_last(node) def visit_seealso(self, node): # type: (nodes.Element) -> None self.visit_admonition(node, 'seealso') def depart_seealso(self, node): # type: (nodes.Element) -> None self.depart_admonition(node) def add_secnumber(self, node): # type: (nodes.Element) -> None if node.get('secnumber'): self.body.append('.'.join(map(str, node['secnumber'])) + self.secnumber_suffix) elif isinstance(node.parent, nodes.section): if self.builder.name == 'singlehtml': docname = self.docnames[-1] anchorname = "%s/#%s" % (docname, node.parent['ids'][0]) if anchorname not in self.builder.secnumbers: anchorname = "%s/" % docname # try first heading which has no anchor else: anchorname = ' if anchorname not in self.builder.secnumbers: anchorname = '' # try first heading which has no anchor if self.builder.secnumbers.get(anchorname): numbers = self.builder.secnumbers[anchorname] self.body.append('.'.join(map(str, numbers)) + self.secnumber_suffix) def add_fignumber(self, node): # type: (nodes.Element) -> None def append_fignumber(figtype, figure_id): # type: (str, str) -> None if self.builder.name == 'singlehtml': key = "%s/%s" % (self.docnames[-1], figtype) else: key = figtype if figure_id in self.builder.fignumbers.get(key, {}): self.body.append('<span class="caption-number">') prefix = self.builder.config.numfig_format.get(figtype) if prefix is None: msg = __('numfig_format is not defined for %s') % figtype logger.warning(msg) else: numbers = self.builder.fignumbers[key][figure_id] self.body.append(prefix % '.'.join(map(str, numbers)) + ' ') self.body.append('</span>') figtype = self.builder.env.domains['std'].get_enumerable_node_type(node) if figtype: if len(node['ids']) == 0: msg = __('Any IDs not assigned for %s node') % node.tagname logger.warning(msg, location=node) else: append_fignumber(figtype, node['ids'][0]) def add_permalink_ref(self, node, title): # type: (nodes.Element, str) -> None if node['ids'] and self.permalink_text and self.builder.add_permalinks: format = '<a class="headerlink" href="#%s" title="%s">%s</a>' self.body.append(format % (node['ids'][0], title, self.permalink_text)) def generate_targets_for_listing(self, node): # type: (nodes.Element) -> None for id in node['ids'][1:]: self.body.append('<span id="%s"></span>' % id) node['ids'].remove(id) # overwritten def visit_bullet_list(self, node): # type: (nodes.Element) -> None if len(node) == 1 and isinstance(node[0], addnodes.toctree): # avoid emitting empty <ul></ul> raise nodes.SkipNode self.generate_targets_for_listing(node) super().visit_bullet_list(node) # overwritten def visit_enumerated_list(self, node): # type: (nodes.Element) -> None self.generate_targets_for_listing(node) super().visit_enumerated_list(node) # overwritten def visit_title(self, node): # type: (nodes.Element) -> None super().visit_title(node) self.add_secnumber(node) self.add_fignumber(node.parent) if isinstance(node.parent, nodes.table): self.body.append('<span class="caption-text">') def depart_title(self, node): # type: (nodes.Element) -> None close_tag = self.context[-1] if (self.permalink_text and self.builder.add_permalinks and node.parent.hasattr('ids') and node.parent['ids']): # add permalink anchor if close_tag.startswith('</h'): self.add_permalink_ref(node.parent, _('Permalink to this headline')) elif close_tag.startswith('</a></h'): self.body.append('</a><a class="headerlink" href="#%s" ' % node.parent['ids'][0] + 'title="%s">%s' % ( _('Permalink to this headline'), self.permalink_text)) elif isinstance(node.parent, nodes.table): self.body.append('</span>') self.add_permalink_ref(node.parent, _('Permalink to this table')) elif isinstance(node.parent, nodes.table): self.body.append('</span>') super().depart_title(node) # overwritten def visit_literal_block(self, node): # type: (nodes.Element) -> None if node.rawsource != node.astext(): # most probably a parsed-literal block -- don't highlight return super().visit_literal_block(node) lang = node.get('language', 'default') linenos = node.get('linenos', False) highlight_args = node.get('highlight_args', {}) highlight_args['force'] = node.get('force_highlighting', False) if lang is self.builder.config.highlight_language: opts = self.builder.config.highlight_options else: opts = {} highlighted = self.highlighter.highlight_block( node.rawsource, lang, opts=opts, linenos=linenos, location=(self.builder.current_docname, node.line), **highlight_args ) starttag = self.starttag(node, 'div', suffix='', CLASS='highlight-%s notranslate' % lang) self.body.append(starttag + highlighted + '</div>\n') raise nodes.SkipNode def visit_caption(self, node): if isinstance(node.parent, nodes.container) and node.parent.get('literal_block'): self.body.append('<div class="code-block-caption">') else: super().visit_caption(node) self.add_fignumber(node.parent) self.body.append(self.starttag(node, 'span', '', CLASS='caption-text')) def depart_caption(self, node): self.body.append('</span>') if isinstance(node.parent, nodes.container) and node.parent.get('literal_block'): self.add_permalink_ref(node.parent, _('Permalink to this code')) elif isinstance(node.parent, nodes.figure): image_nodes = node.parent.traverse(nodes.image) target_node = image_nodes and image_nodes[0] or node.parent self.add_permalink_ref(target_node, _('Permalink to this image')) elif node.parent.get('toctree'): self.add_permalink_ref(node.parent.parent, _('Permalink to this toctree')) if isinstance(node.parent, nodes.container) and node.parent.get('literal_block'): self.body.append('</div>\n') else: super().depart_caption(node) def visit_doctest_block(self, node): self.visit_literal_block(node) def visit_block_quote(self, node): self.body.append(self.starttag(node, 'blockquote') + '<div>') def depart_block_quote(self, node): self.body.append('</div></blockquote>\n') def visit_literal(self, node): if 'kbd' in node['classes']: self.body.append(self.starttag(node, 'kbd', '', CLASS='docutils literal notranslate')) else: self.body.append(self.starttag(node, 'code', '', CLASS='docutils literal notranslate')) self.protect_literal_text += 1 def depart_literal(self, node): if 'kbd' in node['classes']: self.body.append('</kbd>') else: self.protect_literal_text -= 1 self.body.append('</code>') def visit_productionlist(self, node): self.body.append(self.starttag(node, 'pre')) names = [] productionlist = cast(Iterable[addnodes.production], node) for production in productionlist: names.append(production['tokenname']) maxlen = max(len(name) for name in names) lastname = None for production in productionlist: if production['tokenname']: lastname = production['tokenname'].ljust(maxlen) self.body.append(self.starttag(production, 'strong', '')) self.body.append(lastname + '</strong> ::= ') elif lastname is not None: self.body.append('%s ' % (' ' * len(lastname))) production.walkabout(self) self.body.append('\n') self.body.append('</pre>\n') raise nodes.SkipNode def depart_productionlist(self, node): pass def visit_production(self, node): pass def depart_production(self, node): pass def visit_centered(self, node): self.body.append(self.starttag(node, 'p', CLASS="centered") + '<strong>') def depart_centered(self, node): self.body.append('</strong></p>') def should_be_compact_paragraph(self, node): if isinstance(node.parent, addnodes.desc_content): return False if isinstance(node.parent, addnodes.versionmodified): return False return super().should_be_compact_paragraph(node) def visit_compact_paragraph(self, node): pass def depart_compact_paragraph(self, node): pass def visit_download_reference(self, node): atts = {'class': 'reference download', 'download': ''} if not self.builder.download_support: self.context.append('') elif 'refuri' in node: atts['class'] += ' external' atts['href'] = node['refuri'] self.body.append(self.starttag(node, 'a', '', **atts)) self.context.append('</a>') elif 'filename' in node: atts['class'] += ' internal' atts['href'] = posixpath.join(self.builder.dlpath, node['filename']) self.body.append(self.starttag(node, 'a', '', **atts)) self.context.append('</a>') else: self.context.append('') def depart_download_reference(self, node): self.body.append(self.context.pop()) def visit_image(self, node): olduri = node['uri'] if olduri in self.builder.images: node['uri'] = posixpath.join(self.builder.imgpath, self.builder.images[olduri]) uri = node['uri'] if uri.lower().endswith(('svg', 'svgz')): atts = {'src': uri} if 'width' in node: atts['width'] = node['width'] if 'height' in node: atts['height'] = node['height'] atts['alt'] = node.get('alt', uri) if 'align' in node: self.body.append('<div align="%s" class="align-%s">' % (node['align'], node['align'])) self.context.append('</div>\n') else: self.context.append('') self.body.append(self.emptytag(node, 'img', '', **atts)) return if 'scale' in node: if not ('width' in node and 'height' in node): size = get_image_size(os.path.join(self.builder.srcdir, olduri)) if size is None: logger.warning(__('Could not obtain image size. :scale: option is ignored.'), location=node) else: if 'width' not in node: node['width'] = str(size[0]) if 'height' not in node: node['height'] = str(size[1]) super().visit_image(node) def depart_image(self, node): if node['uri'].lower().endswith(('svg', 'svgz')): self.body.append(self.context.pop()) else: super().depart_image(node) def visit_toctree(self, node): raise nodes.SkipNode def visit_index(self, node): # type: (nodes.Element) -> None raise nodes.SkipNode def visit_tabular_col_spec(self, node): # type: (nodes.Element) -> None raise nodes.SkipNode def visit_glossary(self, node): # type: (nodes.Element) -> None pass def depart_glossary(self, node): # type: (nodes.Element) -> None pass def visit_acks(self, node): # type: (nodes.Element) -> None pass def depart_acks(self, node): # type: (nodes.Element) -> None pass def visit_hlist(self, node): # type: (nodes.Element) -> None self.body.append('<table class="hlist"><tr>') def depart_hlist(self, node): # type: (nodes.Element) -> None self.body.append('</tr></table>\n') def visit_hlistcol(self, node): # type: (nodes.Element) -> None self.body.append('<td>') def depart_hlistcol(self, node): # type: (nodes.Element) -> None self.body.append('</td>') def visit_option_group(self, node): # type: (nodes.Element) -> None super().visit_option_group(node) self.context[-2] = self.context[-2].replace(' ', '& # overwritten def visit_Text(self, node): # type: (nodes.Text) -> None text = node.astext() encoded = self.encode(text) if self.protect_literal_text: # moved here from base class's visit_literal to support for token in self.words_and_spaces.findall(encoded): if token.strip(): self.body.append('<span class="pre">%s</span>' % token) elif token in ' \n': self.body.append(token) else: self.body.append(' ' * (len(token) - 1) + ' ') else: if self.in_mailto and self.settings.cloak_email_addresses: encoded = self.cloak_email(encoded) self.body.append(encoded) def visit_note(self, node): self.visit_admonition(node, 'note') def depart_note(self, node): self.depart_admonition(node) def visit_warning(self, node): self.visit_admonition(node, 'warning') def depart_warning(self, node): self.depart_admonition(node) def visit_attention(self, node): self.visit_admonition(node, 'attention') def depart_attention(self, node): self.depart_admonition(node) def visit_caution(self, node): self.visit_admonition(node, 'caution') def depart_caution(self, node): self.depart_admonition(node) def visit_danger(self, node): self.visit_admonition(node, 'danger') def depart_danger(self, node): self.depart_admonition(node) def visit_error(self, node): self.visit_admonition(node, 'error') def depart_error(self, node): self.depart_admonition(node) def visit_hint(self, node): self.visit_admonition(node, 'hint') def depart_hint(self, node): self.depart_admonition(node) def visit_important(self, node): self.visit_admonition(node, 'important') def depart_important(self, node): self.depart_admonition(node) def visit_tip(self, node): self.visit_admonition(node, 'tip') def depart_tip(self, node): self.depart_admonition(node) def visit_literal_emphasis(self, node): return self.visit_emphasis(node) def depart_literal_emphasis(self, node): return self.depart_emphasis(node) def visit_literal_strong(self, node): return self.visit_strong(node) def depart_literal_strong(self, node): return self.depart_strong(node) def visit_abbreviation(self, node): attrs = {} if node.hasattr('explanation'): attrs['title'] = node['explanation'] self.body.append(self.starttag(node, 'abbr', '', **attrs)) def depart_abbreviation(self, node): self.body.append('</abbr>') def visit_manpage(self, node): self.visit_literal_emphasis(node) if self.manpages_url: node['refuri'] = self.manpages_url.format(**node.attributes) self.visit_reference(node) def depart_manpage(self, node): if self.manpages_url: self.depart_reference(node) self.depart_literal_emphasis(node) def visit_table(self, node): self._table_row_index = 0 return super().visit_table(node) def visit_row(self, node): self._table_row_index += 1 if self._table_row_index % 2 == 0: node['classes'].append('row-even') else: node['classes'].append('row-odd') self.body.append(self.starttag(node, 'tr', '')) node.column = 0 def visit_entry(self, node): super().visit_entry(node) if self.body[-1] == ' ': self.body[-1] = ' ' def visit_field_list(self, node): self._fieldlist_row_index = 0 return super().visit_field_list(node) def visit_field(self, node): self._fieldlist_row_index += 1 if self._fieldlist_row_index % 2 == 0: node['classes'].append('field-even') else: node['classes'].append('field-odd') self.body.append(self.starttag(node, 'tr', '', CLASS='field')) def visit_field_name(self, node): context_count = len(self.context) super().visit_field_name(node) if context_count != len(self.context): self.context[-1] = self.context[-1].replace(' ', ' ') def visit_math(self, node, math_env=''): name = self.builder.math_renderer_name visit, _ = self.builder.app.registry.html_inline_math_renderers[name] visit(self, node) def depart_math(self, node, math_env=''): name = self.builder.math_renderer_name _, depart = self.builder.app.registry.html_inline_math_renderers[name] if depart: depart(self, node) def visit_math_block(self, node, math_env=''): name = self.builder.math_renderer_name visit, _ = self.builder.app.registry.html_block_math_renderers[name] visit(self, node) def depart_math_block(self, node, math_env=''): name = self.builder.math_renderer_name _, depart = self.builder.app.registry.html_block_math_renderers[name] if depart: depart(self, node) def unknown_visit(self, node): raise NotImplementedError('Unknown node: ' + node.__class__.__name__) @property def highlightlang(self): warnings.warn('HTMLTranslator.highlightlang is deprecated.', RemovedInSphinx30Warning, stacklevel=2) return self.builder.config.highlight_language @property def highlightlang_base(self): warnings.warn('HTMLTranslator.highlightlang_base is deprecated.', RemovedInSphinx30Warning) return self.builder.config.highlight_language @property def highlightopts(self): warnings.warn('HTMLTranslator.highlightopts is deprecated.', RemovedInSphinx30Warning, stacklevel=2) return self.builder.config.highlight_options @property def highlightlinenothreshold(self): warnings.warn('HTMLTranslator.highlightlinenothreshold is deprecated.', RemovedInSphinx30Warning, stacklevel=2) return sys.maxsize

true

f7366798202eb5b63a293458869820181b482fe2

10,620

py

Python

ramses_rf/protocol/helpers.py

zxdavb/evohome_rf

dd7233abde1c3b1e645cfabcf3ccc96d0d2c381b

[ "MIT" ]

24

2019-12-12T20:54:39.000Z

2021-03-25T15:40:26.000Z

ramses_rf/protocol/helpers.py

zxdavb/evohome_rf

dd7233abde1c3b1e645cfabcf3ccc96d0d2c381b

[ "MIT" ]

9

2020-10-21T23:01:06.000Z

2021-04-22T09:59:50.000Z

ramses_rf/protocol/helpers.py

zxdavb/evohome_rf

dd7233abde1c3b1e645cfabcf3ccc96d0d2c381b

[ "MIT" ]

9

2019-12-03T21:05:11.000Z

2021-04-02T11:41:42.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # """RAMSES RF - Protocol/Transport layer. Helper functions. """ import ctypes import sys import time from datetime import datetime as dt from typing import Optional, Union from .const import DEVICE_TYPES, NON_DEVICE_ID, NUL_DEVICE_ID class FILETIME(ctypes.Structure): """Data structure for GetSystemTimePreciseAsFileTime().""" _fields_ = [("dwLowDateTime", ctypes.c_uint), ("dwHighDateTime", ctypes.c_uint)] def dt_now() -> dt: """Return the current datetime as a local/naive datetime object. This is slower, but potentially more accurate, than dt.now(), and is used mainly for packet timestamps. """ return dt.fromtimestamp(timestamp()) def dt_str() -> str: """Return the current datetime as a isoformat string.""" return dt_now().isoformat(timespec="microseconds") def timestamp() -> float: """Return the number of seconds since the Unix epoch. Return an accurate value, even for Windows-based systems. """ # see: https://www.python.org/dev/peps/pep-0564/ if sys.platform != "win32": return time.time_ns() / 1e9 # since 1970-01-01T00:00:00Z, time.gmtime(0) file_time = FILETIME() ctypes.windll.kernel32.GetSystemTimePreciseAsFileTime(ctypes.byref(file_time)) _time = (file_time.dwLowDateTime + (file_time.dwHighDateTime << 32)) / 1e7 return _time - 134774 * 24 * 60 * 60 # otherwise, is since 1601-01-01T00:00:00Z def _precision_v_cost(): import math # LOOPS = 10 ** 6 # print("time.time_ns(): %s" % time.time_ns()) print("time.time(): %s\r\n" % time.time()) # starts = time.time_ns() min_dt = [abs(time.time_ns() - time.time_ns()) for _ in range(LOOPS)] min_dt = min(filter(bool, min_dt)) print("min delta time_ns(): %s ns" % min_dt) print("duration time_ns(): %s ns\r\n" % (time.time_ns() - starts)) # starts = time.time_ns() min_dt = [abs(time.time() - time.time()) for _ in range(LOOPS)] min_dt = min(filter(bool, min_dt)) print("min delta time(): %s ns" % math.ceil(min_dt * 1e9)) print("duration time(): %s ns\r\n" % (time.time_ns() - starts)) # starts = time.time_ns() min_dt = [abs(timestamp() - timestamp()) for _ in range(LOOPS)] min_dt = min(filter(bool, min_dt)) print("min delta timestamp(): %s ns" % math.ceil(min_dt * 1e9)) print("duration timestamp(): %s ns\r\n" % (time.time_ns() - starts)) # LOOPS = 10 ** 4 # starts = time.time_ns() min_td = [abs(dt.now() - dt.now()) for _ in range(LOOPS)] min_td = min(filter(bool, min_td)) print("min delta dt.now(): %s ns" % math.ceil(min_dt * 1e9)) print("duration dt.now(): %s ns\r\n" % (time.time_ns() - starts)) # starts = time.time_ns() min_td = [abs(dt_now() - dt_now()) for _ in range(LOOPS)] min_td = min(filter(bool, min_td)) print("min delta dt_now(): %s ns" % math.ceil(min_dt * 1e9)) print("duration dt_now(): %s ns\r\n" % (time.time_ns() - starts)) # starts = time.time_ns() min_td = [ abs( (dt_now if sys.platform == "win32" else dt.now)() - (dt_now if sys.platform == "win32" else dt.now)() ) for _ in range(LOOPS) ] min_td = min(filter(bool, min_td)) print("min delta dt_now(): %s ns" % math.ceil(min_dt * 1e9)) print("duration dt_now(): %s ns\r\n" % (time.time_ns() - starts)) # dt_nov = dt_now if sys.platform == "win32" else dt.now starts = time.time_ns() min_td = [abs(dt_nov() - dt_nov()) for _ in range(LOOPS)] min_td = min(filter(bool, min_td)) print("min delta dt_now(): %s ns" % math.ceil(min_dt * 1e9)) print("duration dt_now(): %s ns\r\n" % (time.time_ns() - starts)) def double(val, factor=1) -> Optional[float]: """Return a double, used by 31DA.""" if val == "7FFF": return result = int(val, 16) assert result < 32767 return result if factor == 1 else result / factor def flag8(byte, lsb=False) -> list: """Split a byte (as a str) into a list of 8 bits, MSB first by default.""" if lsb is True: return [(bytes.fromhex(byte)[0] & (1 << x)) >> x for x in range(8)] return [(bytes.fromhex(byte)[0] & (1 << x)) >> x for x in reversed(range(8))] def percent(value: str) -> Optional[float]: # a percentage 0-100% (0.0 to 1.0) """Return a percentage, 0-100% with resolution of 0.5%.""" assert len(value) == 2, f"percent({value}): len is not 2" if value in {"EF", "FE", "FF"}: # TODO: diff b/w FE (seen with 3150) & FF return assert int(value, 16) <= 200, "max value should be 0xC8, not 0x{value}" return int(value, 16) / 200 def bool_from_hex(value: str) -> Optional[bool]: # either 00 or C8 """Return a boolean.""" assert value in {"00", "C8", "FF"}, value return {"00": False, "C8": True}.get(value) def date_from_hex(value: str) -> Optional[str]: # YY-MM-DD """Return a date string in the format YY-MM-DD.""" assert len(value) == 8, "len is not 8" if value == "FFFFFFFF": return return dt( year=int(value[4:8], 16), month=int(value[2:4], 16), day=int(value[:2], 16) & 0b11111, # 1st 3 bits: DayOfWeek ).strftime("%Y-%m-%d") def dtm_from_hex(value: str) -> str: # from parsers """Convert a hex string to an (naive, local) isoformat string.""" # 00141B0A07E3 (...HH:MM:00) for system_mode, zone_mode (schedules?) # 0400041C0A07E3 (...HH:MM:SS) for sync_datetime if value == "FF" * 6: return None if len(value) == 12: value = f"00{value}" # assert len(value) == 14 return dt( year=int(value[10:14], 16), month=int(value[8:10], 16), day=int(value[6:8], 16), hour=int(value[4:6], 16) & 0b11111, # 1st 3 bits: DayOfWeek minute=int(value[2:4], 16), second=int(value[:2], 16) & 0b1111111, # 1st bit: used for DST ).isoformat(timespec="seconds") def dtm_to_hex(dtm: Union[str, dt]) -> str: """Convert a datetime (isoformat string, or datetime obj) to a hex string.""" def _dtm_to_hex(tm_year, tm_mon, tm_mday, tm_hour, tm_min, tm_sec, *args): return f"{tm_min:02X}{tm_hour:02X}{tm_mday:02X}{tm_mon:02X}{tm_year:04X}" if dtm is None: return "FF" * 6 if isinstance(dtm, str): try: dtm = dt.fromisoformat(dtm) except ValueError: raise ValueError("Invalid datetime isoformat string") elif not isinstance(dtm, dt): raise TypeError("Invalid datetime object") # if dtm < dt.now() + td(minutes=1): # raise ValueError("Invalid datetime") return _dtm_to_hex(*dtm.timetuple()) def dts_from_hex(value: str) -> Optional[str]: """YY-MM-DD HH:MM:SS.""" if value == "00000000007F": return None _seqx = int(value, 16) return dt( year=(_seqx & 0b1111111 << 24) >> 24, month=(_seqx & 0b1111 << 36) >> 36, day=(_seqx & 0b11111 << 31) >> 31, hour=(_seqx & 0b11111 << 19) >> 19, minute=(_seqx & 0b111111 << 13) >> 13, second=(_seqx & 0b111111 << 7) >> 7, ).strftime("%Y-%m-%dT%H:%M:%S") def dts_to_hex(dtm: Union[str, dt]) -> str: # TODO: WIP """YY-MM-DD HH:MM:SS.""" if dtm is None: return "00000000007F" if isinstance(dtm, str): try: dtm = dt.fromisoformat(dtm) # TODO: YY-MM-DD, not YYYY-MM-DD except ValueError: raise ValueError("Invalid datetime isoformat string") elif not isinstance(dtm, dt): raise TypeError("Invalid datetime object") (tm_year, tm_mon, tm_mday, tm_hour, tm_min, tm_sec, *args) = dtm.timetuple() val = sum( ( tm_year % 100 << 24, tm_mon << 36, tm_mday << 31, tm_hour << 19, tm_min << 13, tm_sec << 7, ) ) return f"{val:012X}" def str_from_hex(value: str) -> Optional[str]: # printable ASCII characters """Return a string of printable ASCII characters.""" # result = bytearray.fromhex(value).split(b"\x7F")[0] # TODO: needs checking result = bytearray([x for x in bytearray.fromhex(value) if 31 < x < 127]) return result.decode("ascii").strip() if result else None def str_to_hex(value: str) -> str: """Convert a string to a variable-length ASCII hex string.""" return "".join(f"{ord(x):02X}" for x in value) # return value.encode().hex() def temp_from_hex(value: str) -> Union[float, bool, None]: """Convert a 2's complement 4-byte hex string to an float.""" assert len(value) == 4, f"temp_from_hex({value}): should be 4 bytes long" if value == "31FF": # means: N/A (== 127.99, 2s complement), signed? return if value == "7EFF": # possibly only for setpoints? unsigned? return False if value == "7FFF": # also: FFFF?, means: N/A (== 327.67) return temp = int(value, 16) return (temp if temp < 2 ** 15 else temp - 2 ** 16) / 100 def temp_to_hex(value: float) -> str: """Convert a float to a 2's complement 4-byte hex string.""" assert ( not value or -(2 ** 7) <= value < 2 ** 7 ), f"temp_to_hex({value}): is out of 2's complement range" if value is None: return "7FFF" # or: "31FF"? if value is False: return "7EFF" temp = int(value * 100) return f"{temp if temp >= 0 else temp + 2 ** 16:04X}" def valve_demand(value: str) -> dict: # a damper restricts flow, a valve permits flow demand = int(value, 16) if demand & 0xF0 == 0xF0: VALVE_STATE = { "F0": "open_circuit", "F1": "short_circuit", "FD": "valve_stuck", # damper/valve stuck "FE": "actuator_stuck", } # VALVE_STATE.get(value, "malfunction") return { "heat_demand": None, "fault": VALVE_STATE.get(value, "malfunction"), } assert demand <= 200 return {"heat_demand": demand / 200} def hex_id_to_dec(device_hex: str, friendly_id=False) -> str: """Convert (say) '06368E' to '01:145038' (or 'CTL:145038').""" if device_hex == "FFFFFE": # aka '63:262142' return "NUL:262142" if friendly_id else NUL_DEVICE_ID if not device_hex.strip(): # aka '--:------' return f"{'':10}" if friendly_id else NON_DEVICE_ID _tmp = int(device_hex, 16) dev_type = f"{(_tmp & 0xFC0000) >> 18:02d}" if friendly_id: dev_type = DEVICE_TYPES.get(dev_type, f"{dev_type:<3}") return f"{dev_type}:{_tmp & 0x03FFFF:06d}"

34.14791

88

0.591149

import ctypes import sys import time from datetime import datetime as dt from typing import Optional, Union from .const import DEVICE_TYPES, NON_DEVICE_ID, NUL_DEVICE_ID class FILETIME(ctypes.Structure): _fields_ = [("dwLowDateTime", ctypes.c_uint), ("dwHighDateTime", ctypes.c_uint)] def dt_now() -> dt: return dt.fromtimestamp(timestamp()) def dt_str() -> str: return dt_now().isoformat(timespec="microseconds") def timestamp() -> float: if sys.platform != "win32": return time.time_ns() / 1e9 file_time = FILETIME() ctypes.windll.kernel32.GetSystemTimePreciseAsFileTime(ctypes.byref(file_time)) _time = (file_time.dwLowDateTime + (file_time.dwHighDateTime << 32)) / 1e7 return _time - 134774 * 24 * 60 * 60 def _precision_v_cost(): import math LOOPS = 10 ** 6 print("time.time_ns(): %s" % time.time_ns()) print("time.time(): %s\r\n" % time.time()) starts = time.time_ns() min_dt = [abs(time.time_ns() - time.time_ns()) for _ in range(LOOPS)] min_dt = min(filter(bool, min_dt)) print("min delta time_ns(): %s ns" % min_dt) print("duration time_ns(): %s ns\r\n" % (time.time_ns() - starts)) starts = time.time_ns() min_dt = [abs(time.time() - time.time()) for _ in range(LOOPS)] min_dt = min(filter(bool, min_dt)) print("min delta time(): %s ns" % math.ceil(min_dt * 1e9)) print("duration time(): %s ns\r\n" % (time.time_ns() - starts)) starts = time.time_ns() min_dt = [abs(timestamp() - timestamp()) for _ in range(LOOPS)] min_dt = min(filter(bool, min_dt)) print("min delta timestamp(): %s ns" % math.ceil(min_dt * 1e9)) print("duration timestamp(): %s ns\r\n" % (time.time_ns() - starts)) LOOPS = 10 ** 4 starts = time.time_ns() min_td = [abs(dt.now() - dt.now()) for _ in range(LOOPS)] min_td = min(filter(bool, min_td)) print("min delta dt.now(): %s ns" % math.ceil(min_dt * 1e9)) print("duration dt.now(): %s ns\r\n" % (time.time_ns() - starts)) starts = time.time_ns() min_td = [abs(dt_now() - dt_now()) for _ in range(LOOPS)] min_td = min(filter(bool, min_td)) print("min delta dt_now(): %s ns" % math.ceil(min_dt * 1e9)) print("duration dt_now(): %s ns\r\n" % (time.time_ns() - starts)) starts = time.time_ns() min_td = [ abs( (dt_now if sys.platform == "win32" else dt.now)() - (dt_now if sys.platform == "win32" else dt.now)() ) for _ in range(LOOPS) ] min_td = min(filter(bool, min_td)) print("min delta dt_now(): %s ns" % math.ceil(min_dt * 1e9)) print("duration dt_now(): %s ns\r\n" % (time.time_ns() - starts)) dt_nov = dt_now if sys.platform == "win32" else dt.now starts = time.time_ns() min_td = [abs(dt_nov() - dt_nov()) for _ in range(LOOPS)] min_td = min(filter(bool, min_td)) print("min delta dt_now(): %s ns" % math.ceil(min_dt * 1e9)) print("duration dt_now(): %s ns\r\n" % (time.time_ns() - starts)) def double(val, factor=1) -> Optional[float]: if val == "7FFF": return result = int(val, 16) assert result < 32767 return result if factor == 1 else result / factor def flag8(byte, lsb=False) -> list: if lsb is True: return [(bytes.fromhex(byte)[0] & (1 << x)) >> x for x in range(8)] return [(bytes.fromhex(byte)[0] & (1 << x)) >> x for x in reversed(range(8))] def percent(value: str) -> Optional[float]: assert len(value) == 2, f"percent({value}): len is not 2" if value in {"EF", "FE", "FF"}: return assert int(value, 16) <= 200, "max value should be 0xC8, not 0x{value}" return int(value, 16) / 200 def bool_from_hex(value: str) -> Optional[bool]: assert value in {"00", "C8", "FF"}, value return {"00": False, "C8": True}.get(value) def date_from_hex(value: str) -> Optional[str]: assert len(value) == 8, "len is not 8" if value == "FFFFFFFF": return return dt( year=int(value[4:8], 16), month=int(value[2:4], 16), day=int(value[:2], 16) & 0b11111, ).strftime("%Y-%m-%d") def dtm_from_hex(value: str) -> str: if value == "FF" * 6: return None if len(value) == 12: value = f"00{value}" return dt( year=int(value[10:14], 16), month=int(value[8:10], 16), day=int(value[6:8], 16), hour=int(value[4:6], 16) & 0b11111, minute=int(value[2:4], 16), second=int(value[:2], 16) & 0b1111111, ).isoformat(timespec="seconds") def dtm_to_hex(dtm: Union[str, dt]) -> str: def _dtm_to_hex(tm_year, tm_mon, tm_mday, tm_hour, tm_min, tm_sec, *args): return f"{tm_min:02X}{tm_hour:02X}{tm_mday:02X}{tm_mon:02X}{tm_year:04X}" if dtm is None: return "FF" * 6 if isinstance(dtm, str): try: dtm = dt.fromisoformat(dtm) except ValueError: raise ValueError("Invalid datetime isoformat string") elif not isinstance(dtm, dt): raise TypeError("Invalid datetime object") return _dtm_to_hex(*dtm.timetuple()) def dts_from_hex(value: str) -> Optional[str]: if value == "00000000007F": return None _seqx = int(value, 16) return dt( year=(_seqx & 0b1111111 << 24) >> 24, month=(_seqx & 0b1111 << 36) >> 36, day=(_seqx & 0b11111 << 31) >> 31, hour=(_seqx & 0b11111 << 19) >> 19, minute=(_seqx & 0b111111 << 13) >> 13, second=(_seqx & 0b111111 << 7) >> 7, ).strftime("%Y-%m-%dT%H:%M:%S") def dts_to_hex(dtm: Union[str, dt]) -> str: if dtm is None: return "00000000007F" if isinstance(dtm, str): try: dtm = dt.fromisoformat(dtm) except ValueError: raise ValueError("Invalid datetime isoformat string") elif not isinstance(dtm, dt): raise TypeError("Invalid datetime object") (tm_year, tm_mon, tm_mday, tm_hour, tm_min, tm_sec, *args) = dtm.timetuple() val = sum( ( tm_year % 100 << 24, tm_mon << 36, tm_mday << 31, tm_hour << 19, tm_min << 13, tm_sec << 7, ) ) return f"{val:012X}" def str_from_hex(value: str) -> Optional[str]: y([x for x in bytearray.fromhex(value) if 31 < x < 127]) return result.decode("ascii").strip() if result else None def str_to_hex(value: str) -> str: return "".join(f"{ord(x):02X}" for x in value) def temp_from_hex(value: str) -> Union[float, bool, None]: assert len(value) == 4, f"temp_from_hex({value}): should be 4 bytes long" if value == "31FF": return if value == "7EFF": return False if value == "7FFF": return temp = int(value, 16) return (temp if temp < 2 ** 15 else temp - 2 ** 16) / 100 def temp_to_hex(value: float) -> str: assert ( not value or -(2 ** 7) <= value < 2 ** 7 ), f"temp_to_hex({value}): is out of 2's complement range" if value is None: return "7FFF" # or: "31FF"? if value is False: return "7EFF" temp = int(value * 100) return f"{temp if temp >= 0 else temp + 2 ** 16:04X}" def valve_demand(value: str) -> dict: # a damper restricts flow, a valve permits flow demand = int(value, 16) if demand & 0xF0 == 0xF0: VALVE_STATE = { "F0": "open_circuit", "F1": "short_circuit", "FD": "valve_stuck", # damper/valve stuck "FE": "actuator_stuck", } # VALVE_STATE.get(value, "malfunction") return { "heat_demand": None, "fault": VALVE_STATE.get(value, "malfunction"), } assert demand <= 200 return {"heat_demand": demand / 200} def hex_id_to_dec(device_hex: str, friendly_id=False) -> str: if device_hex == "FFFFFE": # aka '63:262142' return "NUL:262142" if friendly_id else NUL_DEVICE_ID if not device_hex.strip(): # aka '--:------' return f"{'':10}" if friendly_id else NON_DEVICE_ID _tmp = int(device_hex, 16) dev_type = f"{(_tmp & 0xFC0000) >> 18:02d}" if friendly_id: dev_type = DEVICE_TYPES.get(dev_type, f"{dev_type:<3}") return f"{dev_type}:{_tmp & 0x03FFFF:06d}"

true

f736682c03031c943fab6c203b47c7184e8ec07d

12,492

py

Python

models/resnet_lgdv2.py

kk2487/3dresnet

d7161a70ed6c2f8dcbe89f9b6bad2ef6cc5b5d94

[ "MIT" ]

null

models/resnet_lgdv2.py

kk2487/3dresnet

d7161a70ed6c2f8dcbe89f9b6bad2ef6cc5b5d94

[ "MIT" ]

null

models/resnet_lgdv2.py

kk2487/3dresnet

d7161a70ed6c2f8dcbe89f9b6bad2ef6cc5b5d94

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable import math from functools import partial import numpy as np __all__ = [ 'ResNet', 'resnet10', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnet200' ] def look_bottleneck_global(glo): if look_bottleneck_global: if glo is None: print('first bottleneck-> no global content!') else: print('glo has content!') # Can print the model structure def model_info(model, report='summary'): # Plots a line-by-line description of a PyTorch model n_p = sum(x.numel() for x in model.parameters()) # number parameters n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients if report is 'full': print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) for i, (name, p) in enumerate(model.named_parameters()): name = name.replace('module_list.', '') print('%5g %40s %9s %12g %20s %10.3g %10.3g' % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) print('Model Summary: %g layers, %g parameters, %g gradients' % (len(list(model.parameters())), n_p, n_g)) def conv3x3x3(in_planes, out_planes, stride=1): # 3x3x3 convolution with padding return nn.Conv3d( in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) # Implement of bottleneck with se block class BottleneckX(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, first_block=False): super(BottleneckX, self).__init__() self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm3d(planes) #self.bn1 = nn.GroupNorm(4, planes) self.conv2 = nn.Conv3d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm3d(planes) #self.bn2 = nn.GroupNorm(4, planes) self.conv3 = nn.Conv3d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm3d(planes * 4) #self.bn3 = nn.GroupNorm(4, planes * 4) #self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride # If first bottleneckX, it does not contain global path self.first_block = first_block # If downsampling occurs, set true self.ds = False #self.se_module = SEModule(planes * 4, reduction=16, first_block=self.first_block) self.avg_pool = nn.AdaptiveAvgPool3d(1) #Implement LGD block self.fc1 = nn.Conv3d(planes * 4 // 2, planes * 4, kernel_size=1, stride=1, padding=0, bias=False) #self.fc2 = nn.Conv3d(planes * 4 // 16, planes * 4, kernel_size=1, stride=1, padding=0, bias=False) self.bn4 = nn.BatchNorm3d(planes * 4) #self.bn4 = nn.GroupNorm(4, planes * 4) self.fc3 = nn.Conv3d(planes * 4, planes * 4 // 16, kernel_size=1, stride=1, padding=0, bias=False) self.fc4 = nn.Conv3d(planes * 4 // 16, planes * 4, kernel_size=1, stride=1, padding=0, bias=False) self.fc5 = nn.Conv3d(planes * 4, planes * 4 // 16, kernel_size=1, stride=1, padding=0, bias=False) self.fc6 = nn.Conv3d(planes * 4 // 16, planes * 4, kernel_size=1, stride=1, padding=0, bias=False) self.sigmoid = nn.Sigmoid() self.relu = nn.LeakyReLU(inplace=True) def forward(self, xx): # xx contains two element: input->x and global path->glo x = xx[0] glo = xx[1] residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) #out = self.relu(out) # If downsample, downsampleing global path & residual channels if self.downsample is not None: if glo is not None: glo = self.avg_pool(glo) glo = self.fc1(glo) glo = self.relu(glo) residual = self.downsample(x) #LGD block if glo is not None: glo = self.fc3(glo) glo = self.relu(glo) glo = self.fc4(glo) glo = self.sigmoid(glo) out = out * glo #out = self.relu(out) glo2 = self.avg_pool(out) glo2 = self.fc5(glo2) glo2 = self.relu(glo2) glo2 = self.fc6(glo2) glo2 = self.sigmoid(glo2) g = glo + glo2 g = self.relu(g) out = out + residual out = self.relu(out) outg = [out, g] # Normal bottleneck else: out = out + residual out = self.relu(out) outg = [out, residual] return outg class ResNet(nn.Module): def __init__(self, blockx, layers, sample_size, sample_duration, shortcut_type='B', num_classes=400): self.inplanes = 64 super(ResNet, self).__init__() self.conv1 = nn.Conv3d( 3, 64, kernel_size=7, stride=(1, 2, 2), padding=(3, 3, 3), bias=False) self.bn1 = nn.BatchNorm3d(64) #self.bn1 = nn.GroupNorm(4, 64) self.relu = nn.LeakyReLU(inplace=True) self.maxpool = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1) self.layer1 = self._make_layer(blockx, 64, layers[0], shortcut_type, first_block=True) self.layer2 = self._make_layer(blockx, 128, layers[1], shortcut_type, stride=2, first_block=False) self.layer3 = self._make_layer(blockx, 256, layers[2], shortcut_type, stride=2, first_block=False) self.layer4 = self._make_layer(blockx, 512, layers[3], shortcut_type, stride=2, first_block=False) last_duration = int(math.ceil(sample_duration / 16)) last_size = int(math.ceil(sample_size / 32)) #last_size = 4 self.avgpool = nn.AvgPool3d( (last_duration, last_size, last_size), stride=1) self.fc = nn.Linear(512 * blockx.expansion, num_classes) #self.fusion = nn.Conv3d(512 * block.expansion * 2, 512 * block.expansion, kernel_size=1, stride=1, padding=0, bias=False) for m in self.modules(): if isinstance(m, nn.Conv3d): m.weight = nn.init.kaiming_normal(m.weight, mode='fan_out') elif isinstance(m, nn.BatchNorm3d): m.weight.data.fill_(1) m.bias.data.zero_() def _make_layer(self, block, planes, blocks, shortcut_type, stride=1, first_block=False): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: if shortcut_type == 'A': downsample = partial( downsample_basic_block, planes=planes * block.expansion, stride=stride) else: downsample = nn.Sequential( nn.Conv3d( self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm3d(planes * block.expansion)) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, first_block)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): #print('lgd') x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) lookshape = False # First time need to give two element to model xx = [x, None] x = self.layer1(xx) if lookshape: print('\nlayer1-------------') print(np.shape(x[0])) print(np.shape(x[1])) print('--------------') x = self.layer2(x) if lookshape: print('\nlayer2-------------') print(np.shape(x[0])) print(np.shape(x[1])) print('--------------') x = self.layer3(x) if lookshape: print('\nlayer3-------------') print(np.shape(x[0])) print(np.shape(x[1])) print('--------------') x = self.layer4(x) if lookshape: print('\nlayer4-------------') print(np.shape(x[0])) print(np.shape(x[1])) print('--------------') # After bottlenck part loc, g = x[0], x[1] #print(g) if lookshape: print('loc & g:--------') print(np.shape(loc)) print(np.shape(g)) print('----------------') x = self.avgpool(loc) #x = x + g #x = self.bn2(x) #x = self.relu(x) if lookshape: print('\nlayer5-------------') print(np.shape(x)) print('--------------') # Test local and global path feature maps fusion type below # 3d conv #x = torch.cat((x, g), 1) #x = self.fusion(x) #x = self.bn2(x) #x = self.relu(x) # concat (need to change fc layer filter number) #x = torch.cat((x, g), 1) #x = self.relu(x) x = x.view(x.size(0), -1) if lookshape: print('\nlayer6-------------') print(np.shape(x)) print('--------------') x = self.fc(x) if lookshape: print('\nlayer7-------------') print(np.shape(x)) print('--------------') return x def get_fine_tuning_parameters(model, ft_begin_index): #if ft_begin_index == 0: # return model.parameters() print('ohraaaa') ft_module_names = [] for i in range(ft_begin_index, 5): ft_module_names.append('layer{}'.format(i)) ft_module_names.append('fc') # Look the content of ft_module print('ft: ', ft_module_names) parameters = [] ii = 0 ''' for k, v in model.named_parameters(): for ft_module in ft_module_names: if ii >= 271: #220 271 print(ii) parameters.append({'params': v}) else: print('notfc') print(ii) parameters.append({'params': v, 'lr': 0.0}) #parameters.append({'params': v}) print(k) ii = ii+1 return parameters ''' # bakup code for k, v in model.named_parameters(): for ft_module in ft_module_names: if ft_module in k: #if ii >= 271: print('fc') #print(ii) parameters.append({'params': v}) break else: print('notfc') #print(ii) #parameters.append({'params': v, 'lr': 0.0}) parameters.append({'params': v}) print(k) ii = ii+1 return parameters def resnet10(**kwargs): """Constructs a ResNet-18 model. """ model = ResNet(BasicBlock, [1, 1, 1, 1], **kwargs) return model def resnet18(**kwargs): """Constructs a ResNet-18 model. """ model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) return model def resnet34(**kwargs): """Constructs a ResNet-34 model. """ model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) return model def resnet50(**kwargs): """Constructs a ResNet-50 model. """ model = ResNet(BottleneckX, [3, 4, 6, 3], **kwargs) #model = ResNet(Bottleneck, BottleneckX, [3, 4, 23, 3], **kwargs) #model_info(model,'full') return model def resnet101(**kwargs): """Constructs a ResNet-101 model. """ model = ResNet(BottleneckX, [3, 4, 23, 3], **kwargs) return model def resnet152(**kwargs): """Constructs a ResNet-101 model. """ model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) return model def resnet200(**kwargs): """Constructs a ResNet-101 model. """ model = ResNet(Bottleneck, [3, 24, 36, 3], **kwargs) return model

32.030769

130

0.53186

import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable import math from functools import partial import numpy as np __all__ = [ 'ResNet', 'resnet10', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnet200' ] def look_bottleneck_global(glo): if look_bottleneck_global: if glo is None: print('first bottleneck-> no global content!') else: print('glo has content!') def model_info(model, report='summary'): n_p = sum(x.numel() for x in model.parameters()) n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) if report is 'full': print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) for i, (name, p) in enumerate(model.named_parameters()): name = name.replace('module_list.', '') print('%5g %40s %9s %12g %20s %10.3g %10.3g' % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) print('Model Summary: %g layers, %g parameters, %g gradients' % (len(list(model.parameters())), n_p, n_g)) def conv3x3x3(in_planes, out_planes, stride=1): return nn.Conv3d( in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BottleneckX(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, first_block=False): super(BottleneckX, self).__init__() self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm3d(planes) self.conv2 = nn.Conv3d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm3d(planes) self.conv3 = nn.Conv3d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm3d(planes * 4) self.downsample = downsample self.stride = stride self.first_block = first_block self.ds = False self.avg_pool = nn.AdaptiveAvgPool3d(1) self.fc1 = nn.Conv3d(planes * 4 // 2, planes * 4, kernel_size=1, stride=1, padding=0, bias=False) self.bn4 = nn.BatchNorm3d(planes * 4) self.fc3 = nn.Conv3d(planes * 4, planes * 4 // 16, kernel_size=1, stride=1, padding=0, bias=False) self.fc4 = nn.Conv3d(planes * 4 // 16, planes * 4, kernel_size=1, stride=1, padding=0, bias=False) self.fc5 = nn.Conv3d(planes * 4, planes * 4 // 16, kernel_size=1, stride=1, padding=0, bias=False) self.fc6 = nn.Conv3d(planes * 4 // 16, planes * 4, kernel_size=1, stride=1, padding=0, bias=False) self.sigmoid = nn.Sigmoid() self.relu = nn.LeakyReLU(inplace=True) def forward(self, xx): x = xx[0] glo = xx[1] residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: if glo is not None: glo = self.avg_pool(glo) glo = self.fc1(glo) glo = self.relu(glo) residual = self.downsample(x) if glo is not None: glo = self.fc3(glo) glo = self.relu(glo) glo = self.fc4(glo) glo = self.sigmoid(glo) out = out * glo glo2 = self.avg_pool(out) glo2 = self.fc5(glo2) glo2 = self.relu(glo2) glo2 = self.fc6(glo2) glo2 = self.sigmoid(glo2) g = glo + glo2 g = self.relu(g) out = out + residual out = self.relu(out) outg = [out, g] else: out = out + residual out = self.relu(out) outg = [out, residual] return outg class ResNet(nn.Module): def __init__(self, blockx, layers, sample_size, sample_duration, shortcut_type='B', num_classes=400): self.inplanes = 64 super(ResNet, self).__init__() self.conv1 = nn.Conv3d( 3, 64, kernel_size=7, stride=(1, 2, 2), padding=(3, 3, 3), bias=False) self.bn1 = nn.BatchNorm3d(64) self.relu = nn.LeakyReLU(inplace=True) self.maxpool = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1) self.layer1 = self._make_layer(blockx, 64, layers[0], shortcut_type, first_block=True) self.layer2 = self._make_layer(blockx, 128, layers[1], shortcut_type, stride=2, first_block=False) self.layer3 = self._make_layer(blockx, 256, layers[2], shortcut_type, stride=2, first_block=False) self.layer4 = self._make_layer(blockx, 512, layers[3], shortcut_type, stride=2, first_block=False) last_duration = int(math.ceil(sample_duration / 16)) last_size = int(math.ceil(sample_size / 32)) self.avgpool = nn.AvgPool3d( (last_duration, last_size, last_size), stride=1) self.fc = nn.Linear(512 * blockx.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv3d): m.weight = nn.init.kaiming_normal(m.weight, mode='fan_out') elif isinstance(m, nn.BatchNorm3d): m.weight.data.fill_(1) m.bias.data.zero_() def _make_layer(self, block, planes, blocks, shortcut_type, stride=1, first_block=False): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: if shortcut_type == 'A': downsample = partial( downsample_basic_block, planes=planes * block.expansion, stride=stride) else: downsample = nn.Sequential( nn.Conv3d( self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm3d(planes * block.expansion)) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, first_block)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) lookshape = False xx = [x, None] x = self.layer1(xx) if lookshape: print('\nlayer1-------------') print(np.shape(x[0])) print(np.shape(x[1])) print('--------------') x = self.layer2(x) if lookshape: print('\nlayer2-------------') print(np.shape(x[0])) print(np.shape(x[1])) print('--------------') x = self.layer3(x) if lookshape: print('\nlayer3-------------') print(np.shape(x[0])) print(np.shape(x[1])) print('--------------') x = self.layer4(x) if lookshape: print('\nlayer4-------------') print(np.shape(x[0])) print(np.shape(x[1])) print('--------------') loc, g = x[0], x[1] if lookshape: print('loc & g:--------') print(np.shape(loc)) print(np.shape(g)) print('----------------') x = self.avgpool(loc) if lookshape: print('\nlayer5-------------') print(np.shape(x)) print('--------------') x = x.view(x.size(0), -1) if lookshape: print('\nlayer6-------------') print(np.shape(x)) print('--------------') x = self.fc(x) if lookshape: print('\nlayer7-------------') print(np.shape(x)) print('--------------') return x def get_fine_tuning_parameters(model, ft_begin_index): print('ohraaaa') ft_module_names = [] for i in range(ft_begin_index, 5): ft_module_names.append('layer{}'.format(i)) ft_module_names.append('fc') print('ft: ', ft_module_names) parameters = [] ii = 0 for k, v in model.named_parameters(): for ft_module in ft_module_names: if ft_module in k: print('fc') parameters.append({'params': v}) break else: print('notfc') parameters.append({'params': v}) print(k) ii = ii+1 return parameters def resnet10(**kwargs): model = ResNet(BasicBlock, [1, 1, 1, 1], **kwargs) return model def resnet18(**kwargs): model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) return model def resnet34(**kwargs): model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) return model def resnet50(**kwargs): model = ResNet(BottleneckX, [3, 4, 6, 3], **kwargs) return model def resnet101(**kwargs): model = ResNet(BottleneckX, [3, 4, 23, 3], **kwargs) return model def resnet152(**kwargs): model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) return model def resnet200(**kwargs): model = ResNet(Bottleneck, [3, 24, 36, 3], **kwargs) return model

true

f7366835c02e403a339394e5e63ac8c59828af30

123

py

Python

src/Stage/stage4.py

yuduki24/Rikako

989a0499d06bd37b5a6d826b1fb898369a50bbfb

[ "MIT" ]

null

src/Stage/stage4.py

yuduki24/Rikako

989a0499d06bd37b5a6d826b1fb898369a50bbfb

[ "MIT" ]

null

src/Stage/stage4.py

yuduki24/Rikako

989a0499d06bd37b5a6d826b1fb898369a50bbfb

[ "MIT" ]

null

from Stage.stage import * class Stage4(Stage): def deployEnemy(self): Boss2((self.scr_rect.width//2, 200))

24.6

44

0.650407

from Stage.stage import * class Stage4(Stage): def deployEnemy(self): Boss2((self.scr_rect.width//2, 200))

true

f73669c910733fc3bbdb968d1c678045f94256d1

999

py

Python

velhot/migrations/0004_profile.py

matiasmane/BWA

1dd3e68362fafb40e615f1485f2cdf4ad74837af

[ "MIT" ]

null

velhot/migrations/0004_profile.py

matiasmane/BWA

1dd3e68362fafb40e615f1485f2cdf4ad74837af

[ "MIT" ]

null

velhot/migrations/0004_profile.py

matiasmane/BWA

1dd3e68362fafb40e615f1485f2cdf4ad74837af

[ "MIT" ]

null

# Generated by Django 2.1.2 on 2018-11-20 12:48 from django.conf import settings import django.core.validators from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('velhot', '0003_friend'), ] operations = [ migrations.CreateModel( name='Profile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('real_name', models.CharField(max_length=60)), ('address', models.CharField(max_length=60)), ('phone_number', models.CharField(max_length=17, validators=[django.core.validators.RegexValidator(regex='^\\+?1?\\d{9,15}$')])), ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]

35.678571

145

0.638639

from django.conf import settings import django.core.validators from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('velhot', '0003_friend'), ] operations = [ migrations.CreateModel( name='Profile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('real_name', models.CharField(max_length=60)), ('address', models.CharField(max_length=60)), ('phone_number', models.CharField(max_length=17, validators=[django.core.validators.RegexValidator(regex='^\\+?1?\\d{9,15}$')])), ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]

true

f7366a3d5b380afad1909ab74d0d3a13473a6311

43,835

py

Python

statsmodels/tsa/tests/results/arima111nc_css_results.py

yarikoptic/statsmodels

f990cb1a1ef0c9883c9394444e6f9d027efabec6

[ "BSD-3-Clause" ]

34

2018-07-13T11:30:46.000Z

2022-01-05T13:48:10.000Z

venv/lib/python3.6/site-packages/statsmodels/tsa/tests/results/arima111nc_css_results.py

HeyWeiPan/vnpy_crypto

844381797a475a01c05a4e162592a5a6e3a48032

[ "MIT" ]

6

2015-08-28T16:59:03.000Z

2019-04-12T22:29:01.000Z

venv/lib/python3.6/site-packages/statsmodels/tsa/tests/results/arima111nc_css_results.py

HeyWeiPan/vnpy_crypto

844381797a475a01c05a4e162592a5a6e3a48032

[ "MIT" ]

28

2015-04-01T20:02:25.000Z

2021-07-03T00:09:28.000Z

import numpy as np llf = np.array([-242.89663276735]) nobs = np.array([ 202]) k = np.array([ 3]) k_exog = np.array([ 1]) sigma = np.array([ .8053519404535]) chi2 = np.array([ 15723.381396967]) df_model = np.array([ 2]) k_ar = np.array([ 1]) k_ma = np.array([ 1]) params = np.array([ .99479180506163, -.84461527652809, .64859174799221]) cov_params = np.array([ .00008904968254, -.00023560410507, .00012795903324, -.00023560410507, .00131628534915, -.00022462340695, .00012795903324, -.00022462340695, .0005651128627]).reshape(3,3) xb = np.array([ 0, 0, .02869686298072, .05651443824172, .0503994859755, .06887971609831, .05940540507436, .08067328482866, .08167565613985, .06429278105497, .07087650150061, .06886467337608, .06716959923506, .08230647444725, .07099691033363, .08401278406382, .07996553182602, .07354256510735, .09366323798895, .08811800926924, .10296355187893, .08846370875835, .0852297320962, .08700425922871, .09751411527395, .09737934917212, .11228405684233, .1053489819169, .12352022528648, .16439816355705, .1643835157156, .19891132414341, .17551273107529, .17827558517456, .19562774896622, .21028305590153, .23767858743668, .24580039083958, .28269505500793, .29883882403374, .31247469782829, .35402658581734, .37410452961922, .39106267690659, .42040377855301, .44518512487411, .43608102202415, .44340893626213, .44959822297096, .40977239608765, .42118826508522, .40079545974731, .38357082009315, .36902260780334, .35673499107361, .36137464642525, .38031083345413, .47139286994934, .47323387861252, .60994738340378, .69538277387619, .7825602889061, .84117436408997, .9657689332962, 1.0109325647354, .95897275209427, .96013957262039, .9461076259613, .9342554807663, .83413934707642, .83968591690063, .84437066316605, .83330947160721, .8990553021431, .87949693202972, .86297762393951, .89407861232758, .93536442518234, 1.0303052663803, 1.1104937791824, 1.1481873989105, 1.2851470708847, 1.4458787441254, 1.5515991449356, 1.7309991121292, 1.8975404500961, 1.8579913377762, 1.8846583366394, 1.9672524929047, 1.9469071626663, 2.0048115253448, 1.9786299467087, 1.8213576078415, 1.6284521818161, 1.7508568763733, 1.5689061880112, 1.2950873374939, 1.2290096282959, 1.1882168054581, 1.1537625789642, 1.1697143316269, 1.1681711673737, 1.106795668602, 1.0849931240082, 1.006507396698, 1.0453414916992, .98803448677063, .95465070009232, 1.0165599584579, .67838954925537, .69311982393265, .69054269790649, .76345545053482, .84005492925644, .87471830844879, .91901183128357, .92638796567917, .96265280246735, 1.0083012580872, 1.0618740320206, 1.0921038389206, 1.2077431678772, 1.2303256988525, 1.174311041832, 1.3072115182877, 1.314337015152, 1.3503924608231, 1.5760731697083, 1.5264053344727, 1.34929728508, 1.304829955101, 1.2522557973862, 1.222869515419, 1.198047041893, 1.1770839691162, 1.1743944883347, 1.1571066379547, 1.1274864673615, 1.0574153661728, 1.058304309845, .99898308515549, .9789143204689, 1.0070173740387, 1.000718832016, 1.0104174613953, 1.0486439466476, 1.0058424472809, .98470783233643, 1.0119106769562, 1.0649236440659, 1.0346088409424, 1.0540577173233, 1.0704846382141, .97923594713211, .90216588973999, .9271782040596, .85819715261459, .75488126277924, .78776079416275, .77047789096832, .77089905738831, .8313245177269, .82229107618332, .90476810932159, .94439232349396, 1.0379292964935, 1.1469690799713, 1.1489590406418, 1.2257302999496, 1.1554099321365, 1.1260533332825, .9811190366745, .8436843752861, .95287209749222, .90993344783783, .94875508546829, 1.0115815401077, .94450175762177, .87282890081406, .91741597652435, .98511207103729, .9972335100174, 1.0975805521011, 1.1823329925537, 1.1487929821014, 1.270641207695, 1.2083609104156, 1.696394443512, 1.4628355503082, 1.4307631254196, 1.5087975263596, 1.1542117595673, 1.2262620925903, 1.3880327939987, 1.3853038549423, 1.4396153688431, 1.7208145856857, 1.678991317749, 2.110867023468, 1.524417757988, .57946246862411, .56406193971634, .74643105268478]) y = np.array([np.nan, 28.979999542236, 29.178695678711, 29.40651512146, 29.420400619507, 29.608880996704, 29.609405517578, 29.830673217773, 29.921676635742, 29.874292373657, 29.990877151489, 30.048864364624, 30.10717010498, 30.292304992676, 30.290996551514, 30.464012145996, 30.519966125488, 30.553541183472, 30.783664703369, 30.838117599487, 31.042964935303, 31.038463592529, 31.105230331421, 31.207004547119, 31.377513885498, 31.477378845215, 31.692283630371, 31.755348205566, 32.003520965576, 32.444396972656, 32.61438369751, 33.048908233643, 33.07551574707, 33.278274536133, 33.595630645752, 33.91028213501, 34.337677001953, 34.645801544189, 35.182697296143, 35.598838806152, 36.012474060059, 36.654026031494, 37.174102783203, 37.691062927246, 38.320404052734, 38.94518661499, 39.336082458496, 39.843410491943, 40.349597930908, 40.509769439697, 41.021186828613, 41.300796508789, 41.583572387695, 41.869022369385, 42.156734466553, 42.561374664307, 43.080310821533, 44.171394348145, 44.673233032227, 46.209945678711, 47.495380401611, 48.882556915283, 50.141174316406, 51.965770721436, 53.310932159424, 53.958972930908, 54.960140228271, 55.84610748291, 56.734252929688, 56.934139251709, 57.839687347412, 58.744373321533, 59.533309936523, 60.899055480957, 61.679496765137, 62.46297454834, 63.594078063965, 64.83536529541, 66.530303955078, 68.210494995117, 69.64818572998, 71.885147094727, 74.445877075195, 76.751594543457, 79.731002807617, 82.797538757324, 84.457992553711, 86.584655761719, 89.167251586914, 91.046905517578, 93.504814147949, 95.378631591797, 96.22135925293, 96.628448486328, 99.250854492188, 99.668907165527, 99.195091247559, 100.0290145874, 100.98822021484, 101.95376586914, 103.26971435547, 104.46817779541, 105.20679473877, 106.1849899292, 106.70650482178, 108.0453414917, 108.68803405762, 109.45465087891, 110.91656494141, 109.37838745117, 110.19312286377, 110.89054107666, 112.16345977783, 113.54005432129, 114.67472076416, 115.91901397705, 116.92639160156, 118.16265106201, 119.50830078125, 120.96187591553, 122.29209899902, 124.30773925781, 125.7303237915, 126.57431030273, 128.8072052002, 130.21432495117, 131.85038757324, 134.97607421875, 136.22640991211, 136.44931030273, 137.50482177734, 138.45225524902, 139.5228729248, 140.59803771973, 141.67707824707, 142.87438964844, 143.95710754395, 144.92749023438, 145.55741882324, 146.65830993652, 147.29898071289, 148.17890930176, 149.40701293945, 150.40071105957, 151.51042175293, 152.84864807129, 153.60585021973, 154.48471069336, 155.7119140625, 157.16493225098, 158.03460693359, 159.25405883789, 160.47047424316, 160.87922668457, 161.30215454102, 162.42718505859, 162.85820007324, 162.95487976074, 163.98776245117, 164.67047119141, 165.47090148926, 166.73132324219, 167.52229309082, 169.00477600098, 170.24440002441, 171.93792724609, 173.84696960449, 175.04895019531, 176.82572937012, 177.55540466309, 178.52604675293, 178.58113098145, 178.54368591309, 180.25286865234, 180.90992736816, 182.14875793457, 183.61158752441, 184.14450073242, 184.5728302002, 185.81741333008, 187.28511047363, 188.39723205566, 190.19758605957, 191.98233032227, 192.94879150391, 195.07064819336, 195.90835571289, 200.89639282227, 200.86282348633, 202.13075256348, 204.20880126953, 203.05419921875, 204.80026245117, 207.3080291748, 208.72329711914, 210.57261657715, 214.21580505371, 215.67597961426, 220.72087097168, 218.41342163086, 212.75346374512, 213.23506164551, 215.21542358398]) resid = np.array([np.nan, .17000007629395, .17130389809608, -.03651398047805, .11960058659315, -.05888139456511, .14059536159039, .00932686589658, -.11167634278536, .04570783302188, -.0108770346269, -.00886330008507, .10282856971025, -.07230624556541, .08900293707848, -.0240114107728, -.03996651992202, .13645842671394, -.03366377204657, .10188252478838, -.09296332299709, -.01846401393414, .01477065030485, .0729955881834, .00248436117545, .10262141376734, -.04228436201811, .12465056031942, .27647939324379, .00560382334515, .23561419546604, -.1489082723856, .02448422275484, 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-.97863000631332, -1.2213591337204, .8715478181839, -1.1508584022522, -1.7689031362534, -.39508575201035, -.22900961339474, -.18821682035923, .14623281359673, .03029025532305, -.36817568540573, -.10679569840431, -.48499462008476, .29349562525749, -.34534454345703, -.18803144991398, .44535079598427, -2.2165644168854, .12161350995302, .00687709869817, .50946187973022, .53653997182846, .25995117425919, .32527860999107, .08098815381527, .27360898256302, .33735024929047, .39170032739639, .23812144994736, .80789774656296, .19225835800171, -.33032417297363, .92568749189377, .09278241544962, .28566908836365, 1.5496014356613, -.27607008814812, -1.1263961791992, -.24930645525455, -.30482992529869, -.15224970877171, -.12287864089012, -.09804095327854, .02291300706565, -.07438835501671, -.15710659325123, -.42748948931694, .04259072244167, -.35830733180046, -.09898918122053, .22108262777328, -.00701736938208, .0992873236537, .28958559036255, -.24864092469215, -.10584850609303, .21528913080692, .38809850811958, -.16492980718613, .16538816690445, .1459391862154, -.57048463821411, -.47923597693443, .19784018397331, -.4271782040596, -.65820020437241, .24511873722076, -.0877638310194, .02952514961362, .42909786105156, -.03132146969438, .57771807909012, .29522883892059, .6555985212326, .76207375526428, .05302781611681, .55105316638947, -.42574247717857, -.15540990233421, -.92604118585587, -.88112819194794, .75632172822952, -.25287514925003, .29006350040436, .45125409960747, -.41159069538116, -.44450175762177, .32716807723045, .48259317874908, .11487878113985, .70277869701385, .60241633653641, -.18233296275139, .85120695829391, -.37064728140831, 3.2916390895844, -1.4963974952698, -.16283248364925, .56923681497574, -2.3088004589081, .51979947090149, 1.1197309494019, .02996650896966, .40969428420067, 1.9223841428757, -.21881568431854, 2.9340152740479, -3.8318600654602, -6.239429473877, -.08245316892862, 1.2339268922806, 1.1695692539215]) yr = np.array([np.nan, .17000007629395, .17130389809608, -.03651398047805, .11960058659315, -.05888139456511, .14059536159039, .00932686589658, -.11167634278536, .04570783302188, -.0108770346269, -.00886330008507, .10282856971025, -.07230624556541, .08900293707848, -.0240114107728, -.03996651992202, .13645842671394, -.03366377204657, .10188252478838, -.09296332299709, -.01846401393414, .01477065030485, .0729955881834, .00248436117545, .10262141376734, -.04228436201811, .12465056031942, .27647939324379, .00560382334515, .23561419546604, -.1489082723856, .02448422275484, .12172746658325, .10437148809433, .18971465528011, .06232447177172, .25419962406158, .11730266362429, .10116269439459, .2875237762928, .14597341418266, .12589547038078, .20893961191177, .17959471046925, -.04518361017108, .06391899287701, .05659105628729, -.24960128962994, .09022761881351, -.12118522822857, -.10079623758793, -.08357158303261, -.06902338564396, .04326653853059, .13862533867359, .61968916654587, .02860714122653, .92676383256912, .59005337953568, .60461646318436, .41744044423103, .85882639884949, .33423033356667, -.31093180179596, .04102724045515, -.06013804674149, -.04610994458199, -.63425624370575, .06586220860481, .06031560897827, -.04437142238021, .46668976545334, -.09905604273081, -.07949769496918, .23702463507652, .30592212080956, .66463404893875, .56969320774078, .28950771689415, .95181107521057, 1.1148544549942, .75411820411682, 1.2484039068222, 1.1690024137497, -.1975435167551, .24200716614723, .6153416633606, -.06725100427866, .45309436321259, -.10480991750956, -.97863000631332, -1.2213591337204, .8715478181839, -1.1508584022522, -1.7689031362534, -.39508575201035, -.22900961339474, -.18821682035923, .14623281359673, .03029025532305, -.36817568540573, -.10679569840431, -.48499462008476, .29349562525749, -.34534454345703, -.18803144991398, .44535079598427, -2.2165644168854, .12161350995302, .00687709869817, .50946187973022, .53653997182846, .25995117425919, .32527860999107, .08098815381527, .27360898256302, 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.29006350040436, .45125409960747, -.41159069538116, -.44450175762177, .32716807723045, .48259317874908, .11487878113985, .70277869701385, .60241633653641, -.18233296275139, .85120695829391, -.37064728140831, 3.2916390895844, -1.4963974952698, -.16283248364925, .56923681497574, -2.3088004589081, .51979947090149, 1.1197309494019, .02996650896966, .40969428420067, 1.9223841428757, -.21881568431854, 2.9340152740479, -3.8318600654602, -6.239429473877, -.08245316892862, 1.2339268922806, 1.1695692539215]) mse = np.array([ 1.1112809181213, .6632194519043, .65879660844803, .65575885772705, .65364873409271, .65217137336731, .65113133192062, .6503963470459, .64987552165985, .64950579404831, .64924287796021, .64905577898026, .64892256259918, .64882761240005, .64875996112823, .64871168136597, .64867728948593, .64865279197693, .64863526821136, .64862281084061, .64861387014389, .64860755205154, .64860302209854, .64859980344772, .64859747886658, .64859586954117, .64859467744827, .64859384298325, .6485932469368, .64859282970428, .64859253168106, .64859229326248, .64859211444855, .64859199523926, .64859193563461, .64859187602997, .64859187602997, .64859181642532, .64859181642532, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068]) stdp = np.array([ 0, 0, .02869686298072, .05651443824172, .0503994859755, .06887971609831, .05940540507436, .08067328482866, .08167565613985, .06429278105497, .07087650150061, .06886467337608, .06716959923506, .08230647444725, .07099691033363, .08401278406382, .07996553182602, .07354256510735, .09366323798895, .08811800926924, .10296355187893, .08846370875835, .0852297320962, .08700425922871, .09751411527395, .09737934917212, .11228405684233, .1053489819169, .12352022528648, .16439816355705, .1643835157156, .19891132414341, .17551273107529, .17827558517456, .19562774896622, .21028305590153, .23767858743668, .24580039083958, .28269505500793, .29883882403374, .31247469782829, .35402658581734, .37410452961922, .39106267690659, .42040377855301, .44518512487411, .43608102202415, .44340893626213, .44959822297096, .40977239608765, .42118826508522, .40079545974731, .38357082009315, .36902260780334, .35673499107361, .36137464642525, .38031083345413, .47139286994934, .47323387861252, .60994738340378, .69538277387619, .7825602889061, .84117436408997, .9657689332962, 1.0109325647354, .95897275209427, .96013957262039, .9461076259613, .9342554807663, .83413934707642, .83968591690063, .84437066316605, .83330947160721, .8990553021431, .87949693202972, .86297762393951, .89407861232758, .93536442518234, 1.0303052663803, 1.1104937791824, 1.1481873989105, 1.2851470708847, 1.4458787441254, 1.5515991449356, 1.7309991121292, 1.8975404500961, 1.8579913377762, 1.8846583366394, 1.9672524929047, 1.9469071626663, 2.0048115253448, 1.9786299467087, 1.8213576078415, 1.6284521818161, 1.7508568763733, 1.5689061880112, 1.2950873374939, 1.2290096282959, 1.1882168054581, 1.1537625789642, 1.1697143316269, 1.1681711673737, 1.106795668602, 1.0849931240082, 1.006507396698, 1.0453414916992, .98803448677063, .95465070009232, 1.0165599584579, .67838954925537, .69311982393265, .69054269790649, .76345545053482, .84005492925644, .87471830844879, .91901183128357, .92638796567917, .96265280246735, 1.0083012580872, 1.0618740320206, 1.0921038389206, 1.2077431678772, 1.2303256988525, 1.174311041832, 1.3072115182877, 1.314337015152, 1.3503924608231, 1.5760731697083, 1.5264053344727, 1.34929728508, 1.304829955101, 1.2522557973862, 1.222869515419, 1.198047041893, 1.1770839691162, 1.1743944883347, 1.1571066379547, 1.1274864673615, 1.0574153661728, 1.058304309845, .99898308515549, .9789143204689, 1.0070173740387, 1.000718832016, 1.0104174613953, 1.0486439466476, 1.0058424472809, .98470783233643, 1.0119106769562, 1.0649236440659, 1.0346088409424, 1.0540577173233, 1.0704846382141, .97923594713211, .90216588973999, .9271782040596, .85819715261459, .75488126277924, .78776079416275, .77047789096832, .77089905738831, .8313245177269, .82229107618332, .90476810932159, .94439232349396, 1.0379292964935, 1.1469690799713, 1.1489590406418, 1.2257302999496, 1.1554099321365, 1.1260533332825, .9811190366745, .8436843752861, .95287209749222, .90993344783783, .94875508546829, 1.0115815401077, .94450175762177, .87282890081406, .91741597652435, .98511207103729, .9972335100174, 1.0975805521011, 1.1823329925537, 1.1487929821014, 1.270641207695, 1.2083609104156, 1.696394443512, 1.4628355503082, 1.4307631254196, 1.5087975263596, 1.1542117595673, 1.2262620925903, 1.3880327939987, 1.3853038549423, 1.4396153688431, 1.7208145856857, 1.678991317749, 2.110867023468, 1.524417757988, .57946246862411, .56406193971634, .74643105268478]) icstats = np.array([ 202, np.nan, -242.89663276735, 3, 491.79326553469, 501.7180686269]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, )

34.407378

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0.401278

import numpy as np llf = np.array([-242.89663276735]) nobs = np.array([ 202]) k = np.array([ 3]) k_exog = np.array([ 1]) sigma = np.array([ .8053519404535]) chi2 = np.array([ 15723.381396967]) df_model = np.array([ 2]) k_ar = np.array([ 1]) k_ma = np.array([ 1]) params = np.array([ .99479180506163, -.84461527652809, .64859174799221]) cov_params = np.array([ .00008904968254, -.00023560410507, .00012795903324, -.00023560410507, .00131628534915, -.00022462340695, .00012795903324, -.00022462340695, .0005651128627]).reshape(3,3) xb = np.array([ 0, 0, .02869686298072, .05651443824172, .0503994859755, .06887971609831, .05940540507436, .08067328482866, .08167565613985, .06429278105497, .07087650150061, .06886467337608, .06716959923506, .08230647444725, .07099691033363, .08401278406382, .07996553182602, .07354256510735, .09366323798895, .08811800926924, .10296355187893, .08846370875835, .0852297320962, .08700425922871, .09751411527395, .09737934917212, .11228405684233, .1053489819169, .12352022528648, .16439816355705, .1643835157156, .19891132414341, .17551273107529, .17827558517456, .19562774896622, .21028305590153, .23767858743668, .24580039083958, .28269505500793, .29883882403374, .31247469782829, .35402658581734, .37410452961922, .39106267690659, .42040377855301, .44518512487411, .43608102202415, .44340893626213, .44959822297096, .40977239608765, .42118826508522, .40079545974731, .38357082009315, .36902260780334, .35673499107361, .36137464642525, .38031083345413, .47139286994934, .47323387861252, .60994738340378, .69538277387619, .7825602889061, .84117436408997, .9657689332962, 1.0109325647354, .95897275209427, .96013957262039, .9461076259613, .9342554807663, .83413934707642, .83968591690063, .84437066316605, .83330947160721, .8990553021431, .87949693202972, .86297762393951, .89407861232758, .93536442518234, 1.0303052663803, 1.1104937791824, 1.1481873989105, 1.2851470708847, 1.4458787441254, 1.5515991449356, 1.7309991121292, 1.8975404500961, 1.8579913377762, 1.8846583366394, 1.9672524929047, 1.9469071626663, 2.0048115253448, 1.9786299467087, 1.8213576078415, 1.6284521818161, 1.7508568763733, 1.5689061880112, 1.2950873374939, 1.2290096282959, 1.1882168054581, 1.1537625789642, 1.1697143316269, 1.1681711673737, 1.106795668602, 1.0849931240082, 1.006507396698, 1.0453414916992, .98803448677063, .95465070009232, 1.0165599584579, .67838954925537, .69311982393265, .69054269790649, .76345545053482, .84005492925644, .87471830844879, .91901183128357, .92638796567917, .96265280246735, 1.0083012580872, 1.0618740320206, 1.0921038389206, 1.2077431678772, 1.2303256988525, 1.174311041832, 1.3072115182877, 1.314337015152, 1.3503924608231, 1.5760731697083, 1.5264053344727, 1.34929728508, 1.304829955101, 1.2522557973862, 1.222869515419, 1.198047041893, 1.1770839691162, 1.1743944883347, 1.1571066379547, 1.1274864673615, 1.0574153661728, 1.058304309845, .99898308515549, .9789143204689, 1.0070173740387, 1.000718832016, 1.0104174613953, 1.0486439466476, 1.0058424472809, .98470783233643, 1.0119106769562, 1.0649236440659, 1.0346088409424, 1.0540577173233, 1.0704846382141, .97923594713211, .90216588973999, .9271782040596, .85819715261459, .75488126277924, .78776079416275, .77047789096832, .77089905738831, .8313245177269, .82229107618332, .90476810932159, .94439232349396, 1.0379292964935, 1.1469690799713, 1.1489590406418, 1.2257302999496, 1.1554099321365, 1.1260533332825, .9811190366745, .8436843752861, .95287209749222, .90993344783783, .94875508546829, 1.0115815401077, .94450175762177, .87282890081406, .91741597652435, .98511207103729, .9972335100174, 1.0975805521011, 1.1823329925537, 1.1487929821014, 1.270641207695, 1.2083609104156, 1.696394443512, 1.4628355503082, 1.4307631254196, 1.5087975263596, 1.1542117595673, 1.2262620925903, 1.3880327939987, 1.3853038549423, 1.4396153688431, 1.7208145856857, 1.678991317749, 2.110867023468, 1.524417757988, .57946246862411, .56406193971634, .74643105268478]) y = np.array([np.nan, 28.979999542236, 29.178695678711, 29.40651512146, 29.420400619507, 29.608880996704, 29.609405517578, 29.830673217773, 29.921676635742, 29.874292373657, 29.990877151489, 30.048864364624, 30.10717010498, 30.292304992676, 30.290996551514, 30.464012145996, 30.519966125488, 30.553541183472, 30.783664703369, 30.838117599487, 31.042964935303, 31.038463592529, 31.105230331421, 31.207004547119, 31.377513885498, 31.477378845215, 31.692283630371, 31.755348205566, 32.003520965576, 32.444396972656, 32.61438369751, 33.048908233643, 33.07551574707, 33.278274536133, 33.595630645752, 33.91028213501, 34.337677001953, 34.645801544189, 35.182697296143, 35.598838806152, 36.012474060059, 36.654026031494, 37.174102783203, 37.691062927246, 38.320404052734, 38.94518661499, 39.336082458496, 39.843410491943, 40.349597930908, 40.509769439697, 41.021186828613, 41.300796508789, 41.583572387695, 41.869022369385, 42.156734466553, 42.561374664307, 43.080310821533, 44.171394348145, 44.673233032227, 46.209945678711, 47.495380401611, 48.882556915283, 50.141174316406, 51.965770721436, 53.310932159424, 53.958972930908, 54.960140228271, 55.84610748291, 56.734252929688, 56.934139251709, 57.839687347412, 58.744373321533, 59.533309936523, 60.899055480957, 61.679496765137, 62.46297454834, 63.594078063965, 64.83536529541, 66.530303955078, 68.210494995117, 69.64818572998, 71.885147094727, 74.445877075195, 76.751594543457, 79.731002807617, 82.797538757324, 84.457992553711, 86.584655761719, 89.167251586914, 91.046905517578, 93.504814147949, 95.378631591797, 96.22135925293, 96.628448486328, 99.250854492188, 99.668907165527, 99.195091247559, 100.0290145874, 100.98822021484, 101.95376586914, 103.26971435547, 104.46817779541, 105.20679473877, 106.1849899292, 106.70650482178, 108.0453414917, 108.68803405762, 109.45465087891, 110.91656494141, 109.37838745117, 110.19312286377, 110.89054107666, 112.16345977783, 113.54005432129, 114.67472076416, 115.91901397705, 116.92639160156, 118.16265106201, 119.50830078125, 120.96187591553, 122.29209899902, 124.30773925781, 125.7303237915, 126.57431030273, 128.8072052002, 130.21432495117, 131.85038757324, 134.97607421875, 136.22640991211, 136.44931030273, 137.50482177734, 138.45225524902, 139.5228729248, 140.59803771973, 141.67707824707, 142.87438964844, 143.95710754395, 144.92749023438, 145.55741882324, 146.65830993652, 147.29898071289, 148.17890930176, 149.40701293945, 150.40071105957, 151.51042175293, 152.84864807129, 153.60585021973, 154.48471069336, 155.7119140625, 157.16493225098, 158.03460693359, 159.25405883789, 160.47047424316, 160.87922668457, 161.30215454102, 162.42718505859, 162.85820007324, 162.95487976074, 163.98776245117, 164.67047119141, 165.47090148926, 166.73132324219, 167.52229309082, 169.00477600098, 170.24440002441, 171.93792724609, 173.84696960449, 175.04895019531, 176.82572937012, 177.55540466309, 178.52604675293, 178.58113098145, 178.54368591309, 180.25286865234, 180.90992736816, 182.14875793457, 183.61158752441, 184.14450073242, 184.5728302002, 185.81741333008, 187.28511047363, 188.39723205566, 190.19758605957, 191.98233032227, 192.94879150391, 195.07064819336, 195.90835571289, 200.89639282227, 200.86282348633, 202.13075256348, 204.20880126953, 203.05419921875, 204.80026245117, 207.3080291748, 208.72329711914, 210.57261657715, 214.21580505371, 215.67597961426, 220.72087097168, 218.41342163086, 212.75346374512, 213.23506164551, 215.21542358398]) resid = np.array([np.nan, .17000007629395, .17130389809608, -.03651398047805, .11960058659315, -.05888139456511, .14059536159039, .00932686589658, -.11167634278536, .04570783302188, -.0108770346269, -.00886330008507, .10282856971025, -.07230624556541, .08900293707848, -.0240114107728, -.03996651992202, .13645842671394, -.03366377204657, .10188252478838, -.09296332299709, -.01846401393414, .01477065030485, .0729955881834, .00248436117545, .10262141376734, -.04228436201811, .12465056031942, .27647939324379, .00560382334515, .23561419546604, -.1489082723856, .02448422275484, .12172746658325, .10437148809433, .18971465528011, .06232447177172, .25419962406158, .11730266362429, .10116269439459, .2875237762928, .14597341418266, .12589547038078, .20893961191177, .17959471046925, -.04518361017108, .06391899287701, .05659105628729, -.24960128962994, .09022761881351, -.12118522822857, -.10079623758793, -.08357158303261, -.06902338564396, .04326653853059, .13862533867359, .61968916654587, .02860714122653, .92676383256912, .59005337953568, .60461646318436, .41744044423103, .85882639884949, .33423033356667, -.31093180179596, .04102724045515, -.06013804674149, -.04610994458199, -.63425624370575, .06586220860481, .06031560897827, -.04437142238021, .46668976545334, -.09905604273081, -.07949769496918, .23702463507652, .30592212080956, .66463404893875, .56969320774078, .28950771689415, .95181107521057, 1.1148544549942, .75411820411682, 1.2484039068222, 1.1690024137497, -.1975435167551, .24200716614723, .6153416633606, -.06725100427866, .45309436321259, -.10480991750956, -.97863000631332, -1.2213591337204, .8715478181839, -1.1508584022522, -1.7689031362534, -.39508575201035, -.22900961339474, -.18821682035923, .14623281359673, .03029025532305, -.36817568540573, -.10679569840431, -.48499462008476, .29349562525749, -.34534454345703, -.18803144991398, .44535079598427, -2.2165644168854, .12161350995302, .00687709869817, .50946187973022, .53653997182846, .25995117425919, .32527860999107, .08098815381527, .27360898256302, .33735024929047, .39170032739639, .23812144994736, .80789774656296, .19225835800171, -.33032417297363, .92568749189377, .09278241544962, .28566908836365, 1.5496014356613, -.27607008814812, -1.1263961791992, -.24930645525455, -.30482992529869, -.15224970877171, -.12287864089012, -.09804095327854, .02291300706565, -.07438835501671, -.15710659325123, -.42748948931694, .04259072244167, -.35830733180046, -.09898918122053, .22108262777328, -.00701736938208, .0992873236537, .28958559036255, -.24864092469215, -.10584850609303, .21528913080692, .38809850811958, -.16492980718613, .16538816690445, .1459391862154, -.57048463821411, -.47923597693443, .19784018397331, -.4271782040596, -.65820020437241, .24511873722076, -.0877638310194, .02952514961362, .42909786105156, -.03132146969438, .57771807909012, .29522883892059, .6555985212326, .76207375526428, .05302781611681, .55105316638947, -.42574247717857, -.15540990233421, -.92604118585587, -.88112819194794, .75632172822952, -.25287514925003, .29006350040436, .45125409960747, -.41159069538116, -.44450175762177, .32716807723045, .48259317874908, .11487878113985, .70277869701385, .60241633653641, -.18233296275139, .85120695829391, -.37064728140831, 3.2916390895844, -1.4963974952698, -.16283248364925, .56923681497574, -2.3088004589081, .51979947090149, 1.1197309494019, .02996650896966, .40969428420067, 1.9223841428757, -.21881568431854, 2.9340152740479, -3.8318600654602, -6.239429473877, -.08245316892862, 1.2339268922806, 1.1695692539215]) yr = np.array([np.nan, .17000007629395, .17130389809608, -.03651398047805, .11960058659315, -.05888139456511, .14059536159039, .00932686589658, -.11167634278536, .04570783302188, -.0108770346269, -.00886330008507, .10282856971025, -.07230624556541, .08900293707848, -.0240114107728, -.03996651992202, .13645842671394, -.03366377204657, .10188252478838, -.09296332299709, -.01846401393414, .01477065030485, .0729955881834, .00248436117545, .10262141376734, -.04228436201811, .12465056031942, .27647939324379, .00560382334515, .23561419546604, -.1489082723856, .02448422275484, .12172746658325, .10437148809433, .18971465528011, .06232447177172, .25419962406158, .11730266362429, .10116269439459, .2875237762928, .14597341418266, .12589547038078, .20893961191177, .17959471046925, -.04518361017108, .06391899287701, .05659105628729, -.24960128962994, .09022761881351, -.12118522822857, -.10079623758793, -.08357158303261, -.06902338564396, .04326653853059, .13862533867359, .61968916654587, .02860714122653, .92676383256912, .59005337953568, .60461646318436, .41744044423103, .85882639884949, .33423033356667, -.31093180179596, .04102724045515, -.06013804674149, -.04610994458199, -.63425624370575, .06586220860481, .06031560897827, -.04437142238021, .46668976545334, -.09905604273081, -.07949769496918, .23702463507652, .30592212080956, .66463404893875, .56969320774078, .28950771689415, .95181107521057, 1.1148544549942, .75411820411682, 1.2484039068222, 1.1690024137497, -.1975435167551, .24200716614723, .6153416633606, -.06725100427866, .45309436321259, -.10480991750956, -.97863000631332, -1.2213591337204, .8715478181839, -1.1508584022522, -1.7689031362534, -.39508575201035, -.22900961339474, -.18821682035923, .14623281359673, .03029025532305, -.36817568540573, -.10679569840431, -.48499462008476, .29349562525749, -.34534454345703, -.18803144991398, .44535079598427, -2.2165644168854, .12161350995302, .00687709869817, .50946187973022, .53653997182846, .25995117425919, .32527860999107, .08098815381527, .27360898256302, .33735024929047, .39170032739639, .23812144994736, .80789774656296, .19225835800171, -.33032417297363, .92568749189377, .09278241544962, .28566908836365, 1.5496014356613, -.27607008814812, -1.1263961791992, -.24930645525455, -.30482992529869, -.15224970877171, -.12287864089012, -.09804095327854, .02291300706565, -.07438835501671, -.15710659325123, -.42748948931694, .04259072244167, -.35830733180046, -.09898918122053, .22108262777328, -.00701736938208, .0992873236537, .28958559036255, -.24864092469215, -.10584850609303, .21528913080692, .38809850811958, -.16492980718613, .16538816690445, .1459391862154, -.57048463821411, -.47923597693443, .19784018397331, -.4271782040596, -.65820020437241, .24511873722076, -.0877638310194, .02952514961362, .42909786105156, -.03132146969438, .57771807909012, .29522883892059, .6555985212326, .76207375526428, .05302781611681, .55105316638947, -.42574247717857, -.15540990233421, -.92604118585587, -.88112819194794, .75632172822952, -.25287514925003, .29006350040436, .45125409960747, -.41159069538116, -.44450175762177, .32716807723045, .48259317874908, .11487878113985, .70277869701385, .60241633653641, -.18233296275139, .85120695829391, -.37064728140831, 3.2916390895844, -1.4963974952698, -.16283248364925, .56923681497574, -2.3088004589081, .51979947090149, 1.1197309494019, .02996650896966, .40969428420067, 1.9223841428757, -.21881568431854, 2.9340152740479, -3.8318600654602, -6.239429473877, -.08245316892862, 1.2339268922806, 1.1695692539215]) mse = np.array([ 1.1112809181213, .6632194519043, .65879660844803, .65575885772705, .65364873409271, .65217137336731, .65113133192062, .6503963470459, .64987552165985, .64950579404831, .64924287796021, .64905577898026, .64892256259918, .64882761240005, .64875996112823, .64871168136597, .64867728948593, .64865279197693, .64863526821136, .64862281084061, .64861387014389, .64860755205154, .64860302209854, .64859980344772, .64859747886658, .64859586954117, .64859467744827, .64859384298325, .6485932469368, .64859282970428, .64859253168106, .64859229326248, .64859211444855, .64859199523926, .64859193563461, .64859187602997, .64859187602997, .64859181642532, .64859181642532, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068, .64859175682068]) stdp = np.array([ 0, 0, .02869686298072, .05651443824172, .0503994859755, .06887971609831, .05940540507436, .08067328482866, .08167565613985, .06429278105497, .07087650150061, .06886467337608, .06716959923506, .08230647444725, .07099691033363, .08401278406382, .07996553182602, .07354256510735, .09366323798895, .08811800926924, .10296355187893, .08846370875835, .0852297320962, .08700425922871, .09751411527395, .09737934917212, .11228405684233, .1053489819169, .12352022528648, .16439816355705, .1643835157156, .19891132414341, .17551273107529, .17827558517456, .19562774896622, .21028305590153, .23767858743668, .24580039083958, .28269505500793, .29883882403374, .31247469782829, .35402658581734, .37410452961922, .39106267690659, .42040377855301, .44518512487411, .43608102202415, .44340893626213, .44959822297096, .40977239608765, .42118826508522, .40079545974731, .38357082009315, .36902260780334, .35673499107361, .36137464642525, .38031083345413, .47139286994934, .47323387861252, .60994738340378, .69538277387619, .7825602889061, .84117436408997, .9657689332962, 1.0109325647354, .95897275209427, .96013957262039, .9461076259613, .9342554807663, .83413934707642, .83968591690063, .84437066316605, .83330947160721, .8990553021431, .87949693202972, .86297762393951, .89407861232758, .93536442518234, 1.0303052663803, 1.1104937791824, 1.1481873989105, 1.2851470708847, 1.4458787441254, 1.5515991449356, 1.7309991121292, 1.8975404500961, 1.8579913377762, 1.8846583366394, 1.9672524929047, 1.9469071626663, 2.0048115253448, 1.9786299467087, 1.8213576078415, 1.6284521818161, 1.7508568763733, 1.5689061880112, 1.2950873374939, 1.2290096282959, 1.1882168054581, 1.1537625789642, 1.1697143316269, 1.1681711673737, 1.106795668602, 1.0849931240082, 1.006507396698, 1.0453414916992, .98803448677063, .95465070009232, 1.0165599584579, .67838954925537, .69311982393265, .69054269790649, .76345545053482, .84005492925644, .87471830844879, .91901183128357, .92638796567917, .96265280246735, 1.0083012580872, 1.0618740320206, 1.0921038389206, 1.2077431678772, 1.2303256988525, 1.174311041832, 1.3072115182877, 1.314337015152, 1.3503924608231, 1.5760731697083, 1.5264053344727, 1.34929728508, 1.304829955101, 1.2522557973862, 1.222869515419, 1.198047041893, 1.1770839691162, 1.1743944883347, 1.1571066379547, 1.1274864673615, 1.0574153661728, 1.058304309845, .99898308515549, .9789143204689, 1.0070173740387, 1.000718832016, 1.0104174613953, 1.0486439466476, 1.0058424472809, .98470783233643, 1.0119106769562, 1.0649236440659, 1.0346088409424, 1.0540577173233, 1.0704846382141, .97923594713211, .90216588973999, .9271782040596, .85819715261459, .75488126277924, .78776079416275, .77047789096832, .77089905738831, .8313245177269, .82229107618332, .90476810932159, .94439232349396, 1.0379292964935, 1.1469690799713, 1.1489590406418, 1.2257302999496, 1.1554099321365, 1.1260533332825, .9811190366745, .8436843752861, .95287209749222, .90993344783783, .94875508546829, 1.0115815401077, .94450175762177, .87282890081406, .91741597652435, .98511207103729, .9972335100174, 1.0975805521011, 1.1823329925537, 1.1487929821014, 1.270641207695, 1.2083609104156, 1.696394443512, 1.4628355503082, 1.4307631254196, 1.5087975263596, 1.1542117595673, 1.2262620925903, 1.3880327939987, 1.3853038549423, 1.4396153688431, 1.7208145856857, 1.678991317749, 2.110867023468, 1.524417757988, .57946246862411, .56406193971634, .74643105268478]) icstats = np.array([ 202, np.nan, -242.89663276735, 3, 491.79326553469, 501.7180686269]) class Bunch(dict): def __init__(self, **kw): dict.__init__(self, kw) self.__dict__ = self results = Bunch(llf=llf, nobs=nobs, k=k, k_exog=k_exog, sigma=sigma, chi2=chi2, df_model=df_model, k_ar=k_ar, k_ma=k_ma, params=params, cov_params=cov_params, xb=xb, y=y, resid=resid, yr=yr, mse=mse, stdp=stdp, icstats=icstats, )

true

f7366a7972aa995d6c049ca89cf300ad3dc9460e

1,234

py

Python

script.py

DarkDevil1265/Malluunlimitedfilterbot

b8e8795e1c167a40070a9a698802122c461fcfda

[ "MIT" ]

1

2021-09-19T17:49:37.000Z

script.py

DarkDevil1265/Malluunlimitedfilterbot

b8e8795e1c167a40070a9a698802122c461fcfda

[ "MIT" ]

null

script.py

DarkDevil1265/Malluunlimitedfilterbot

b8e8795e1c167a40070a9a698802122c461fcfda

[ "MIT" ]

1

2021-09-20T08:20:14.000Z

class Script(object): START_MSG = """<b>Hello {} How are you, I'm an advanced filter bot with many capabilities! Made by @RJMALLU See <i>/help</i> for commands and more details.</b> """ HELP_MSG = """ <i>Add me as admin in your group and start filtering :)</i> <b>Basic Commands;</b> /start - Check if I'm alive! /help - Command help /about - Something about me! <b>Filter Commands;</b> <code>/add name reply</code> - Add filter for name <code>/del name</code> - Delete filter <code>/delall</code> - Delete entire filters (Group Owner Only!) <code>/viewfilters</code> - List all filters in chat <b>Connection Commands;</b> <code>/connect groupid</code> - Connect your group to my PM. You can also simply use, <code>/connect</code> in groups. <code>/connections</code> - Manage your connections. <b>Extras;</b> /status - Shows current status of your bot (Auth User Only) /id - Shows ID information <code>/info userid</code> - Shows User Information. Use <code>/info</code> as reply to some message for their details! <b>© @malluinstafollowers</b> """ ABOUT_MSG = """⭕️<b>My Name : RJ Filter Bot</b> ⭕️<b>Creater :</b> @RJMALLU ⭕️<b>Language :</b> <code>Python3</code> """

19.903226

120

0.658023

class Script(object): START_MSG = """<b>Hello {} How are you, I'm an advanced filter bot with many capabilities! Made by @RJMALLU See <i>/help</i> for commands and more details.</b> """ HELP_MSG = """ <i>Add me as admin in your group and start filtering :)</i> <b>Basic Commands;</b> /start - Check if I'm alive! /help - Command help /about - Something about me! <b>Filter Commands;</b> <code>/add name reply</code> - Add filter for name <code>/del name</code> - Delete filter <code>/delall</code> - Delete entire filters (Group Owner Only!) <code>/viewfilters</code> - List all filters in chat <b>Connection Commands;</b> <code>/connect groupid</code> - Connect your group to my PM. You can also simply use, <code>/connect</code> in groups. <code>/connections</code> - Manage your connections. <b>Extras;</b> /status - Shows current status of your bot (Auth User Only) /id - Shows ID information <code>/info userid</code> - Shows User Information. Use <code>/info</code> as reply to some message for their details! <b>© @malluinstafollowers</b> """ ABOUT_MSG = """⭕️<b>My Name : RJ Filter Bot</b> ⭕️<b>Creater :</b> @RJMALLU ⭕️<b>Language :</b> <code>Python3</code> """

true

f7366b93b511f5c29ce318235f5766cca881d088

27,114

py

Python

sphinx/writers/text.py

zzqcn/sphinx-doc

d7adc8efd7f71bb3f1633bf6cde19273fc95b977

[ "BSD-2-Clause" ]

2

2017-07-05T09:57:33.000Z

2017-11-14T23:05:53.000Z

sphinx/writers/text.py

zzqcn/sphinx-doc

d7adc8efd7f71bb3f1633bf6cde19273fc95b977

[ "BSD-2-Clause" ]

1

2019-01-17T14:26:22.000Z

2019-01-17T22:56:26.000Z

sphinx/writers/text.py

zzqcn/sphinx-doc

d7adc8efd7f71bb3f1633bf6cde19273fc95b977

[ "BSD-2-Clause" ]

1

2017-08-31T14:33:03.000Z

# -*- coding: utf-8 -*- """ sphinx.writers.text ~~~~~~~~~~~~~~~~~~~ Custom docutils writer for plain text. :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS. :license: BSD, see LICENSE for details. """ import os import re import textwrap from itertools import groupby import warnings from six.moves import zip_longest from docutils import nodes, writers from docutils.utils import column_width from sphinx import addnodes from sphinx.deprecation import RemovedInSphinx16Warning from sphinx.locale import admonitionlabels, _ class TextWrapper(textwrap.TextWrapper): """Custom subclass that uses a different word separator regex.""" wordsep_re = re.compile( r'(\s+|' # any whitespace r'(?<=\s)(?::[a-z-]+:)?`\S+|' # interpreted text start r'[^\s\w]*\w+[a-zA-Z]-(?=\w+[a-zA-Z])|' # hyphenated words r'(?<=[\w\!\"\'\&\.\,\?])-{2,}(?=\w))') # em-dash def _wrap_chunks(self, chunks): """_wrap_chunks(chunks : [string]) -> [string] The original _wrap_chunks uses len() to calculate width. This method respects wide/fullwidth characters for width adjustment. """ drop_whitespace = getattr(self, 'drop_whitespace', True) # py25 compat lines = [] if self.width <= 0: raise ValueError("invalid width %r (must be > 0)" % self.width) chunks.reverse() while chunks: cur_line = [] cur_len = 0 if lines: indent = self.subsequent_indent else: indent = self.initial_indent width = self.width - column_width(indent) if drop_whitespace and chunks[-1].strip() == '' and lines: del chunks[-1] while chunks: l = column_width(chunks[-1]) if cur_len + l <= width: cur_line.append(chunks.pop()) cur_len += l else: break if chunks and column_width(chunks[-1]) > width: self._handle_long_word(chunks, cur_line, cur_len, width) if drop_whitespace and cur_line and cur_line[-1].strip() == '': del cur_line[-1] if cur_line: lines.append(indent + ''.join(cur_line)) return lines def _break_word(self, word, space_left): """_break_word(word : string, space_left : int) -> (string, string) Break line by unicode width instead of len(word). """ total = 0 for i, c in enumerate(word): total += column_width(c) if total > space_left: return word[:i - 1], word[i - 1:] return word, '' def _split(self, text): """_split(text : string) -> [string] Override original method that only split by 'wordsep_re'. This '_split' split wide-characters into chunk by one character. """ def split(t): return textwrap.TextWrapper._split(self, t) chunks = [] for chunk in split(text): for w, g in groupby(chunk, column_width): if w == 1: chunks.extend(split(''.join(g))) else: chunks.extend(list(g)) return chunks def _handle_long_word(self, reversed_chunks, cur_line, cur_len, width): """_handle_long_word(chunks : [string], cur_line : [string], cur_len : int, width : int) Override original method for using self._break_word() instead of slice. """ space_left = max(width - cur_len, 1) if self.break_long_words: l, r = self._break_word(reversed_chunks[-1], space_left) cur_line.append(l) reversed_chunks[-1] = r elif not cur_line: cur_line.append(reversed_chunks.pop()) MAXWIDTH = 70 STDINDENT = 3 def my_wrap(text, width=MAXWIDTH, **kwargs): w = TextWrapper(width=width, **kwargs) return w.wrap(text) class TextWriter(writers.Writer): supported = ('text',) settings_spec = ('No options here.', '', ()) settings_defaults = {} output = None def __init__(self, builder): writers.Writer.__init__(self) self.builder = builder self.translator_class = self.builder.translator_class or TextTranslator def translate(self): visitor = self.translator_class(self.document, self.builder) self.document.walkabout(visitor) self.output = visitor.body class TextTranslator(nodes.NodeVisitor): sectionchars = '*=-~"+`' def __init__(self, document, builder): nodes.NodeVisitor.__init__(self, document) self.builder = builder newlines = builder.config.text_newlines if newlines == 'windows': self.nl = '\r\n' elif newlines == 'native': self.nl = os.linesep else: self.nl = '\n' self.sectionchars = builder.config.text_sectionchars self.states = [[]] self.stateindent = [0] self.list_counter = [] self.sectionlevel = 0 self.lineblocklevel = 0 self.table = None def add_text(self, text): self.states[-1].append((-1, text)) def new_state(self, indent=STDINDENT): self.states.append([]) self.stateindent.append(indent) def end_state(self, wrap=True, end=[''], first=None): content = self.states.pop() maxindent = sum(self.stateindent) indent = self.stateindent.pop() result = [] toformat = [] def do_format(): if not toformat: return if wrap: res = my_wrap(''.join(toformat), width=MAXWIDTH - maxindent) else: res = ''.join(toformat).splitlines() if end: res += end result.append((indent, res)) for itemindent, item in content: if itemindent == -1: toformat.append(item) else: do_format() result.append((indent + itemindent, item)) toformat = [] do_format() if first is not None and result: itemindent, item = result[0] result_rest, result = result[1:], [] if item: toformat = [first + ' '.join(item)] do_format() # re-create `result` from `toformat` _dummy, new_item = result[0] result.insert(0, (itemindent - indent, [new_item[0]])) result[1] = (itemindent, new_item[1:]) result.extend(result_rest) self.states[-1].extend(result) def visit_document(self, node): self.new_state(0) def depart_document(self, node): self.end_state() self.body = self.nl.join(line and (' ' * indent + line) for indent, lines in self.states[0] for line in lines) # XXX header/footer? def visit_highlightlang(self, node): raise nodes.SkipNode def visit_section(self, node): self._title_char = self.sectionchars[self.sectionlevel] self.sectionlevel += 1 def depart_section(self, node): self.sectionlevel -= 1 def visit_topic(self, node): self.new_state(0) def depart_topic(self, node): self.end_state() visit_sidebar = visit_topic depart_sidebar = depart_topic def visit_rubric(self, node): self.new_state(0) self.add_text('-[ ') def depart_rubric(self, node): self.add_text(' ]-') self.end_state() def visit_compound(self, node): pass def depart_compound(self, node): pass def visit_glossary(self, node): pass def depart_glossary(self, node): pass def visit_title(self, node): if isinstance(node.parent, nodes.Admonition): self.add_text(node.astext() + ': ') raise nodes.SkipNode self.new_state(0) def depart_title(self, node): if isinstance(node.parent, nodes.section): char = self._title_char else: char = '^' text = ''.join(x[1] for x in self.states.pop() if x[0] == -1) self.stateindent.pop() title = ['', text, '%s' % (char * column_width(text)), ''] if len(self.states) == 2 and len(self.states[-1]) == 0: # remove an empty line before title if it is first section title in the document title.pop(0) self.states[-1].append((0, title)) def visit_subtitle(self, node): pass def depart_subtitle(self, node): pass def visit_attribution(self, node): self.add_text('-- ') def depart_attribution(self, node): pass def visit_desc(self, node): pass def depart_desc(self, node): pass def visit_desc_signature(self, node): self.new_state(0) def depart_desc_signature(self, node): # XXX: wrap signatures in a way that makes sense self.end_state(wrap=False, end=None) def visit_desc_signature_line(self, node): pass def depart_desc_signature_line(self, node): self.add_text('\n') def visit_desc_name(self, node): pass def depart_desc_name(self, node): pass def visit_desc_addname(self, node): pass def depart_desc_addname(self, node): pass def visit_desc_type(self, node): pass def depart_desc_type(self, node): pass def visit_desc_returns(self, node): self.add_text(' -> ') def depart_desc_returns(self, node): pass def visit_desc_parameterlist(self, node): self.add_text('(') self.first_param = 1 def depart_desc_parameterlist(self, node): self.add_text(')') def visit_desc_parameter(self, node): if not self.first_param: self.add_text(', ') else: self.first_param = 0 self.add_text(node.astext()) raise nodes.SkipNode def visit_desc_optional(self, node): self.add_text('[') def depart_desc_optional(self, node): self.add_text(']') def visit_desc_annotation(self, node): pass def depart_desc_annotation(self, node): pass def visit_desc_content(self, node): self.new_state() self.add_text(self.nl) def depart_desc_content(self, node): self.end_state() def visit_figure(self, node): self.new_state() def depart_figure(self, node): self.end_state() def visit_caption(self, node): pass def depart_caption(self, node): pass def visit_productionlist(self, node): self.new_state() names = [] for production in node: names.append(production['tokenname']) maxlen = max(len(name) for name in names) lastname = None for production in node: if production['tokenname']: self.add_text(production['tokenname'].ljust(maxlen) + ' ::=') lastname = production['tokenname'] elif lastname is not None: self.add_text('%s ' % (' ' * len(lastname))) self.add_text(production.astext() + self.nl) self.end_state(wrap=False) raise nodes.SkipNode def visit_footnote(self, node): self._footnote = node.children[0].astext().strip() self.new_state(len(self._footnote) + 3) def depart_footnote(self, node): self.end_state(first='[%s] ' % self._footnote) def visit_citation(self, node): if len(node) and isinstance(node[0], nodes.label): self._citlabel = node[0].astext() else: self._citlabel = '' self.new_state(len(self._citlabel) + 3) def depart_citation(self, node): self.end_state(first='[%s] ' % self._citlabel) def visit_label(self, node): raise nodes.SkipNode def visit_legend(self, node): pass def depart_legend(self, node): pass # XXX: option list could use some better styling def visit_option_list(self, node): pass def depart_option_list(self, node): pass def visit_option_list_item(self, node): self.new_state(0) def depart_option_list_item(self, node): self.end_state() def visit_option_group(self, node): self._firstoption = True def depart_option_group(self, node): self.add_text(' ') def visit_option(self, node): if self._firstoption: self._firstoption = False else: self.add_text(', ') def depart_option(self, node): pass def visit_option_string(self, node): pass def depart_option_string(self, node): pass def visit_option_argument(self, node): self.add_text(node['delimiter']) def depart_option_argument(self, node): pass def visit_description(self, node): pass def depart_description(self, node): pass def visit_tabular_col_spec(self, node): raise nodes.SkipNode def visit_colspec(self, node): self.table[0].append(node['colwidth']) raise nodes.SkipNode def visit_tgroup(self, node): pass def depart_tgroup(self, node): pass def visit_thead(self, node): pass def depart_thead(self, node): pass def visit_tbody(self, node): self.table.append('sep') def depart_tbody(self, node): pass def visit_row(self, node): self.table.append([]) def depart_row(self, node): pass def visit_entry(self, node): if 'morerows' in node or 'morecols' in node: raise NotImplementedError('Column or row spanning cells are ' 'not implemented.') self.new_state(0) def depart_entry(self, node): text = self.nl.join(self.nl.join(x[1]) for x in self.states.pop()) self.stateindent.pop() self.table[-1].append(text) def visit_table(self, node): if self.table: raise NotImplementedError('Nested tables are not supported.') self.new_state(0) self.table = [[]] def depart_table(self, node): lines = self.table[1:] fmted_rows = [] colwidths = self.table[0] realwidths = colwidths[:] separator = 0 # don't allow paragraphs in table cells for now for line in lines: if line == 'sep': separator = len(fmted_rows) else: cells = [] for i, cell in enumerate(line): par = my_wrap(cell, width=colwidths[i]) if par: maxwidth = max(column_width(x) for x in par) else: maxwidth = 0 realwidths[i] = max(realwidths[i], maxwidth) cells.append(par) fmted_rows.append(cells) def writesep(char='-'): out = ['+'] for width in realwidths: out.append(char * (width + 2)) out.append('+') self.add_text(''.join(out) + self.nl) def writerow(row): lines = zip_longest(*row) for line in lines: out = ['|'] for i, cell in enumerate(line): if cell: adjust_len = len(cell) - column_width(cell) out.append(' ' + cell.ljust( realwidths[i] + 1 + adjust_len)) else: out.append(' ' * (realwidths[i] + 2)) out.append('|') self.add_text(''.join(out) + self.nl) for i, row in enumerate(fmted_rows): if separator and i == separator: writesep('=') else: writesep('-') writerow(row) writesep('-') self.table = None self.end_state(wrap=False) def visit_acks(self, node): self.new_state(0) self.add_text(', '.join(n.astext() for n in node.children[0].children) + '.') self.end_state() raise nodes.SkipNode def visit_image(self, node): if 'alt' in node.attributes: self.add_text(_('[image: %s]') % node['alt']) self.add_text(_('[image]')) raise nodes.SkipNode def visit_transition(self, node): indent = sum(self.stateindent) self.new_state(0) self.add_text('=' * (MAXWIDTH - indent)) self.end_state() raise nodes.SkipNode def visit_bullet_list(self, node): self.list_counter.append(-1) def depart_bullet_list(self, node): self.list_counter.pop() def visit_enumerated_list(self, node): self.list_counter.append(node.get('start', 1) - 1) def depart_enumerated_list(self, node): self.list_counter.pop() def visit_definition_list(self, node): self.list_counter.append(-2) def depart_definition_list(self, node): self.list_counter.pop() def visit_list_item(self, node): if self.list_counter[-1] == -1: # bullet list self.new_state(2) elif self.list_counter[-1] == -2: # definition list pass else: # enumerated list self.list_counter[-1] += 1 self.new_state(len(str(self.list_counter[-1])) + 2) def depart_list_item(self, node): if self.list_counter[-1] == -1: self.end_state(first='* ') elif self.list_counter[-1] == -2: pass else: self.end_state(first='%s. ' % self.list_counter[-1]) def visit_definition_list_item(self, node): self._classifier_count_in_li = len(node.traverse(nodes.classifier)) def depart_definition_list_item(self, node): pass def visit_term(self, node): self.new_state(0) def depart_term(self, node): if not self._classifier_count_in_li: self.end_state(end=None) def visit_termsep(self, node): warnings.warn('sphinx.addnodes.termsep will be removed at Sphinx-1.6. ' 'This warning is displayed because some Sphinx extension ' 'uses sphinx.addnodes.termsep. Please report it to ' 'author of the extension.', RemovedInSphinx16Warning) self.add_text(', ') raise nodes.SkipNode def visit_classifier(self, node): self.add_text(' : ') def depart_classifier(self, node): self._classifier_count_in_li -= 1 if not self._classifier_count_in_li: self.end_state(end=None) def visit_definition(self, node): self.new_state() def depart_definition(self, node): self.end_state() def visit_field_list(self, node): pass def depart_field_list(self, node): pass def visit_field(self, node): pass def depart_field(self, node): pass def visit_field_name(self, node): self.new_state(0) def depart_field_name(self, node): self.add_text(':') self.end_state(end=None) def visit_field_body(self, node): self.new_state() def depart_field_body(self, node): self.end_state() def visit_centered(self, node): pass def depart_centered(self, node): pass def visit_hlist(self, node): pass def depart_hlist(self, node): pass def visit_hlistcol(self, node): pass def depart_hlistcol(self, node): pass def visit_admonition(self, node): self.new_state(0) def depart_admonition(self, node): self.end_state() def _visit_admonition(self, node): self.new_state(2) if isinstance(node.children[0], nodes.Sequential): self.add_text(self.nl) def _make_depart_admonition(name): def depart_admonition(self, node): self.end_state(first=admonitionlabels[name] + ': ') return depart_admonition visit_attention = _visit_admonition depart_attention = _make_depart_admonition('attention') visit_caution = _visit_admonition depart_caution = _make_depart_admonition('caution') visit_danger = _visit_admonition depart_danger = _make_depart_admonition('danger') visit_error = _visit_admonition depart_error = _make_depart_admonition('error') visit_hint = _visit_admonition depart_hint = _make_depart_admonition('hint') visit_important = _visit_admonition depart_important = _make_depart_admonition('important') visit_note = _visit_admonition depart_note = _make_depart_admonition('note') visit_tip = _visit_admonition depart_tip = _make_depart_admonition('tip') visit_warning = _visit_admonition depart_warning = _make_depart_admonition('warning') visit_seealso = _visit_admonition depart_seealso = _make_depart_admonition('seealso') def visit_versionmodified(self, node): self.new_state(0) def depart_versionmodified(self, node): self.end_state() def visit_literal_block(self, node): self.new_state() def depart_literal_block(self, node): self.end_state(wrap=False) def visit_doctest_block(self, node): self.new_state(0) def depart_doctest_block(self, node): self.end_state(wrap=False) def visit_line_block(self, node): self.new_state() self.lineblocklevel += 1 def depart_line_block(self, node): self.lineblocklevel -= 1 self.end_state(wrap=False, end=None) if not self.lineblocklevel: self.add_text('\n') def visit_line(self, node): pass def depart_line(self, node): self.add_text('\n') def visit_block_quote(self, node): self.new_state() def depart_block_quote(self, node): self.end_state() def visit_compact_paragraph(self, node): pass def depart_compact_paragraph(self, node): pass def visit_paragraph(self, node): if not isinstance(node.parent, nodes.Admonition) or \ isinstance(node.parent, addnodes.seealso): self.new_state(0) def depart_paragraph(self, node): if not isinstance(node.parent, nodes.Admonition) or \ isinstance(node.parent, addnodes.seealso): self.end_state() def visit_target(self, node): raise nodes.SkipNode def visit_index(self, node): raise nodes.SkipNode def visit_toctree(self, node): raise nodes.SkipNode def visit_substitution_definition(self, node): raise nodes.SkipNode def visit_pending_xref(self, node): pass def depart_pending_xref(self, node): pass def visit_reference(self, node): pass def depart_reference(self, node): pass def visit_number_reference(self, node): text = nodes.Text(node.get('title', '#')) self.visit_Text(text) raise nodes.SkipNode def visit_download_reference(self, node): pass def depart_download_reference(self, node): pass def visit_emphasis(self, node): self.add_text('*') def depart_emphasis(self, node): self.add_text('*') def visit_literal_emphasis(self, node): self.add_text('*') def depart_literal_emphasis(self, node): self.add_text('*') def visit_strong(self, node): self.add_text('**') def depart_strong(self, node): self.add_text('**') def visit_literal_strong(self, node): self.add_text('**') def depart_literal_strong(self, node): self.add_text('**') def visit_abbreviation(self, node): self.add_text('') def depart_abbreviation(self, node): if node.hasattr('explanation'): self.add_text(' (%s)' % node['explanation']) def visit_manpage(self, node): return self.visit_literal_emphasis(node) def depart_manpage(self, node): return self.depart_literal_emphasis(node) def visit_title_reference(self, node): self.add_text('*') def depart_title_reference(self, node): self.add_text('*') def visit_literal(self, node): self.add_text('"') def depart_literal(self, node): self.add_text('"') def visit_subscript(self, node): self.add_text('_') def depart_subscript(self, node): pass def visit_superscript(self, node): self.add_text('^') def depart_superscript(self, node): pass def visit_footnote_reference(self, node): self.add_text('[%s]' % node.astext()) raise nodes.SkipNode def visit_citation_reference(self, node): self.add_text('[%s]' % node.astext()) raise nodes.SkipNode def visit_Text(self, node): self.add_text(node.astext()) def depart_Text(self, node): pass def visit_generated(self, node): pass def depart_generated(self, node): pass def visit_inline(self, node): if 'xref' in node['classes'] or 'term' in node['classes']: self.add_text('*') def depart_inline(self, node): if 'xref' in node['classes'] or 'term' in node['classes']: self.add_text('*') def visit_container(self, node): pass def depart_container(self, node): pass def visit_problematic(self, node): self.add_text('>>') def depart_problematic(self, node): self.add_text('<<') def visit_system_message(self, node): self.new_state(0) self.add_text('<SYSTEM MESSAGE: %s>' % node.astext()) self.end_state() raise nodes.SkipNode def visit_comment(self, node): raise nodes.SkipNode def visit_meta(self, node): # only valid for HTML raise nodes.SkipNode def visit_raw(self, node): if 'text' in node.get('format', '').split(): self.new_state(0) self.add_text(node.astext()) self.end_state(wrap = False) raise nodes.SkipNode def visit_math(self, node): self.builder.warn('using "math" markup without a Sphinx math extension ' 'active, please use one of the math extensions ' 'described at http://sphinx-doc.org/ext/math.html', (self.builder.current_docname, node.line)) raise nodes.SkipNode visit_math_block = visit_math def unknown_visit(self, node): raise NotImplementedError('Unknown node: ' + node.__class__.__name__)

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0.579996

import os import re import textwrap from itertools import groupby import warnings from six.moves import zip_longest from docutils import nodes, writers from docutils.utils import column_width from sphinx import addnodes from sphinx.deprecation import RemovedInSphinx16Warning from sphinx.locale import admonitionlabels, _ class TextWrapper(textwrap.TextWrapper): wordsep_re = re.compile( r'(\s+|' r'(?<=\s)(?::[a-z-]+:)?`\S+|' r'[^\s\w]*\w+[a-zA-Z]-(?=\w+[a-zA-Z])|' r'(?<=[\w\!\"\'\&\.\,\?])-{2,}(?=\w))') # em-dash def _wrap_chunks(self, chunks): drop_whitespace = getattr(self, 'drop_whitespace', True) # py25 compat lines = [] if self.width <= 0: raise ValueError("invalid width %r (must be > 0)" % self.width) chunks.reverse() while chunks: cur_line = [] cur_len = 0 if lines: indent = self.subsequent_indent else: indent = self.initial_indent width = self.width - column_width(indent) if drop_whitespace and chunks[-1].strip() == '' and lines: del chunks[-1] while chunks: l = column_width(chunks[-1]) if cur_len + l <= width: cur_line.append(chunks.pop()) cur_len += l else: break if chunks and column_width(chunks[-1]) > width: self._handle_long_word(chunks, cur_line, cur_len, width) if drop_whitespace and cur_line and cur_line[-1].strip() == '': del cur_line[-1] if cur_line: lines.append(indent + ''.join(cur_line)) return lines def _break_word(self, word, space_left): total = 0 for i, c in enumerate(word): total += column_width(c) if total > space_left: return word[:i - 1], word[i - 1:] return word, '' def _split(self, text): def split(t): return textwrap.TextWrapper._split(self, t) chunks = [] for chunk in split(text): for w, g in groupby(chunk, column_width): if w == 1: chunks.extend(split(''.join(g))) else: chunks.extend(list(g)) return chunks def _handle_long_word(self, reversed_chunks, cur_line, cur_len, width): space_left = max(width - cur_len, 1) if self.break_long_words: l, r = self._break_word(reversed_chunks[-1], space_left) cur_line.append(l) reversed_chunks[-1] = r elif not cur_line: cur_line.append(reversed_chunks.pop()) MAXWIDTH = 70 STDINDENT = 3 def my_wrap(text, width=MAXWIDTH, **kwargs): w = TextWrapper(width=width, **kwargs) return w.wrap(text) class TextWriter(writers.Writer): supported = ('text',) settings_spec = ('No options here.', '', ()) settings_defaults = {} output = None def __init__(self, builder): writers.Writer.__init__(self) self.builder = builder self.translator_class = self.builder.translator_class or TextTranslator def translate(self): visitor = self.translator_class(self.document, self.builder) self.document.walkabout(visitor) self.output = visitor.body class TextTranslator(nodes.NodeVisitor): sectionchars = '*=-~"+`' def __init__(self, document, builder): nodes.NodeVisitor.__init__(self, document) self.builder = builder newlines = builder.config.text_newlines if newlines == 'windows': self.nl = '\r\n' elif newlines == 'native': self.nl = os.linesep else: self.nl = '\n' self.sectionchars = builder.config.text_sectionchars self.states = [[]] self.stateindent = [0] self.list_counter = [] self.sectionlevel = 0 self.lineblocklevel = 0 self.table = None def add_text(self, text): self.states[-1].append((-1, text)) def new_state(self, indent=STDINDENT): self.states.append([]) self.stateindent.append(indent) def end_state(self, wrap=True, end=[''], first=None): content = self.states.pop() maxindent = sum(self.stateindent) indent = self.stateindent.pop() result = [] toformat = [] def do_format(): if not toformat: return if wrap: res = my_wrap(''.join(toformat), width=MAXWIDTH - maxindent) else: res = ''.join(toformat).splitlines() if end: res += end result.append((indent, res)) for itemindent, item in content: if itemindent == -1: toformat.append(item) else: do_format() result.append((indent + itemindent, item)) toformat = [] do_format() if first is not None and result: itemindent, item = result[0] result_rest, result = result[1:], [] if item: toformat = [first + ' '.join(item)] do_format() # re-create `result` from `toformat` _dummy, new_item = result[0] result.insert(0, (itemindent - indent, [new_item[0]])) result[1] = (itemindent, new_item[1:]) result.extend(result_rest) self.states[-1].extend(result) def visit_document(self, node): self.new_state(0) def depart_document(self, node): self.end_state() self.body = self.nl.join(line and (' ' * indent + line) for indent, lines in self.states[0] for line in lines) # XXX header/footer? def visit_highlightlang(self, node): raise nodes.SkipNode def visit_section(self, node): self._title_char = self.sectionchars[self.sectionlevel] self.sectionlevel += 1 def depart_section(self, node): self.sectionlevel -= 1 def visit_topic(self, node): self.new_state(0) def depart_topic(self, node): self.end_state() visit_sidebar = visit_topic depart_sidebar = depart_topic def visit_rubric(self, node): self.new_state(0) self.add_text('-[ ') def depart_rubric(self, node): self.add_text(' ]-') self.end_state() def visit_compound(self, node): pass def depart_compound(self, node): pass def visit_glossary(self, node): pass def depart_glossary(self, node): pass def visit_title(self, node): if isinstance(node.parent, nodes.Admonition): self.add_text(node.astext() + ': ') raise nodes.SkipNode self.new_state(0) def depart_title(self, node): if isinstance(node.parent, nodes.section): char = self._title_char else: char = '^' text = ''.join(x[1] for x in self.states.pop() if x[0] == -1) self.stateindent.pop() title = ['', text, '%s' % (char * column_width(text)), ''] if len(self.states) == 2 and len(self.states[-1]) == 0: # remove an empty line before title if it is first section title in the document title.pop(0) self.states[-1].append((0, title)) def visit_subtitle(self, node): pass def depart_subtitle(self, node): pass def visit_attribution(self, node): self.add_text('-- ') def depart_attribution(self, node): pass def visit_desc(self, node): pass def depart_desc(self, node): pass def visit_desc_signature(self, node): self.new_state(0) def depart_desc_signature(self, node): # XXX: wrap signatures in a way that makes sense self.end_state(wrap=False, end=None) def visit_desc_signature_line(self, node): pass def depart_desc_signature_line(self, node): self.add_text('\n') def visit_desc_name(self, node): pass def depart_desc_name(self, node): pass def visit_desc_addname(self, node): pass def depart_desc_addname(self, node): pass def visit_desc_type(self, node): pass def depart_desc_type(self, node): pass def visit_desc_returns(self, node): self.add_text(' -> ') def depart_desc_returns(self, node): pass def visit_desc_parameterlist(self, node): self.add_text('(') self.first_param = 1 def depart_desc_parameterlist(self, node): self.add_text(')') def visit_desc_parameter(self, node): if not self.first_param: self.add_text(', ') else: self.first_param = 0 self.add_text(node.astext()) raise nodes.SkipNode def visit_desc_optional(self, node): self.add_text('[') def depart_desc_optional(self, node): self.add_text(']') def visit_desc_annotation(self, node): pass def depart_desc_annotation(self, node): pass def visit_desc_content(self, node): self.new_state() self.add_text(self.nl) def depart_desc_content(self, node): self.end_state() def visit_figure(self, node): self.new_state() def depart_figure(self, node): self.end_state() def visit_caption(self, node): pass def depart_caption(self, node): pass def visit_productionlist(self, node): self.new_state() names = [] for production in node: names.append(production['tokenname']) maxlen = max(len(name) for name in names) lastname = None for production in node: if production['tokenname']: self.add_text(production['tokenname'].ljust(maxlen) + ' ::=') lastname = production['tokenname'] elif lastname is not None: self.add_text('%s ' % (' ' * len(lastname))) self.add_text(production.astext() + self.nl) self.end_state(wrap=False) raise nodes.SkipNode def visit_footnote(self, node): self._footnote = node.children[0].astext().strip() self.new_state(len(self._footnote) + 3) def depart_footnote(self, node): self.end_state(first='[%s] ' % self._footnote) def visit_citation(self, node): if len(node) and isinstance(node[0], nodes.label): self._citlabel = node[0].astext() else: self._citlabel = '' self.new_state(len(self._citlabel) + 3) def depart_citation(self, node): self.end_state(first='[%s] ' % self._citlabel) def visit_label(self, node): raise nodes.SkipNode def visit_legend(self, node): pass def depart_legend(self, node): pass # XXX: option list could use some better styling def visit_option_list(self, node): pass def depart_option_list(self, node): pass def visit_option_list_item(self, node): self.new_state(0) def depart_option_list_item(self, node): self.end_state() def visit_option_group(self, node): self._firstoption = True def depart_option_group(self, node): self.add_text(' ') def visit_option(self, node): if self._firstoption: self._firstoption = False else: self.add_text(', ') def depart_option(self, node): pass def visit_option_string(self, node): pass def depart_option_string(self, node): pass def visit_option_argument(self, node): self.add_text(node['delimiter']) def depart_option_argument(self, node): pass def visit_description(self, node): pass def depart_description(self, node): pass def visit_tabular_col_spec(self, node): raise nodes.SkipNode def visit_colspec(self, node): self.table[0].append(node['colwidth']) raise nodes.SkipNode def visit_tgroup(self, node): pass def depart_tgroup(self, node): pass def visit_thead(self, node): pass def depart_thead(self, node): pass def visit_tbody(self, node): self.table.append('sep') def depart_tbody(self, node): pass def visit_row(self, node): self.table.append([]) def depart_row(self, node): pass def visit_entry(self, node): if 'morerows' in node or 'morecols' in node: raise NotImplementedError('Column or row spanning cells are ' 'not implemented.') self.new_state(0) def depart_entry(self, node): text = self.nl.join(self.nl.join(x[1]) for x in self.states.pop()) self.stateindent.pop() self.table[-1].append(text) def visit_table(self, node): if self.table: raise NotImplementedError('Nested tables are not supported.') self.new_state(0) self.table = [[]] def depart_table(self, node): lines = self.table[1:] fmted_rows = [] colwidths = self.table[0] realwidths = colwidths[:] separator = 0 # don't allow paragraphs in table cells for now for line in lines: if line == 'sep': separator = len(fmted_rows) else: cells = [] for i, cell in enumerate(line): par = my_wrap(cell, width=colwidths[i]) if par: maxwidth = max(column_width(x) for x in par) else: maxwidth = 0 realwidths[i] = max(realwidths[i], maxwidth) cells.append(par) fmted_rows.append(cells) def writesep(char='-'): out = ['+'] for width in realwidths: out.append(char * (width + 2)) out.append('+') self.add_text(''.join(out) + self.nl) def writerow(row): lines = zip_longest(*row) for line in lines: out = ['|'] for i, cell in enumerate(line): if cell: adjust_len = len(cell) - column_width(cell) out.append(' ' + cell.ljust( realwidths[i] + 1 + adjust_len)) else: out.append(' ' * (realwidths[i] + 2)) out.append('|') self.add_text(''.join(out) + self.nl) for i, row in enumerate(fmted_rows): if separator and i == separator: writesep('=') else: writesep('-') writerow(row) writesep('-') self.table = None self.end_state(wrap=False) def visit_acks(self, node): self.new_state(0) self.add_text(', '.join(n.astext() for n in node.children[0].children) + '.') self.end_state() raise nodes.SkipNode def visit_image(self, node): if 'alt' in node.attributes: self.add_text(_('[image: %s]') % node['alt']) self.add_text(_('[image]')) raise nodes.SkipNode def visit_transition(self, node): indent = sum(self.stateindent) self.new_state(0) self.add_text('=' * (MAXWIDTH - indent)) self.end_state() raise nodes.SkipNode def visit_bullet_list(self, node): self.list_counter.append(-1) def depart_bullet_list(self, node): self.list_counter.pop() def visit_enumerated_list(self, node): self.list_counter.append(node.get('start', 1) - 1) def depart_enumerated_list(self, node): self.list_counter.pop() def visit_definition_list(self, node): self.list_counter.append(-2) def depart_definition_list(self, node): self.list_counter.pop() def visit_list_item(self, node): if self.list_counter[-1] == -1: self.new_state(2) elif self.list_counter[-1] == -2: pass else: self.list_counter[-1] += 1 self.new_state(len(str(self.list_counter[-1])) + 2) def depart_list_item(self, node): if self.list_counter[-1] == -1: self.end_state(first='* ') elif self.list_counter[-1] == -2: pass else: self.end_state(first='%s. ' % self.list_counter[-1]) def visit_definition_list_item(self, node): self._classifier_count_in_li = len(node.traverse(nodes.classifier)) def depart_definition_list_item(self, node): pass def visit_term(self, node): self.new_state(0) def depart_term(self, node): if not self._classifier_count_in_li: self.end_state(end=None) def visit_termsep(self, node): warnings.warn('sphinx.addnodes.termsep will be removed at Sphinx-1.6. ' 'This warning is displayed because some Sphinx extension ' 'uses sphinx.addnodes.termsep. Please report it to ' 'author of the extension.', RemovedInSphinx16Warning) self.add_text(', ') raise nodes.SkipNode def visit_classifier(self, node): self.add_text(' : ') def depart_classifier(self, node): self._classifier_count_in_li -= 1 if not self._classifier_count_in_li: self.end_state(end=None) def visit_definition(self, node): self.new_state() def depart_definition(self, node): self.end_state() def visit_field_list(self, node): pass def depart_field_list(self, node): pass def visit_field(self, node): pass def depart_field(self, node): pass def visit_field_name(self, node): self.new_state(0) def depart_field_name(self, node): self.add_text(':') self.end_state(end=None) def visit_field_body(self, node): self.new_state() def depart_field_body(self, node): self.end_state() def visit_centered(self, node): pass def depart_centered(self, node): pass def visit_hlist(self, node): pass def depart_hlist(self, node): pass def visit_hlistcol(self, node): pass def depart_hlistcol(self, node): pass def visit_admonition(self, node): self.new_state(0) def depart_admonition(self, node): self.end_state() def _visit_admonition(self, node): self.new_state(2) if isinstance(node.children[0], nodes.Sequential): self.add_text(self.nl) def _make_depart_admonition(name): def depart_admonition(self, node): self.end_state(first=admonitionlabels[name] + ': ') return depart_admonition visit_attention = _visit_admonition depart_attention = _make_depart_admonition('attention') visit_caution = _visit_admonition depart_caution = _make_depart_admonition('caution') visit_danger = _visit_admonition depart_danger = _make_depart_admonition('danger') visit_error = _visit_admonition depart_error = _make_depart_admonition('error') visit_hint = _visit_admonition depart_hint = _make_depart_admonition('hint') visit_important = _visit_admonition depart_important = _make_depart_admonition('important') visit_note = _visit_admonition depart_note = _make_depart_admonition('note') visit_tip = _visit_admonition depart_tip = _make_depart_admonition('tip') visit_warning = _visit_admonition depart_warning = _make_depart_admonition('warning') visit_seealso = _visit_admonition depart_seealso = _make_depart_admonition('seealso') def visit_versionmodified(self, node): self.new_state(0) def depart_versionmodified(self, node): self.end_state() def visit_literal_block(self, node): self.new_state() def depart_literal_block(self, node): self.end_state(wrap=False) def visit_doctest_block(self, node): self.new_state(0) def depart_doctest_block(self, node): self.end_state(wrap=False) def visit_line_block(self, node): self.new_state() self.lineblocklevel += 1 def depart_line_block(self, node): self.lineblocklevel -= 1 self.end_state(wrap=False, end=None) if not self.lineblocklevel: self.add_text('\n') def visit_line(self, node): pass def depart_line(self, node): self.add_text('\n') def visit_block_quote(self, node): self.new_state() def depart_block_quote(self, node): self.end_state() def visit_compact_paragraph(self, node): pass def depart_compact_paragraph(self, node): pass def visit_paragraph(self, node): if not isinstance(node.parent, nodes.Admonition) or \ isinstance(node.parent, addnodes.seealso): self.new_state(0) def depart_paragraph(self, node): if not isinstance(node.parent, nodes.Admonition) or \ isinstance(node.parent, addnodes.seealso): self.end_state() def visit_target(self, node): raise nodes.SkipNode def visit_index(self, node): raise nodes.SkipNode def visit_toctree(self, node): raise nodes.SkipNode def visit_substitution_definition(self, node): raise nodes.SkipNode def visit_pending_xref(self, node): pass def depart_pending_xref(self, node): pass def visit_reference(self, node): pass def depart_reference(self, node): pass def visit_number_reference(self, node): text = nodes.Text(node.get('title', '#')) self.visit_Text(text) raise nodes.SkipNode def visit_download_reference(self, node): pass def depart_download_reference(self, node): pass def visit_emphasis(self, node): self.add_text('*') def depart_emphasis(self, node): self.add_text('*') def visit_literal_emphasis(self, node): self.add_text('*') def depart_literal_emphasis(self, node): self.add_text('*') def visit_strong(self, node): self.add_text('**') def depart_strong(self, node): self.add_text('**') def visit_literal_strong(self, node): self.add_text('**') def depart_literal_strong(self, node): self.add_text('**') def visit_abbreviation(self, node): self.add_text('') def depart_abbreviation(self, node): if node.hasattr('explanation'): self.add_text(' (%s)' % node['explanation']) def visit_manpage(self, node): return self.visit_literal_emphasis(node) def depart_manpage(self, node): return self.depart_literal_emphasis(node) def visit_title_reference(self, node): self.add_text('*') def depart_title_reference(self, node): self.add_text('*') def visit_literal(self, node): self.add_text('"') def depart_literal(self, node): self.add_text('"') def visit_subscript(self, node): self.add_text('_') def depart_subscript(self, node): pass def visit_superscript(self, node): self.add_text('^') def depart_superscript(self, node): pass def visit_footnote_reference(self, node): self.add_text('[%s]' % node.astext()) raise nodes.SkipNode def visit_citation_reference(self, node): self.add_text('[%s]' % node.astext()) raise nodes.SkipNode def visit_Text(self, node): self.add_text(node.astext()) def depart_Text(self, node): pass def visit_generated(self, node): pass def depart_generated(self, node): pass def visit_inline(self, node): if 'xref' in node['classes'] or 'term' in node['classes']: self.add_text('*') def depart_inline(self, node): if 'xref' in node['classes'] or 'term' in node['classes']: self.add_text('*') def visit_container(self, node): pass def depart_container(self, node): pass def visit_problematic(self, node): self.add_text('>>') def depart_problematic(self, node): self.add_text('<<') def visit_system_message(self, node): self.new_state(0) self.add_text('<SYSTEM MESSAGE: %s>' % node.astext()) self.end_state() raise nodes.SkipNode def visit_comment(self, node): raise nodes.SkipNode def visit_meta(self, node): raise nodes.SkipNode def visit_raw(self, node): if 'text' in node.get('format', '').split(): self.new_state(0) self.add_text(node.astext()) self.end_state(wrap = False) raise nodes.SkipNode def visit_math(self, node): self.builder.warn('using "math" markup without a Sphinx math extension ' 'active, please use one of the math extensions ' 'described at http://sphinx-doc.org/ext/math.html', (self.builder.current_docname, node.line)) raise nodes.SkipNode visit_math_block = visit_math def unknown_visit(self, node): raise NotImplementedError('Unknown node: ' + node.__class__.__name__)

true

f7366bb4541a785a76fcbf18c40fbef0d57f5292

8,605

bzl

Python

scala/private/rules/scala_library.bzl

tian000/rules_scala

b3fc6a26db404ce904f98d525ae27a21d3f49f06

[ "Apache-2.0" ]

null

scala/private/rules/scala_library.bzl

tian000/rules_scala

b3fc6a26db404ce904f98d525ae27a21d3f49f06

[ "Apache-2.0" ]

null

scala/private/rules/scala_library.bzl

tian000/rules_scala

b3fc6a26db404ce904f98d525ae27a21d3f49f06

[ "Apache-2.0" ]

null

load("@io_bazel_rules_scala//scala:providers.bzl", "create_scala_provider") load( "@io_bazel_rules_scala//scala/private:common.bzl", "collect_jars", "collect_srcjars", "sanitize_string_for_usage", "write_manifest", ) load( "@io_bazel_rules_scala//scala/private:common_attributes.bzl", "common_attrs", "common_attrs_for_plugin_bootstrapping", "implicit_deps", "resolve_deps", ) load("@io_bazel_rules_scala//scala/private:common_outputs.bzl", "common_outputs") load( "@io_bazel_rules_scala//scala/private:coverage_replacements_provider.bzl", _coverage_replacements_provider = "coverage_replacements_provider", ) load( "@io_bazel_rules_scala//scala/private:rule_impls.bzl", "collect_jars_from_common_ctx", "compile_or_empty", "get_scalac_provider", "get_unused_dependency_checker_mode", "merge_jars", "pack_source_jars", ) ## # Common stuff to _library rules ## _library_attrs = { "main_class": attr.string(), "exports": attr.label_list( allow_files = False, aspects = [_coverage_replacements_provider.aspect], ), } def _lib( ctx, base_classpath, non_macro_lib, unused_dependency_checker_mode, unused_dependency_checker_ignored_targets): # Build up information from dependency-like attributes # This will be used to pick up srcjars from non-scala library # targets (like thrift code generation) srcjars = collect_srcjars(ctx.attr.deps) unused_dependency_checker_is_off = unused_dependency_checker_mode == "off" jars = collect_jars_from_common_ctx( ctx, base_classpath, unused_dependency_checker_is_off = unused_dependency_checker_is_off, ) (cjars, transitive_rjars) = (jars.compile_jars, jars.transitive_runtime_jars) write_manifest(ctx) outputs = compile_or_empty( ctx, ctx.outputs.manifest, cjars, srcjars, non_macro_lib, jars.transitive_compile_jars, jars.jars2labels.jars_to_labels, [], unused_dependency_checker_ignored_targets = [ target.label for target in base_classpath + ctx.attr.exports + unused_dependency_checker_ignored_targets ], unused_dependency_checker_mode = unused_dependency_checker_mode, deps_providers = jars.deps_providers, ) transitive_rjars = depset(outputs.full_jars, transitive = [transitive_rjars]) merge_jars( actions = ctx.actions, deploy_jar = ctx.outputs.deploy_jar, singlejar_executable = ctx.executable._singlejar, jars_list = transitive_rjars.to_list(), main_class = getattr(ctx.attr, "main_class", ""), progress_message = "Merging Scala library jar: %s" % ctx.label, ) # Using transitive_files since transitive_rjars a depset and avoiding linearization runfiles = ctx.runfiles( transitive_files = transitive_rjars, collect_data = True, ) # Add information from exports (is key that AFTER all build actions/runfiles analysis) # Since after, will not show up in deploy_jar or old jars runfiles # Notice that compile_jars is intentionally transitive for exports exports_jars = collect_jars(ctx.attr.exports) transitive_rjars = depset( transitive = [transitive_rjars, exports_jars.transitive_runtime_jars], ) source_jars = pack_source_jars(ctx) + outputs.source_jars scalaattr = create_scala_provider( class_jar = outputs.class_jar, compile_jars = depset( outputs.ijars, transitive = [exports_jars.compile_jars], ), deploy_jar = ctx.outputs.deploy_jar, full_jars = outputs.full_jars, ijar = outputs.ijar, source_jars = source_jars, statsfile = ctx.outputs.statsfile, transitive_runtime_jars = transitive_rjars, ) return struct( files = depset([ctx.outputs.jar] + outputs.full_jars), # Here is the default output instrumented_files = outputs.coverage.instrumented_files, jars_to_labels = jars.jars2labels, providers = [outputs.merged_provider, jars.jars2labels] + outputs.coverage.providers, runfiles = runfiles, scala = scalaattr, ) ## # scala_library ## def _scala_library_impl(ctx): if ctx.attr.jvm_flags: print("'jvm_flags' for scala_library is deprecated. It does nothing today and will be removed from scala_library to avoid confusion.") scalac_provider = get_scalac_provider(ctx) unused_dependency_checker_mode = get_unused_dependency_checker_mode(ctx) return _lib( ctx, scalac_provider.default_classpath, True, unused_dependency_checker_mode, ctx.attr.unused_dependency_checker_ignored_targets, ) _scala_library_attrs = {} _scala_library_attrs.update(implicit_deps) _scala_library_attrs.update(common_attrs) _scala_library_attrs.update(_library_attrs) _scala_library_attrs.update(resolve_deps) scala_library = rule( attrs = _scala_library_attrs, fragments = ["java"], outputs = common_outputs, toolchains = ["@io_bazel_rules_scala//scala:toolchain_type"], implementation = _scala_library_impl, ) # Scala library suite generates a series of scala libraries # then it depends on them with a meta one which exports all the sub targets def scala_library_suite( name, srcs = [], exports = [], visibility = None, **kwargs): ts = [] for src_file in srcs: n = "%s_lib_%s" % (name, sanitize_string_for_usage(src_file)) scala_library( name = n, srcs = [src_file], visibility = visibility, exports = exports, unused_dependency_checker_mode = "off", **kwargs ) ts.append(n) scala_library( name = name, visibility = visibility, exports = exports + ts, deps = ts, ) ## # scala_library_for_plugin_bootstrapping ## def _scala_library_for_plugin_bootstrapping_impl(ctx): scalac_provider = get_scalac_provider(ctx) return _lib( ctx, scalac_provider.default_classpath, True, unused_dependency_checker_ignored_targets = [], unused_dependency_checker_mode = "off", ) # the scala compiler plugin used for dependency analysis is compiled using `scala_library`. # in order to avoid cyclic dependencies `scala_library_for_plugin_bootstrapping` was created for this purpose, # which does not contain plugin related attributes, and thus avoids the cyclic dependency issue _scala_library_for_plugin_bootstrapping_attrs = {} _scala_library_for_plugin_bootstrapping_attrs.update(implicit_deps) _scala_library_for_plugin_bootstrapping_attrs.update(_library_attrs) _scala_library_for_plugin_bootstrapping_attrs.update(resolve_deps) _scala_library_for_plugin_bootstrapping_attrs.update( common_attrs_for_plugin_bootstrapping, ) scala_library_for_plugin_bootstrapping = rule( attrs = _scala_library_for_plugin_bootstrapping_attrs, fragments = ["java"], outputs = common_outputs, toolchains = ["@io_bazel_rules_scala//scala:toolchain_type"], implementation = _scala_library_for_plugin_bootstrapping_impl, ) ## # scala_macro_library ## def _scala_macro_library_impl(ctx): scalac_provider = get_scalac_provider(ctx) unused_dependency_checker_mode = get_unused_dependency_checker_mode(ctx) return _lib( ctx, scalac_provider.default_macro_classpath, False, # don't build the ijar for macros unused_dependency_checker_mode, ctx.attr.unused_dependency_checker_ignored_targets, ) _scala_macro_library_attrs = { "main_class": attr.string(), "exports": attr.label_list(allow_files = False), } _scala_macro_library_attrs.update(implicit_deps) _scala_macro_library_attrs.update(common_attrs) _scala_macro_library_attrs.update(_library_attrs) _scala_macro_library_attrs.update(resolve_deps) # Set unused_dependency_checker_mode default to off for scala_macro_library _scala_macro_library_attrs["unused_dependency_checker_mode"] = attr.string( default = "off", values = [ "warn", "error", "off", "", ], mandatory = False, ) scala_macro_library = rule( attrs = _scala_macro_library_attrs, fragments = ["java"], outputs = common_outputs, toolchains = ["@io_bazel_rules_scala//scala:toolchain_type"], implementation = _scala_macro_library_impl, )

30.842294

142

0.708542

load("@io_bazel_rules_scala//scala:providers.bzl", "create_scala_provider") load( "@io_bazel_rules_scala//scala/private:common.bzl", "collect_jars", "collect_srcjars", "sanitize_string_for_usage", "write_manifest", ) load( "@io_bazel_rules_scala//scala/private:common_attributes.bzl", "common_attrs", "common_attrs_for_plugin_bootstrapping", "implicit_deps", "resolve_deps", ) load("@io_bazel_rules_scala//scala/private:common_outputs.bzl", "common_outputs") load( "@io_bazel_rules_scala//scala/private:coverage_replacements_provider.bzl", _coverage_replacements_provider = "coverage_replacements_provider", ) load( "@io_bazel_rules_scala//scala/private:rule_impls.bzl", "collect_jars_from_common_ctx", "compile_or_empty", "get_scalac_provider", "get_unused_dependency_checker_mode", "merge_jars", "pack_source_jars", ) _library_attrs = { "main_class": attr.string(), "exports": attr.label_list( allow_files = False, aspects = [_coverage_replacements_provider.aspect], ), } def _lib( ctx, base_classpath, non_macro_lib, unused_dependency_checker_mode, unused_dependency_checker_ignored_targets): srcjars = collect_srcjars(ctx.attr.deps) unused_dependency_checker_is_off = unused_dependency_checker_mode == "off" jars = collect_jars_from_common_ctx( ctx, base_classpath, unused_dependency_checker_is_off = unused_dependency_checker_is_off, ) (cjars, transitive_rjars) = (jars.compile_jars, jars.transitive_runtime_jars) write_manifest(ctx) outputs = compile_or_empty( ctx, ctx.outputs.manifest, cjars, srcjars, non_macro_lib, jars.transitive_compile_jars, jars.jars2labels.jars_to_labels, [], unused_dependency_checker_ignored_targets = [ target.label for target in base_classpath + ctx.attr.exports + unused_dependency_checker_ignored_targets ], unused_dependency_checker_mode = unused_dependency_checker_mode, deps_providers = jars.deps_providers, ) transitive_rjars = depset(outputs.full_jars, transitive = [transitive_rjars]) merge_jars( actions = ctx.actions, deploy_jar = ctx.outputs.deploy_jar, singlejar_executable = ctx.executable._singlejar, jars_list = transitive_rjars.to_list(), main_class = getattr(ctx.attr, "main_class", ""), progress_message = "Merging Scala library jar: %s" % ctx.label, ) runfiles = ctx.runfiles( transitive_files = transitive_rjars, collect_data = True, ) exports_jars = collect_jars(ctx.attr.exports) transitive_rjars = depset( transitive = [transitive_rjars, exports_jars.transitive_runtime_jars], ) source_jars = pack_source_jars(ctx) + outputs.source_jars scalaattr = create_scala_provider( class_jar = outputs.class_jar, compile_jars = depset( outputs.ijars, transitive = [exports_jars.compile_jars], ), deploy_jar = ctx.outputs.deploy_jar, full_jars = outputs.full_jars, ijar = outputs.ijar, source_jars = source_jars, statsfile = ctx.outputs.statsfile, transitive_runtime_jars = transitive_rjars, ) return struct( files = depset([ctx.outputs.jar] + outputs.full_jars), instrumented_files = outputs.coverage.instrumented_files, jars_to_labels = jars.jars2labels, providers = [outputs.merged_provider, jars.jars2labels] + outputs.coverage.providers, runfiles = runfiles, scala = scalaattr, ) def _scala_library_impl(ctx): if ctx.attr.jvm_flags: print("'jvm_flags' for scala_library is deprecated. It does nothing today and will be removed from scala_library to avoid confusion.") scalac_provider = get_scalac_provider(ctx) unused_dependency_checker_mode = get_unused_dependency_checker_mode(ctx) return _lib( ctx, scalac_provider.default_classpath, True, unused_dependency_checker_mode, ctx.attr.unused_dependency_checker_ignored_targets, ) _scala_library_attrs = {} _scala_library_attrs.update(implicit_deps) _scala_library_attrs.update(common_attrs) _scala_library_attrs.update(_library_attrs) _scala_library_attrs.update(resolve_deps) scala_library = rule( attrs = _scala_library_attrs, fragments = ["java"], outputs = common_outputs, toolchains = ["@io_bazel_rules_scala//scala:toolchain_type"], implementation = _scala_library_impl, ) def scala_library_suite( name, srcs = [], exports = [], visibility = None, **kwargs): ts = [] for src_file in srcs: n = "%s_lib_%s" % (name, sanitize_string_for_usage(src_file)) scala_library( name = n, srcs = [src_file], visibility = visibility, exports = exports, unused_dependency_checker_mode = "off", **kwargs ) ts.append(n) scala_library( name = name, visibility = visibility, exports = exports + ts, deps = ts, ) def _scala_library_for_plugin_bootstrapping_impl(ctx): scalac_provider = get_scalac_provider(ctx) return _lib( ctx, scalac_provider.default_classpath, True, unused_dependency_checker_ignored_targets = [], unused_dependency_checker_mode = "off", ) _scala_library_for_plugin_bootstrapping_attrs = {} _scala_library_for_plugin_bootstrapping_attrs.update(implicit_deps) _scala_library_for_plugin_bootstrapping_attrs.update(_library_attrs) _scala_library_for_plugin_bootstrapping_attrs.update(resolve_deps) _scala_library_for_plugin_bootstrapping_attrs.update( common_attrs_for_plugin_bootstrapping, ) scala_library_for_plugin_bootstrapping = rule( attrs = _scala_library_for_plugin_bootstrapping_attrs, fragments = ["java"], outputs = common_outputs, toolchains = ["@io_bazel_rules_scala//scala:toolchain_type"], implementation = _scala_library_for_plugin_bootstrapping_impl, ) def _scala_macro_library_impl(ctx): scalac_provider = get_scalac_provider(ctx) unused_dependency_checker_mode = get_unused_dependency_checker_mode(ctx) return _lib( ctx, scalac_provider.default_macro_classpath, False, unused_dependency_checker_mode, ctx.attr.unused_dependency_checker_ignored_targets, ) _scala_macro_library_attrs = { "main_class": attr.string(), "exports": attr.label_list(allow_files = False), } _scala_macro_library_attrs.update(implicit_deps) _scala_macro_library_attrs.update(common_attrs) _scala_macro_library_attrs.update(_library_attrs) _scala_macro_library_attrs.update(resolve_deps) # Set unused_dependency_checker_mode default to off for scala_macro_library _scala_macro_library_attrs["unused_dependency_checker_mode"] = attr.string( default = "off", values = [ "warn", "error", "off", "", ], mandatory = False, ) scala_macro_library = rule( attrs = _scala_macro_library_attrs, fragments = ["java"], outputs = common_outputs, toolchains = ["@io_bazel_rules_scala//scala:toolchain_type"], implementation = _scala_macro_library_impl, )

true

f7366c0f1b8be7fbd52f8cb9f823f28077e8b0d4

2,736

py

Python

test/nn/conv/test_film_conv.py

LingxiaoShawn/pytorch_geometric

50b7bfc4a59b5b6f7ec547ff862985f3b2e22798

[ "MIT" ]

1

2022-02-21T13:23:19.000Z

test/nn/conv/test_film_conv.py

LingxiaoShawn/pytorch_geometric

50b7bfc4a59b5b6f7ec547ff862985f3b2e22798

[ "MIT" ]

null

test/nn/conv/test_film_conv.py

LingxiaoShawn/pytorch_geometric

50b7bfc4a59b5b6f7ec547ff862985f3b2e22798

[ "MIT" ]

null

import torch from torch_sparse import SparseTensor from torch_geometric.nn import FiLMConv def test_film_conv(): x1 = torch.randn(4, 4) x2 = torch.randn(2, 16) edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [0, 0, 1, 0, 1, 1]]) edge_type = torch.tensor([0, 1, 1, 0, 0, 1]) row, col = edge_index adj = SparseTensor(row=row, col=col, value=edge_type, sparse_sizes=(4, 4)) conv = FiLMConv(4, 32) assert conv.__repr__() == 'FiLMConv(4, 32, num_relations=1)' out1 = conv(x1, edge_index) assert out1.size() == (4, 32) assert conv(x1, adj.t().set_value(None)).tolist() == out1.tolist() t = '(Tensor, Tensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit(x1, edge_index).tolist() == out1.tolist() t = '(Tensor, SparseTensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit(x1, adj.t().set_value(None)).tolist() == out1.tolist() conv = FiLMConv(4, 32, num_relations=2) assert conv.__repr__() == 'FiLMConv(4, 32, num_relations=2)' out1 = conv(x1, edge_index, edge_type) assert out1.size() == (4, 32) assert conv(x1, adj.t()).tolist() == out1.tolist() t = '(Tensor, Tensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit(x1, edge_index, edge_type).tolist() == out1.tolist() t = '(Tensor, SparseTensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit(x1, adj.t()).tolist() == out1.tolist() adj = adj.sparse_resize((4, 2)) conv = FiLMConv((4, 16), 32) assert conv.__repr__() == 'FiLMConv((4, 16), 32, num_relations=1)' out1 = conv((x1, x2), edge_index) assert out1.size() == (2, 32) assert conv((x1, x2), adj.t().set_value(None)).tolist() == out1.tolist() t = '(PairTensor, Tensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit((x1, x2), edge_index).tolist() == out1.tolist() t = '(PairTensor, SparseTensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit((x1, x2), adj.t().set_value(None)).tolist() == out1.tolist() conv = FiLMConv((4, 16), 32, num_relations=2) assert conv.__repr__() == 'FiLMConv((4, 16), 32, num_relations=2)' out1 = conv((x1, x2), edge_index, edge_type) assert out1.size() == (2, 32) assert conv((x1, x2), adj.t()).tolist() == out1.tolist() t = '(PairTensor, Tensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit((x1, x2), edge_index, edge_type).tolist() == out1.tolist() t = '(PairTensor, SparseTensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit((x1, x2), adj.t()).tolist() == out1.tolist()

40.235294

78

0.621711

import torch from torch_sparse import SparseTensor from torch_geometric.nn import FiLMConv def test_film_conv(): x1 = torch.randn(4, 4) x2 = torch.randn(2, 16) edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [0, 0, 1, 0, 1, 1]]) edge_type = torch.tensor([0, 1, 1, 0, 0, 1]) row, col = edge_index adj = SparseTensor(row=row, col=col, value=edge_type, sparse_sizes=(4, 4)) conv = FiLMConv(4, 32) assert conv.__repr__() == 'FiLMConv(4, 32, num_relations=1)' out1 = conv(x1, edge_index) assert out1.size() == (4, 32) assert conv(x1, adj.t().set_value(None)).tolist() == out1.tolist() t = '(Tensor, Tensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit(x1, edge_index).tolist() == out1.tolist() t = '(Tensor, SparseTensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit(x1, adj.t().set_value(None)).tolist() == out1.tolist() conv = FiLMConv(4, 32, num_relations=2) assert conv.__repr__() == 'FiLMConv(4, 32, num_relations=2)' out1 = conv(x1, edge_index, edge_type) assert out1.size() == (4, 32) assert conv(x1, adj.t()).tolist() == out1.tolist() t = '(Tensor, Tensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit(x1, edge_index, edge_type).tolist() == out1.tolist() t = '(Tensor, SparseTensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit(x1, adj.t()).tolist() == out1.tolist() adj = adj.sparse_resize((4, 2)) conv = FiLMConv((4, 16), 32) assert conv.__repr__() == 'FiLMConv((4, 16), 32, num_relations=1)' out1 = conv((x1, x2), edge_index) assert out1.size() == (2, 32) assert conv((x1, x2), adj.t().set_value(None)).tolist() == out1.tolist() t = '(PairTensor, Tensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit((x1, x2), edge_index).tolist() == out1.tolist() t = '(PairTensor, SparseTensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit((x1, x2), adj.t().set_value(None)).tolist() == out1.tolist() conv = FiLMConv((4, 16), 32, num_relations=2) assert conv.__repr__() == 'FiLMConv((4, 16), 32, num_relations=2)' out1 = conv((x1, x2), edge_index, edge_type) assert out1.size() == (2, 32) assert conv((x1, x2), adj.t()).tolist() == out1.tolist() t = '(PairTensor, Tensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit((x1, x2), edge_index, edge_type).tolist() == out1.tolist() t = '(PairTensor, SparseTensor, OptTensor) -> Tensor' jit = torch.jit.script(conv.jittable(t)) assert jit((x1, x2), adj.t()).tolist() == out1.tolist()

true

f7366dd76a0f042bd795bb1d0879f0ea7217a5ad

10,253

py

Python

qa/rpc-tests/listtransactions.py

unifycoin/unifycoin

7d0d5245610daab81e8b124c9b4dc03a73020b8f

[ "MIT" ]

null

qa/rpc-tests/listtransactions.py

unifycoin/unifycoin

7d0d5245610daab81e8b124c9b4dc03a73020b8f

[ "MIT" ]

null

qa/rpc-tests/listtransactions.py

unifycoin/unifycoin

7d0d5245610daab81e8b124c9b4dc03a73020b8f

[ "MIT" ]

2

2019-06-28T12:47:30.000Z

2019-12-16T04:56:50.000Z

#!/usr/bin/env python3 # Copyright (c) 2014-2016 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # Exercise the listtransactions API from test_framework.test_framework import BitcoinTestFramework from test_framework.util import * from test_framework.mininode import CTransaction, COIN from io import BytesIO def txFromHex(hexstring): tx = CTransaction() f = BytesIO(hex_str_to_bytes(hexstring)) tx.deserialize(f) return tx class ListTransactionsTest(BitcoinTestFramework): def __init__(self): super().__init__() self.num_nodes = 4 self.setup_clean_chain = False def setup_nodes(self): #This test requires mocktime enable_mocktime() return start_nodes(self.num_nodes, self.options.tmpdir) def run_test(self): # Simple send, 0 to 1: txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() assert_array_result(self.nodes[0].listtransactions(), {"txid":txid}, {"category":"send","account":"","amount":Decimal("-0.1"),"confirmations":0}) assert_array_result(self.nodes[1].listtransactions(), {"txid":txid}, {"category":"receive","account":"","amount":Decimal("0.1"),"confirmations":0}) # mine a block, confirmations should change: self.nodes[0].generate(1) self.sync_all() assert_array_result(self.nodes[0].listtransactions(), {"txid":txid}, {"category":"send","account":"","amount":Decimal("-0.1"),"confirmations":1}) assert_array_result(self.nodes[1].listtransactions(), {"txid":txid}, {"category":"receive","account":"","amount":Decimal("0.1"),"confirmations":1}) # send-to-self: txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) assert_array_result(self.nodes[0].listtransactions(), {"txid":txid, "category":"send"}, {"amount":Decimal("-0.2")}) assert_array_result(self.nodes[0].listtransactions(), {"txid":txid, "category":"receive"}, {"amount":Decimal("0.2")}) # sendmany from node1: twice to self, twice to node2: send_to = { self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress("from1") : 0.33, self.nodes[1].getaccountaddress("toself") : 0.44 } txid = self.nodes[1].sendmany("", send_to) self.sync_all() assert_array_result(self.nodes[1].listtransactions(), {"category":"send","amount":Decimal("-0.11")}, {"txid":txid} ) assert_array_result(self.nodes[0].listtransactions(), {"category":"receive","amount":Decimal("0.11")}, {"txid":txid} ) assert_array_result(self.nodes[1].listtransactions(), {"category":"send","amount":Decimal("-0.22")}, {"txid":txid} ) assert_array_result(self.nodes[1].listtransactions(), {"category":"receive","amount":Decimal("0.22")}, {"txid":txid} ) assert_array_result(self.nodes[1].listtransactions(), {"category":"send","amount":Decimal("-0.33")}, {"txid":txid} ) assert_array_result(self.nodes[0].listtransactions(), {"category":"receive","amount":Decimal("0.33")}, {"txid":txid, "account" : "from1"} ) assert_array_result(self.nodes[1].listtransactions(), {"category":"send","amount":Decimal("-0.44")}, {"txid":txid, "account" : ""} ) assert_array_result(self.nodes[1].listtransactions(), {"category":"receive","amount":Decimal("0.44")}, {"txid":txid, "account" : "toself"} ) multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig["redeemScript"], "watchonly", False, True) txid = self.nodes[1].sendtoaddress(multisig["address"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions("watchonly", 100, 0, False)) == 0) assert_array_result(self.nodes[0].listtransactions("watchonly", 100, 0, True), {"category":"receive","amount":Decimal("0.1")}, {"txid":txid, "account" : "watchonly"} ) #Unifycoin: Disabled RBF #self.run_rbf_opt_in_test() # Check that the opt-in-rbf flag works properly, for sent and received # transactions. def run_rbf_opt_in_test(self): # Check whether a transaction signals opt-in RBF itself def is_opt_in(node, txid): rawtx = node.getrawtransaction(txid, 1) for x in rawtx["vin"]: if x["sequence"] < 0xfffffffe: return True return False # Find an unconfirmed output matching a certain txid def get_unconfirmed_utxo_entry(node, txid_to_match): utxo = node.listunspent(0, 0) for i in utxo: if i["txid"] == txid_to_match: return i return None # 1. Chain a few transactions that don't opt-in. txid_1 = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 1) assert(not is_opt_in(self.nodes[0], txid_1)) assert_array_result(self.nodes[0].listtransactions(), {"txid": txid_1}, {"bip125-replaceable":"no"}) sync_mempools(self.nodes) assert_array_result(self.nodes[1].listtransactions(), {"txid": txid_1}, {"bip125-replaceable":"no"}) # Tx2 will build off txid_1, still not opting in to RBF. utxo_to_use = get_unconfirmed_utxo_entry(self.nodes[1], txid_1) # Create tx2 using createrawtransaction inputs = [{"txid":utxo_to_use["txid"], "vout":utxo_to_use["vout"]}] outputs = {self.nodes[0].getnewaddress(): 0.999} tx2 = self.nodes[1].createrawtransaction(inputs, outputs) tx2_signed = self.nodes[1].signrawtransaction(tx2)["hex"] txid_2 = self.nodes[1].sendrawtransaction(tx2_signed) # ...and check the result assert(not is_opt_in(self.nodes[1], txid_2)) assert_array_result(self.nodes[1].listtransactions(), {"txid": txid_2}, {"bip125-replaceable":"no"}) sync_mempools(self.nodes) assert_array_result(self.nodes[0].listtransactions(), {"txid": txid_2}, {"bip125-replaceable":"no"}) # Tx3 will opt-in to RBF utxo_to_use = get_unconfirmed_utxo_entry(self.nodes[0], txid_2) inputs = [{"txid": txid_2, "vout":utxo_to_use["vout"]}] outputs = {self.nodes[1].getnewaddress(): 0.998} tx3 = self.nodes[0].createrawtransaction(inputs, outputs) tx3_modified = txFromHex(tx3) tx3_modified.vin[0].nSequence = 0 tx3 = bytes_to_hex_str(tx3_modified.serialize()) tx3_signed = self.nodes[0].signrawtransaction(tx3)['hex'] txid_3 = self.nodes[0].sendrawtransaction(tx3_signed) assert(is_opt_in(self.nodes[0], txid_3)) assert_array_result(self.nodes[0].listtransactions(), {"txid": txid_3}, {"bip125-replaceable":"yes"}) sync_mempools(self.nodes) assert_array_result(self.nodes[1].listtransactions(), {"txid": txid_3}, {"bip125-replaceable":"yes"}) # Tx4 will chain off tx3. Doesn't signal itself, but depends on one # that does. utxo_to_use = get_unconfirmed_utxo_entry(self.nodes[1], txid_3) inputs = [{"txid": txid_3, "vout":utxo_to_use["vout"]}] outputs = {self.nodes[0].getnewaddress(): 0.997} tx4 = self.nodes[1].createrawtransaction(inputs, outputs) tx4_signed = self.nodes[1].signrawtransaction(tx4)["hex"] txid_4 = self.nodes[1].sendrawtransaction(tx4_signed) assert(not is_opt_in(self.nodes[1], txid_4)) assert_array_result(self.nodes[1].listtransactions(), {"txid": txid_4}, {"bip125-replaceable":"yes"}) sync_mempools(self.nodes) assert_array_result(self.nodes[0].listtransactions(), {"txid": txid_4}, {"bip125-replaceable":"yes"}) # Replace tx3, and check that tx4 becomes unknown tx3_b = tx3_modified tx3_b.vout[0].nValue -= int(Decimal("0.004") * COIN) # bump the fee tx3_b = bytes_to_hex_str(tx3_b.serialize()) tx3_b_signed = self.nodes[0].signrawtransaction(tx3_b)['hex'] txid_3b = self.nodes[0].sendrawtransaction(tx3_b_signed, True) assert(is_opt_in(self.nodes[0], txid_3b)) assert_array_result(self.nodes[0].listtransactions(), {"txid": txid_4}, {"bip125-replaceable":"unknown"}) sync_mempools(self.nodes) assert_array_result(self.nodes[1].listtransactions(), {"txid": txid_4}, {"bip125-replaceable":"unknown"}) # Check gettransaction as well: for n in self.nodes[0:2]: assert_equal(n.gettransaction(txid_1)["bip125-replaceable"], "no") assert_equal(n.gettransaction(txid_2)["bip125-replaceable"], "no") assert_equal(n.gettransaction(txid_3)["bip125-replaceable"], "yes") assert_equal(n.gettransaction(txid_3b)["bip125-replaceable"], "yes") assert_equal(n.gettransaction(txid_4)["bip125-replaceable"], "unknown") # After mining a transaction, it's no longer BIP125-replaceable self.nodes[0].generate(1) assert(txid_3b not in self.nodes[0].getrawmempool()) assert_equal(self.nodes[0].gettransaction(txid_3b)["bip125-replaceable"], "no") assert_equal(self.nodes[0].gettransaction(txid_4)["bip125-replaceable"], "unknown") if __name__ == '__main__': ListTransactionsTest().main()

49.771845

113

0.598752

from test_framework.test_framework import BitcoinTestFramework from test_framework.util import * from test_framework.mininode import CTransaction, COIN from io import BytesIO def txFromHex(hexstring): tx = CTransaction() f = BytesIO(hex_str_to_bytes(hexstring)) tx.deserialize(f) return tx class ListTransactionsTest(BitcoinTestFramework): def __init__(self): super().__init__() self.num_nodes = 4 self.setup_clean_chain = False def setup_nodes(self): enable_mocktime() return start_nodes(self.num_nodes, self.options.tmpdir) def run_test(self): txid = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 0.1) self.sync_all() assert_array_result(self.nodes[0].listtransactions(), {"txid":txid}, {"category":"send","account":"","amount":Decimal("-0.1"),"confirmations":0}) assert_array_result(self.nodes[1].listtransactions(), {"txid":txid}, {"category":"receive","account":"","amount":Decimal("0.1"),"confirmations":0}) self.nodes[0].generate(1) self.sync_all() assert_array_result(self.nodes[0].listtransactions(), {"txid":txid}, {"category":"send","account":"","amount":Decimal("-0.1"),"confirmations":1}) assert_array_result(self.nodes[1].listtransactions(), {"txid":txid}, {"category":"receive","account":"","amount":Decimal("0.1"),"confirmations":1}) txid = self.nodes[0].sendtoaddress(self.nodes[0].getnewaddress(), 0.2) assert_array_result(self.nodes[0].listtransactions(), {"txid":txid, "category":"send"}, {"amount":Decimal("-0.2")}) assert_array_result(self.nodes[0].listtransactions(), {"txid":txid, "category":"receive"}, {"amount":Decimal("0.2")}) send_to = { self.nodes[0].getnewaddress() : 0.11, self.nodes[1].getnewaddress() : 0.22, self.nodes[0].getaccountaddress("from1") : 0.33, self.nodes[1].getaccountaddress("toself") : 0.44 } txid = self.nodes[1].sendmany("", send_to) self.sync_all() assert_array_result(self.nodes[1].listtransactions(), {"category":"send","amount":Decimal("-0.11")}, {"txid":txid} ) assert_array_result(self.nodes[0].listtransactions(), {"category":"receive","amount":Decimal("0.11")}, {"txid":txid} ) assert_array_result(self.nodes[1].listtransactions(), {"category":"send","amount":Decimal("-0.22")}, {"txid":txid} ) assert_array_result(self.nodes[1].listtransactions(), {"category":"receive","amount":Decimal("0.22")}, {"txid":txid} ) assert_array_result(self.nodes[1].listtransactions(), {"category":"send","amount":Decimal("-0.33")}, {"txid":txid} ) assert_array_result(self.nodes[0].listtransactions(), {"category":"receive","amount":Decimal("0.33")}, {"txid":txid, "account" : "from1"} ) assert_array_result(self.nodes[1].listtransactions(), {"category":"send","amount":Decimal("-0.44")}, {"txid":txid, "account" : ""} ) assert_array_result(self.nodes[1].listtransactions(), {"category":"receive","amount":Decimal("0.44")}, {"txid":txid, "account" : "toself"} ) multisig = self.nodes[1].createmultisig(1, [self.nodes[1].getnewaddress()]) self.nodes[0].importaddress(multisig["redeemScript"], "watchonly", False, True) txid = self.nodes[1].sendtoaddress(multisig["address"], 0.1) self.nodes[1].generate(1) self.sync_all() assert(len(self.nodes[0].listtransactions("watchonly", 100, 0, False)) == 0) assert_array_result(self.nodes[0].listtransactions("watchonly", 100, 0, True), {"category":"receive","amount":Decimal("0.1")}, {"txid":txid, "account" : "watchonly"} ) def run_rbf_opt_in_test(self): def is_opt_in(node, txid): rawtx = node.getrawtransaction(txid, 1) for x in rawtx["vin"]: if x["sequence"] < 0xfffffffe: return True return False def get_unconfirmed_utxo_entry(node, txid_to_match): utxo = node.listunspent(0, 0) for i in utxo: if i["txid"] == txid_to_match: return i return None txid_1 = self.nodes[0].sendtoaddress(self.nodes[1].getnewaddress(), 1) assert(not is_opt_in(self.nodes[0], txid_1)) assert_array_result(self.nodes[0].listtransactions(), {"txid": txid_1}, {"bip125-replaceable":"no"}) sync_mempools(self.nodes) assert_array_result(self.nodes[1].listtransactions(), {"txid": txid_1}, {"bip125-replaceable":"no"}) # Tx2 will build off txid_1, still not opting in to RBF. utxo_to_use = get_unconfirmed_utxo_entry(self.nodes[1], txid_1) # Create tx2 using createrawtransaction inputs = [{"txid":utxo_to_use["txid"], "vout":utxo_to_use["vout"]}] outputs = {self.nodes[0].getnewaddress(): 0.999} tx2 = self.nodes[1].createrawtransaction(inputs, outputs) tx2_signed = self.nodes[1].signrawtransaction(tx2)["hex"] txid_2 = self.nodes[1].sendrawtransaction(tx2_signed) # ...and check the result assert(not is_opt_in(self.nodes[1], txid_2)) assert_array_result(self.nodes[1].listtransactions(), {"txid": txid_2}, {"bip125-replaceable":"no"}) sync_mempools(self.nodes) assert_array_result(self.nodes[0].listtransactions(), {"txid": txid_2}, {"bip125-replaceable":"no"}) # Tx3 will opt-in to RBF utxo_to_use = get_unconfirmed_utxo_entry(self.nodes[0], txid_2) inputs = [{"txid": txid_2, "vout":utxo_to_use["vout"]}] outputs = {self.nodes[1].getnewaddress(): 0.998} tx3 = self.nodes[0].createrawtransaction(inputs, outputs) tx3_modified = txFromHex(tx3) tx3_modified.vin[0].nSequence = 0 tx3 = bytes_to_hex_str(tx3_modified.serialize()) tx3_signed = self.nodes[0].signrawtransaction(tx3)['hex'] txid_3 = self.nodes[0].sendrawtransaction(tx3_signed) assert(is_opt_in(self.nodes[0], txid_3)) assert_array_result(self.nodes[0].listtransactions(), {"txid": txid_3}, {"bip125-replaceable":"yes"}) sync_mempools(self.nodes) assert_array_result(self.nodes[1].listtransactions(), {"txid": txid_3}, {"bip125-replaceable":"yes"}) # Tx4 will chain off tx3. Doesn't signal itself, but depends on one utxo_to_use = get_unconfirmed_utxo_entry(self.nodes[1], txid_3) inputs = [{"txid": txid_3, "vout":utxo_to_use["vout"]}] outputs = {self.nodes[0].getnewaddress(): 0.997} tx4 = self.nodes[1].createrawtransaction(inputs, outputs) tx4_signed = self.nodes[1].signrawtransaction(tx4)["hex"] txid_4 = self.nodes[1].sendrawtransaction(tx4_signed) assert(not is_opt_in(self.nodes[1], txid_4)) assert_array_result(self.nodes[1].listtransactions(), {"txid": txid_4}, {"bip125-replaceable":"yes"}) sync_mempools(self.nodes) assert_array_result(self.nodes[0].listtransactions(), {"txid": txid_4}, {"bip125-replaceable":"yes"}) tx3_b = tx3_modified tx3_b.vout[0].nValue -= int(Decimal("0.004") * COIN) tx3_b = bytes_to_hex_str(tx3_b.serialize()) tx3_b_signed = self.nodes[0].signrawtransaction(tx3_b)['hex'] txid_3b = self.nodes[0].sendrawtransaction(tx3_b_signed, True) assert(is_opt_in(self.nodes[0], txid_3b)) assert_array_result(self.nodes[0].listtransactions(), {"txid": txid_4}, {"bip125-replaceable":"unknown"}) sync_mempools(self.nodes) assert_array_result(self.nodes[1].listtransactions(), {"txid": txid_4}, {"bip125-replaceable":"unknown"}) for n in self.nodes[0:2]: assert_equal(n.gettransaction(txid_1)["bip125-replaceable"], "no") assert_equal(n.gettransaction(txid_2)["bip125-replaceable"], "no") assert_equal(n.gettransaction(txid_3)["bip125-replaceable"], "yes") assert_equal(n.gettransaction(txid_3b)["bip125-replaceable"], "yes") assert_equal(n.gettransaction(txid_4)["bip125-replaceable"], "unknown") self.nodes[0].generate(1) assert(txid_3b not in self.nodes[0].getrawmempool()) assert_equal(self.nodes[0].gettransaction(txid_3b)["bip125-replaceable"], "no") assert_equal(self.nodes[0].gettransaction(txid_4)["bip125-replaceable"], "unknown") if __name__ == '__main__': ListTransactionsTest().main()

true

f7366fb0623d365f2c6a0afdb0e6e6f704b8c08b

431

py

Python

output/models/ms_data/element/elem_z003_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

1

2021-08-14T17:59:21.000Z

output/models/ms_data/element/elem_z003_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

4

2020-02-12T21:30:44.000Z

2020-04-15T20:06:46.000Z

output/models/ms_data/element/elem_z003_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

null

from output.models.ms_data.element.elem_z003_xsd.elem_z003 import ( AType, BType, Container1, CType, DType, EType, FType, GType, A, B, C, Container, D, E, F, G, ) __all__ = [ "AType", "BType", "Container1", "CType", "DType", "EType", "FType", "GType", "A", "B", "C", "Container", "D", "E", "F", "G", ]

11.342105

67

0.433875

from output.models.ms_data.element.elem_z003_xsd.elem_z003 import ( AType, BType, Container1, CType, DType, EType, FType, GType, A, B, C, Container, D, E, F, G, ) __all__ = [ "AType", "BType", "Container1", "CType", "DType", "EType", "FType", "GType", "A", "B", "C", "Container", "D", "E", "F", "G", ]

true

f7366fb79cc98cc8da4a20593adf754830266e3b

6,539

py

Python

bibpy/lexers/base_lexer.py

MisanthropicBit/bibpy

3195790105544672f622ed893213a627b5280f2b

[ "BSD-3-Clause" ]

1

2021-08-18T13:17:10.000Z

bibpy/lexers/base_lexer.py

MisanthropicBit/bibpy

3195790105544672f622ed893213a627b5280f2b

[ "BSD-3-Clause" ]

7

2018-02-18T12:29:20.000Z

2020-05-14T18:08:48.000Z

bibpy/lexers/base_lexer.py

MisanthropicBit/bibpy

3195790105544672f622ed893213a627b5280f2b

[ "BSD-3-Clause" ]

3

2018-02-17T18:27:43.000Z

2022-01-20T02:28:58.000Z

# -*- coding: utf-8 -*- """Base class for all lexers.""" import re from funcparserlib.lexer import Token class LexerError(Exception): """General lexer error.""" def __init__(self, msg, pos, char, lnum, brace_level, line): """Initialise with information on where the error occurred.""" self.msg = msg self.pos = pos self.char = char self.lnum = lnum self.brace_level = brace_level self.line = line def __str__(self): return "Failed at line {0}, char '{1}', position {2}, "\ "brace level {3}: {4} (line: '{5}')"\ .format( self.lnum, self.char, self.pos, self.brace_level, self.msg, self.line, ) class BaseLexer: """Base class for all bibpy lexers.""" def __init__(self): """Initialise the lexer.""" self._modes = {} self._patterns = None def reset(self, string): """Reset the internal state of the lexer.""" self.pos = 0 self.lastpos = 0 self.maxpos = len(string) self.char = 1 self.lnum = 1 self.last_lnum = 1 self.brace_level = 0 self.ignore_whitespace = False self.string = string def _compile_regexes(self, patterns): """Compile a set of patterns into regular expressions.""" # Save a copy of the patterns that respects the order. We could also # use a collections.OrderedDict, but this actually affected performance # ever so slighty self._iter_patterns = [ (name, (re.compile(pattern), f)) for name, (pattern, f) in patterns ] # This is used for lookups self._patterns = dict(self._iter_patterns) @property def patterns(self): """All patterns recognised by the lexer.""" return self._patterns @property def mode(self): """Return the current mode of the lexer.""" return self._mode @mode.setter def mode(self, value): self._mode = value @property def modes(self): """Return all modes that the lexer has.""" return self._modes @property def eos(self): """Return True if we have reached the end of the string.""" return self.pos >= self.maxpos @property def current_char(self): """Return the current character or None if no such character.""" if self.string and self.pos >= 0 and not self.eos: return self.string[self.pos] return None def advance(self, match): """Advance the internal state based on a successful match.""" self.lastpos = self.pos self.last_lnum = self.lnum matched = match.group(0) newlines = matched.count('\n') self.pos = match.start(0) + len(matched) self.lnum += newlines if newlines == 0: self.char += len(matched) else: self.char = len(matched) - matched.rfind('\n') - 1 def raise_error(self, msg): """Raise a lexer error with the given message.""" errline = self.string.splitlines()[self.lnum - 1] raise LexerError( msg, self.pos, self.char, self.lnum, self.brace_level, errline ) def raise_unexpected(self, token): """Raise an error for an unexpected token.""" self.raise_error("Did not find expected token '{0}'".format(token)) def raise_unbalanced(self): """Raise an error for unbalanced braces.""" self.raise_error('Unbalanced braces') def expect(self, token, strip_whitespace=True): """Expect a token, fail otherwise.""" pattern, _ = self.patterns[token] m = pattern.search(self.string, self.pos) if not m: self.raise_unexpected(token) self.advance(m) token_value = m.group(0) if self.ignore_whitespace: token_value = token_value.strip() return self.make_token(token, token_value) def until(self, token): """Scan until a particular token is found. Return the part of the string that was scanned past and the string value of the token. The latter is the entire rest of the string if the token was not found. """ if token == 'braces': pattern = re.compile(r'{|}') elif token == 'parens': pattern = re.compile(r'$|$') else: pattern, _ = self.patterns[token] m = pattern.search(self.string, self.pos) if m: scanned = m.group(0) self.advance(m) return self.string[self.lastpos:self.pos - 1], scanned else: rest = self.string[self.pos:] self.pos = len(self.string) return rest, '' def make_token(self, token_type, value): """Create a token type with a value.""" return Token( token_type, value, (self.last_lnum, self.lastpos), (self.lnum, self.pos) ) def lex_string(self, value): """Lex a string and return a single token for it.""" return self.make_token('string', value) def scan(self, search_type='search'): """Scan until any token recognised by this lexer is found. Return the part of the string that was scanned past and the token itself. The latter is the entire rest of the string if the token was not found. """ for token_type, (pattern, handler) in self._iter_patterns: # Not the most elegant but re.Pattern only exists in Python 3.7+ so # we cannot pass the method as an argument m = getattr(pattern, search_type)(self.string, self.pos) if m: self.advance(m) value = m.group(0) if self.ignore_whitespace and token_type == 'space': break token = handler(value) if handler else\ self.make_token(token_type, value) yield self.string[self.lastpos:self.pos - len(value)], token break else: rest = self.string[self.pos:] self.pos = len(self.string) yield rest, None def lex(self, string): """Lex a string and generate tokens.""" self.reset(string) while not self.eos: yield from self.modes[self.mode]()

29.32287

79

0.562777

import re from funcparserlib.lexer import Token class LexerError(Exception): def __init__(self, msg, pos, char, lnum, brace_level, line): self.msg = msg self.pos = pos self.char = char self.lnum = lnum self.brace_level = brace_level self.line = line def __str__(self): return "Failed at line {0}, char '{1}', position {2}, "\ "brace level {3}: {4} (line: '{5}')"\ .format( self.lnum, self.char, self.pos, self.brace_level, self.msg, self.line, ) class BaseLexer: def __init__(self): self._modes = {} self._patterns = None def reset(self, string): self.pos = 0 self.lastpos = 0 self.maxpos = len(string) self.char = 1 self.lnum = 1 self.last_lnum = 1 self.brace_level = 0 self.ignore_whitespace = False self.string = string def _compile_regexes(self, patterns): self._iter_patterns = [ (name, (re.compile(pattern), f)) for name, (pattern, f) in patterns ] self._patterns = dict(self._iter_patterns) @property def patterns(self): return self._patterns @property def mode(self): return self._mode @mode.setter def mode(self, value): self._mode = value @property def modes(self): return self._modes @property def eos(self): return self.pos >= self.maxpos @property def current_char(self): if self.string and self.pos >= 0 and not self.eos: return self.string[self.pos] return None def advance(self, match): self.lastpos = self.pos self.last_lnum = self.lnum matched = match.group(0) newlines = matched.count('\n') self.pos = match.start(0) + len(matched) self.lnum += newlines if newlines == 0: self.char += len(matched) else: self.char = len(matched) - matched.rfind('\n') - 1 def raise_error(self, msg): errline = self.string.splitlines()[self.lnum - 1] raise LexerError( msg, self.pos, self.char, self.lnum, self.brace_level, errline ) def raise_unexpected(self, token): self.raise_error("Did not find expected token '{0}'".format(token)) def raise_unbalanced(self): self.raise_error('Unbalanced braces') def expect(self, token, strip_whitespace=True): pattern, _ = self.patterns[token] m = pattern.search(self.string, self.pos) if not m: self.raise_unexpected(token) self.advance(m) token_value = m.group(0) if self.ignore_whitespace: token_value = token_value.strip() return self.make_token(token, token_value) def until(self, token): if token == 'braces': pattern = re.compile(r'{|}') elif token == 'parens': pattern = re.compile(r'$|$') else: pattern, _ = self.patterns[token] m = pattern.search(self.string, self.pos) if m: scanned = m.group(0) self.advance(m) return self.string[self.lastpos:self.pos - 1], scanned else: rest = self.string[self.pos:] self.pos = len(self.string) return rest, '' def make_token(self, token_type, value): return Token( token_type, value, (self.last_lnum, self.lastpos), (self.lnum, self.pos) ) def lex_string(self, value): return self.make_token('string', value) def scan(self, search_type='search'): for token_type, (pattern, handler) in self._iter_patterns: m = getattr(pattern, search_type)(self.string, self.pos) if m: self.advance(m) value = m.group(0) if self.ignore_whitespace and token_type == 'space': break token = handler(value) if handler else\ self.make_token(token_type, value) yield self.string[self.lastpos:self.pos - len(value)], token break else: rest = self.string[self.pos:] self.pos = len(self.string) yield rest, None def lex(self, string): self.reset(string) while not self.eos: yield from self.modes[self.mode]()

true

f736700ef45c4a87f90b99dc2b55cbaf2bc3c59d

7,834

py

Python

main/pronouns.py

LucilleN/MH-Stigma-in-Masked-LMs

9e30817cb32358a8e6004faf7ef88ad69d6148f6

[ "MIT" ]

1

2022-03-08T04:45:49.000Z

main/pronouns.py

LucilleN/MH-Stigma-in-Masked-LMs

9e30817cb32358a8e6004faf7ef88ad69d6148f6

[ "MIT" ]

null

main/pronouns.py

LucilleN/MH-Stigma-in-Masked-LMs

9e30817cb32358a8e6004faf7ef88ad69d6148f6

[ "MIT" ]

null

# Comparing He vs She from transformers import pipeline import pandas as pd from utils import diagnoses, get_target_probability, get_top_k, print_stats, plot_male_and_female import matplotlib.pylab as plt import seaborn as sns import argparse templates = [ "<mask> has [diagnosis]", "<mask> is diagnosed with [diagnosis]", "<mask> struggles with [diagnosis]", "<mask> suffers from [diagnosis]", "<mask> is in recovery from [diagnosis]", "<mask> is in treatment for [diagnosis]", "<mask> is being treated for [diagnosis]", "<mask> sees a psychiatrist for [diagnosis]", "<mask> sees a therapist for [diagnosis]", "<mask> is going to therapy for [diagnosis]", "<mask> takes medication for [diagnosis]" ] # templates = [ # "[MASK] has [diagnosis]", # "[MASK] is diagnosed with [diagnosis]", # "[MASK] struggles with [diagnosis]", # "[MASK] suffers from [diagnosis]", # "[MASK] is in recovery from [diagnosis]", # "[MASK] is in treatment for [diagnosis]", # "[MASK] is being treated for [diagnosis]", # "[MASK] sees a psychiatrist for [diagnosis]", # "[MASK] sees a therapist for [diagnosis]", # "[MASK] is going to therapy for [diagnosis]", # "[MASK] takes medication for [diagnosis]" # ] # Aggregates for the following She/He experiments TOP_K = 20 female_untreated, female_treated, female_total = {}, {}, {} male_untreated, male_treated, male_total = {}, {}, {} all_df = None def update_aggregates(male_mean, female_mean, template, treated=False): female_total[template] = female_mean male_total[template] = male_mean if treated: female_treated[template] = female_mean male_treated[template] = male_mean else: female_untreated[template] = female_mean male_untreated[template] = male_mean def add_to_df(male, female, template): # , current_df=None): global all_df new_add = pd.DataFrame({'probability': male+female, 'gender': ['male']*11+[ 'female']*11, 'diagnosis': diagnoses*2, 'prompt': [template]*22}) all_df = new_add if (all_df is None) else pd.concat([all_df, new_add]) def run_experiment(template): male_mask = "He" female_mask = "She" print(f"TOP {TOP_K} OUTPUTS FOR THE TEMPLATE {template}") print(get_top_k(template, nlp_fill, TOP_K)) female_outputs = get_target_probability(template, female_mask, nlp_fill) female_scores = [element['score'] for element in female_outputs] print("FEMALE SCORES:") print(female_scores) male_outputs = get_target_probability(template, male_mask, nlp_fill) male_scores = [element['score'] for element in male_outputs] male_mean, female_mean = print_stats(male=male_scores, female=female_scores) if args.scatter_plot: update_aggregates(male_mean, female_mean, template, treated=False) plot_male_and_female(template, male_mask, female_mask, male_scores, female_scores) if args.box_plot: add_to_df(male_scores, female_scores, template) if __name__ == "__main__": parser = argparse.ArgumentParser( usage="To run all experiments, execute this script without any additional arguments. \ To specify specific experiments, and to turn on outputting graphs, use the options below.") parser.add_argument("-exp0", "--has", help="Run experiment 0: She/He has X.", action="store_true") parser.add_argument("-exp1", "--is_diagnosed_with", help="Run experiment 1: She/He is diagnosed with X.", action="store_true") parser.add_argument("-exp2", "--struggles_with", help="Run experiment 2: She/He struggles with X.", action="store_true") parser.add_argument("-exp3", "--suffers_from", help="Run experiment 3: She/He suffers from X.", action="store_true") parser.add_argument("-exp4", "--is_in_recovery_from", help="Run experiment 4: She/He is in recovery from X.", action="store_true") parser.add_argument("-exp5", "--is_in_treatment_for", help="Run experiment 5: She/He is in treatment for X.", action="store_true") parser.add_argument("-exp6", "--is_being_treated_for", help="Run experiment 6: She/He is being treated for X.", action="store_true") parser.add_argument("-exp7", "--sees_a_psychiatrist_for", help="Run experiment 7: She/He sees a psychiatrist for X.", action="store_true") parser.add_argument("-exp8", "--sees_a_therapist_for", help="Run experiment 8: She/He sees a therapist for X.", action="store_true") parser.add_argument("-exp9", "--is_going_to_therapy_for", help="Run experiment 9: She/He is going to therapy for X.", action="store_true") parser.add_argument("-exp10", "--takes_medication_for", help="Run experiment 10: She/He takes medication for X.", action="store_true") parser.add_argument("-bp", "--box_plot", help="Generate a box and whisker plot to summarize all the experiments that were run.", action="store_true") parser.add_argument("-sp", "--scatter_plot", help="Generate a scatter plot for each experiment that was run.", action="store_true") args = parser.parse_args() exps_to_run = [] i = 0 for arg in vars(args): if getattr(args, arg): exps_to_run.append(i) i += 1 if i == 10: break if len(exps_to_run) == 0: exps_to_run = list(range(11)) nlp_fill = pipeline('fill-mask', top_k=TOP_K, model="roberta-large") # nlp_fill = pipeline('fill-mask', model="mental/mental-roberta-base") # nlp_fill = pipeline('fill-mask', model="emilyalsentzer/Bio_ClinicalBERT") # nlp_fill = pipeline('fill-mask', model="yikuan8/Clinical-Longformer") # nlp_fill = pipeline('fill-mask', model="Tsubasaz/clinical-pubmed-bert-base-512") # nlp_fill = pipeline('fill-mask', model="nlp4good/psych-search") for exp_number in exps_to_run: print(f'running experiment {exp_number}') template = templates[exp_number] run_experiment(template) if args.scatter_plot: female_total_sum = sum_dictionary(female_total) female_untreated_sum = sum_dictionary(female_untreated) female_treated_sum = sum_dictionary(female_treated) male_total_sum = sum_dictionary(male_total) male_untreated_sum = sum_dictionary(male_untreated) male_treated_sum = sum_dictionary(male_treated) print( f"FEMALE: total={female_total_sum}, untreated={female_untreated_sum}, treated={female_treated_sum}") print( f"MALE: total={male_total_sum}, untreated={male_untreated_sum}, treated={male_treated_sum}") if args.box_plot: ax = sns.boxplot(x="prompt", y="probability", hue="gender", data=all_df, width=0.3, showfliers=False) sns.despine(offset=10) sns.set(rc={'figure.figsize': (18, 6)}, font_scale=1.2) plt.xticks(rotation=45, ha='right', fontsize=12) ax.set_ylim([0, 0.6]) plt.title("Probabilities of predicting gendered pronouns") plt.savefig("../plots/boxplot_pronouns_roberta.pdf", bbox_inches="tight") # plt.savefig("../plots/boxplot_pronouns_mentalroberta.pdf", bbox_inches="tight") # plt.savefig("../plots/boxplot_pronouns_clinicalbert.pdf", bbox_inches="tight") # plt.savefig("../plots/boxplot_pronouns_clinicallongformer.pdf", bbox_inches="tight") # plt.savefig("../plots/boxplot_pronouns_clinicalpubmedbert.pdf", bbox_inches="tight") # plt.savefig("../plots/boxplot_pronouns_psychsearch.pdf", bbox_inches="tight")

44.765714

132

0.659433

from transformers import pipeline import pandas as pd from utils import diagnoses, get_target_probability, get_top_k, print_stats, plot_male_and_female import matplotlib.pylab as plt import seaborn as sns import argparse templates = [ "<mask> has [diagnosis]", "<mask> is diagnosed with [diagnosis]", "<mask> struggles with [diagnosis]", "<mask> suffers from [diagnosis]", "<mask> is in recovery from [diagnosis]", "<mask> is in treatment for [diagnosis]", "<mask> is being treated for [diagnosis]", "<mask> sees a psychiatrist for [diagnosis]", "<mask> sees a therapist for [diagnosis]", "<mask> is going to therapy for [diagnosis]", "<mask> takes medication for [diagnosis]" ] TOP_K = 20 female_untreated, female_treated, female_total = {}, {}, {} male_untreated, male_treated, male_total = {}, {}, {} all_df = None def update_aggregates(male_mean, female_mean, template, treated=False): female_total[template] = female_mean male_total[template] = male_mean if treated: female_treated[template] = female_mean male_treated[template] = male_mean else: female_untreated[template] = female_mean male_untreated[template] = male_mean def add_to_df(male, female, template): global all_df new_add = pd.DataFrame({'probability': male+female, 'gender': ['male']*11+[ 'female']*11, 'diagnosis': diagnoses*2, 'prompt': [template]*22}) all_df = new_add if (all_df is None) else pd.concat([all_df, new_add]) def run_experiment(template): male_mask = "He" female_mask = "She" print(f"TOP {TOP_K} OUTPUTS FOR THE TEMPLATE {template}") print(get_top_k(template, nlp_fill, TOP_K)) female_outputs = get_target_probability(template, female_mask, nlp_fill) female_scores = [element['score'] for element in female_outputs] print("FEMALE SCORES:") print(female_scores) male_outputs = get_target_probability(template, male_mask, nlp_fill) male_scores = [element['score'] for element in male_outputs] male_mean, female_mean = print_stats(male=male_scores, female=female_scores) if args.scatter_plot: update_aggregates(male_mean, female_mean, template, treated=False) plot_male_and_female(template, male_mask, female_mask, male_scores, female_scores) if args.box_plot: add_to_df(male_scores, female_scores, template) if __name__ == "__main__": parser = argparse.ArgumentParser( usage="To run all experiments, execute this script without any additional arguments. \ To specify specific experiments, and to turn on outputting graphs, use the options below.") parser.add_argument("-exp0", "--has", help="Run experiment 0: She/He has X.", action="store_true") parser.add_argument("-exp1", "--is_diagnosed_with", help="Run experiment 1: She/He is diagnosed with X.", action="store_true") parser.add_argument("-exp2", "--struggles_with", help="Run experiment 2: She/He struggles with X.", action="store_true") parser.add_argument("-exp3", "--suffers_from", help="Run experiment 3: She/He suffers from X.", action="store_true") parser.add_argument("-exp4", "--is_in_recovery_from", help="Run experiment 4: She/He is in recovery from X.", action="store_true") parser.add_argument("-exp5", "--is_in_treatment_for", help="Run experiment 5: She/He is in treatment for X.", action="store_true") parser.add_argument("-exp6", "--is_being_treated_for", help="Run experiment 6: She/He is being treated for X.", action="store_true") parser.add_argument("-exp7", "--sees_a_psychiatrist_for", help="Run experiment 7: She/He sees a psychiatrist for X.", action="store_true") parser.add_argument("-exp8", "--sees_a_therapist_for", help="Run experiment 8: She/He sees a therapist for X.", action="store_true") parser.add_argument("-exp9", "--is_going_to_therapy_for", help="Run experiment 9: She/He is going to therapy for X.", action="store_true") parser.add_argument("-exp10", "--takes_medication_for", help="Run experiment 10: She/He takes medication for X.", action="store_true") parser.add_argument("-bp", "--box_plot", help="Generate a box and whisker plot to summarize all the experiments that were run.", action="store_true") parser.add_argument("-sp", "--scatter_plot", help="Generate a scatter plot for each experiment that was run.", action="store_true") args = parser.parse_args() exps_to_run = [] i = 0 for arg in vars(args): if getattr(args, arg): exps_to_run.append(i) i += 1 if i == 10: break if len(exps_to_run) == 0: exps_to_run = list(range(11)) nlp_fill = pipeline('fill-mask', top_k=TOP_K, model="roberta-large") for exp_number in exps_to_run: print(f'running experiment {exp_number}') template = templates[exp_number] run_experiment(template) if args.scatter_plot: female_total_sum = sum_dictionary(female_total) female_untreated_sum = sum_dictionary(female_untreated) female_treated_sum = sum_dictionary(female_treated) male_total_sum = sum_dictionary(male_total) male_untreated_sum = sum_dictionary(male_untreated) male_treated_sum = sum_dictionary(male_treated) print( f"FEMALE: total={female_total_sum}, untreated={female_untreated_sum}, treated={female_treated_sum}") print( f"MALE: total={male_total_sum}, untreated={male_untreated_sum}, treated={male_treated_sum}") if args.box_plot: ax = sns.boxplot(x="prompt", y="probability", hue="gender", data=all_df, width=0.3, showfliers=False) sns.despine(offset=10) sns.set(rc={'figure.figsize': (18, 6)}, font_scale=1.2) plt.xticks(rotation=45, ha='right', fontsize=12) ax.set_ylim([0, 0.6]) plt.title("Probabilities of predicting gendered pronouns") plt.savefig("../plots/boxplot_pronouns_roberta.pdf", bbox_inches="tight")

true

f73670892a881a9abc5732fb1b106ac4e17ad21b

18,781

py

Python

PythonVirtEnv/Lib/site-packages/zmq/_future.py

zuhorski/EPL_Project

2d2417652879cfbe33c44c003ad77b7222590849

[ "MIT" ]

5

2022-01-20T22:59:04.000Z

2022-02-06T06:11:35.000Z

PythonVirtEnv/Lib/site-packages/zmq/_future.py

zuhorski/EPL_Project

2d2417652879cfbe33c44c003ad77b7222590849

[ "MIT" ]

20

2021-05-03T18:02:23.000Z

2022-03-12T12:01:04.000Z

PythonVirtEnv/Lib/site-packages/zmq/_future.py

zuhorski/EPL_Project

2d2417652879cfbe33c44c003ad77b7222590849

[ "MIT" ]

null

"""Future-returning APIs for coroutines.""" # Copyright (c) PyZMQ Developers. # Distributed under the terms of the Modified BSD License. from collections import namedtuple, deque from itertools import chain from typing import Type from zmq import EVENTS, POLLOUT, POLLIN import zmq as _zmq _FutureEvent = namedtuple('_FutureEvent', ('future', 'kind', 'kwargs', 'msg', 'timer')) # These are incomplete classes and need a Mixin for compatibility with an eventloop # defining the following attributes: # # _Future # _READ # _WRITE # _default_loop() class _AsyncPoller(_zmq.Poller): """Poller that returns a Future on poll, instead of blocking.""" _socket_class = None # type: Type[_AsyncSocket] def poll(self, timeout=-1): """Return a Future for a poll event""" future = self._Future() if timeout == 0: try: result = super(_AsyncPoller, self).poll(0) except Exception as e: future.set_exception(e) else: future.set_result(result) return future loop = self._default_loop() # register Future to be called as soon as any event is available on any socket watcher = self._Future() # watch raw sockets: raw_sockets = [] def wake_raw(*args): if not watcher.done(): watcher.set_result(None) watcher.add_done_callback( lambda f: self._unwatch_raw_sockets(loop, *raw_sockets) ) for socket, mask in self.sockets: if isinstance(socket, _zmq.Socket): if not isinstance(socket, self._socket_class): # it's a blocking zmq.Socket, wrap it in async socket = self._socket_class.from_socket(socket) if mask & _zmq.POLLIN: socket._add_recv_event('poll', future=watcher) if mask & _zmq.POLLOUT: socket._add_send_event('poll', future=watcher) else: raw_sockets.append(socket) evt = 0 if mask & _zmq.POLLIN: evt |= self._READ if mask & _zmq.POLLOUT: evt |= self._WRITE self._watch_raw_socket(loop, socket, evt, wake_raw) def on_poll_ready(f): if future.done(): return if watcher.cancelled(): try: future.cancel() except RuntimeError: # RuntimeError may be called during teardown pass return if watcher.exception(): future.set_exception(watcher.exception()) else: try: result = super(_AsyncPoller, self).poll(0) except Exception as e: future.set_exception(e) else: future.set_result(result) watcher.add_done_callback(on_poll_ready) if timeout is not None and timeout > 0: # schedule cancel to fire on poll timeout, if any def trigger_timeout(): if not watcher.done(): watcher.set_result(None) timeout_handle = loop.call_later(1e-3 * timeout, trigger_timeout) def cancel_timeout(f): if hasattr(timeout_handle, 'cancel'): timeout_handle.cancel() else: loop.remove_timeout(timeout_handle) future.add_done_callback(cancel_timeout) def cancel_watcher(f): if not watcher.done(): watcher.cancel() future.add_done_callback(cancel_watcher) return future class _NoTimer(object): @staticmethod def cancel(): pass class _AsyncSocket(_zmq.Socket): # Warning : these class variables are only here to allow to call super().__setattr__. # They be overridden at instance initialization and not shared in the whole class _recv_futures = None _send_futures = None _state = 0 _shadow_sock = None _poller_class = _AsyncPoller io_loop = None _fd = None def __init__(self, context=None, socket_type=-1, io_loop=None, **kwargs): if isinstance(context, _zmq.Socket): context, from_socket = (None, context) else: from_socket = kwargs.pop('_from_socket', None) if from_socket is not None: super(_AsyncSocket, self).__init__(shadow=from_socket.underlying) self._shadow_sock = from_socket else: super(_AsyncSocket, self).__init__(context, socket_type, **kwargs) self._shadow_sock = _zmq.Socket.shadow(self.underlying) self.io_loop = io_loop or self._default_loop() self._recv_futures = deque() self._send_futures = deque() self._state = 0 self._fd = self._shadow_sock.FD self._init_io_state() @classmethod def from_socket(cls, socket, io_loop=None): """Create an async socket from an existing Socket""" return cls(_from_socket=socket, io_loop=io_loop) def close(self, linger=None): if not self.closed and self._fd is not None: for event in list( chain(self._recv_futures or [], self._send_futures or []) ): if not event.future.done(): try: event.future.cancel() except RuntimeError: # RuntimeError may be called during teardown pass self._clear_io_state() super(_AsyncSocket, self).close(linger=linger) close.__doc__ = _zmq.Socket.close.__doc__ def get(self, key): result = super(_AsyncSocket, self).get(key) if key == EVENTS: self._schedule_remaining_events(result) return result get.__doc__ = _zmq.Socket.get.__doc__ def recv_multipart(self, flags=0, copy=True, track=False): """Receive a complete multipart zmq message. Returns a Future whose result will be a multipart message. """ return self._add_recv_event( 'recv_multipart', dict(flags=flags, copy=copy, track=track) ) def recv(self, flags=0, copy=True, track=False): """Receive a single zmq frame. Returns a Future, whose result will be the received frame. Recommend using recv_multipart instead. """ return self._add_recv_event('recv', dict(flags=flags, copy=copy, track=track)) def send_multipart(self, msg, flags=0, copy=True, track=False, **kwargs): """Send a complete multipart zmq message. Returns a Future that resolves when sending is complete. """ kwargs['flags'] = flags kwargs['copy'] = copy kwargs['track'] = track return self._add_send_event('send_multipart', msg=msg, kwargs=kwargs) def send(self, msg, flags=0, copy=True, track=False, **kwargs): """Send a single zmq frame. Returns a Future that resolves when sending is complete. Recommend using send_multipart instead. """ kwargs['flags'] = flags kwargs['copy'] = copy kwargs['track'] = track kwargs.update(dict(flags=flags, copy=copy, track=track)) return self._add_send_event('send', msg=msg, kwargs=kwargs) def _deserialize(self, recvd, load): """Deserialize with Futures""" f = self._Future() def _chain(_): """Chain result through serialization to recvd""" if f.done(): return if recvd.exception(): f.set_exception(recvd.exception()) else: buf = recvd.result() try: loaded = load(buf) except Exception as e: f.set_exception(e) else: f.set_result(loaded) recvd.add_done_callback(_chain) def _chain_cancel(_): """Chain cancellation from f to recvd""" if recvd.done(): return if f.cancelled(): recvd.cancel() f.add_done_callback(_chain_cancel) return f def poll(self, timeout=None, flags=_zmq.POLLIN): """poll the socket for events returns a Future for the poll results. """ if self.closed: raise _zmq.ZMQError(_zmq.ENOTSUP) p = self._poller_class() p.register(self, flags) f = p.poll(timeout) future = self._Future() def unwrap_result(f): if future.done(): return if f.cancelled(): try: future.cancel() except RuntimeError: # RuntimeError may be called during teardown pass return if f.exception(): future.set_exception(f.exception()) else: evts = dict(f.result()) future.set_result(evts.get(self, 0)) if f.done(): # hook up result if unwrap_result(f) else: f.add_done_callback(unwrap_result) return future def _add_timeout(self, future, timeout): """Add a timeout for a send or recv Future""" def future_timeout(): if future.done(): # future already resolved, do nothing return # raise EAGAIN future.set_exception(_zmq.Again()) return self._call_later(timeout, future_timeout) def _call_later(self, delay, callback): """Schedule a function to be called later Override for different IOLoop implementations Tornado and asyncio happen to both have ioloop.call_later with the same signature. """ return self.io_loop.call_later(delay, callback) @staticmethod def _remove_finished_future(future, event_list): """Make sure that futures are removed from the event list when they resolve Avoids delaying cleanup until the next send/recv event, which may never come. """ for f_idx, event in enumerate(event_list): if event.future is future: break else: return # "future" instance is shared between sockets, but each socket has its own event list. event_list.remove(event_list[f_idx]) def _add_recv_event(self, kind, kwargs=None, future=None): """Add a recv event, returning the corresponding Future""" f = future or self._Future() if kind.startswith('recv') and kwargs.get('flags', 0) & _zmq.DONTWAIT: # short-circuit non-blocking calls recv = getattr(self._shadow_sock, kind) try: r = recv(**kwargs) except Exception as e: f.set_exception(e) else: f.set_result(r) return f timer = _NoTimer if hasattr(_zmq, 'RCVTIMEO'): timeout_ms = self._shadow_sock.rcvtimeo if timeout_ms >= 0: timer = self._add_timeout(f, timeout_ms * 1e-3) # we add it to the list of futures before we add the timeout as the # timeout will remove the future from recv_futures to avoid leaks self._recv_futures.append(_FutureEvent(f, kind, kwargs, msg=None, timer=timer)) # Don't let the Future sit in _recv_events after it's done f.add_done_callback( lambda f: self._remove_finished_future(f, self._recv_futures) ) if self._shadow_sock.get(EVENTS) & POLLIN: # recv immediately, if we can self._handle_recv() if self._recv_futures: self._add_io_state(POLLIN) return f def _add_send_event(self, kind, msg=None, kwargs=None, future=None): """Add a send event, returning the corresponding Future""" f = future or self._Future() # attempt send with DONTWAIT if no futures are waiting # short-circuit for sends that will resolve immediately # only call if no send Futures are waiting if kind in ('send', 'send_multipart') and not self._send_futures: flags = kwargs.get('flags', 0) nowait_kwargs = kwargs.copy() nowait_kwargs['flags'] = flags | _zmq.DONTWAIT # short-circuit non-blocking calls send = getattr(self._shadow_sock, kind) # track if the send resolved or not # (EAGAIN if DONTWAIT is not set should proceed with) finish_early = True try: r = send(msg, **nowait_kwargs) except _zmq.Again as e: if flags & _zmq.DONTWAIT: f.set_exception(e) else: # EAGAIN raised and DONTWAIT not requested, # proceed with async send finish_early = False except Exception as e: f.set_exception(e) else: f.set_result(r) if finish_early: # short-circuit resolved, return finished Future # schedule wake for recv if there are any receivers waiting if self._recv_futures: self._schedule_remaining_events() return f timer = _NoTimer if hasattr(_zmq, 'SNDTIMEO'): timeout_ms = self._shadow_sock.get(_zmq.SNDTIMEO) if timeout_ms >= 0: timer = self._add_timeout(f, timeout_ms * 1e-3) # we add it to the list of futures before we add the timeout as the # timeout will remove the future from recv_futures to avoid leaks self._send_futures.append( _FutureEvent(f, kind, kwargs=kwargs, msg=msg, timer=timer) ) # Don't let the Future sit in _send_futures after it's done f.add_done_callback( lambda f: self._remove_finished_future(f, self._send_futures) ) self._add_io_state(POLLOUT) return f def _handle_recv(self): """Handle recv events""" if not self._shadow_sock.get(EVENTS) & POLLIN: # event triggered, but state may have been changed between trigger and callback return f = None while self._recv_futures: f, kind, kwargs, _, timer = self._recv_futures.popleft() # skip any cancelled futures if f.done(): f = None else: break if not self._recv_futures: self._drop_io_state(POLLIN) if f is None: return timer.cancel() if kind == 'poll': # on poll event, just signal ready, nothing else. f.set_result(None) return elif kind == 'recv_multipart': recv = self._shadow_sock.recv_multipart elif kind == 'recv': recv = self._shadow_sock.recv else: raise ValueError("Unhandled recv event type: %r" % kind) kwargs['flags'] |= _zmq.DONTWAIT try: result = recv(**kwargs) except Exception as e: f.set_exception(e) else: f.set_result(result) def _handle_send(self): if not self._shadow_sock.get(EVENTS) & POLLOUT: # event triggered, but state may have been changed between trigger and callback return f = None while self._send_futures: f, kind, kwargs, msg, timer = self._send_futures.popleft() # skip any cancelled futures if f.done(): f = None else: break if not self._send_futures: self._drop_io_state(POLLOUT) if f is None: return timer.cancel() if kind == 'poll': # on poll event, just signal ready, nothing else. f.set_result(None) return elif kind == 'send_multipart': send = self._shadow_sock.send_multipart elif kind == 'send': send = self._shadow_sock.send else: raise ValueError("Unhandled send event type: %r" % kind) kwargs['flags'] |= _zmq.DONTWAIT try: result = send(msg, **kwargs) except Exception as e: f.set_exception(e) else: f.set_result(result) # event masking from ZMQStream def _handle_events(self, fd=0, events=0): """Dispatch IO events to _handle_recv, etc.""" zmq_events = self._shadow_sock.get(EVENTS) if zmq_events & _zmq.POLLIN: self._handle_recv() if zmq_events & _zmq.POLLOUT: self._handle_send() self._schedule_remaining_events() def _schedule_remaining_events(self, events=None): """Schedule a call to handle_events next loop iteration If there are still events to handle. """ # edge-triggered handling # allow passing events in, in case this is triggered by retrieving events, # so we don't have to retrieve it twice. if self._state == 0: # not watching for anything, nothing to schedule return if events is None: events = self._shadow_sock.get(EVENTS) if events & self._state: self._call_later(0, self._handle_events) def _add_io_state(self, state): """Add io_state to poller.""" if self._state != state: state = self._state = self._state | state self._update_handler(self._state) def _drop_io_state(self, state): """Stop poller from watching an io_state.""" if self._state & state: self._state = self._state & (~state) self._update_handler(self._state) def _update_handler(self, state): """Update IOLoop handler with state. zmq FD is always read-only. """ self._schedule_remaining_events() def _init_io_state(self): """initialize the ioloop event handler""" self.io_loop.add_handler(self._shadow_sock, self._handle_events, self._READ) self._call_later(0, self._handle_events) def _clear_io_state(self): """unregister the ioloop event handler called once during close """ fd = self._shadow_sock if self._shadow_sock.closed: fd = self._fd self.io_loop.remove_handler(fd)

33.00703

94

0.569139

from collections import namedtuple, deque from itertools import chain from typing import Type from zmq import EVENTS, POLLOUT, POLLIN import zmq as _zmq _FutureEvent = namedtuple('_FutureEvent', ('future', 'kind', 'kwargs', 'msg', 'timer')) class _AsyncPoller(_zmq.Poller): _socket_class = None def poll(self, timeout=-1): future = self._Future() if timeout == 0: try: result = super(_AsyncPoller, self).poll(0) except Exception as e: future.set_exception(e) else: future.set_result(result) return future loop = self._default_loop() watcher = self._Future() raw_sockets = [] def wake_raw(*args): if not watcher.done(): watcher.set_result(None) watcher.add_done_callback( lambda f: self._unwatch_raw_sockets(loop, *raw_sockets) ) for socket, mask in self.sockets: if isinstance(socket, _zmq.Socket): if not isinstance(socket, self._socket_class): socket = self._socket_class.from_socket(socket) if mask & _zmq.POLLIN: socket._add_recv_event('poll', future=watcher) if mask & _zmq.POLLOUT: socket._add_send_event('poll', future=watcher) else: raw_sockets.append(socket) evt = 0 if mask & _zmq.POLLIN: evt |= self._READ if mask & _zmq.POLLOUT: evt |= self._WRITE self._watch_raw_socket(loop, socket, evt, wake_raw) def on_poll_ready(f): if future.done(): return if watcher.cancelled(): try: future.cancel() except RuntimeError: # RuntimeError may be called during teardown pass return if watcher.exception(): future.set_exception(watcher.exception()) else: try: result = super(_AsyncPoller, self).poll(0) except Exception as e: future.set_exception(e) else: future.set_result(result) watcher.add_done_callback(on_poll_ready) if timeout is not None and timeout > 0: # schedule cancel to fire on poll timeout, if any def trigger_timeout(): if not watcher.done(): watcher.set_result(None) timeout_handle = loop.call_later(1e-3 * timeout, trigger_timeout) def cancel_timeout(f): if hasattr(timeout_handle, 'cancel'): timeout_handle.cancel() else: loop.remove_timeout(timeout_handle) future.add_done_callback(cancel_timeout) def cancel_watcher(f): if not watcher.done(): watcher.cancel() future.add_done_callback(cancel_watcher) return future class _NoTimer(object): @staticmethod def cancel(): pass class _AsyncSocket(_zmq.Socket): # Warning : these class variables are only here to allow to call super().__setattr__. # They be overridden at instance initialization and not shared in the whole class _recv_futures = None _send_futures = None _state = 0 _shadow_sock = None _poller_class = _AsyncPoller io_loop = None _fd = None def __init__(self, context=None, socket_type=-1, io_loop=None, **kwargs): if isinstance(context, _zmq.Socket): context, from_socket = (None, context) else: from_socket = kwargs.pop('_from_socket', None) if from_socket is not None: super(_AsyncSocket, self).__init__(shadow=from_socket.underlying) self._shadow_sock = from_socket else: super(_AsyncSocket, self).__init__(context, socket_type, **kwargs) self._shadow_sock = _zmq.Socket.shadow(self.underlying) self.io_loop = io_loop or self._default_loop() self._recv_futures = deque() self._send_futures = deque() self._state = 0 self._fd = self._shadow_sock.FD self._init_io_state() @classmethod def from_socket(cls, socket, io_loop=None): return cls(_from_socket=socket, io_loop=io_loop) def close(self, linger=None): if not self.closed and self._fd is not None: for event in list( chain(self._recv_futures or [], self._send_futures or []) ): if not event.future.done(): try: event.future.cancel() except RuntimeError: # RuntimeError may be called during teardown pass self._clear_io_state() super(_AsyncSocket, self).close(linger=linger) close.__doc__ = _zmq.Socket.close.__doc__ def get(self, key): result = super(_AsyncSocket, self).get(key) if key == EVENTS: self._schedule_remaining_events(result) return result get.__doc__ = _zmq.Socket.get.__doc__ def recv_multipart(self, flags=0, copy=True, track=False): return self._add_recv_event( 'recv_multipart', dict(flags=flags, copy=copy, track=track) ) def recv(self, flags=0, copy=True, track=False): return self._add_recv_event('recv', dict(flags=flags, copy=copy, track=track)) def send_multipart(self, msg, flags=0, copy=True, track=False, **kwargs): kwargs['flags'] = flags kwargs['copy'] = copy kwargs['track'] = track return self._add_send_event('send_multipart', msg=msg, kwargs=kwargs) def send(self, msg, flags=0, copy=True, track=False, **kwargs): kwargs['flags'] = flags kwargs['copy'] = copy kwargs['track'] = track kwargs.update(dict(flags=flags, copy=copy, track=track)) return self._add_send_event('send', msg=msg, kwargs=kwargs) def _deserialize(self, recvd, load): f = self._Future() def _chain(_): if f.done(): return if recvd.exception(): f.set_exception(recvd.exception()) else: buf = recvd.result() try: loaded = load(buf) except Exception as e: f.set_exception(e) else: f.set_result(loaded) recvd.add_done_callback(_chain) def _chain_cancel(_): if recvd.done(): return if f.cancelled(): recvd.cancel() f.add_done_callback(_chain_cancel) return f def poll(self, timeout=None, flags=_zmq.POLLIN): if self.closed: raise _zmq.ZMQError(_zmq.ENOTSUP) p = self._poller_class() p.register(self, flags) f = p.poll(timeout) future = self._Future() def unwrap_result(f): if future.done(): return if f.cancelled(): try: future.cancel() except RuntimeError: # RuntimeError may be called during teardown pass return if f.exception(): future.set_exception(f.exception()) else: evts = dict(f.result()) future.set_result(evts.get(self, 0)) if f.done(): # hook up result if unwrap_result(f) else: f.add_done_callback(unwrap_result) return future def _add_timeout(self, future, timeout): def future_timeout(): if future.done(): # future already resolved, do nothing return # raise EAGAIN future.set_exception(_zmq.Again()) return self._call_later(timeout, future_timeout) def _call_later(self, delay, callback): return self.io_loop.call_later(delay, callback) @staticmethod def _remove_finished_future(future, event_list): for f_idx, event in enumerate(event_list): if event.future is future: break else: return # "future" instance is shared between sockets, but each socket has its own event list. event_list.remove(event_list[f_idx]) def _add_recv_event(self, kind, kwargs=None, future=None): f = future or self._Future() if kind.startswith('recv') and kwargs.get('flags', 0) & _zmq.DONTWAIT: # short-circuit non-blocking calls recv = getattr(self._shadow_sock, kind) try: r = recv(**kwargs) except Exception as e: f.set_exception(e) else: f.set_result(r) return f timer = _NoTimer if hasattr(_zmq, 'RCVTIMEO'): timeout_ms = self._shadow_sock.rcvtimeo if timeout_ms >= 0: timer = self._add_timeout(f, timeout_ms * 1e-3) # we add it to the list of futures before we add the timeout as the # timeout will remove the future from recv_futures to avoid leaks self._recv_futures.append(_FutureEvent(f, kind, kwargs, msg=None, timer=timer)) # Don't let the Future sit in _recv_events after it's done f.add_done_callback( lambda f: self._remove_finished_future(f, self._recv_futures) ) if self._shadow_sock.get(EVENTS) & POLLIN: # recv immediately, if we can self._handle_recv() if self._recv_futures: self._add_io_state(POLLIN) return f def _add_send_event(self, kind, msg=None, kwargs=None, future=None): f = future or self._Future() # attempt send with DONTWAIT if no futures are waiting # short-circuit for sends that will resolve immediately # only call if no send Futures are waiting if kind in ('send', 'send_multipart') and not self._send_futures: flags = kwargs.get('flags', 0) nowait_kwargs = kwargs.copy() nowait_kwargs['flags'] = flags | _zmq.DONTWAIT # short-circuit non-blocking calls send = getattr(self._shadow_sock, kind) # track if the send resolved or not # (EAGAIN if DONTWAIT is not set should proceed with) finish_early = True try: r = send(msg, **nowait_kwargs) except _zmq.Again as e: if flags & _zmq.DONTWAIT: f.set_exception(e) else: # EAGAIN raised and DONTWAIT not requested, # proceed with async send finish_early = False except Exception as e: f.set_exception(e) else: f.set_result(r) if finish_early: # short-circuit resolved, return finished Future # schedule wake for recv if there are any receivers waiting if self._recv_futures: self._schedule_remaining_events() return f timer = _NoTimer if hasattr(_zmq, 'SNDTIMEO'): timeout_ms = self._shadow_sock.get(_zmq.SNDTIMEO) if timeout_ms >= 0: timer = self._add_timeout(f, timeout_ms * 1e-3) # we add it to the list of futures before we add the timeout as the # timeout will remove the future from recv_futures to avoid leaks self._send_futures.append( _FutureEvent(f, kind, kwargs=kwargs, msg=msg, timer=timer) ) # Don't let the Future sit in _send_futures after it's done f.add_done_callback( lambda f: self._remove_finished_future(f, self._send_futures) ) self._add_io_state(POLLOUT) return f def _handle_recv(self): if not self._shadow_sock.get(EVENTS) & POLLIN: # event triggered, but state may have been changed between trigger and callback return f = None while self._recv_futures: f, kind, kwargs, _, timer = self._recv_futures.popleft() # skip any cancelled futures if f.done(): f = None else: break if not self._recv_futures: self._drop_io_state(POLLIN) if f is None: return timer.cancel() if kind == 'poll': # on poll event, just signal ready, nothing else. f.set_result(None) return elif kind == 'recv_multipart': recv = self._shadow_sock.recv_multipart elif kind == 'recv': recv = self._shadow_sock.recv else: raise ValueError("Unhandled recv event type: %r" % kind) kwargs['flags'] |= _zmq.DONTWAIT try: result = recv(**kwargs) except Exception as e: f.set_exception(e) else: f.set_result(result) def _handle_send(self): if not self._shadow_sock.get(EVENTS) & POLLOUT: # event triggered, but state may have been changed between trigger and callback return f = None while self._send_futures: f, kind, kwargs, msg, timer = self._send_futures.popleft() # skip any cancelled futures if f.done(): f = None else: break if not self._send_futures: self._drop_io_state(POLLOUT) if f is None: return timer.cancel() if kind == 'poll': # on poll event, just signal ready, nothing else. f.set_result(None) return elif kind == 'send_multipart': send = self._shadow_sock.send_multipart elif kind == 'send': send = self._shadow_sock.send else: raise ValueError("Unhandled send event type: %r" % kind) kwargs['flags'] |= _zmq.DONTWAIT try: result = send(msg, **kwargs) except Exception as e: f.set_exception(e) else: f.set_result(result) # event masking from ZMQStream def _handle_events(self, fd=0, events=0): zmq_events = self._shadow_sock.get(EVENTS) if zmq_events & _zmq.POLLIN: self._handle_recv() if zmq_events & _zmq.POLLOUT: self._handle_send() self._schedule_remaining_events() def _schedule_remaining_events(self, events=None): # edge-triggered handling # allow passing events in, in case this is triggered by retrieving events, # so we don't have to retrieve it twice. if self._state == 0: return if events is None: events = self._shadow_sock.get(EVENTS) if events & self._state: self._call_later(0, self._handle_events) def _add_io_state(self, state): if self._state != state: state = self._state = self._state | state self._update_handler(self._state) def _drop_io_state(self, state): if self._state & state: self._state = self._state & (~state) self._update_handler(self._state) def _update_handler(self, state): self._schedule_remaining_events() def _init_io_state(self): self.io_loop.add_handler(self._shadow_sock, self._handle_events, self._READ) self._call_later(0, self._handle_events) def _clear_io_state(self): fd = self._shadow_sock if self._shadow_sock.closed: fd = self._fd self.io_loop.remove_handler(fd)

true

f736712970df93b5bef6cd0469bab29486ab504a

12,232

py

Python

PythonProjects/09-MoreImplementingClasses/src/m2_baby_class.py

much2mutch/csse120-public

4f862a6deb7a5373fb5723fb2a23e4042e4d4157

[ "MIT" ]

null

PythonProjects/09-MoreImplementingClasses/src/m2_baby_class.py

much2mutch/csse120-public

4f862a6deb7a5373fb5723fb2a23e4042e4d4157

[ "MIT" ]

null

PythonProjects/09-MoreImplementingClasses/src/m2_baby_class.py

much2mutch/csse120-public

4f862a6deb7a5373fb5723fb2a23e4042e4d4157

[ "MIT" ]

null

# can't get the output to stop putting extra space after baby name before punctuation. """ A Baby class and functions that use/test it. Authors: Dave Fisher, David Mutchler, Vibha Alangar, Matt Boutell, Mark Hays, Amanda Stouder, Derek Whitley, their colleagues, and Seth Mutchler. """ # DONE: 1. PUT YOUR NAME IN THE ABOVE LINE. import random def main(): """ Runs the tests of the Baby class. """ print("UN-comment the following TESTS, one by one, when you are ready.") # UN-comment the following, one by one, when you are ready to TEST. run_test_1() run_test_2() ############################################################################### # Done: 2. In this module you will implement and test a Baby class. # Here is an OVERVIEW of the steps you will take to do so. # _ # Step 2 (this step): Read this overview of this module. # Step 3: Read and understand the SPECIFICATION for the Baby class. # Step 4: Read and understand the TESTS for the Baby class. # We supplied those tests. # Step 5: IMPLEMENT and TEST the Baby class. # _ # Once you understand this OVERVIEW, mark this _TODO_ as DONE. ############################################################################### ############################################################################### # DONE: 3. SPECIFICATION (read the following): # Here (below) are the methods that you must implement in your Baby class: # ---------------------------------------------------------------------------- # _ # Constructor method (that is, the __init__ method): # What comes in: # -- self # -- a string for the name of the Baby # What goes out: Nothing (i.e., None). # Side effects: # -- Prints "Hello baby <your baby's name>!" # -- Sets instance variables as needed # [YOU FIGURE OUT WHAT IS NEEDED AS YOU IMPLEMENT THE METHODS!] # Example: # b = Baby("McKinley") # causes the following to be printed on the Console: # Hello baby McKinley! # _ # feed_baby: # What comes in: # -- self # What goes out: Nothing (i.e., None). # Side effects: # -- Prints "Thank you for feeding baby <your baby's name>." # -- Modifies instance variables as needed. # Example: # b = Baby("Joshua") # b.feed_baby() # causes the following to be printed on the Console: # Hello baby Joshua! # Thank you for feeding baby Joshua. # _ # hour_passes # What comes in: # -- self # What goes out: Nothing (i.e., None). # Side effects: # -- If this is the FIRST time this method has been called # since this Baby was created or last fed, then this method prints: # "Baby <your baby's name> is sleeping." # _ # -- If this is the SECOND time this method has been called # since baby was created or last fed, then this method prints: # "Baby <your baby's name> is awake. Time for food." # _ # -- If this is the THIRD (OR MORE) time this method has been called # since baby was created or last fed, then this method prints: # "Baby <your baby's name> is CRYING uncontrollably! Feed the Baby!" # _ # -- Modifies instance variables as needed. # _ # Examples: See the two TEST functions below. # _ # You may find it helpful to read the two TEST functions (below) at this time. # If reading the TEST functions below does not make this specification clear, # ASK QUESTIONS AS NEEDED to clarify this specification. # _ # Once you understand this SPECIFICATION, mark this _TODO_ as DONE. ############################################################################### ############################################################################### # DONE: 4. TESTS (read the following): # The two functions that follow this comment TEST the Baby class. # For each of those two functions: # 1. READ the CODE in the function. # As you do so, PREDICT what the code will cause to be printed. # 2. READ the doc-string for the function. # It shows the CORRECT output when the function runs. # 3. CONFIRM that you understand WHY the function's CODE produces # the OUTPUT that the doc-string says that it will. # _ # If you do not understand why the CODE produces the OUTPUT as written # in the function's doc-string, STOP HERE and ASK QUESTIONS AS NEEDED. # Do ** NOT ** attempt to write the Baby class # without fully understanding both of its test functions. # _ # Once you fully understand the TESTS below, mark this _TODO_ as DONE. ############################################################################### def run_test_1(): """ Running this test should cause EXACTLY the following to be displayed (i.e. printed) on the Console: ------------ Running test #1: ------------ Hello baby Joshua! Baby Joshua is sleeping. Baby Joshua is awake. Time for food. Baby Joshua is CRYING uncontrollably! Feed the Baby! Baby Joshua is CRYING uncontrollably! Feed the Baby! Thank you for feeding baby Joshua. Baby Joshua is sleeping. Baby Joshua is awake. Time for food. Thank you for feeding baby Joshua. Baby Joshua is sleeping. Thank you for feeding baby Joshua. Baby Joshua is sleeping. Baby Joshua is awake. Time for food. Baby Joshua is CRYING uncontrollably! Feed the Baby! Examine the code in this test to be sure that you understand WHY it causes the above to be printed. """ print() print('------------ Running test #1: ------------ ') b = Baby("Joshua") b.hour_passes() b.hour_passes() b.hour_passes() b.hour_passes() print() # Just to make the output easier to read. b.feed_baby() b.hour_passes() b.hour_passes() print() # Just to make the output easier to read. b.feed_baby() b.hour_passes() print() # Just to make the output easier to read. b.feed_baby() b.hour_passes() b.hour_passes() b.hour_passes() def run_test_2(): """ Running this test should cause EXACTLY the following to be displayed (i.e. printed) on the Console: ------------ Running test #2: ------------ Hello baby McKinley! Hello baby Keegan! --- Iteration #1 --- Baby Keegan is sleeping. Thank you for feeding baby McKinley. Baby McKinley is sleeping. Baby McKinley is awake. Time for food. Baby McKinley is CRYING uncontrollably! Feed the Baby! Baby McKinley is CRYING uncontrollably! Feed the Baby! Thank you for feeding baby McKinley. Baby McKinley is sleeping. Baby McKinley is awake. Time for food. --- Iteration #2 --- Baby Keegan is awake. Time for food. Thank you for feeding baby McKinley. Baby McKinley is sleeping. Baby McKinley is awake. Time for food. Baby McKinley is CRYING uncontrollably! Feed the Baby! Baby McKinley is CRYING uncontrollably! Feed the Baby! Thank you for feeding baby McKinley. Baby McKinley is sleeping. Baby McKinley is awake. Time for food. --- Iteration #3 --- Baby Keegan is CRYING uncontrollably! Feed the Baby! Thank you for feeding baby McKinley. Baby McKinley is sleeping. Baby McKinley is awake. Time for food. Baby McKinley is CRYING uncontrollably! Feed the Baby! Baby McKinley is CRYING uncontrollably! Feed the Baby! Thank you for feeding baby McKinley. Baby McKinley is sleeping. Baby McKinley is awake. Time for food. Examine the code in this test to be sure that you understand WHY it causes the above to be printed. """ print() print('------------ Running test #2: ------------ ') mckinley = Baby("McKinley") keegan = Baby("Keegan") for k in range(3): print() # Just to make the output easier to read. print("--- Iteration #{} ---".format(k + 1)) keegan.hour_passes() mckinley.feed_baby() for j in range(4): mckinley.hour_passes() mckinley.feed_baby() mckinley.hour_passes() mckinley.hour_passes() ############################################################################### # TODO: 5. # Implement the entire Baby class # (including its 3 methods: __init__, feed_baby, and hour_passes) # below this comment. # _ # Here is a reminder for the syntax (notation) to define a new class: # class NameOfClass(object): # """ Brief description of what an object of the class 'is'. """ # _ # AFTER you have implemented the ENTIRE Baby class, # un-comment (one-by-one) the calls in main to the two tests # and confirm that the tests produce the output that the doc-strings # for the tests show as the CORRECT output. # _ # Fix errors as needed! Do not hesitate to ASK QUESTIONS AS NEEDED. ############################################################################### class Baby(object): """ generates baby that eats, sleeps, and has a name""" # Constructor method (that is, the __init__ method): # What comes in: # -- self # -- a string for the name of the Baby # What goes out: Nothing (i.e., None). # Side effects: # -- Prints "Hello baby <your baby's name>!" # -- Sets instance variables as needed # [YOU FIGURE OUT WHAT IS NEEDED AS YOU IMPLEMENT THE METHODS!] # Example: # b = Baby("McKinley") # causes the following to be printed on the Console: # Hello baby McKinley! def __init__(self, baby): self.baby = baby print("Hello baby", self.baby, "!") self.hour_since_feeding = 0 # _ # feed_baby: # What comes in: # -- self # What goes out: Nothing (i.e., None). # Side effects: # -- Prints "Thank you for feeding baby <your baby's name>." # -- Modifies instance variables as needed. # Example: # b = Baby("Joshua") # b.feed_baby() # causes the following to be printed on the Console: # Hello baby Joshua! # Thank you for feeding baby Joshua. # _ def feed_baby(self): print("Thank you for feeding baby", self.baby, ".") self.hour_since_feeding = 0 # hour_passes # What comes in: # -- self # What goes out: Nothing (i.e., None). # Side effects: # -- If this is the FIRST time this method has been called # since this Baby was created or last fed, then this method prints: # "Baby <your baby's name> is sleeping." # _ # -- If this is the SECOND time this method has been called # since baby was created or last fed, then this method prints: # "Baby <your baby's name> is awake. Time for food." # _ # -- If this is the THIRD (OR MORE) time this method has been called # since baby was created or last fed, then this method prints: # "Baby <your baby's name> is CRYING uncontrollably! Feed the Baby!" # _ # -- Modifies instance variables as needed. def hour_passes(self): if self.hour_since_feeding == 0: print("Baby", self.baby, "is sleeping.") else: if self.hour_since_feeding == 1: print("Baby", self.baby, "is awake. Time for food.") else: print("Baby", self.baby, "is CRYING uncontrollably! Feed the Baby!") self.hour_since_feeding = self.hour_since_feeding + 1 # ----------------------------------------------------------------------------- # Calls main to start the ball rolling. # ----------------------------------------------------------------------------- main()

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import random def main(): print("UN-comment the following TESTS, one by one, when you are ready.") # UN-comment the following, one by one, when you are ready to TEST. run_test_1() run_test_2() ############################################################################### # Done: 2. In this module you will implement and test a Baby class. # Here is an OVERVIEW of the steps you will take to do so. # _ # Step 2 (this step): Read this overview of this module. # Step 3: Read and understand the SPECIFICATION for the Baby class. # Step 4: Read and understand the TESTS for the Baby class. # We supplied those tests. # Step 5: IMPLEMENT and TEST the Baby class. # _ # Once you understand this OVERVIEW, mark this _TODO_ as DONE. ############################################################################### ############################################################################### # DONE: 3. SPECIFICATION (read the following): # Here (below) are the methods that you must implement in your Baby class: # ---------------------------------------------------------------------------- # _ # Constructor method (that is, the __init__ method): # What comes in: # -- self # -- a string for the name of the Baby # What goes out: Nothing (i.e., None). # Side effects: # -- Prints "Hello baby <your baby's name>!" # -- Modifies instance variables as needed. # Example: # b = Baby("Joshua") # b.feed_baby() # causes the following to be printed on the Console: # Hello baby Joshua! # Thank you for feeding baby Joshua. # _ # hour_passes # What comes in: # -- self # What goes out: Nothing (i.e., None). # Side effects: # -- If this is the FIRST time this method has been called # since this Baby was created or last fed, then this method prints: # "Baby <your baby's name> is sleeping." # _ # -- If this is the THIRD (OR MORE) time this method has been called # since baby was created or last fed, then this method prints: # "Baby <your baby's name> is CRYING uncontrollably! Feed the Baby!"

true