xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

8d60c377538ddae6447654f6c37f24bae517225c

3,629

py

Python

convert.py

Ellen7ions/bin2mem

51e3216cbf5e78547751968ef1619a925f2f55ef

[ "MIT" ]

3

2021-05-18T13:07:39.000Z

2021-05-24T12:46:43.000Z

convert.py

Ellen7ions/bin2mem

51e3216cbf5e78547751968ef1619a925f2f55ef

[ "MIT" ]

null

convert.py

Ellen7ions/bin2mem

51e3216cbf5e78547751968ef1619a925f2f55ef

[ "MIT" ]

null

import os, sys import json class Config: def __init__(self, config_path='./config.json'): super(Config, self).__init__() self.config_path = config_path self.bin2mem_path = None self.init_configs(json.load(open(config_path))) def init_configs(self, json_data): self.bin2mem_path = json_data['bin2mem.path'] Config.check_file_exists(self.bin2mem_path) @staticmethod def check_file_exists(file_name): if not os.path.exists(file_name): raise Exception(f'{file_name} not found!') class Convert: def __init__(self): super(Convert, self).__init__() self.config = Config() self.FLAG_SAVE_FILES = False self.FLAG_FILE_NAME = '' self.FLAG_CLEAN_ALL = False self.workspace_name = '' self.file_name = '' self.o_file_path = '' self.bin_file_path = '' self.coe_file_path = '' self.init_flags() self.make_workspace() self.set_files_path() def init_flags(self): for i in sys.argv: if i == '-s': self.FLAG_SAVE_FILES = True if i.endswith('.s'): self.FLAG_FILE_NAME = i if i == 'clean': self.FLAG_CLEAN_ALL = True if self.FLAG_FILE_NAME == '': if os.path.exists('main.s'): self.FLAG_FILE_NAME = 'main.s' else: raise Exception('Where is your input file :(') self.workspace_name = self.FLAG_FILE_NAME[:-2] self.file_name = self.FLAG_FILE_NAME[:-2] def make_workspace(self): if not os.path.exists(self.workspace_name): os.mkdir(self.workspace_name) def set_files_path(self): self.o_file_path = f'.\\{self.workspace_name}\\{self.file_name}.o' self.bin_file_path = f'.\\{self.workspace_name}\\{self.file_name}.bin' self.coe_file_path = f'.\\{self.workspace_name}\\{self.file_name}.txt' def mips_gcc_c(self): os.system(f'mips-sde-elf-gcc -c {self.FLAG_FILE_NAME} -o {self.o_file_path}') def mips_objcopy(self): os.system(f'mips-sde-elf-objcopy -O binary {self.o_file_path} {self.bin_file_path}') def mips_bin2mem(self): os.system(f'{self.config.bin2mem_path} {self.bin_file_path} {self.coe_file_path}') def clean_process_files(self): try: Config.check_file_exists(self.o_file_path) os.system(f'del {self.o_file_path}') except Exception as e: pass try: Config.check_file_exists(self.bin_file_path) os.system(f'del {self.bin_file_path}') except Exception as e: pass def run(self): self.mips_gcc_c() self.mips_objcopy() self.mips_bin2mem() def clean(self): self.clean_process_files() try: Config.check_file_exists(self.coe_file_path) os.system(f'del {self.coe_file_path}') except Exception as e: pass os.removedirs(self.workspace_name) def mips_objdump(self): if os.path.exists(self.o_file_path): os.system(f'mips-sde-elf-objdump -d {self.o_file_path}') def apply(self): if self.FLAG_CLEAN_ALL: self.clean() return self.run() if not self.FLAG_SAVE_FILES: self.clean_process_files() return self.mips_objdump() if __name__ == '__main__': c = Convert() c.apply() # c.mips_gcc_c() # c.mips_objcopy() # c.mips_bin2mem() # config = Config()

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491

3,629

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0.076459

0.06841

0.052314

0.399396

0.312374

0.234406

0.13833

0.091549

0

0.003882

0.290163

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0.767857

0.018187

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false

0.03125

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0.21875

0

null

0

null

0

1

0

8d61a4b35ddf035024fe7d951c745cb83a2a9d4d

3,161

py

Python

stats.py

DisinfoResearch/TwitterCollector

183b6761cca54b5db5b98a2f9f86bd8bcc98a7cb

[ "MIT" ]

null

stats.py

DisinfoResearch/TwitterCollector

183b6761cca54b5db5b98a2f9f86bd8bcc98a7cb

[ "MIT" ]

null

stats.py

DisinfoResearch/TwitterCollector

183b6761cca54b5db5b98a2f9f86bd8bcc98a7cb

[ "MIT" ]

null

#!/bin/python3 # Copyright (C) 2021, Michigan State University. # # 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 csv import json import argparse import sys import datetime from dateutil.parser import parse def calc_row(u): created_date = parse(u['created_at']) t = today - created_date.date() # Prevent divide by zero ff_ratio = 0 if int(u['friends_count']) != 0: ff_ratio = int(u['followers_count'])/int(u['friends_count']) # Force conversions to int, as you never know with Twitter return {'Twitter_ID':u['id'], 'Handle':u['screen_name'], 'Followed':u['friends_count'], 'Followers':u['followers_count'], 'Followers/Followed':ff_ratio, 'Tweets':u['statuses_count'], 'Days_old':int(t.days), 'Tweets/Days_old':int(u['statuses_count'])/int(t.days), 'Followers/Days_old':int(u['followers_count'])/int(t.days)} def process_csv(inp, out): # Uses a Tuple to ensure a specific column order csv_writer = csv.DictWriter(out, fieldnames=('Twitter_ID', 'Handle', 'Followed', 'Followers', 'Followers/Followed', 'Tweets', 'Days_old', 'Tweets/Days_old', 'Followers/Days_old')) csv_writer.writeheader() for line in inp: csv_writer.writerow(calc_row(json.loads(line))) def process_json(inp, out): for line in inp: j = json.loads(line) out.write(json.dumps(calc_row(j))+"\n") parser = argparse.ArgumentParser(description='Convert JSON to CSV', epilog='P.S. Trust The Plan') parser.add_argument('--format', help='either JSON or CSV', required=True) parser.add_argument('input', help='JSON File, or stdin if not specified', type=argparse.FileType('r', encoding='utf-8'), default=sys.stdin) parser.add_argument('output', help='output to File, or stdout if not specified', type=argparse.FileType('w', encoding='utf-8'), default=sys.stdout) args = parser.parse_args() today = datetime.date.today() if args.format.upper() == 'CSV': process_csv(args.input, args.output) elif args.format.upper() == 'JSON': process_json(args.input, args.output) else: print(f"Error: '{args.format}' is an invalid format, must be CSV or JSON.", end="\n\n") parser.print_help() exit(-1)

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326

0.726669

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3,161

4.716075

0.425887

0.038955

0.017264

0.014166

0.068172

0.030102

0

0.003709

0.147105

3,161

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45.157143

0.834199

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false

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0.057143

0

null

0

null

0

1

0

8d61d1b5d6b0de975b9d576cfadcd886cc44204a

10,970

py

Python

Scratch/lstm.py

imadtoubal/MultimodalDeepfakeDetection

46539e16c988ee9fdfb714893788bbbf72836595

[ "MIT" ]

2

2022-03-12T09:18:13.000Z

2022-03-23T08:29:10.000Z

Scratch/lstm.py

imadtoubal/MultimodalDeepfakeDetection

46539e16c988ee9fdfb714893788bbbf72836595

[ "MIT" ]

null

Scratch/lstm.py

imadtoubal/MultimodalDeepfakeDetection

46539e16c988ee9fdfb714893788bbbf72836595

[ "MIT" ]

null

import torch from torch import nn import torch.nn.functional as F import torch.optim as optim from preprocess import * from torch.utils.data import Dataset, DataLoader from blazeface import BlazeFace import os import cv2 import numpy as np from matplotlib import pyplot as plt import random import pickle DATA_FOLDER = '../input/deepfake-detection-challenge' TRAIN_SAMPLE_FOLDER = 'train_sample_videos' TEST_FOLDER = 'test_videos' device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") NET = BlazeFace().to(device) NET.load_weights("../input/blazeface.pth") NET.load_anchors("../input/anchors.npy") class MyLSTM(nn.Module): def __init__(self, num_layers=2, num_hidden_nodes=512): super(MyLSTM, self).__init__() self.num_layers = num_layers self.num_hidden_nodes = num_hidden_nodes # input dim is 167, output 200 self.lstm = nn.LSTM(167, num_hidden_nodes, batch_first=True, num_layers=num_layers) # fully connected self.fc1 = nn.Linear(num_hidden_nodes, num_hidden_nodes) self.act = nn.Sigmoid() self.fc2 = nn.Linear(num_hidden_nodes, 2) self.softmax = nn.Softmax() def forward(self, x, hidden): y, hidden = self.lstm(x, hidden) # returns the two outputs y = y[:, -1, :] # get only the last output y = self.fc1(y) y = self.fc2(y) y = F.softmax(y, dim=1) return y, hidden def init_hidden(self, batch_size): weight = next(self.parameters()).data hidden = (weight.new(self.num_layers, batch_size, self.num_hidden_nodes).zero_(), weight.new(self.num_layers, batch_size, self.num_hidden_nodes).zero_()) return hidden class FourierDataset(Dataset): def __init__(self, data): """ data: a list of (label: string, fourier_data: numpy array, name: string) """ self.data = [] for elt in data: label, spects, name = elt label = torch.tensor(0 if label == 'FAKE' else 1) # Moving window sequence generation without overalap # other ideas: 1. Random sampling, 2. Moving qindow with overlap # this data will be shuffled for i in range(0, 24 * (spects.shape[0] // 24), 24): spect = torch.tensor(spects[i:i+24, :]) self.data.append((spect, label)) def __getitem__(self, idx): return self.data[idx] # spect (24, 167), label (2) def __len__(self): return len(self.data) sequence = 24 # 1 sec of video feature_size = 167 # length of spatial frequency def read_video(filename): vidcap = cv2.VideoCapture(filename) success, image = vidcap.read() count = 0 images = [] while success: tiles, resize_info = stride_search(image) detections = NET.predict_on_image(tiles[1]) blazeface_endpoints = get_face_endpoints(tiles[1], detections)[ 0] # take the first face only # we need to resize them on the original image and get the amount shifted to prevent negative values # in this case it will be 1080 split_size = 128 * resize_info[1] # determine how much we shifted for this tile x_shift = (image.shape[1] - split_size) // 2 face_endpoints = (int(blazeface_endpoints[0] * resize_info[0]), int(blazeface_endpoints[1] * resize_info[0] + x_shift), int(blazeface_endpoints[2] * resize_info[0]), int(blazeface_endpoints[3] * resize_info[0] + x_shift)) # next we need to expand the rectangle to be 240, 240 pixels (for this training example) # we can do this equally in each direction, kind of face_width = face_endpoints[3] - face_endpoints[1] face_height = face_endpoints[2] - face_endpoints[0] buffer = 20 face_box = image[max(0, face_endpoints[0] - buffer): min(face_endpoints[2] + buffer, image.shape[0]), max(0, face_endpoints[1] - buffer): min(face_endpoints[3] + buffer, image.shape[1])] # print(face_box.shape) # almost a square or very close to it face = cv2.resize(face_box, (240, 240)) images.append(face) # cv2.imshow("face", face) success, image = vidcap.read() count += 1 if cv2.waitKey(1) & 0xFF == ord('q'): break if images: return np.stack(images) def get_spects(vid): spects = [] for i in range(vid.shape[0]): img = vid[i] spects.append(fourier_tranform(img, '')) return np.stack(spects) def get_face_endpoints(img, detections, with_keypoints=False): if isinstance(detections, torch.Tensor): detections = detections.cpu().numpy() if detections.ndim == 1: detections = np.expand_dims(detections, axis=0) detected_faces_endpoints = [] for i in range(detections.shape[0]): # dependent on number of faces found ymin = detections[i, 0] * img.shape[0] xmin = detections[i, 1] * img.shape[1] ymax = detections[i, 2] * img.shape[0] xmax = detections[i, 3] * img.shape[1] detected_faces_endpoints.append((ymin, xmin, ymax, xmax)) cv2.rectangle(img, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 0, 255), 2) if with_keypoints: for k in range(6): kp_x = detections[i, 4 + k*2] * img.shape[1] kp_y = detections[i, 4 + k*2 + 1] * img.shape[0] circle = patches.Circle((kp_x, kp_y), radius=0.5, linewidth=1, edgecolor="lightskyblue", facecolor="none", alpha=detections[i, 16]) return detected_faces_endpoints def prepare_data(): # Here we check the train data files extensions. train_list = list(os.listdir( os.path.join(DATA_FOLDER, TRAIN_SAMPLE_FOLDER))) ext_dict = [] for file in train_list: file_ext = file.split('.')[1] if (file_ext not in ext_dict): ext_dict.append(file_ext) print(f"Extensions: {ext_dict}") # Let's count how many files with each extensions there are. for file_ext in ext_dict: print( f"Files with extension `{file_ext}`: {len([file for file in train_list if file.endswith(file_ext)])}") test_list = list(os.listdir(os.path.join(DATA_FOLDER, TEST_FOLDER))) ext_dict = [] for file in test_list: file_ext = file.split('.')[1] if (file_ext not in ext_dict): ext_dict.append(file_ext) print(f"Extensions: {ext_dict}") for file_ext in ext_dict: print( f"Files with extension `{file_ext}`: {len([file for file in train_list if file.endswith(file_ext)])}") json_file = [file for file in train_list if file.endswith('json')][0] print(f"JSON file: {json_file}") meta_train_df = get_meta_from_json(TRAIN_SAMPLE_FOLDER, json_file) meta_train_df.head() fake_train_sample_video = list( meta_train_df.loc[meta_train_df.label == 'FAKE'].sample(90).index) real_train_sample_video = list( meta_train_df.loc[meta_train_df.label == 'REAL'].index) training_data = [] for video_file in fake_train_sample_video: try: data = process_video_data(os.path.join( DATA_FOLDER, TRAIN_SAMPLE_FOLDER, video_file)) training_data.append(('FAKE', data, video_file)) # (X, 24, 167) except: continue for video_file in real_train_sample_video: try: data = process_video_data(os.path.join( DATA_FOLDER, TRAIN_SAMPLE_FOLDER, video_file)) training_data.append(('REAL', data, video_file)) except: continue random.shuffle(training_data) with open('train_data.txt', 'wb') as fp: # pickling pickle.dump(training_data, fp) return training_data def read_data(): with open("train_data.txt", "rb") as fp: # Unpickling training_data = pickle.load(fp) return training_data def process_video_data(video_file): stack = read_video(video_file) stack = stack.mean(axis=-1) / 255 return get_spects(stack) def prepare_spect(spect): return torch.tensor(spect) def convert_scores(label): return torch.tensor([1, 0]) if label == 'FAKE' else torch.tensor([0, 1]) def train(training_data): batch_size = 69 model = MyLSTM() loss_function = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) training_data = FourierDataset(training_data) trainloader = DataLoader( training_data, batch_size=batch_size, shuffle=True) hidden = model.init_hidden(batch_size) print_every = 10 for epoch in range(100): # again, normally you would NOT do 100 epochs, it is toy data running_loss = 0.0 running_acc = 0.0 i = 0 for inp, labels in trainloader: # renamed sequence to inp because inp is a batch of sequences # Step 1. Remember that Pytorch accumulates gradients. # We need to clear them out before each instance model.zero_grad() inp = inp.float() # Step 2. Run our forward pass. tag_scores, h = model(inp, hidden) # Step 3. Compute the loss, gradients, and update the parameters by # calling optimizer.step() loss = loss_function(tag_scores, labels) loss.backward() optimizer.step() running_acc += torch.mean((tag_scores.argmax(dim=1) == labels).float()).item() # print statistics running_loss += loss.item() if i % print_every == print_every-1: print('[%d, %5d] loss: %.3f - acc: %.3f' % (epoch + 1, i + 1, running_loss / print_every, running_acc * 100 / print_every)) running_loss = 0.0 running_acc = 0.0 i += 1 def main(): # prepare_data() ''' stack = read_video(os.path.join(DATA_FOLDER, TRAIN_SAMPLE_FOLDER, 'aagfhgtpmv.mp4')) print(stack.shape) stack = stack.mean(axis=-1) / 255 spects = get_spects(stack) # print(spects.shape) print(spects[0]) plt.plot(spects[0]) plt.xlabel('Spatial Frequency') plt.ylabel('Power Spectrum') plt.show() ''' training_data = read_data() train(training_data) if __name__ == '__main__': main()

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0.150241

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0

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0.301094

10,970

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1

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false

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null

0

null

0

1

0

8d63217e5fdc8f7f711034a43dd2b7d398591281

18,373

py

Python

analysis/plot/python/plot_groups/estimator.py

leozz37/makani

c94d5c2b600b98002f932e80a313a06b9285cc1b

[ "Apache-2.0" ]

1,178

2020-09-10T17:15:42.000Z

2022-03-31T14:59:35.000Z

analysis/plot/python/plot_groups/estimator.py

leozz37/makani

c94d5c2b600b98002f932e80a313a06b9285cc1b

[ "Apache-2.0" ]

1

2020-05-22T05:22:35.000Z

analysis/plot/python/plot_groups/estimator.py

leozz37/makani

c94d5c2b600b98002f932e80a313a06b9285cc1b

[ "Apache-2.0" ]

107

2020-09-10T17:29:30.000Z

2022-03-18T09:00:14.000Z

# Copyright 2020 Makani Technologies LLC # # 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. """Plots relating to the estimator.""" from makani.analysis.plot.python import mplot from makani.avionics.common import plc_messages from makani.control import control_types from makani.lib.python import c_helpers from makani.lib.python.h5_utils import numpy_utils from matplotlib.pyplot import plot from matplotlib.pyplot import yticks import numpy as np from scipy import interpolate MFig = mplot.PlotGroup.MFig # pylint: disable=invalid-name _WING_GPS_RECEIVER_HELPER = c_helpers.EnumHelper( 'WingGpsReceiver', control_types) _GROUND_STATION_MODE_HELPER = c_helpers.EnumHelper( 'GroundStationMode', plc_messages) def _QuatToVec(q): dims = ['q0', 'q1', 'q2', 'q3'] return np.array([q[d] for d in dims]) class Plots(mplot.PlotGroup): """Plots of the estimator.""" @MFig(title='Filtered Velocity', ylabel='Velocity [m/s]', xlabel='Time [s]') def PlotFilteredVelocity(self, e, c, s, params): mplot.PlotVec3(c['time'], c['state_est']['Vg'], label='Vg', linestyle='-') mplot.PlotVec3(c['time'], c['state_est']['Vg_f'], label='Vg_f', linestyle='-.') if s is not None: mplot.PlotVec3(s['time'], s['wing']['Vg'], label='sim', linestyle=':') @MFig(title='Acc Norm f', ylabel='Acc. [m/s^2]', xlabel='Time [s]') def PlotAccNormF(self, e, c, s, params, imu_index=0): plot(c['time'], c['state_est']['acc_norm_f']) @MFig(title='Gyros', ylabel='Rate [rad/s]', xlabel='Time [s]') def PlotGyros(self, e, c, s, params, imu_index=0): mplot.PlotVec3(c['time'], c['control_input']['imus']['gyro'][:, imu_index]) @MFig(title='Filtered Body Rates', ylabel='Rate [rad/s]', xlabel='Time [s]') def PlotBodyRates(self, e, c, s, params): mplot.PlotVec3(c['time'], c['state_est']['pqr_f']) @MFig(title='Attitude Error', ylabel='Error [deg]', xlabel='Time [s]') def PlotAttitudeError(self, e, c, s, params): for imu_index in range(3): if s is not None: dims = ['q0', 'q1', 'q2', 'q3'] q_s = {d: np.zeros(c['time'].shape) for d in dims} for d in dims: q_s[d] = interpolate.interp1d(s['time'], s['wing']['q'][d], bounds_error=False)(c['time']) q_s = _QuatToVec(q_s) if 'q_g2b' in e.dtype.names: q_c = e['q_g2b'][:, imu_index] q_c = _QuatToVec(q_c) plot(c['time'], np.rad2deg( np.arccos(1.0 - 2.0 * (1.0 - np.sum(q_c * q_s, axis=0)**2.0))), label='Imu %d' % imu_index) if 'mahony_states' in e.dtype.names: q_c = e['mahony_states']['q'][:, imu_index] q_c = _QuatToVec(q_c) plot(c['time'], np.rad2deg( np.arccos(1.0 - 2.0 * (1.0 - np.sum(q_c * q_s, axis=0)**2.0))), label='Imu %d' % imu_index) @MFig(title='Gyro Biases', ylabel='Biases [rad/s]', xlabel='Time [s]') def PlotGyroBiases(self, e, c, s, params, imu_index=0): mplot.PlotVec3(c['time'], e['gyro_biases'][:, imu_index], label='IMU %d' % imu_index) if s is not None: mplot.PlotVec3(s['time'], s['imus']['gyro_bias_b'][:, imu_index], linestyle=':') @MFig(title='Acc Biases', ylabel='Biases [m/s^1]', xlabel='Time [s]') def PlotAccBiases(self, e, c, s, params, imu_index=0): mplot.PlotVec3(c['time'], e['acc_b_estimates'][:, imu_index], label='IMU %d' % imu_index) @MFig(title='Air Speed', ylabel='Speed [m/s]', xlabel='Time [s]') def PlotAirspeed(self, e, c, s, params): plot(c['time'], c['state_est']['apparent_wind']['sph']['v'], 'b', label='est') plot(c['time'], c['state_est']['apparent_wind']['sph_f']['v'], 'g', label='filt') if s is not None: plot(s['time'], s['wing']['apparent_wind_b']['v'], 'b:', label='sim') @MFig(title='Magnetometer', ylabel='Field [Gauss]', xlabel='Time [s]') def PlotMagnetometer(self, e, c, s, params): mplot.PlotVec3(c['time'], c['control_input']['imus']['mag'][:, 0], linestyle='-', label='A') mplot.PlotVec3(c['time'], c['control_input']['imus']['mag'][:, 1], linestyle=':', label='B') mplot.PlotVec3(c['time'], c['control_input']['imus']['mag'][:, 2], linestyle='-.', label='C') @MFig(title='Specific Force', ylabel='Specific Force [m/s^2]', xlabel='Time [s]') def PlotAccelerometer(self, e, c, s, params): mplot.PlotVec3(c['time'], c['control_input']['imus']['acc'][:, 0], linestyle='-', label='A') mplot.PlotVec3(c['time'], c['control_input']['imus']['acc'][:, 1], linestyle=':', label='B') mplot.PlotVec3(c['time'], c['control_input']['imus']['acc'][:, 2], linestyle='-.', label='C') @MFig(title='Accel.', ylabel='Specific Force [m/s^2]', xlabel='Time [s]') def PlotSpecificForce(self, e, c, s, params): mplot.PlotVec3(c['time'], c['estimator']['acc_b_estimates'][:, 0], linestyle='-', label='A') mplot.PlotVec3(c['time'], c['estimator']['acc_b_estimates'][:, 1], linestyle=':', label='B') mplot.PlotVec3(c['time'], c['estimator']['acc_b_estimates'][:, 2], linestyle='-.', label='C') @MFig(title='Magnetometer Diff', ylabel='Field [Gauss]', xlabel='Time [s]') def PlotMagnetometerDiff(self, e, c, s, params, dimension='x'): plot(c['time'], c['control_input']['imus']['mag'][dimension][:, 0] - c['control_input']['imus']['mag'][dimension][:, 1], 'b', label='A-B ' + dimension) plot(c['time'], c['control_input']['imus']['mag'][dimension][:, 1] - c['control_input']['imus']['mag'][dimension][:, 2], 'g', label='B-C ' + dimension) plot(c['time'], c['control_input']['imus']['mag'][dimension][:, 2] - c['control_input']['imus']['mag'][dimension][:, 0], 'r', label='C-A ' + dimension) @MFig(title='Current GPS', ylabel='GPS Receiver', xlabel='Time [s]') def PlotGpsReceiver(self, e, c, s, params): plot(c['time'], e['current_gps_receiver'], label='current_receiver') yticks(_WING_GPS_RECEIVER_HELPER.Values(), _WING_GPS_RECEIVER_HELPER.ShortNames()) def _PlotGpsPositionEcefChannel(self, c, d): sigma = c['control_input']['wing_gps']['pos_sigma'][d] wing_gps_pos = np.array(c['control_input']['wing_gps']['pos'][d]) wing_gps_pos[wing_gps_pos == 0] = float('nan') plot(c['time'], wing_gps_pos[:, 0], 'b', label='0:%s ECEF' % d) plot(c['time'], wing_gps_pos[:, 0] + sigma[:, 0], 'b:') plot(c['time'], wing_gps_pos[:, 0] - sigma[:, 0], 'b:') plot(c['time'], wing_gps_pos[:, 1], 'g', label='1:%s ECEF' % d) plot(c['time'], wing_gps_pos[:, 1] + sigma[:, 1], 'g:') plot(c['time'], wing_gps_pos[:, 1] - sigma[:, 1], 'g:') @MFig(title='GPS Position ECEF', ylabel='Position [m]', xlabel='Time [s]') def PlotGpsPositionEcefX(self, e, c, s, params): self._PlotGpsPositionEcefChannel(c, 'x') @MFig(title='GPS Position ECEF', ylabel='Position [m]', xlabel='Time [s]') def PlotGpsPositionEcefY(self, e, c, s, params): self._PlotGpsPositionEcefChannel(c, 'y') @MFig(title='GPS Position ECEF', ylabel='Position [m]', xlabel='Time [s]') def PlotGpsPositionEcefZ(self, e, c, s, params): self._PlotGpsPositionEcefChannel(c, 'z') @MFig(title='Kite Velocity Sigma', ylabel='Sigma Velocity [m/s]', xlabel='Time [s]') def PlotVelocitySigmas(self, e, c, s, params, plot_glas=True): if 'cov_vel_g' in e.dtype.names: plot(c['time'], e['cov_vel_g']['x']**0.5, 'b', label='Vg_x est') plot(c['time'], e['cov_vel_g']['y']**0.5, 'g', label='Vg_y est') plot(c['time'], e['cov_vel_g']['z']**0.5, 'r', label='Vg_z est') if 'gps' in e.dtype.names: aux_indices = np.argwhere(e['current_gps_receiver'] == 1) vg = e['gps']['sigma_Vg'][:, 0] vg[aux_indices] = e['gps']['sigma_Vg'][aux_indices, 1] plot(c['time'], vg['x'], 'b-.', label='Vg_x gps') plot(c['time'], vg['y'], 'g-.', label='Vg_y gps') plot(c['time'], vg['z'], 'r-.', label='Vg_z gps') @MFig(title='Kite Position Sigma', ylabel='Sigma Position [m]', xlabel='Time [s]') def PlotPositionSigmas(self, e, c, s, params, plot_glas=True): if 'cov_vel_g' in e.dtype.names: plot(c['time'], e['cov_pos_g']['x']**0.5, 'b', label='Xg_x est') plot(c['time'], e['cov_pos_g']['y']**0.5, 'g', label='Xg_y est') plot(c['time'], e['cov_pos_g']['z']**0.5, 'r', label='Xg_z est') if 'gps' in e.dtype.names: aux_indices = np.argwhere(e['current_gps_receiver'] == 1) xg = e['gps']['sigma_Xg'][:, 0] xg[aux_indices] = e['gps']['sigma_Xg'][aux_indices, 1] plot(c['time'], xg['x'], 'b-.', label='Xg_x gps') plot(c['time'], xg['y'], 'g-.', label='Xg_y gps') plot(c['time'], xg['z'], 'r-.', label='Xg_z gps') @MFig(title='Kite Velocity', ylabel='Velocity [m/s]', xlabel='Time [s]') def PlotVelocity(self, e, c, s, params, plot_glas=True): plot(c['time'], c['state_est']['Vg']['x'], 'b', label='Vg_x est') plot(c['time'], c['state_est']['Vg']['y'], 'g', label='Vg_y est') plot(c['time'], c['state_est']['Vg']['z'], 'r', label='Vg_z est') if 'Vg_gps' in e.dtype.names: plot(c['time'], e['Vg_gps']['x'], 'b-.', label='Vg_x gps') plot(c['time'], e['Vg_gps']['y'], 'g-.', label='Vg_y gps') plot(c['time'], e['Vg_gps']['z'], 'r-.', label='Vg_z gps') if 'gps' in e.dtype.names: aux_indices = np.argwhere(e['current_gps_receiver'] == 1) vg = e['gps']['Vg'][:, 0] vg[aux_indices] = e['gps']['Vg'][aux_indices, 1] plot(c['time'], vg['x'], 'b-.', label='Vg_x gps') plot(c['time'], vg['y'], 'g-.', label='Vg_y gps') plot(c['time'], vg['z'], 'r-.', label='Vg_z gps') if plot_glas and 'Vg_glas' in e.dtype.names: plot(c['time'], e['Vg_glas']['x'], 'b:', label='Vg_x glas') plot(c['time'], e['Vg_glas']['y'], 'g:', label='Vg_y glas') plot(c['time'], e['Vg_glas']['z'], 'r:', label='Vg_z glas') if s is not None: plot(s['time'], s['wing']['Vg']['x'], 'b-o', label='Vg_x sim') plot(s['time'], s['wing']['Vg']['y'], 'g-o', label='Vg_y sim') plot(s['time'], s['wing']['Vg']['z'], 'r-o', label='Vg_z sim') @MFig(title='Payout', ylabel='Payout [m]', xlabel='Time [s]') def PlotPayout(self, e, c, s, params): plot(c['time'], c['state_est']['winch']['payout'], label='Payout') @MFig(title='Tension', ylabel='Tension [N]', xlabel='Time [s]') def PlotTension(self, e, c, s, params): plot(c['time'], c['state_est']['tether_force_b']['sph']['tension'], label='Tension est') @MFig(title='Tether Angles', ylabel='Angles [deg]', xlabel='Time [s]') def PlotTetherAngles(self, e, c, s, params): plot(c['time'], np.rad2deg(c['state_est']['tether_force_b']['sph']['roll']), label='Tether Roll') plot(c['time'], np.rad2deg(c['state_est']['tether_force_b']['sph']['pitch']), label='Tether Pitch') @MFig(title='Kite Position', ylabel='Position [m]', xlabel='Time [s]') def PlotPosition(self, e, c, s, params, plot_glas=True): for (d, clr) in [('x', 'b'), ('y', 'g'), ('z', 'r')]: plot(c['time'], c['state_est']['Xg'][d], clr, label='Xg_%s est' % d) plot(c['time'], c['state_est']['Xg'][d] + c['estimator']['cov_pos_g'][d]**0.5, clr+':', label='Xg_%s est' % d) plot(c['time'], c['state_est']['Xg'][d] - c['estimator']['cov_pos_g'][d]**0.5, clr+':', label='Xg_%s est' % d) plot(c['time'], e['gps']['Xg'][d][:], clr+'--', label='Xg_%s gps' % d) plot(c['time'], e['gps']['Xg'][d][:] + e['gps']['sigma_Xg'][d][:], clr+':', label='Xg_%s gps' % d) plot(c['time'], e['gps']['Xg'][d][:] - e['gps']['sigma_Xg'][d][:], clr+':', label='Xg_%s gps' % d) if plot_glas: plot(c['time'], e['glas']['Xg'][d][:], clr+'-.', label='Xg_%s glas' % d) plot(c['time'], e['glas']['Xg'][d][:] + e['glas']['sigma_Xg'][d][:], clr+':', label='Xg_%s glas' % d) plot(c['time'], e['glas']['Xg'][d][:] - e['glas']['sigma_Xg'][d][:], clr+':', label='Xg_%s glas' % d) clr = 'r' # z-color from above loop plot(c['time'], e['baro']['Xg_z'], clr+'-*', label='Xg_z baro') if s is not None: plot(s['time'], s['wing']['Xg']['x'], 'b-o', label='Xg_x sim') plot(s['time'], s['wing']['Xg']['y'], 'g-o', label='Xg_y sim') plot(s['time'], s['wing']['Xg']['z'], 'r-o', label='Xg_z sim') @MFig(title='GSG Biases', ylabel='Angles [deg]', xlabel='Time [s]') def PlotGsgBias(self, e, c, s, params): plot(c['time'], np.rad2deg(e['gsg_bias']['azi']), 'b', label='Azi Bias') plot(c['time'], np.rad2deg(e['gsg_bias']['ele']), 'g', label='Ele Bias') @MFig(title='GPS Bias', ylabel='Position [m]', xlabel='Time [s]') def PlotGpsBias(self, e, c, s, params): mplot.PlotVec3(c['time'], e['Xg_gps_biases'][:, 0], label='GPS A bias') mplot.PlotVec3(c['time'], e['Xg_gps_biases'][:, 1], label='GPS B bias') @MFig(title='Wind Speed', ylabel='Wind Speed [m/s]', xlabel='Time [s]') def PlotWindSpeed(self, e, c, s, params): if s is not None: wind_g = s['wind_sensor']['wind_g'] plot(s['time'], numpy_utils.Vec3Norm(wind_g), 'C1--', label='wind speed at wind sensor [sim]') wind_g = s['wing']['wind_g'] plot(s['time'], numpy_utils.Vec3Norm(wind_g), 'C2:', label='wind speed at kite [sim]') # Plot the estimated wind speed last so that it will be on top. plot(c['time'], c['state_est']['wind_g']['speed_f'], 'C0-', linewidth=2, label='wind speed [est]') @MFig(title='Kite Azimuth', ylabel='Azimuth [deg]', xlabel='Time [s]') def PlotKiteAzimuth(self, e, c, s, params): xg = c['state_est']['Xg'] plot(c['time'], np.rad2deg(np.arctan2(xg['y'], xg['x'])), 'b') @MFig(title='Wind Direction (FROM)', ylabel='Direction [deg]', xlabel='Time [s]') def PlotWindDir(self, e, c, s, params): if s is not None: wind_g = s['wind_sensor']['wind_g'] plot(s['time'], np.rad2deg(np.arctan2(-wind_g['y'], -wind_g['x'])), 'C1--', label='wind direction at wind sensor [sim]') wind_g = s['wing']['wind_g'] plot(s['time'], np.rad2deg(np.arctan2(-wind_g['y'], -wind_g['x'])), 'C1--', label='wind direction at kite [sim]') # The estimator's "dir_f" is the TO direction. Here we convert to a # FROM direction. dir_f = np.rad2deg(c['state_est']['wind_g']['dir_f']) + 180.0 dir_f[dir_f > 360.0] -= 360.0 # Plot the estimated wind speed last so that it will be on top. plot(c['time'], dir_f, 'C0-', linewidth=2, label='wind direction [est]') @MFig(title='Tether Elevation', ylabel='[deg]', xlabel='Time [s]') def PlotTetherElevation(self, e, c, s, params): elevation = c['state_est']['tether_ground_angles']['elevation'] elevation[np.logical_not( c['state_est']['tether_ground_angles']['elevation_valid'] )] = float('nan') plot(c['time'], np.rad2deg(elevation), label='Est') if s is not None: plot(s['time'], np.rad2deg(s['tether']['Xv_start_elevation']), '--', label='Sim') @MFig(title='Ground Station Mode', ylabel='Mode [enum]', xlabel='Time [s]') def PlotGroundStationMode(self, e, c, s, params): plot(c['time'], c['control_input']['gs_sensors']['mode'], label='ci') plot(c['time'], c['state_est']['gs_mode'], label='est') if s is not None: plot(s['time'], s['gs02']['mode'], '-.', label='Sim') yticks(_GROUND_STATION_MODE_HELPER.Values(), _GROUND_STATION_MODE_HELPER.ShortNames()) @MFig(title='Ground Station Transform Stage', ylabel='Stage [#]', xlabel='Time [s]') def PlotGroundStationTransformStage(self, e, c, s, params): plot(c['time'], c['control_input']['gs_sensors']['transform_stage'], label='ci') plot(c['time'], c['state_est']['gs_transform_stage'], label='est') if s is not None: # This value is not yet in simulator telemetry. pass # TODO: Create separate 'simulator' plot group. @MFig(title='Moments', ylabel='Nm', xlabel='Time [s]') def PlotKiteMoments(self, e, c, s, params, axis='y'): for what in ['aero', 'gravity', 'tether', 'rotors', 'disturb', 'blown_wing', 'total']: plot(s['time'], s['wing']['fm_'+what]['moment'][axis], label='fm_'+what) @MFig(title='Kite Azimuth and Elevation', ylabel='Angle [deg]', xlabel='Time [s]') def PlotKiteAzimuthAndElevation(self, e, c, s, params): wing_pos_g = s['wing']['Xg'] plot(s['time'], np.rad2deg(np.arctan2(wing_pos_g['y'], wing_pos_g['x'])), label='kite azimuth') plot(s['time'], np.rad2deg(np.arctan2(-wing_pos_g['z'], np.hypot(wing_pos_g['x'], wing_pos_g['y']))), label='kite elevation') @MFig(title='Air Density (Measured at Ground Station)', ylabel='Density [kg/m^3]', xlabel='Time [s]') def PlotDensity(self, e, c, s, params): plot(c['time'], c['state_est']['rho'], label='state_est.rho') plot(c['time'], np.full_like(c['time'], params['system_params']['phys']['rho']), label='hard-coded value') @MFig(title='Tether Anchor Point', ylabel='[m]', xlabel='Time [s]') def PlotTetherAnchorPoint(self, e, c, s, params): mplot.PlotVec3(c['time'], c['state_est']['tether_anchor']['pos_g'], label='pos_g [est]', linestyle='-') mplot.PlotVec3(c['time'], c['state_est']['tether_anchor']['pos_g_f'], label='pos_g_f [est]', linestyle='--')

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8d638991d71730377e930b6afff8fce13cde7b4a

4,453

py

Python

siptrackdlib/objectregistry.py

sii/siptrackd

f124f750c5c826156c31ae8699e90ff95a964a02

[ "Apache-2.0" ]

null

siptrackdlib/objectregistry.py

sii/siptrackd

f124f750c5c826156c31ae8699e90ff95a964a02

[ "Apache-2.0" ]

14

2016-03-18T13:28:16.000Z

2019-06-02T21:11:29.000Z

siptrackdlib/objectregistry.py

sii/siptrackd

f124f750c5c826156c31ae8699e90ff95a964a02

[ "Apache-2.0" ]

7

2016-03-18T13:04:54.000Z

2021-06-22T10:39:04.000Z

from siptrackdlib import errors from siptrackdlib import log class ObjectClass(object): """A class definition in the object registry. Stores a reference to the class itself and also a list of valid child classes (class_ids). """ def __init__(self, class_reference): self.class_reference = class_reference self.valid_children = {} def registerChild(self, class_reference): """Register a class as a valid child class.""" self.valid_children[class_reference.class_id] = None class ObjectRegistry(object): """Keeps track of registered classes and their valid children. The object registry is used to keep track of valid classes and what classes are valid children of a class. It also allocates object ids and can be used to create new objects based on the registry. """ def __init__(self): self.object_classes = {} self.object_classes_by_name = {} self.next_oid = 0 def registerClass(self, class_reference): """Register a new class. This creates a new ObjectClass and stores it in the registry, enabling creation of objects of the given class. The returned ObjectClass object can be used to register valid children of the class. """ object_class = ObjectClass(class_reference) self.object_classes[class_reference.class_id] = \ object_class self.object_classes_by_name[class_reference.class_name] = \ object_class return object_class def isValidChild(self, parent_id, child_id): """Check if a class is a valid child of another class.""" if not parent_id in self.object_classes: return False parent = self.object_classes[parent_id] if child_id not in parent.valid_children: return False return True def getClass(self, class_name): """Returns the class reference for class registered with class_name.""" if class_name in self.object_classes_by_name: return self.object_classes_by_name[class_name].class_reference return None def getClassById(self, class_id): """Returns the class reference for class registered with class_name.""" if class_id in self.object_classes: return self.object_classes[class_id].class_reference return None def getIDByName(self, class_name): """Return a classes id given it's name.""" if class_name in self.object_classes_by_name: object_class = self.object_classes_by_name[class_name] return object_class.class_reference.class_id return None def allocateOID(self): """Allocate a new oid.""" ret = str(self.next_oid) self.next_oid += 1 return ret def revertOID(self): """Revert an oid allocation.""" self.next_oid -= 1 def createObject(self, class_id, parent_branch, *args, **kwargs): """Try to create a new object based on a registered class. This will try to create a new object of 'class_id' type, allocating it it's own oid. A new branch will also be created in the object tree to hold the object. """ if class_id not in self.object_classes: raise errors.SiptrackError( 'trying to create object with invalid class id \'%s\'' % (class_id)) object_class = self.object_classes[class_id] oid = self.allocateOID() branch = parent_branch.add(oid) try: obj = object_class.class_reference(oid, branch, *args, **kwargs) except Exception as e: branch.remove(recursive = False, callback_data = None) self.revertOID() raise branch.ext_data = obj return obj def _createObject(self, class_id, branch): """Try to create _only_ a new object based on an oid and class id. Similar to createObject, but takes a class id and an oid and only creates a new object, no branch etc. """ if class_id not in self.object_classes: raise errors.SiptrackError( 'trying to create object with invalid class id \'%s\'' % (class_id)) object_class = self.object_classes[class_id] obj = object_class.class_reference(branch.oid, branch) return obj object_registry = ObjectRegistry()

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false

0

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0

0.42029

0

null

0

null

0

1

0

8d66576529e5704ad9e6b2d90cc87687907b8c91

1,139

py

Python

src/kol/request/CombatRequest.py

ZJ/pykol

c0523a4a4d09bcdf16f8c86c78da96914e961076

[ "BSD-3-Clause" ]

1

2016-05-08T13:26:56.000Z

src/kol/request/CombatRequest.py

ZJ/pykol

c0523a4a4d09bcdf16f8c86c78da96914e961076

[ "BSD-3-Clause" ]

null

src/kol/request/CombatRequest.py

ZJ/pykol

c0523a4a4d09bcdf16f8c86c78da96914e961076

[ "BSD-3-Clause" ]

null

from GenericAdventuringRequest import GenericAdventuringRequest class CombatRequest(GenericAdventuringRequest): """ A request used for a single round of combat. The user may attack, use an item or skill, or attempt to run away. """ # What follows are a list of available actions. ATTACK = 0 USE_ITEM = 1 USE_SKILL = 2 RUN_AWAY = 3 def __init__(self, session, action, param=None): """ In this constructor, action should be set to CombatRequest.ATTACK, CombatRequest.USE_ITEM, CombatRequest.USE_SKILL, or CombatRequest.RUN_AWAY. If a skill or item is to be used, the caller should also specify param to be the number of the item or skill the user wishes to use. """ super(CombatRequest, self).__init__(session) self.url = session.serverURL + "fight.php" if action == ATTACK: self.requestData["action"] = "attack" elif action == USE_ITEM: self.requestData["action"] = "useitem" self.requestData["whichitem"] = param elif action == USE_SKILL: self.requestData["action"] = "skill" self.requestData["whichskill"] = param elif action == RUN_AWAY: self.requestData["action"] = "runaway"

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92

0.72432

155

1,139

5.212903

0.432258

0.111386

0.10396

0

0.004269

0.177349

1,139

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0

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0.052632

false

0

0.052632

0

0.368421

0

null

0

null

0

1

0

8d6a85cb3cf62644daa8bec049af6d5de6f147e2

632

py

Python

src/modules/dates/searchDates.py

leonardoleyva/api-agenda-uas

697740a0a3feebb2ada01133db020fcf5127e1de

[ "MIT" ]

1

2022-03-13T02:28:29.000Z

src/modules/dates/searchDates.py

leonardoleyva/api-agenda-uas

697740a0a3feebb2ada01133db020fcf5127e1de

[ "MIT" ]

null

src/modules/dates/searchDates.py

leonardoleyva/api-agenda-uas

697740a0a3feebb2ada01133db020fcf5127e1de

[ "MIT" ]

null

from .date import Date from ..response import handleResponse from datetime import datetime def searchDates(): req = Date().searchAll() message = "Listado de citas" if req['status'] == True else "No se pudo conseguir el listado de citas, inténtelo más tarde" dateToday = datetime.now().isoformat().split('T')[0] dates = [] for date in req['dates']: dateDict = date.to_dict() dateYYMMDD = dateDict['date'].split('T')[0] if dateYYMMDD >= dateToday: dates.append({**dateDict, 'id': date.id}) response = handleResponse(req['status'], message, dates) return response

27.478261

126

0.643987

77

632

5.272727

0.545455

0.044335

0.068966

0

0.004057

0.219937

632

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0.819473

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false

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0

0.333333

0

null

0

null

0

1

0

8d6cc5852312640c236532b7026c1ac08efbc30f

13,148

py

Python

core/views/misc.py

ditttu/gymkhana-Nominations

2a0e993c1b8362c456a9369b0b549d1c809a21df

[ "MIT" ]

3

2018-02-27T13:48:28.000Z

2018-03-03T21:57:50.000Z

core/views/misc.py

ditttu/gymkhana-Nominations

2a0e993c1b8362c456a9369b0b549d1c809a21df

[ "MIT" ]

6

2020-02-12T00:07:46.000Z

2022-03-11T23:25:59.000Z

core/views/misc.py

ditttu/gymkhana-Nominations

2a0e993c1b8362c456a9369b0b549d1c809a21df

[ "MIT" ]

1

2019-03-26T20:19:57.000Z

from django.contrib.auth.decorators import login_required from django.core.exceptions import ObjectDoesNotExist from django.core.urlresolvers import reverse, reverse_lazy from django.http import HttpResponseRedirect from django.shortcuts import render,HttpResponse from django.views.generic.edit import CreateView, UpdateView, DeleteView import csv, json from datetime import date,datetime from itertools import chain from operator import attrgetter from forms.models import Questionnaire from forms.views import replicate from core.models import * from core.forms import * from .nomi_cr import get_access_and_post_for_result, get_access_and_post @login_required def ratify(request, nomi_pk): nomi = Nomination.objects.get(pk=nomi_pk) access, view_post = get_access_and_post_for_result(request,nomi_pk) if access: if view_post.perms == "can ratify the post": nomi.append() return HttpResponseRedirect(reverse('applicants', kwargs={'pk': nomi_pk})) else: return render(request, 'no_access.html') else: return render(request, 'no_access.html') @login_required def request_ratify(request, nomi_pk): nomi = Nomination.objects.get(pk=nomi_pk) access, view_post = get_access_and_post_for_result(request,nomi_pk) if access: if view_post.parent: to_add = view_post.parent nomi.result_approvals.add(to_add) nomi.nomi_approvals.add(to_add) nomi.status = 'Sent for ratification' nomi.save() return HttpResponseRedirect(reverse('applicants', kwargs={'pk': nomi_pk})) else: return render(request, 'no_access.html') @login_required def cancel_ratify(request, nomi_pk): nomi = Nomination.objects.get(pk=nomi_pk) access, view_post = get_access_and_post_for_result(request,nomi_pk) if access: if view_post.parent: to_remove = view_post.parent nomi.result_approvals.remove(to_remove) nomi.nomi_approvals.remove(to_remove) nomi.status = 'Interview period' nomi.save() return HttpResponseRedirect(reverse('applicants', kwargs={'pk': nomi_pk})) else: return render(request, 'no_access.html') @login_required def cancel_result_approval(request, nomi_pk): nomi = Nomination.objects.get(pk=nomi_pk) access, view_post = get_access_and_post_for_result(request,nomi_pk) if access: to_remove = view_post.parent if to_remove.parent not in nomi.result_approvals.all(): nomi.result_approvals.remove(to_remove) return HttpResponseRedirect(reverse('applicants', kwargs={'pk': nomi_pk})) else: return render(request, 'no_access.html') @login_required def result_approval(request, nomi_pk): nomi = Nomination.objects.get(pk=nomi_pk) access, view_post = get_access_and_post_for_result(request,nomi_pk) if access: if view_post == nomi.nomi_post.parent: nomi.show_result = True to_add = view_post.parent nomi.result_approvals.add(to_add) return HttpResponseRedirect(reverse('applicants', kwargs={'pk': nomi_pk})) else: return render(request, 'no_access.html') @login_required def create_deratification_request(request, post_pk, user_pk ,type): post = Post.objects.get(pk=post_pk) user =User.objects.get(pk=user_pk) if request.user in post.parent.post_holders.all(): Deratification.objects.create(name=user, post=post,status = type, deratify_approval=post.parent) return HttpResponseRedirect(reverse('child_post', kwargs={'pk': post_pk})) @login_required def approve_deratification_request(request,pk): to_deratify = Deratification.objects.get(pk = pk) view = to_deratify.deratify_approval if request.user in view.post_holders.all(): if view.perms == "can ratify the post": to_deratify.post.post_holders.remove(to_deratify.name) history=PostHistory.objects.filter(user=to_deratify.name).filter(post = to_deratify.post).first() if to_deratify.status=='remove from post': history.delete() to_deratify.status = 'removed' else: history.end = date.today() history.save() to_deratify.status = 'deratified' to_deratify.save() else: to_deratify.deratify_approval = view.parent to_deratify.save() return HttpResponseRedirect(reverse('post_view', kwargs={'pk':view.pk})) else: return render(request, 'no_access.html') @login_required def reject_deratification_request(request, pk): to_deratify = Deratification.objects.get(pk=pk) view = to_deratify.deratify_approval if request.user in view.post_holders.all(): to_deratify.delete() return HttpResponseRedirect(reverse('post_view', kwargs={'pk':view.pk})) else: return render(request, 'no_access.html') ''' mark_as_interviewed, reject_nomination, accept_nomination: Changes the interview status/ nomination_instance status of the applicant ''' def get_access_and_post_for_selection(request, nomi_pk): nomi =Nomination.objects.get(pk=nomi_pk) access = False view_post = None for post in nomi.result_approvals.all(): if request.user in post.post_holders.all(): access = True view_post = post break return access, view_post @login_required def mark_as_interviewed(request, pk): application = NominationInstance.objects.get(pk=pk) id_nomi = application.nomination.pk nomination = Nomination.objects.get(pk=id_nomi) access, view_post = get_access_and_post_for_selection(request,id_nomi) if access or request.user in nomination.interview_panel.all(): application.interview_status = 'Interview Done' application.save() return HttpResponseRedirect(reverse('nomi_answer', kwargs={'pk': pk})) else: return render(request, 'no_access.html') @login_required def accept_nomination(request, pk): application = NominationInstance.objects.get(pk=pk) id_accept = application.nomination.pk nomination = Nomination.objects.get(pk=id_accept) access, view_post = get_access_and_post_for_selection(request, id_accept) if access or request.user in nomination.interview_panel.all(): application.status = 'Accepted' application.save() comment = '<strong>' + str(request.user.userprofile.name) + '</strong>' + ' Accepted '\ + '<strong>' + str(application.user.userprofile.name) + '</strong>' status = Commment.objects.create(comments=comment, nomi_instance=application) return HttpResponseRedirect(reverse('applicants', kwargs={'pk': id_accept})) else: return render(request, 'no_access.html') @login_required def reject_nomination(request, pk): application = NominationInstance.objects.get(pk=pk) id_reject = application.nomination.pk nomination = Nomination.objects.get(pk=id_reject) access, view_post = get_access_and_post_for_selection(request, id_reject) if access or request.user in nomination.interview_panel.all(): application.status = 'Rejected' application.save() comment = '<strong>' + str(request.user.userprofile.name) + '</strong>' + ' Rejected ' \ + '<strong>' + str(application.user.userprofile.name) + '</strong>' status = Commment.objects.create(comments=comment, nomi_instance=application) return HttpResponseRedirect(reverse('applicants', kwargs={'pk': id_reject})) else: return render(request, 'no_access.html') ''' append_user, replace_user: Adds and Removes the current post-holders according to their selection status ''' @login_required def append_user(request, pk): posts = request.user.posts.all() access = False for post in posts: if post.perms == "can ratify the post": access = True break if access: nomi = Nomination.objects.get(pk=pk) nomi.append() return HttpResponseRedirect(reverse('applicants', kwargs={'pk': pk})) else: return render(request, 'no_access.html') @login_required def end_tenure(request): posts = request.user.posts.all() access = False for post in posts: if post.perms == "can ratify the post": access = True break if access: posts = Post.objects.all() for post in posts: for holder in post.post_holders.all(): try: history = PostHistory.objects.get(post=post, user=holder) if history.end: if date.today() >= history.end: post.post_holders.remove(holder) except ObjectDoesNotExist: pass return HttpResponseRedirect(reverse('index')) else: return render(request, 'no_access.html') # Import all posts of all clubs # Check if their session has expired (31-3-2018 has passed) # Remove them from the post # Create the post history (No need, its already created) ## ------------------------------------------------------------------------------------------------------------------ ## ############################################ PROFILE VIEWS ################################################## ## ------------------------------------------------------------------------------------------------------------------ ## @login_required def profile_view(request): pk = request.user.pk my_posts = Post.objects.filter(post_holders=request.user) history = PostHistory.objects.filter(user=request.user).order_by('start') pending_nomi = NominationInstance.objects.filter(user=request.user).filter(nomination__status='Nomination out') pending_re_nomi = NominationInstance.objects.filter(user=request.user).\ filter(nomination__status='Interview period and Nomination reopened') pending_nomi = pending_nomi | pending_re_nomi # show the instances that user finally submitted.. not the saved one interview_re_nomi = NominationInstance.objects.filter(user=request.user).filter(submission_status = True).filter(nomination__status='Interview period and Reopening initiated') interview_nomi = NominationInstance.objects.filter(user=request.user).filter(submission_status = True).filter(nomination__status='Interview period') interview_nomi = interview_nomi | interview_re_nomi declared_nomi = NominationInstance.objects.filter(user=request.user).filter(submission_status = True).filter(nomination__status='Sent for ratification') try: user_profile = UserProfile.objects.get(user__id=pk) post_exclude_history = [] # In case a post is not registered in history post_history = [] for his in history: post_history.append(his.post) for post in my_posts: if post not in post_history: post_exclude_history.append(post) return render(request, 'profile.html', context={'user_profile': user_profile, 'history': history, 'pending_nomi': pending_nomi, 'declared_nomi': declared_nomi, 'interview_nomi': interview_nomi, 'my_posts': my_posts, 'excluded_posts': post_exclude_history}) except ObjectDoesNotExist: return HttpResponseRedirect('create') @login_required def public_profile(request, pk): student = UserProfile.objects.get(pk=pk) student_user = student.user history = PostHistory.objects.filter(user=student_user) my_posts = Post.objects.filter(post_holders=student_user) return render(request, 'public_profile.html', context={'student': student, 'history': history, 'my_posts': my_posts}) def UserProfileUpdate(request,pk): profile = UserProfile.objects.get(pk = pk) if profile.user == request.user: form = ProfileForm(request.POST or None, instance=profile) if form.is_valid(): form.save() return HttpResponseRedirect(reverse('profile')) return render(request, 'nomi/userprofile_form.html', context={'form': form}) else: return render(request, 'no_access.html') class CommentUpdate(UpdateView): model = Commment fields = ['comments'] def get_success_url(self): form_pk = self.kwargs['form_pk'] return reverse('nomi_answer', kwargs={'pk': form_pk}) class CommentDelete(DeleteView): model = Commment def get_success_url(self): form_pk = self.kwargs['form_pk'] return reverse('nomi_answer', kwargs={'pk': form_pk}) def all_nominations(request): all_nomi = Nomination.objects.all().exclude(status='Nomination created') return render(request, 'all_nominations.html', context={'all_nomi': all_nomi})

34.783069

179

0.6601

1,537

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5.446975

0.127521

0.015767

0.027234

0.038462

0.579312

0.535834

0.508958

0.484711

0.477186

0.449833

0

0.00068

0.217143

13,148

377

180

34.875332

0.812688

0.040843

0

0.503788

0

0.082776

0.002122

0.007576

0

1

0.075758

false

0.003788

0.056818

0

0.287879

0

null

0

null

0

1

0

8d6deeb2db5e44e12af11dde00260d1e8aae607e

29,706

py

Python

make_paper_plots.py

mjbasso/asymptotic_formulae_examples

a1ba177426bf82e2a58e7b54e1874b088a86595f

[ "MIT" ]

1

2021-08-06T14:58:51.000Z

make_paper_plots.py

mjbasso/asymptotic_formulae_examples

a1ba177426bf82e2a58e7b54e1874b088a86595f

[ "MIT" ]

null

make_paper_plots.py

mjbasso/asymptotic_formulae_examples

a1ba177426bf82e2a58e7b54e1874b088a86595f

[ "MIT" ]

null

#!/usr/bin/env python3 import logging import os import pickle import time from os.path import join as pjoin import matplotlib.pyplot as plt import numpy as np import scipy from matplotlib import rc from scipy.optimize import least_squares import asymptotic_formulae from asymptotic_formulae import GaussZ0 from asymptotic_formulae import GaussZ0_MC from asymptotic_formulae import nCRZ0 from asymptotic_formulae import nCRZ0_MC from asymptotic_formulae import nSRZ0 from asymptotic_formulae import nSRZ0_MC rc('font', **{'family': 'sans-serif','sans-serif': ['Helvetica']}) rc('text', usetex = True) logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) sh = logging.StreamHandler() sh.setFormatter(logging.Formatter('%(asctime)s : %(name)s : %(levelname)s : %(message)s')) logger.addHandler(sh) # For creating a set of uniformly-spaced points on a log scale def logVector(low, high, n): low = np.log(low) / np.log(10) high = np.log(high) / np.log(10) step = (high - low) / n vec = np.array([low + step * i for i in range(n + 1)]) return np.exp(np.log(10) * vec) # As described in Section 2.1.4 def nCRZ0_DiagTau(s, b, tau): ''' Calculate the asymptotic significance for a 1 SR + N CRs, diagonal tau measurement s := expected signal yield in SR (float) b := expected background yields in SR (vector of floats, size N) tau := transfer coefficients, tau[i] carries background i yield in SR to CR i (vector of floats, size N) Returns Z0 (float) ''' # Argument checking b, tau = np.array(b), np.array(tau) s, b, tau = float(s), b.astype(float), tau.astype(float) assert b.ndim == 1 # b should be a vector assert tau.ndim == 1 # tau should be a vector assert len(b) == len(tau) assert (tau >= 0.).all() # Assert tau contains transfer factors (i.e., all positive) n = s + np.sum(b) # System of equations def func(bhh): eqns = [] for k in range(len(b)): eqns.append(n / np.sum(bhh) - 1. + tau[k] * (b[k] / bhh[k] - 1.)) return eqns # Perform our minimization res = least_squares(func, x0 = b, bounds = [tuple(len(b) * [0.]), tuple(len(b) * [np.inf])]) if not res.success: raise RuntimeError('Minimization failed: status = %s, message = \'%s\'' % (res.status, res.message)) bhh = np.array(res.x) # Calculate our significance Z0 = np.sqrt(-2. * np.log((np.sum(bhh) / n) ** n * np.prod([(bhh[k] / b[k]) ** (tau[k] * b[k]) for k in range(len(b))]))) return Z0 # As described in Section 2.4.2 def GaussZ0_Decorr(s, b, sigma): ''' Calculate the asymptotic significance for a 1 SR + N CRs, diagonal tau measurement s := expected signal yield in SR (float) b := expected background yields in SR (vector of floats, size N) sigma := width of Gaussian constraint ("absolute uncertainty") for each background yield (vector of floats, size N) Returns Z0 (float) ''' # Argument checking b, sigma = np.array(b), np.array(sigma) s, b, sigma = float(s), b.astype(float), sigma.astype(float) assert b.ndim == 1 # b should be a vector assert sigma.ndim == 1 # sigma should be a vector assert len(b) == len(sigma) assert (sigma >= 0.).all() # Assert sigma contains widths (i.e., all positive) n = s + np.sum(b) # System of equations def func(bhh): eqns = [] for k in range(len(b)): eqns.append(sigma[k] * (n / np.sum(bhh) - 1.) - (bhh[k] - b[k]) / sigma[k]) return eqns # Perform our minimization res = least_squares(func, x0 = b, bounds = [tuple(len(b) * [0.]), tuple(len(b) * [np.inf])]) if not res.success: raise RuntimeError('Minimization failed: status = %s, message = \'%s\'' % (res.status, res.message)) bhh = np.array(res.x) # Calculate our significance Z0 = np.sqrt(-2. * (n * np.log(np.sum(bhh) / n) + n - np.sum(bhh + 0.5 * ((b - bhh) / sigma) ** 2))) return Z0 def makedir(path): if not os.path.exists(path): os.makedirs(path) return path def load_data_from_pickle(path): if os.path.exists(path): with open(path, 'rb') as f: data = pickle.load(f) else: data = {} return data def dump_data_to_pickle(data, path): if not os.path.exists(path): with open(path, 'wb') as f: data = pickle.dump(data, f) pass pass def main(): basedir = os.path.dirname(os.path.abspath(__file__)) pickledir = makedir(pjoin(basedir, 'pickles/')) plotdir = makedir(pjoin(basedir, 'plots/')) ##################### ### SECTION 2.1.1 ### ##################### def Section2p1p1(): s = 50. b1 = 100. b2 = 50. tau11 = 60. tau22 = 40. tau12 = np.linspace(0., b1 * tau11 / b2, 100) tau21 = np.linspace(0., b2 * tau22 / b1, 100) z0 = np.empty((len(tau12), len(tau21))) for i in range(len(tau12)): for j in range(len(tau21)): z0[i, j] = nCRZ0(s, [b1, b2], [[tau11, tau12[i]], [tau21[j], tau22]]) fig = plt.figure() ax = fig.add_subplot(111) pcm = ax.pcolormesh(tau12 * b2 / (tau11 * b1), tau21 * b1 / (tau22 * b2), z0, cmap = 'magma', shading = 'nearest') pcm.set_edgecolor('face') cbar = plt.colorbar(pcm) ax.set_xlabel('($b_2$ in CR 1) / ($b_1$ in CR 1) [a.u.]') ax.set_ylabel('($b_1$ in CR 2) / ($b_2$ in CR 2) [a.u.]') cbar.set_label('Significance of discovery $\\textrm{med}[Z_0|\\mu^\\prime=1]$ [a.u.]', rotation = 270, labelpad = 20) # ax.set_title('Asymptotic significance for CRs with mixed background processes', pad = 10) plt.savefig(pjoin(plotdir, '1SRNCR_mixed_processes.eps'), format = 'eps', dpi = 1200) plt.close() multi = logVector(1, 1000, 100) z0 = np.empty((len(multi), len(multi))) for i in range(len(multi)): for j in range(len(multi)): z0[i, j] = nCRZ0(s, [b1, b2], [[multi[i], 0.1 * multi[i] * b1 / b2], [0.1 * multi[j] * b2 / b1, multi[j]]]) fig = plt.figure() ax = fig.add_subplot(111) pcm = ax.pcolormesh(multi, multi, z0, cmap = 'magma', shading = 'nearest') pcm.set_edgecolor('face') cbar = plt.colorbar(pcm) ax.set_xlabel('($b_1$ in CR 1) / ($b_1$ in SR) [a.u.]') ax.set_ylabel('($b_2$ in CR 2) / ($b_2$ in SR) [a.u.]') ax.set_xscale('log') ax.set_yscale('log') cbar.set_label('Significance of discovery $\\textrm{med}[Z_0|\\mu^\\prime=1]$ [a.u.]', rotation = 270, labelpad = 20) # ax.set_title('Asymptotic significance for CRs varying transfer factors', pad = 10) plt.savefig(pjoin(plotdir, '1SRNCR_varying_tau.eps'), format = 'eps', dpi = 1200) plt.close() ##################### ### SECTION 2.1.2 ### ##################### def Section2p1p2(): # Set the seed np.random.seed(43) datapath = pjoin(pickledir, 'Section2p1p2.pkl') s = 10. b1 = [round(n) for n in logVector(1., 1000., 10)] b2 = [5., 25., 150.] tau1 = 8. tau2 = 5. colours = ['g', 'b', 'r'] data = load_data_from_pickle(datapath) for _b2, c in zip(b2, colours): k = str(int(_b2)) if not data.get(k, {}): data[k] = {'z0': [], 't0': [], 't1': []} for _b1 in b1: logger.info('On (b1, b2) = (%s, %s).' % (int(_b1), int(_b2))) z0, t0, t1 = nCRZ0_MC(s, [_b1, _b2], [[tau1, 0.], [0., tau2]], return_t0_and_t1 = True, sleep = 0.001, ntoys = 50000) data[k]['z0'].append(z0) data[k]['t0'].append(t0) data[k]['t1'].append(t1) plt.plot(b1, data[k]['z0'], marker = 'o', color = c, linewidth = 0, label = 'Numerical: $b_2 = %s$' % int(_b2)) b1Fine = logVector(b1[0], b1[-1], 1000) plt.plot(b1Fine, [nCRZ0_DiagTau(s, [_b1, _b2], [tau1, tau2]) for _b1 in b1Fine], linestyle = '-', markersize = 0, color = c, label = 'Asymptotic: $b_2 = %s$' % int(_b2)) plt.plot(b1Fine, s / np.sqrt(s + b1Fine + _b2), linestyle = '--', markersize = 0, color = c, label = 'Simple: $b_2 = %s$' % int(_b2)) plt.xlim((b1[0], b1[-1])) plt.ylim((0., 3.5)) plt.xlabel('Background 1 yield in SR $b_1$ [a.u.]') plt.ylabel('Significance of discovery $\\textrm{med}[Z_0|\\mu^\\prime=1]$ [a.u.]') plt.xscale('log') # plt.title('1 SR + 2 CRs Asymptotic Significance: $s = %s$, $\\tau_1 = %s$, $\\tau_2 = %s$' % (int(s), int(tau1), int(tau2))) plt.legend(loc = 'upper right') plt.savefig(pjoin(plotdir, '1SRplus2CR.eps'), format = 'eps', dpi = 1200) plt.close() axrange = (0., 25.) bins = 100 for _b1 in [1., 1000.]: t0, t1 = data['5']['t0'][b1.index(_b1)], data['5']['t1'][b1.index(_b1)] plt.hist(t0, weights = len(t0) * [1. / len(t0)], range = axrange, bins = bins, histtype = 'step', color = 'b', label = '$f(t_0|\\mu^\\prime = 0)$') plt.hist(t1, weights = len(t1) * [1. / len(t1)], range = axrange, bins = bins, histtype = 'step', color = 'r', label = '$f(t_0|\\mu^\\prime = 1)$') plt.xlim(axrange) plt.xlabel('Test statistic $t_0$ [a.u.]') plt.ylabel('Normalized counts [a.u.]') plt.yscale('log') plt.legend() plt.savefig(pjoin(plotdir, '1SRplus2CR_b1eq%s.eps' % int(_b1)), format = 'eps', dpi = 1200) plt.close() dump_data_to_pickle(data, datapath) ##################### ### SECTION 2.2.1 ### ##################### def Section2p2p1(): # Set the seed np.random.seed(44) datapath = pjoin(pickledir, 'Section2p2p1.pkl') s1 = [round(n) for n in logVector(1., 100., 10)] s2 = [25., 10., 10.] s3 = 12. b = [1000., 1000., 3000.] tau1 = 2. tau2 = 10. tau3 = 20. colours = ['g', 'b', 'r'] data = load_data_from_pickle(datapath) for _s2, _b, c in zip(s2, b, colours): k = str(int(_s2)) + '_' + str(int(_b)) if not data.get(k, {}): data[k] = {'z0': [], 't0': [], 't1': []} for _s1 in s1: logger.info('On (s1, s2, b) = (%s, %s, %s).' % (int(_s1), int(_s2), int(_b))) ntoys = 100000 if (_s1 > 75.) else 50000 logger.info('Using %s toys.' % ntoys) z0, t0, t1 = nSRZ0_MC([_s1, _s2, s3], _b, [tau1, tau2, tau3], return_t0_and_t1 = True, sleep = 0.001, ntoys = ntoys) data[k]['z0'].append(z0) data[k]['t0'].append(t0) data[k]['t1'].append(t1) plt.plot(s1, data[k]['z0'], marker = 'o', color = c, linewidth = 0, label = 'Numerical: $(s_2, b) = (%s, %s)$' % (int(_s2), int(_b))) s1Fine = logVector(s1[0], s1[-1], 1000) plt.plot(s1Fine, [nSRZ0([_s1, _s2, s3], _b, [tau1, tau2, tau3]) for _s1 in s1Fine], linestyle = '-', markersize = 0, color = c, label = 'Asymptotic: $(s_2, b) = (%s, %s)$' % (int(_s2), int(_b))) plt.plot(s1Fine, np.sqrt((s1Fine / np.sqrt(s1Fine + _b / tau1)) ** 2 + (_s2 / np.sqrt(_s2 + _b / tau2)) ** 2 + (s3 / np.sqrt(s3 + _b / tau3)) ** 2), linestyle = '--', markersize = 0, color = c, label = 'Simple: $(s_2, b) = (%s, %s)$' % (int(_s2), int(_b))) plt.xlim((s1[0], s1[-1])) plt.ylim((0., 5.0)) plt.xlabel('Signal yield in SR 1 $s_1$ [a.u.]') plt.ylabel('Significance of discovery $\\textrm{med}[Z_0|\\mu^\\prime=1]$ [a.u.]') plt.xscale('log') # plt.title('3 SRs + 1 CR Asymptotic Significance: $s_3 = %s$, $\\tau_1 = %s$, $\\tau_2 = %s$, $\\tau_3 = %s$' % (int(s3), int(tau1), int(tau2), int(tau3))) plt.legend(loc = 'upper left', bbox_to_anchor = (1.0, 1.02)) plt.savefig(pjoin(plotdir, '3SRplus1CR.eps'), format = 'eps', dpi = 1200, bbox_inches = 'tight') plt.close() dump_data_to_pickle(data, datapath) ##################### ### SECTION 2.4.2 ### ##################### def Section2p4p2_vsB1(): # Set the seed np.random.seed(45) datapath = pjoin(pickledir, 'Section2p4p2_vsB1.pkl') sigma1 = 5. sigma2 = 10. s = 10. b1 = [round(n) for n in logVector(1., 1000., 10)] b2 = [5., 25., 150.] R = [[lambda th: 1. + sigma1 / 100. * th, lambda th: 1.], [lambda th: 1., lambda th: 1. + sigma2 / 100. * th]] S = [[1., 0.], [0., 1.]] colours = ['g', 'b', 'r'] data = load_data_from_pickle(datapath) for _b2, c in zip(b2, colours): k = str(int(_b2)) if not data.get(k, {}): data[k] = {'z0': [], 't0': [], 't1': []} for _b1 in b1: logger.info('On (b1, b2) = (%s, %s).' % (int(_b1), int(_b2))) z0, t0, t1 = GaussZ0_MC(s, [_b1, _b2], R, S, return_t0_and_t1 = True, sleep = 0.001, ntoys = 50000) data[k]['z0'].append(z0) data[k]['t0'].append(t0) data[k]['t1'].append(t1) plt.plot(b1, data[k]['z0'], marker = 'o', color = c, linewidth = 0, label = 'Numerical: $b_2 = %s$' % int(_b2)) b1Fine = logVector(b1[0], b1[-1], 1000) plt.plot(b1Fine, [GaussZ0_Decorr(s, [_b1, _b2], [_b1 * sigma1 / 100., _b2 * sigma2 / 100.]) for _b1 in b1Fine], linestyle = '-', markersize = 0, color = c, label = 'Asymptotic: $b_2 = %s$' % int(_b2)) plt.plot(b1Fine, s / np.sqrt(s + b1Fine + _b2 + (sigma1 / 100. * b1Fine) ** 2 + (sigma2 / 100. * _b2) ** 2), linestyle = '--', markersize = 0, color = c, label = 'Simple: $b_2 = %s$' % int(_b2)) plt.xlim((b1[0], b1[-1])) plt.ylim((0., 3.5)) plt.xlabel('Background 1 yield in SR $b_1$ [a.u.]') plt.ylabel('Significance of discovery $\\textrm{med}[Z_0|\\mu^\\prime=1]$ [a.u.]') plt.xscale('log') # plt.title('1 SR + 2 Gaussian Decorrelated Constraints Asymptotic Significance:\n$s = {}$, $\\sigma_1 = {}\\%$, $\\sigma_2 = {}\\%$'.format(int(s), int(sigma1), int(sigma2))) plt.legend(loc = 'upper right') plt.savefig(pjoin(plotdir, '1SRplus2GaussConst.eps'), format = 'eps', dpi = 1200) plt.close() dump_data_to_pickle(data, datapath) def Section2p4p2_vsSigma(): sigma1 = np.hstack([logVector(0.1, 100., 15), logVector(100., 400., 3)[1:]]) sigma2 = [1., 10., 100.] s = 10. b1 = [25., 50., 50., 150.] b2 = [25., 50., 150., 50.] colours = ['gold', 'g', 'b', 'r'] fig, axs = plt.subplots(nrows = 2, ncols = 2, sharex = 'col', sharey = 'row', figsize = [2 * 6.0, 2 * 4.0]) axs[1, 1].axis('off') for i, _sigma2 in enumerate(sigma2): # Set the seed - let's use a fresh seed on each loop iteration, as we are saving separate pickles # (this allows us to cleanly reproduce the results, per pickle, without throwing all of the toys in previous) np.random.seed(60 + i) # Dump a pickle for each sigma2 loop datapath = pjoin(pickledir, 'Section2p4p2_vsSigma_sigma2eq%s.pkl' % int(_sigma2)) data = load_data_from_pickle(datapath) if i == 0: ax = axs[0, 0] elif i == 1: ax = axs[0, 1] elif i == 2: ax = axs[1, 0] elif i == 3: continue else: ax = None for _b1, _b2, c in zip(b1, b2, colours): k = str(int(_b1)) + '_' + str(int(_b2)) if not data.get(k, {}): data[k] = {'z0': [], 't0': [], 't1': []} for _sigma1 in sigma1: logger.info('On (sigma1, sigma2, b1, b2) = (%s, %s, %s, %s).' % (round(_sigma1, 5), round(_sigma2, 5), int(_b1), int(_b2))) z0, t0, t1 = GaussZ0_MC(s, [_b1, _b2], R(_sigma1, _sigma2), S, return_t0_and_t1 = True, sleep = 0.001, ntoys = 50000, retry_first = False, skip_failed_toys = True) data[k]['z0'].append(z0) data[k]['t0'].append(t0) data[k]['t1'].append(t1) ax.plot(sigma1, data[k]['z0'], marker = 'o', color = c, linewidth = 0, label = 'Numerical: $(b_1, b_2) = (%s, %s)$' % (int(_b1), int(_b2)) if i == 0 else '') sigma1Fine = logVector(sigma1[0], sigma1[-1] if sigma1[-1] > 1000. else 1000., 1000) ax.plot(sigma1Fine, [GaussZ0_Decorr(s, [_b1, _b2], [_b1 * _sigma1 / 100., _b2 * _sigma2 / 100.]) for _sigma1 in sigma1Fine], linestyle = '-', markersize = 0, color = c, label = 'Asymptotic: $(b_1, b_2) = (%s, %s)$' % (int(_b1), int(_b2)) if i == 0 else '') ax.plot(sigma1Fine, s / np.sqrt(s + _b1 + _b2 + (sigma1Fine / 100. * _b1) ** 2 + (_sigma2 / 100. * _b2) ** 2), linestyle = '--', markersize = 0, color = c, label = 'Simple: $(b_1, b_2) = (%s, %s)$' % (int(_b1), int(_b2)) if i == 0 else '') ax.set_ylim((0., 1.4)) if i != 1: ax.set_ylabel('Significance of discovery $\\textrm{med}[Z_0|\\mu^\\prime=1]$ [a.u.]') ax.text(40, 1.2, '$s = {}$, $\\sigma_2 = {}\\%$'.format(int(s), int(_sigma2)), fontsize = 12, bbox = {'facecolor': 'white', 'pad': 10}) if i != 0: ax.set_xlim((sigma1[0], sigma1[-1] if sigma1[-1] > 1000. else 1000.)) ax.set_xlabel('Background 1 yield uncertainty in SR $\\sigma_1$ [\\%]') ax.set_xscale('log') if i == 1: ax.xaxis.set_tick_params(labelbottom = True) dump_data_to_pickle(data, datapath) # fig.suptitle('1 SR + 2 Decorrelated Gaussian Constraints Asymptotic Significance') axs[0, 0].legend(loc = 'upper left', bbox_to_anchor = (1.05, -0.15)) plt.subplots_adjust(hspace = 0.05, wspace = 0.05) # , top = 0.95, bottom = 0.05) plt.savefig(pjoin(plotdir, '1SRplus2GaussConst_err.eps'), format = 'eps', dpi = 1200, bbox_inches = 'tight') plt.close() ##################### ### SECTION 2.4.4 ### ##################### def Section2p4p4_Corr(): # Set the seed np.random.seed(47) datapath = pjoin(pickledir, 'Section2p4p4_Corr.pkl') s = 10. b1 = [round(n) for n in logVector(1., 1000., 10)] b2 = 5. sigma1 = 35. sigma2 = 70. R = [[lambda th: 1. + sigma1 / 100. * th, lambda th: 1.], [lambda th: 1., lambda th: 1. + sigma2 / 100. * th]] S = [[1., 0.75], [0.75, 1.]] data = load_data_from_pickle(datapath) if not all(data.get(k, []) for k in ['z0', 't0', 't1']): data.update({'z0': [], 't0': [], 't1': []}) for _b1 in b1: logger.info('On b1 = %s.' % int(_b1)) z0, t0, t1 = GaussZ0_MC(s, [_b1, b2], R, S, return_t0_and_t1 = True, sleep = 0.002, ntoys = 50000) data['z0'].append(z0) data['t0'].append(t0) data['t1'].append(t1) plt.plot(b1, data['z0'], marker = 'o', color = 'r', linewidth = 0, label = 'Numerical') b1Fine = logVector(b1[0], b1[-1], 1000) plt.plot(b1Fine, [GaussZ0(s = s, b = [_b1, b2], R = R, S = S) for _b1 in b1Fine], linestyle = '-', markersize = 0, color = 'r', label = 'Asymptotic (corr.)') plt.plot(b1Fine, [GaussZ0(s = s, b = [_b1, b2], R = R, S = [[1., 0.], [0., 1.]]) for _b1 in b1Fine], linestyle = ':', markersize = 0, color = 'darkred', label = 'Asymptotic (decorr.)') plt.plot(b1Fine, s / np.sqrt(s + b1Fine + b2 + (sigma1 / 100. * b1Fine) ** 2 + (sigma2 / 100. * b2) ** 2), linestyle = '--', markersize = 0, color = 'lightcoral', label = 'Simple') plt.xlim((b1[0], b1[-1])) plt.ylim((0., 2.)) plt.xlabel('Background 1 yield in SR $b_1$ [a.u.]') plt.ylabel('Significance of discovery $\\textrm{med}[Z_0|\\mu^\\prime=1]$ [a.u.]') plt.xscale('log') # plt.title('1 SR + 2 Gaussian Correlated Constraints Asymptotic Significance:\n$s = {}$, $b_2 = {}$, $\\sigma_1 = {}\\%$, $\\sigma_2 = {}\\%$'.format(int(s), int(b2), int(sigma1), int(sigma2))) plt.legend(loc = 'upper right') plt.savefig(pjoin(plotdir, '1SRplus2GaussConst_corr.eps'), format = 'eps', dpi = 1200) plt.close() dump_data_to_pickle(data, datapath) ##################### ### SECTION 2.4.5 ### ##################### def Section2p4p5_Response(): # Set the seed np.random.seed(49) def smooth_interpolate(th, func1, func2, weight): return weight(th) * func1(th) + (1. - weight(th)) * func2(th) def heaviside(th, sigma_lo, sigma_hi): return smooth_interpolate(th, lambda th: 1. + sigma_lo * th, lambda th: 1. + sigma_hi * th, lambda th: 1. - np.heaviside(th, 1.)) def arctan(th, sigma_lo, sigma_hi, k = 10.): return smooth_interpolate(th, lambda th: 1. + sigma_lo * th, lambda th: 1. + sigma_hi * th, lambda th: (1. - 2. / np.pi * np.arctan(np.pi / 2. * k * th)) / 2.) def tanh(th, sigma_lo, sigma_hi, k = 10.): return smooth_interpolate(th, lambda th: 1. + sigma_lo * th, lambda th: 1. + sigma_hi * th, lambda th: (1. - np.tanh(k * th)) / 2.) def erf(th, sigma_lo, sigma_hi, k = 10.): return smooth_interpolate(th, lambda th: 1. + sigma_lo * th, lambda th: 1. + sigma_hi * th, lambda th: (1. - scipy.special.erf(k * th)) / 2.) def sigmoid(th, sigma_lo, sigma_hi, k = 10.): return smooth_interpolate(th, lambda th: 1. + sigma_lo * th, lambda th: 1. + sigma_hi * th, lambda th: 1. - 1. / (1. + np.exp(-k * th))) response_functions = {'Heaviside': (heaviside, 'k', '-'), 'arctan': (arctan, 'g', '--'), 'tanh': (tanh, 'b', ':'), 'erf': (erf, 'r', '-.'), 'sigmoid': (sigmoid, 'gold', '-')} sigma_lo = 0.20 sigma_hi = 0.35 th = np.linspace(-1., +1., 1000) for l, (f, c, ls) in response_functions.items(): plt.plot(th, f(th, sigma_lo, sigma_hi), color = c, label = l, linestyle = ls) plt.xlim((th[0], th[-1])) plt.ylim((1. - sigma_lo, 1. + sigma_hi)) plt.xlabel('Nuisance parameter $\\theta$ [a.u.]') plt.ylabel('Response function $R(\\theta)$ [a.u.]') # plt.title('Different Response Functions') plt.legend(loc = 'upper left') plt.savefig(pjoin(plotdir, 'response_functions.eps'), format = 'eps', dpi = 1200) plt.xlim((-0.2, +0.2)) plt.ylim((0.95, 1.075)) plt.savefig(pjoin(plotdir, 'response_functions_zoomed.eps'), format = 'eps', dpi = 1200) plt.close() # 1st derivatives: th = np.linspace(-1., +1., 1000) for l, (f, c, ls) in response_functions.items(): plt.plot(th, scipy.misc.derivative(lambda th: f(th, sigma_lo, sigma_hi), th, dx = 1e-6), color = c, label = l, linestyle = ls) plt.xlim((th[0], th[-1])) plt.ylim((0.15, 0.40)) plt.xlabel('Nuisance parameter $\\theta$ [a.u.]') plt.ylabel('Derivative of response function $dR(\\theta)/d\\theta$ [a.u.]') # plt.title('Dervatives of Different Response Functions') plt.legend(loc = 'upper left') plt.savefig(pjoin(plotdir, 'response_functions_derivatives.eps'), format = 'eps', dpi = 1200) plt.close() s = 10. b1 = logVector(1., 10000., 100) b2 = 5. sigma1_lo = 20. / 100. sigma1_hi = 35. / 100. sigma2_lo = 70. / 100. sigma2_hi = 90. / 100. R = lambda sigma1_lo, sigma1_hi, sigma2_lo, sigma2_hi: [[lambda th: f(th, sigma1_lo, sigma1_hi), lambda th: 1.], [lambda th: 1., lambda th: f(th, sigma2_lo, sigma2_hi)]] S = [[1., 0.75], [0.75, 1.]] for l, (f, c, ls) in response_functions.items(): plt.plot(b1, [GaussZ0(s = s, b = [_b1, b2], R = R(sigma1_lo, sigma1_hi, sigma2_lo, sigma2_hi), S = S) for _b1 in b1], linestyle = ls, markersize = 0, color = c, label = l) plt.xlim((b1[0], b1[-1])) plt.ylim((0.001, 10.)) plt.xlabel('Background 1 yield in SR $b_1$ [a.u.]') plt.ylabel('Significance of discovery $\\textrm{med}[Z_0|\\mu^\\prime=1]$ [a.u.]') plt.xscale('log') plt.yscale('log') # plt.title('Sensitivities for Different Response Functions:\n$s = {}$, $b_2 = {}$'.format(int(s), int(b2))) plt.legend(loc = 'upper right') plt.savefig(pjoin(plotdir, 'response_functions_z0_b2eq%s.eps' % int(b2)), format = 'eps', dpi = 1200, bbox_inches = 'tight') plt.close() s = 100. b2 = 10000. for l, (f, c, ls) in response_functions.items(): plt.plot(b1, [GaussZ0(s = s, b = [_b1, b2], R = R(sigma1_lo, sigma1_hi, sigma2_lo, sigma2_hi), S = S) for _b1 in b1], linestyle = ls, markersize = 0, color = c, label = l) plt.xlim((b1[0], b1[-1])) plt.ylim((0.008, 0.02)) plt.xlabel('Background 1 yield in SR $b_1$ [a.u.]') plt.ylabel('Significance of discovery $\\textrm{med}[Z_0|\\mu^\\prime=1]$ [a.u.]') plt.xscale('log') plt.yscale('log') # plt.title('Sensitivities for Different Response Functions:\n$s = {}$, $b_2 = {}$'.format(int(s), int(b2))) plt.legend(loc = 'upper right') plt.savefig(pjoin(plotdir, 'response_functions_z0_b2eq%s.eps' % int(b2)), format = 'eps', dpi = 1200, bbox_inches = 'tight') plt.close() ##################### ### SECTION 2.4.6 ### ##################### def Section2p4p6_CPU(): # Set the seed np.random.seed(48) datapath = pjoin(pickledir, 'Section2p4p6_CPU.pkl') s = 10. b1 = 10. b2 = 5. sigma1 = 35. sigma2 = 70. R = [[lambda th: 1. + sigma1 / 100. * th, lambda th: 1.], [lambda th: 1., lambda th: 1. + sigma2 / 100. * th]] S = [[1., 0.75], [0.75, 1.]] ntoys = [round(n) for n in logVector(1000, 1000000, 40)] data = load_data_from_pickle(datapath) if not all(data.get(k, []) for k in ['z0', 't0', 't1', 'cpu']): data.update({'z0': [], 't0': [], 't1': [], 'cpu': []}) for _ntoys in ntoys: logger.info('On ntoys = %s.' % int(_ntoys)) logging.getLogger(asymptotic_formulae.__name__).setLevel(level = logging.WARNING) start = time.clock() z0, t0, t1 = GaussZ0_MC(s, [b1, b2], R, S, return_t0_and_t1 = True, sleep = 0.001, ntoys = _ntoys, retry_first = False, skip_failed_toys = True) stop = time.clock() logging.getLogger(asymptotic_formulae.__name__).setLevel(level = logging.DEBUG) data['z0'].append(z0) data['t0'].append(t0) data['t1'].append(t1) delta = stop - start logger.info('Z0 = %s, CPU time = %s s.' % (z0, delta)) data['cpu'].append(delta) if not all(data.get(k, []) for k in ['cpu_asymptotic', 'z0_asymptotic']): data['cpu_asymptotic'] = [] data['z0_asymptotic'] = [] for i in range(len(ntoys)): logger.info('On iteration %s.' % i) logging.getLogger(asymptotic_formulae.__name__).setLevel(level = logging.WARNING) start = time.clock() z0 = GaussZ0(s = s, b = [b1, b2], R = R, S = S) stop = time.clock() logging.getLogger(asymptotic_formulae.__name__).setLevel(level = logging.DEBUG) delta = stop - start logger.info('CPU time = %s s.' % delta) data['cpu_asymptotic'].append(delta) data['z0_asymptotic'].append(z0) z0 = GaussZ0(s = s, b = [b1, b2], R = R, S = S) fig = plt.figure() fig, axs = plt.subplots(2, 1, sharex = True) fig.subplots_adjust(hspace = 0.1) # fig.suptitle('CPU Comparisons: Numerical vs. Asymptotic for Gaussian Constraints') axs[0].plot(ntoys, data['z0'], color = 'darkorange', label = 'Numerical') axs[0].plot(ntoys, data['z0_asymptotic'], color = 'navy', label = 'Asymptotic') axs[0].set_ylabel('Significance of discovery [a.u.]') axs[0].set_ylim((1.15, 1.30)) axs[0].legend(loc = 'upper right') axs[1].plot(ntoys, data['cpu'], color = 'darkorange', label = 'Numerical') axs[1].plot(ntoys, data['cpu_asymptotic'], color = 'navy', label = 'Asymptotic') axs[1].set_xlabel('Number of toys [a.u.]') axs[1].set_ylabel('CPU time [s]') axs[1].set_xlim((ntoys[0], ntoys[-1])) axs[1].set_ylim((1e-3, 1e4)) axs[1].set_xscale('log') axs[1].set_yscale('log') plt.savefig(pjoin(plotdir, 'Section2p4p2_CPU.eps'), format = 'eps', dpi = 1200) plt.close() dump_data_to_pickle(data, datapath) Section2p1p1() Section2p1p2() Section2p2p1() Section2p4p2_vsB1() Section2p4p2_vsSigma() Section2p4p4_Corr() Section2p4p5_Response() Section2p4p6_CPU() if __name__ == '__main__': main()

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8d783ab1b46b55a24509d554110a68bdbb340935

11,660

py

Python

montecarlo/mcpy/monte_carlo.py

v-asatha/EconML

eb9ac829e93abbc8a163ab09d905b40370b21b1a

[ "MIT" ]

null

montecarlo/mcpy/monte_carlo.py

v-asatha/EconML

eb9ac829e93abbc8a163ab09d905b40370b21b1a

[ "MIT" ]

null

montecarlo/mcpy/monte_carlo.py

v-asatha/EconML

eb9ac829e93abbc8a163ab09d905b40370b21b1a

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. import os import sys import numpy as np from joblib import Parallel, delayed import joblib import argparse import importlib from itertools import product import collections from copy import deepcopy from mcpy.utils import filesafe from mcpy import plotting def check_valid_config(config): """ Performs a basic check of the config file, checking if the necessary subsections are present. If multiple config files are being made that use the same dgps and/or methods, it may be helpful to tailor the config check to those dgps and methods. That way, one can check that the correct parameters are being provided for those dgps and methods. This is specific to one's implementation, however. """ assert 'type' in config, "config dict must specify config type" assert 'dgps' in config, "config dict must contain dgps" assert 'dgp_opts' in config, "config dict must contain dgp_opts" assert 'method_opts' in config, "config dict must contain method_opts" assert 'mc_opts' in config, "config dict must contain mc_opts" assert 'metrics' in config, "config dict must contain metrics" assert 'methods' in config, "config dict must contain methods" assert 'plots' in config, "config dict must contain plots" assert 'single_summary_metrics' in config, "config dict must specify which metrics are plotted in a y-x plot vs. as a single value per dgp and method" assert 'target_dir' in config, "config must contain target_dir" assert 'reload_results' in config, "config must contain reload_results" assert 'n_experiments' in config['mc_opts'], "config[mc_opts] must contain n_experiments" assert 'seed' in config['mc_opts'], "config[mc_opts] must contain seed" class MonteCarlo: """ This class contains methods to run (multiple) monte carlo experiments Experiments are constructed from a config file, which mainly consists of references to the implementations of four different kinds of items, in addition to various parameters for the experiment. See the README for a descriptoin of the config file, or look at an example in the configs directory. The four main items are: - data generating processes (dgps): functions that generate data according to some assumed underlying model - methods: functions that take in data and produce other data. In our case, they train on data produced by DGPs and then produce counterfactual estimates - metrics: functions that take in the results of estimators and calculate metrics - plots: functions that take in the metric results, etc. and generate plots """ def __init__(self, config): self.config = config check_valid_config(self.config) # these param strings are for properly naming results saved to disk config['param_str'] = '_'.join(['{}_{}'.format(filesafe(k), v) for k,v in self.config['mc_opts'].items()]) config['param_str'] += '_' + '_'.join(['{}_{}'.format(filesafe(k), v) for k,v in self.config['dgp_opts'].items()]) config['param_str'] += '_' + '_'.join(['{}_{}'.format(filesafe(k), v) for k,v in self.config['method_opts'].items()]) def experiment(self, instance_params, seed): """ Given instance parameters to pass on to the data generating processes, runs an experiment on a single randomly generated instance of data and returns the parameter estimates for each method and the evaluated metrics for each method. Parameters ---------- instance_params : dictionary instance paramaters that DGP functions may use seed : int random seed for random data generation Returns ------- experiment_results : dictionary results of the experiment, depending on what the methods return. These are stored by dgp_name and then by method_name. true_params : dictionary true parameters of the DGP, indexed by dgp_name, used for metrics calculation downstream """ np.random.seed(seed) experiment_results = {} true_params = {} for dgp_name, dgp_fn in self.config['dgps'].items(): data, true_param = dgp_fn(self.config['dgp_opts'][dgp_name], instance_params[dgp_name], seed) true_params[dgp_name] = true_param experiment_results[dgp_name] = {} for method_name, method in self.config['methods'].items(): experiment_results[dgp_name][method_name] = method(data, self.config['method_opts'][method_name], seed) return experiment_results, true_params def run(self): """ Runs multiple experiments in parallel on randomly generated instances and samples and returns the results for each method and the evaluated metrics for each method across all experiments. Returns ------- simulation_results : dictionary dictionary indexed by [dgp_name][method_name] for individual experiment results metric_results : dictionary dictionary indexed by [dgp_name][method_name][metric_name] true_param : dictinoary dictionary indexed by [dgp_name] """ random_seed = self.config['mc_opts']['seed'] if not os.path.exists(self.config['target_dir']): os.makedirs(self.config['target_dir']) instance_params = {} for dgp_name in self.config['dgps']: instance_params[dgp_name] = self.config['dgp_instance_fns'][dgp_name](self.config['dgp_opts'][dgp_name], random_seed) # results_file = os.path.join(self.config['target_dir'], 'results_{}.jbl'.format(self.config['param_str'])) results_file = os.path.join(self.config['target_dir'], 'results_seed{}.jbl'.format(random_seed)) if self.config['reload_results'] and os.path.exists(results_file): results = joblib.load(results_file) else: results = Parallel(n_jobs=-1, verbose=1)( delayed(self.experiment)(instance_params, random_seed + exp_id) for exp_id in range(self.config['mc_opts']['n_experiments'])) joblib.dump(results, results_file) simulation_results = {} # note that simulation_results is a vector of individual experiment_results. from experiment() metric_results = {} true_params = {} for dgp_name in self.config['dgps'].keys(): simulation_results[dgp_name] = {} metric_results[dgp_name] = {} for method_name in self.config['methods'].keys(): simulation_results[dgp_name][method_name] = [results[i][0][dgp_name][method_name] for i in range(self.config['mc_opts']['n_experiments'])] true_params[dgp_name] = [results[i][1][dgp_name] for i in range(self.config['mc_opts']['n_experiments'])] metric_results[dgp_name][method_name] = {} for metric_name, metric_fn in self.config['metrics'].items(): # for metric_name, metric_fn in self.config['metrics'][method_name].items(): # for method specific parameters metric_results[dgp_name][method_name][metric_name] = metric_fn(simulation_results[dgp_name][method_name], true_params[dgp_name]) for plot_name, plot_fn in self.config['plots'].items(): # for plot_name, plot_fn in self.config['plots'][method_name].items(): # for method specific plots if isinstance(plot_fn, dict): plotting.instance_plot(plot_name, simulation_results, metric_results, self.config, plot_fn) else: plot_fn(plot_name, simulation_results, metric_results, true_params, self.config) return simulation_results, metric_results, true_params class MonteCarloSweep: """ This class contains methods to run sets of multiple monte carlo experiments where each set of experiments has different parameters (for the dgps and methods, etc.). This enables sweeping through parameter values to generate results for each permutation of parameters. For example, running a simulation when the number of samples a specific DGP generates is 100, 1000, or 10000. """ def __init__(self, config): self.config = config check_valid_config(self.config) config['param_str'] = '_'.join(['{}_{}'.format(filesafe(k), self.stringify_param(v)) for k,v in self.config['mc_opts'].items()]) config['param_str'] += '_' + '_'.join(['{}_{}'.format(filesafe(k), self.stringify_param(v)) for k,v in self.config['dgp_opts'].items()]) config['param_str'] += '_' + '_'.join(['{}_{}'.format(filesafe(k), self.stringify_param(v)) for k,v in self.config['method_opts'].items()]) def stringify_param(self, param): """ Parameters ---------- param : list list denoting the various values a parameter should take Returns ------- A string representation of the range of the values that parameter will take """ if hasattr(param, "__len__"): return '{}_to_{}'.format(np.min(param), np.max(param)) else: return param def run(self): """ Runs many monte carlo simulations for all the permutations of parameters specified in the config file. Returns ------- sweep_keys : list list of all the permutations of parameters for each dgp sweep_sim_results : list list of simulation results for each permutation of parameters for each dgp sweep_metrics : list list of metric results for each permutation of parameters for each dgp sweep_true_params : list list of true parameters for each permutation of parameters for each dgp """ # currently duplicates computation for the dgps because all only one dgp param changed each config # need to make it so that every inst_config is different for each dgp for dgp_name in self.config['dgp_opts'].keys(): dgp_sweep_params = [] dgp_sweep_param_vals = [] for dgp_key, dgp_val in self.config['dgp_opts'][dgp_name].items(): if hasattr(dgp_val, "__len__"): dgp_sweep_params.append(dgp_key) dgp_sweep_param_vals.append(dgp_val) sweep_keys = [] sweep_sim_results = [] sweep_metrics = [] sweep_true_params = [] inst_config = deepcopy(self.config) for vec in product(*dgp_sweep_param_vals): setting = list(zip(dgp_sweep_params, vec)) for k,v in setting: inst_config['dgp_opts'][dgp_name][k] = v simulation_results, metrics, true_params = MonteCarlo(inst_config).run() sweep_keys.append(setting) sweep_sim_results.append(simulation_results) sweep_metrics.append(metrics) sweep_true_params.append(true_params) for plot_name, plot_fn in self.config['sweep_plots'].items(): if isinstance(plot_fn, dict): plotting.sweep_plot(plot_key, sweep_keys, sweep_sim_results, sweep_metrics, self.config, plot_fn) else: plot_fn(plot_name, sweep_keys, sweep_sim_results, sweep_metrics, sweep_true_params, self.config) return sweep_keys, sweep_sim_results, sweep_metrics, sweep_true_params

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0.222222

0

null

0

null

0

1

0

8d7ad5ef06de97e8b617443c00cdb60123831b97

5,845

py

Python

MusicGame.py

kfparri/MusicGame

f2914cae7a68585ca1a569c78ac13f68c1adb827

[ "MIT" ]

null

MusicGame.py

kfparri/MusicGame

f2914cae7a68585ca1a569c78ac13f68c1adb827

[ "MIT" ]

null

MusicGame.py

kfparri/MusicGame

f2914cae7a68585ca1a569c78ac13f68c1adb827

[ "MIT" ]

null

#------------------------------------------------------------------------------------------------------ # File Name: MusicGame.py # Author: Kyle Parrish # Date: 7/4/2014 # Description: This is a simple program that I wrote for the raspberry pi so that my daughter can # play with. It is a simple program that plays a different sound with every keystroke. It also # displays a simple shape pattern on the screen with each keypress. The pi can also be setup to # allow users to change the sounds by uploading them to a web form on the pi itself. This code # will be included when it is finished. # Change log: # 4.30.15 - Updated the header to test out Visual Studio Code git integration # 9.18.15 - Started making some changes to the application. Natalie is starting to enjoy # the application so I'm starting to make it do more: # - Updated the code to put circles as well as squares on the screen. #------------------------------------------------------------------------------------------------------ # Basic imports for the game import os,sys,datetime, sqlite3 import pygame # I don't believe that I need the time references anymore, to be removed with next commit #from time import strftime, localtime from random import randint from pygame.locals import * # Setup basic constants test = 640 # Screen height and width SCREEN_WIDTH = test SCREEN_HEIGHT = 480 #CENTER_POINT = (SCREEN_WIDTH / 2, SCREEN_HEIGHT / 2) #LOWER_CENTER = (SCREEN_WIDTH / 2, SCREEN_HEIGHT / 4) #CENTER_RECT_HEIGHT = 40 #CLOCK_TEXT_FONT = 48 # Colors, any of these can be used in the program WHITE = (255, 255, 255) BLACK = (0, 0, 0) RED = (255, 0, 0) GREEN = (0, 255, 0) BLUE = (0, 0, 255) MATRIX_GREEN = (0, 255, 21) # Code taken from: http://code.activestate.com/recipes/521884-play-sound-files-with-pygame-in-a-cross-platform-m/ # global constants FREQ = 44100 # same as audio CD BITSIZE = -16 # unsigned 16 bit CHANNELS = 2 # 1 == mono, 2 == stereo BUFFER = 1024 # audio buffer size in no. of samples FRAMERATE = 30 # how often to check if playback has finished sounds = ["Typewrit-Intermed-538_hifi.ogg", "Typewrit-Bell-Patrick-8344_hifi.ogg", "Arcade_S-wwwbeat-8528_hifi.ogg", "Arcade_S-wwwbeat-8529_hifi.ogg", "Arcade_S-wwwbeat-8530_hifi.ogg", "Arcade_S-wwwbeat-8531_hifi.ogg", "PowerUp-Mark_E_B-8070_hifi.ogg", "PulseGun-Mark_E_B-7843_hifi.ogg", "PulseSho-Mark_E_B-8071_hifi.ogg", "SineySpa-Mark_E_B-7844_hifi.ogg", "ToySpace-Mark_E_B-7846_hifi.ogg", "ZipUp-Mark_E_B-8079_hifi.ogg"] soundFiles = [] def playsound(soundfile): """Play sound through default mixer channel in blocking manner. This will load the whole sound into memory before playback """ soundfile.play() #sound = pygame.mixer.Sound(soundfile) #clock = pygame.time.Clock() #sound.play() #while pygame.mixer.get_busy(): #clock.tick(FRAMERATE) def drawMyRect(surface): #pygame.draw.rect(screen, color, (x,y,width,height), thickness) pygame.draw.rect(surface, RED, (randint(0,600), randint(0,440), 40,40), 5) return surface def drawMyCircle(surface): pygame.draw.circle(surface, GREEN, (randint(0,600), randint(0,440)), 20, 5) return surface def main(): pygame.mixer.pre_init(44100,-16,2, 1024) pygame.init() screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT)) pygame.display.set_caption('Music Game') drawCircle = True # create background background = pygame.Surface(screen.get_size()) background = background.convert() #allocate all the sound files, this should make it work better... for file in sounds: tempsound = pygame.mixer.Sound(file) soundFiles.append(tempsound) # hide the mouse # not used while developing #pygame.mouse.set_visible(False) #pygame.draw.rect(screen, color, (x,y,width,height), thickness) #pygame.draw.rect(background, RED, (10,10,40,40), 5) #drawMyRect(background) screen.blit(background, (0,0)) pygame.display.update() soundfile = "Typewrit-Intermed-538_hifi.ogg" soundfile3 = "Typewrit-Bell-Patrick-8344_hifi.ogg" # main loop while 1: # This needs to change to match the new way of checking that I found on the web # http://stackoverflow.com/questions/12556535/programming-pygame-so-that-i-can-press-multiple-keys-at-once-to-get-my-character updateScreen = False resetScreen = False for event in pygame.event.get(): if event.type == QUIT: pygame.quit() sys.exit() return elif event.type == KEYDOWN: keys = pygame.key.get_pressed() #print(len(keys)) if keys[K_ESCAPE] and keys[K_LCTRL]: pygame.quit() sys.exit() elif keys[K_ESCAPE]: resetScreen = True; soundFiles[1].play() #playsound(soundFiles[1]) else: updateScreen = True soundFiles[0].play() #playsound(soundFiles[0]) if resetScreen: background = pygame.Surface(screen.get_size()) background = background.convert() screen.blit(background, (0,0)) pygame.display.update() if updateScreen: if drawCircle: drawMyCircle(background) else: drawMyRect(background) drawCircle = not drawCircle screen.blit(background, (0,0)) pygame.display.update() if __name__ == '__main__': main()

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Python

DeepLearning/DeepLearning/07_Deep_LeeYS/Week_1/4. Single-Layer NN/4) Neural Network.py

ghost9023/DeepLearningPythonStudy

4d319c8729472cc5f490935854441a2d4b4e8818

[ "MIT" ]

1

2019-06-27T04:05:59.000Z

DeepLearning/DeepLearning/07_Deep_LeeYS/Week_1/4. Single-Layer NN/4) Neural Network.py

ghost9023/DeepLearningPythonStudy

4d319c8729472cc5f490935854441a2d4b4e8818

[ "MIT" ]

null

DeepLearning/DeepLearning/07_Deep_LeeYS/Week_1/4. Single-Layer NN/4) Neural Network.py

ghost9023/DeepLearningPythonStudy

4d319c8729472cc5f490935854441a2d4b4e8818

[ "MIT" ]

null

#4) 신경망 구현하기 ##########KEYWORD############### ################################ #신경망은 입력층에서 출력층으로 기본적으로 한 방향으로 흐른다. 한 싸이클이 끝나면 역전파 알고리즘을 통해 #계속 학습으 진행하지만 역전파 알고리즘과 같은 고급 알고리즘은 다음장에서.. #한 방향으로만 정보가 전방으로 전달되는 신경망을 피드포워드 신경망(Feed Forward NN)이라고 한다. #기본적으로 신경망은 입력층에서 데이터 입력을 받은 뒤 은닉층에서 데이터를 학습하고 출력층으로 결과를 내보낸다. #입력층의 역할은 입력 데이터를 받아들이는 것이고 이를 위해서 입력층의 노드(뉴런) 개수는 입력데이터의 특성 갯수와 일치해야 한다. #은닉층은 학습을 진행하는 층으로 은닉층의 노드 수와 은닉층 Layer 수는 설계자가 경험으로 얻어내야 한다. #뉴런의 수가 너무 많으면 오버피팅이 발생하고 너무 적으면 언더피팅이 발생하여 학습이 되지 않음. #또한 은닉층의 개수가 지나치게 많은 경우 비효율적이다. #단순히 은닉층의 개수를 2배 늘리면 연산에 걸리는 시간은 400% 증가하지만 학습효율은 10%만 증가하기도 한다. #출력층은 은닉층을 거쳐서 얻어낸 결과를 해결하고자 하는 문제에 맞게 만들어 준다. #필기체 숫자 0부터 9까지를 인식하는 신경망이면 출력층이 10개가 될 것이고 개와 고양이를 분류하는 신경망이라면 3개의 출력층이 된다. #다차원 배열을 이용하여 층이 3개인 다층 신경망을 간단하게 구현하자.행렬곱과 각 행렬의 원소의 위치를 잘 확인하면 어렵지 않다. #그림25 P35 # #Layer Node 수 Node Shape Weight Shape Bias Shape 계산식 #입력층 2 2차원 벡터 2 X 3 Matrix 3차원 Vector 은닉층(1) = 활성화함수((입력층*가중치1) + 편향1) #은닉층(1) 3 3차원 벡터 3 X 2 Matrix 2차원 Vector 은닉층(2) = 활성화함수((은닉층1) * 가중치2 + 편향2) #은닉층(2) 2 2차원 벡터 2 X 2 Matrix 2차원 Vector 출력층 = 활성화함수((은닉층2) * 가중치3 + 편향3) #출력층 2 2차원 벡터 #그림을 확인해보면 3층 신경망이 어떻게 구성되어 있는지 확인할 수 있다. #입력층은 2개이며 각 층마다 편향이 존재한다. 은닉층은 2개 층으로 구성되어 있고 출력층의 출력값은 2개이다. #위 그림을 확인해보면 #w12^(1), a2(1) 와 같은 형식으로 표기되어 있는 것을 확인 할 수 있다. 우측 상단의 (1)은 1층의 가중치를 의미한다. #우측 하단의 12에서 1은 다음층의 뉴런번호 2는 앞층의 뉴런 번호를 의미한다. 따라서 w12^(!)은 앞의 1번 뉴런에서 2번 뉴런으로 이동하는 신경망 1층의 가중치 #를 의미한다. #예제 3층 신경망의 구조를 보면 입력층은 2개로 구성되어 있고 1층에서 편향이 1로 존재한다. 여기서 가중치에 의해 #입력 값은 a1(1) ... 에 입력된다. 이 입력값을 수식으로 나타내면 #a1(1) = w11^(1)x1 + w12^(1)x2 + b1^(1) 으로 표현할 수 있다. #이를 행렬 내적으로 표현하면 1층의 노드를 A^(1) = (a1^(1),a2^(1),a3^(1)), 1층의 가중치를 #W^(1) ... #이를 이용해서 numpy의 다차원 배열을 이용하면 신경망 1층을 파이선 코드로 짤 수 있다. #마찬가지로 1층의 출력값을 다시 2층의 입력값으로 넣고 똑같은 방식으로 입력노드 행렬(1층의 출력노드 행렬), 가중치 행렬, 편향 행렬의 #행렬 연산을 통해 2층의 출력 노드 행렬을 구할 수 있게 된다. #마찬가지로 신경망 1층에서 행렬 연산식을 통해 출력값을 구했던 것처럼 1층의 출력값을 2층의 입력값으로 연결해주고 2층의 가중치와 2층의 편향을 #더해주면 2층의 출력값이 완성된다. #마지막으로 그림30 처럼 2층의 출력값을 동일한 방법으로 출력층의 입력값으로 넣고 출력층 사이의 가중치와 편향을 더해준 동일한 방법으로 #식을 계산하면 최정적인 출력값이 뽑히게 된다. 한가지 위 과정과 다른 점이 있다면 출력층의 활성함수는 풀고자하는 문제의 성질에 맞게 정한다. #회귀가 목적인 신경망은 출력층에 항등함수를 사용하고 이중클래스 분류에는 시그모이드 함수를 다중 클래스에는 소프트맥스 함수를 일반적으로 사용. #그럼 출력층에 사용하는 활성함수를 알아보자. #회귀에는 항등함수, 분류에는 소프트맥스 함수를 보통 사용한다. 회귀는 입력데이터의 연속적인 수치를 예측하는 것을 의미하고 분류는 각 데이터가 어떤 #범주에 속하는지 나누는 것을 의미한다. 항등함수는 입력값이 그대로 출력되는 함수로 흔히 알고 있는 f(x) = x 를 의미한다. #파이선 코드로는 def identity_function(x): return x #소프트맥스 함수는 자연상수를 밑수로 하는 지수함수로 이루어진 하나의 함수이다. #소프트맥스 함수가 가지는 의미는 바로 시그모이드 함수를 일반화 한 것. #이를 통해 각 클래스에 대한 확률을 계산 할 수도 있게 됨. #시그모이드 함수를 일반화해서 각 클래스에 대한 확률을 계산 할 수 있다는 것은 모든 소프트맥스 함수의 출력값을 더하면 1이 나온다는 의미이다. #소프트맥스 함수 출력값은 0과 1사이의 값이고 각각의 출력 값은 개별 출력 값에 대한 확률 값이기 때문에 전체 소프트맥스 함수의 합은 항상 #1이 되는 특별한 성질을 가진다. #때문에 소프트 맥스 함수를 출력층의 활성함수로 사용하면 출력결과를 확률적으로 결론낼 수 있다. #예를 들어 #y[0] = 0.018, y[1] = 0.245, y[2] = 0.737로 결과가 출력되었다면 1.8%의 확률로 0번 클래스, 24.5%의 확률로 1번 클래스, 73.7%의 확률로 2번 #클래스일 것이므로 2번 클래스일 확률이 가장 높고 따라서 답은 2번 클래스다. 라는 결과를 도출 할 수 있다. #소프트맥스 함수를 이용해서 통계적(확률적)으로 문제를 대응할 수 있게 되는 것이다. \ #소프트맥스 함수는 단조 증가 함수인 지수함수 exp()를 기반으로 하므로 소프트맥스 함수의 출력값의 대소관계가 그대로 입력된 원소의 대소관계를 이어받는다. #따라서 역으로 소프트맥스 함수를 통해 나온 출력값의 대소관계를 입력값의 대소관계로 판단해도 된다. #그래서 신경망 학습과정에선 출력층의 활성함수로 소프트맥스 함수를 사용하고 학습된 모델을 이용해서 추론(분류 및 회귀)하는 과정에선 소프트맥스 함수를 #활성함수에서 생략해도 된다. 이러한 소프트맥스 함수의 구현엔 주의사항이 있다. #지수함수는 입력값이 커지면 급격하게 무한히 증가한다. 이를 오버플로우(Overflow)라고 한다. #입력값이 100인 exp(100)은 10의 40승이 넘는 수이다. 오버플로를 해결하기 위해선 해당 값을 전체 데이터 셋에서의 최대값으로 뺀 값으로 치환하는 방법을 사용한다. #위 과정을 수식으로 나타낼 수 있다. [수식 13] P40 #소프트맥스 함수의 분모 분자에 C라는 상수를 곱해준다. 같은 상수값을 곱해주었으므로 전체 값엔 변화가 없다. #그리고 여기에 지수함수와 로그함수의 성질 중 하나인 x = a ^ log(a,x)를 이용하여 상수 C를 exp() 함수 안으로 넣는다. #그럼 상수 C는 exp() 함수 내에서 log(e,C) = ln C 로 변화되고 ln C를 상수 C` 로 받게 되면 아래의 수식으로 변형된다. #파이선 코드 import numpy as np a = np.array([1010,1000,990]) np.exp(a) / np.sum(np.exp(a)) #오버플로 발생 #변경된 softmax 함수식 c = np.max(a) np.exp(a-c) / np.sum(np.exp(a-c)) #정상적으로 계산됨 #이처럼 같은 스케일의 변화는 아무런 결과값에 아무런 영향을 주지 않는 점을 이용해서 소프트맥스 함수의 오버플로 현상을 해결할 수 있다. #이를 이용하여 소프트맥스 함수를 파이썬으로 구현하면 아래와 같다. def softmax(a): c=np.max(a) exp_a = np.exp(a-c) sum_exp_a = np.sum(exp_a) y = exp_a / sum_exp_a return y #마지막으로 출력층의 노드 개수를 정하는 방법은 간단하다. 입력한 데이터의 클래스 갯수만큼 출력층의 노드 갯수를 정해주면 된다. #다른 예로 개와 고양이를 분류하고 싶다면 개, 고양이 총 2개의 출력 노드를 만들면 된다. #은닉층이 2개인 다층 신경망(보통 입력층을 제외한 층수로 신경망을 부른다. 따라서 이 경우는 3층 신경망) #을 간단하게 파이선으로 코딩. #이 신경망 모델은 출력층의 활성 함수로 항등함수로 정의한다. #결과적으로 위 과정을 모두 합한 전체적인 은닉층이 2층인 다층 신경망의 파이썬 구현코드는 아래와 같다. import numpy as np #시그모이드 함수 def sigmoid(x): return 1 / (1 + np.exp(-x)) #identify function 항등함수 사용 def identify_function(x): return x #신경망을 초기화. 여기서 가중치와 편향의 다차원 배열을 선언해준다. def init_network(): network = {} network['w1'] = np.array([[0.1,0.3,0.5],[0.2,0.4,0.6]]) network['b1'] = np.array([0.1,0.2,0.3]) network['w2'] = np.array([[0.1,0.4],[0.2,0.5],[0.3,0.6]]) network['b2'] = np.array([0.1,0.2]) network['w3'] = np.array([[0.1,0.3],[0.2,0.4]]) network['b3'] = np.array([0.1,0.2]) return network #순전파 신경망 함수. 가중치와 편향을 입력받아 입력층과 은닉층의 활성함수는 시그모이드 함수로, #출력층의 활성함수는 항등함수를 사용하는 3층 신경망을 함수로 구현 def forward(network,x): w1,w2,w3 = network['w1'],network['w2'],network['w3'] b1,b2,b3 = network['b1'],network['bw'],network['b3'] a1 = np.dot(x,w1) + b1 z1 = sigmoid(a1) a2 = np.dot(z1,w2) + b2 z2 = sigmoid(a2) a3 = np.dot(z2,w3) + b3 y = identify_function(a3) return y network = init_network() #신경망의 가중치와 편향값 인스턴스화 x = np.array([1.0,0.5]) y = forward(network ,x) print(y) #단순한 신경망을 설계하는 것은 어렵지 않다. 다차원 배열을 잘 사용해서 가중치와 입력값 그리고 편향을 잘 도출해서 어떤 활성함수를 사용할지 정해서 #구현한 다음 구현한 활성함수에 값을 잘 넣어준 다음 이전 층의 출력값으로 잘 연결해서 원하는 층만큼 이어주면 된다.

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null

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null

0

1

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8d7eb5aaefc17250eb9787e23ab1f5200d2d65f8

466

py

Python

label_gen.py

avasid/gaze_detection

dbb76a2b3f3eedff5801b53bc95b3a95bc715bc5

[ "MIT" ]

1

2020-02-07T21:34:10.000Z

label_gen.py

avasid/gaze_detection

dbb76a2b3f3eedff5801b53bc95b3a95bc715bc5

[ "MIT" ]

8

2020-11-13T18:37:12.000Z

2022-03-12T00:14:04.000Z

label_gen.py

avasid/gaze_detection

dbb76a2b3f3eedff5801b53bc95b3a95bc715bc5

[ "MIT" ]

null

import os import pandas as pd dictt = {} i = 0 for label in ['down', 'up', 'left', 'right']: img_lst = os.listdir("./data/img_data/" + label + "/") temp_label = [0] * 4 temp_label[i] = 1 for img in img_lst: print(label + " " + img) dictt[img] = temp_label i += 1 label_df = pd.DataFrame(data=dictt, index=['down', 'up', 'left', 'right']).transpose() label_df = label_df.sample(frac=1) label_df.to_csv("./data/label_data.csv")

23.3

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8d7fb31d8d0c397a081d7685e96fa1bf8414f9a6

2,398

py

Python

rubik_race/rubiks_race/solver_test.py

ZengLawrence/rubiks_race

3d78484f0a68c7e483953cea68130f1edde2739a

[ "MIT" ]

null

rubik_race/rubiks_race/solver_test.py

ZengLawrence/rubiks_race

3d78484f0a68c7e483953cea68130f1edde2739a

[ "MIT" ]

null

rubik_race/rubiks_race/solver_test.py

ZengLawrence/rubiks_race

3d78484f0a68c7e483953cea68130f1edde2739a

[ "MIT" ]

null

''' Created on Jun 27, 2017 @author: lawrencezeng ''' import unittest from rubiks_race import solver class Test(unittest.TestCase): def setUp(self): self.initial_position = [ ['g', 'g', 'y', 'r', 'r' ], ['w', 'g', 'w', 'w', 'y' ], ['g', 'o', ' ', 'r', 'o' ], ['o', 'b', 'b', 'y', 'y' ], ['b', 'o', 'w', 'r', 'b' ] ] self.pattern = [ ['g', 'w', 'w'], ['g', 'o', 'r'], ['b', 'b', 'y'] ] final_positions = [ ['g', 'g', 'y', 'r', 'r' ], ['w', 'g', 'w', 'w', 'y' ], [' ', 'g', 'o', 'r', 'o' ], ['o', 'b', 'b', 'y', 'y' ], ['b', 'o', 'w', 'r', 'b' ] ] moves = [ [[2, 1], [2, 2]], [[2, 0], [2, 1]], ] self.result = [final_positions, moves] def tearDown(self): return unittest.TestCase.tearDown(self) def test_solve(self): initial_position = [ ['g', 'g', 'y', 'r', 'r' ], ['w', 'g', 'w', 'w', 'y' ], ['g', 'o', ' ', 'r', 'o' ], ['o', 'b', 'b', 'y', 'y' ], ['b', 'o', 'w', 'r', 'b' ] ] pattern = [ ['g', 'w', 'w'], ['g', 'o', 'r'], ['b', 'b', 'y'] ] final_positions = [ ['g', 'g', 'y', 'r', 'r' ], ['w', 'g', 'w', 'w', 'y' ], [' ', 'g', 'o', 'r', 'o' ], ['o', 'b', 'b', 'y', 'y' ], ['b', 'o', 'w', 'r', 'b' ] ] moves = [ [[2, 1], [2, 2]], [[2, 0], [2, 1]], ] self.assertItemsEqual([final_positions, moves], solver.solve(initial_position, pattern)) def test_solve_pq(self): self.assertItemsEqual(self.result, solver.solve_pq(self.initial_position, self.pattern)) if __name__ == "__main__": #import sys;sys.argv = ['', 'Test.test_solve'] unittest.main()

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null

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8d80488b5bce65f6332a7212b2c16986023812ef

1,625

py

Python

wagtail_translation/migrations/0001_initial.py

patroqueeet/wagtail2-translation

6a7ad4eea5d900c8640f965ebf7a442dd7bc7e74

[ "MIT" ]

null

wagtail_translation/migrations/0001_initial.py

patroqueeet/wagtail2-translation

6a7ad4eea5d900c8640f965ebf7a442dd7bc7e74

[ "MIT" ]

null

wagtail_translation/migrations/0001_initial.py

patroqueeet/wagtail2-translation

6a7ad4eea5d900c8640f965ebf7a442dd7bc7e74

[ "MIT" ]

1

2021-01-08T19:25:46.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from modeltranslation import settings as mt_settings from modeltranslation.utils import build_localized_fieldname, get_translation_fields from django.db import migrations, models def url_path_fix(apps, schema_editor): # cannot use apps.get_model here # because Page instances wouldn't have set_url_path method from wagtail.core.models import Page url_path_fields = get_translation_fields('url_path') for page in Page.objects.order_by('path').iterator(): page.set_url_path(page.get_parent()) # make sure descendant page url paths are not updated at this point # because it would fail page.save(update_fields=url_path_fields) class Migration(migrations.Migration): """ This migration fixes whatever pages you already have in DB so that their titles and slugs in default language are not empty and url_path field translations are updated accordingly. """ dependencies = [ ('wagtailtranslation', '9999_wagtail_translation'), ] operations = [ # 1. copy slugs and titles to corresponding default language fields migrations.RunSQL( ['UPDATE wagtailcore_page SET {}=slug, {}=title'.format( build_localized_fieldname('slug', mt_settings.DEFAULT_LANGUAGE), build_localized_fieldname('title', mt_settings.DEFAULT_LANGUAGE))], migrations.RunSQL.noop), # 2. update url_path in all existing pages for all translations migrations.RunPython(url_path_fix, migrations.RunPython.noop), ]

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null

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8d8293dd05c195d7acdf3af64d74eb27c71ed3fc

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py

Python

WORC/WORC.py

MStarmans91/WORC

b6b8fc2ccb7d443a69b5ca20b1d6efb65b3f0fc7

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

47

2018-01-28T14:08:15.000Z

2022-03-24T16:10:07.000Z

WORC/WORC.py

JZK00/WORC

14e8099835eccb35d49b52b97c0be64ecca3809c

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

13

2018-08-28T13:32:57.000Z

2020-10-26T16:35:59.000Z

WORC/WORC.py

JZK00/WORC

14e8099835eccb35d49b52b97c0be64ecca3809c

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

16

2017-11-13T10:53:36.000Z

2022-03-18T17:02:04.000Z

#!/usr/bin/env python # Copyright 2016-2021 Biomedical Imaging Group Rotterdam, Departments of # Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands # # 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 yaml import fastr import graphviz import configparser from pathlib import Path from random import randint import WORC.IOparser.file_io as io from fastr.api import ResourceLimit from WORC.tools.Slicer import Slicer from WORC.tools.Elastix import Elastix from WORC.tools.Evaluate import Evaluate import WORC.addexceptions as WORCexceptions import WORC.IOparser.config_WORC as config_io from WORC.detectors.detectors import DebugDetector from WORC.export.hyper_params_exporter import export_hyper_params_to_latex from urllib.parse import urlparse from urllib.request import url2pathname class WORC(object): """Workflow for Optimal Radiomics Classification. A Workflow for Optimal Radiomics Classification (WORC) object that serves as a pipeline spawner and manager for optimizating radiomics studies. Depending on the attributes set, the object will spawn an appropriate pipeline and manage it. Note that many attributes are lists and can therefore contain multiple instances. For example, when providing two sequences per patient, the "images" list contains two items. The type of items in the lists is described below. All objects that serve as source for your network, i.e. refer to actual files to be used, should be formatted as fastr sources suited for one of the fastr plugings, see also http://fastr.readthedocs.io/en/stable/fastr.reference.html#ioplugin-reference The objects should be lists of these fastr sources or dictionaries with the sample ID's, e.g. images_train = [{'Patient001': vfs://input/CT001.nii.gz, 'Patient002': vfs://input/CT002.nii.gz}, {'Patient001': vfs://input/MR001.nii.gz, 'Patient002': vfs://input/MR002.nii.gz}] Attributes ------------------ name: String, default 'WORC' name of the network. configs: list, required Configuration parameters, either ConfigParser objects created through the defaultconfig function or paths of config .ini files. (list, required) labels: list, required Paths to files containing patient labels (.txt files). network: automatically generated The FASTR network generated through the "build" function. images: list, optional Paths refering to the images used for Radiomics computation. Images should be of the ITK Image type. segmentations: list, optional Paths refering to the segmentations used for Radiomics computation. Segmentations should be of the ITK Image type. semantics: semantic features per image type (list, optional) masks: state which pixels of images are valid (list, optional) features: input Radiomics features for classification (list, optional) metadata: DICOM headers belonging to images (list, optional) Elastix_Para: parameter files for Elastix (list, optional) fastr_plugin: plugin to use for FASTR execution fastr_tempdir: temporary directory to use for FASTR execution additions: additional inputs for your network (dict, optional) source_data: data to use as sources for FASTR (dict) sink_data: data to use as sinks for FASTR (dict) CopyMetadata: Boolean, default True when using elastix, copy metadata from image to segmentation or not """ def __init__(self, name='test'): """Initialize WORC object. Set the initial variables all to None, except for some defaults. Arguments: name: name of the nework (string, optional) """ self.name = 'WORC_' + name # Initialize several objects self.configs = list() self.fastrconfigs = list() self.images_train = list() self.segmentations_train = list() self.semantics_train = list() self.labels_train = list() self.masks_train = list() self.masks_normalize_train = list() self.features_train = list() self.metadata_train = list() self.images_test = list() self.segmentations_test = list() self.semantics_test = list() self.labels_test = list() self.masks_test = list() self.masks_normalize_test = list() self.features_test = list() self.metadata_test = list() self.Elastix_Para = list() self.label_names = 'Label1, Label2' self.fixedsplits = list() # Set some defaults, name self.fastr_plugin = 'LinearExecution' if name == '': name = [randint(0, 9) for p in range(0, 5)] self.fastr_tmpdir = os.path.join(fastr.config.mounts['tmp'], self.name) self.additions = dict() self.CopyMetadata = True self.segmode = [] self._add_evaluation = False self.TrainTest = False # Memory settings for all fastr nodes self.fastr_memory_parameters = dict() self.fastr_memory_parameters['FeatureCalculator'] = '14G' self.fastr_memory_parameters['Classification'] = '6G' self.fastr_memory_parameters['WORCCastConvert'] = '4G' self.fastr_memory_parameters['Preprocessing'] = '4G' self.fastr_memory_parameters['Elastix'] = '4G' self.fastr_memory_parameters['Transformix'] = '4G' self.fastr_memory_parameters['Segmentix'] = '6G' self.fastr_memory_parameters['ComBat'] = '12G' self.fastr_memory_parameters['PlotEstimator'] = '12G' if DebugDetector().do_detection(): print(fastr.config) def defaultconfig(self): """Generate a configparser object holding all default configuration values. Returns: config: configparser configuration file """ config = configparser.ConfigParser() config.optionxform = str # General configuration of WORC config['General'] = dict() config['General']['cross_validation'] = 'True' config['General']['Segmentix'] = 'True' config['General']['FeatureCalculators'] = '[predict/CalcFeatures:1.0, pyradiomics/Pyradiomics:1.0]' config['General']['Preprocessing'] = 'worc/PreProcess:1.0' config['General']['RegistrationNode'] = "elastix4.8/Elastix:4.8" config['General']['TransformationNode'] = "elastix4.8/Transformix:4.8" config['General']['Joblib_ncores'] = '1' config['General']['Joblib_backend'] = 'threading' config['General']['tempsave'] = 'False' config['General']['AssumeSameImageAndMaskMetadata'] = 'False' config['General']['ComBat'] = 'False' # Options for the object/patient labels that are used config['Labels'] = dict() config['Labels']['label_names'] = 'Label1, Label2' config['Labels']['modus'] = 'singlelabel' config['Labels']['url'] = 'WIP' config['Labels']['projectID'] = 'WIP' # Preprocessing config['Preprocessing'] = dict() config['Preprocessing']['CheckSpacing'] = 'False' config['Preprocessing']['Clipping'] = 'False' config['Preprocessing']['Clipping_Range'] = '-1000.0, 3000.0' config['Preprocessing']['Normalize'] = 'True' config['Preprocessing']['Normalize_ROI'] = 'Full' config['Preprocessing']['Method'] = 'z_score' config['Preprocessing']['ROIDetermine'] = 'Provided' config['Preprocessing']['ROIdilate'] = 'False' config['Preprocessing']['ROIdilateradius'] = '10' config['Preprocessing']['Resampling'] = 'False' config['Preprocessing']['Resampling_spacing'] = '1, 1, 1' config['Preprocessing']['BiasCorrection'] = 'False' config['Preprocessing']['BiasCorrection_Mask'] = 'False' config['Preprocessing']['CheckOrientation'] = 'False' config['Preprocessing']['OrientationPrimaryAxis'] = 'axial' # Segmentix config['Segmentix'] = dict() config['Segmentix']['mask'] = 'subtract' config['Segmentix']['segtype'] = 'None' config['Segmentix']['segradius'] = '5' config['Segmentix']['N_blobs'] = '1' config['Segmentix']['fillholes'] = 'True' config['Segmentix']['remove_small_objects'] = 'False' config['Segmentix']['min_object_size'] = '2' # PREDICT - Feature calculation # Determine which features are calculated config['ImageFeatures'] = dict() config['ImageFeatures']['shape'] = 'True' config['ImageFeatures']['histogram'] = 'True' config['ImageFeatures']['orientation'] = 'True' config['ImageFeatures']['texture_Gabor'] = 'True' config['ImageFeatures']['texture_LBP'] = 'True' config['ImageFeatures']['texture_GLCM'] = 'True' config['ImageFeatures']['texture_GLCMMS'] = 'True' config['ImageFeatures']['texture_GLRLM'] = 'False' config['ImageFeatures']['texture_GLSZM'] = 'False' config['ImageFeatures']['texture_NGTDM'] = 'False' config['ImageFeatures']['coliage'] = 'False' config['ImageFeatures']['vessel'] = 'True' config['ImageFeatures']['log'] = 'True' config['ImageFeatures']['phase'] = 'True' # Parameter settings for PREDICT feature calculation # Defines only naming of modalities config['ImageFeatures']['image_type'] = 'CT' # Define frequencies for gabor filter in pixels config['ImageFeatures']['gabor_frequencies'] = '0.05, 0.2, 0.5' # Gabor, GLCM angles in degrees and radians, respectively config['ImageFeatures']['gabor_angles'] = '0, 45, 90, 135' config['ImageFeatures']['GLCM_angles'] = '0, 0.79, 1.57, 2.36' # GLCM discretization levels, distances in pixels config['ImageFeatures']['GLCM_levels'] = '16' config['ImageFeatures']['GLCM_distances'] = '1, 3' # LBP radius, number of points in pixels config['ImageFeatures']['LBP_radius'] = '3, 8, 15' config['ImageFeatures']['LBP_npoints'] = '12, 24, 36' # Phase features minimal wavelength and number of scales config['ImageFeatures']['phase_minwavelength'] = '3' config['ImageFeatures']['phase_nscale'] = '5' # Log features sigma of Gaussian in pixels config['ImageFeatures']['log_sigma'] = '1, 5, 10' # Vessel features scale range, steps for the range config['ImageFeatures']['vessel_scale_range'] = '1, 10' config['ImageFeatures']['vessel_scale_step'] = '2' # Vessel features radius for erosion to determine boudnary config['ImageFeatures']['vessel_radius'] = '5' # Tags from which to extract features, and how to name them config['ImageFeatures']['dicom_feature_tags'] = '0010 1010, 0010 0040' config['ImageFeatures']['dicom_feature_labels'] = 'age, sex' # PyRadiomics - Feature calculation # Addition to the above, specifically for PyRadiomics # Mostly based on specific MR Settings: see https://github.com/Radiomics/pyradiomics/blob/master/examples/exampleSettings/exampleMR_NoResampling.yaml config['PyRadiomics'] = dict() config['PyRadiomics']['geometryTolerance'] = '0.0001' config['PyRadiomics']['normalize'] = 'False' config['PyRadiomics']['normalizeScale'] = '100' config['PyRadiomics']['resampledPixelSpacing'] = 'None' config['PyRadiomics']['interpolator'] = 'sitkBSpline' config['PyRadiomics']['preCrop'] = 'True' config['PyRadiomics']['binCount'] = config['ImageFeatures']['GLCM_levels'] # BinWidth to sensitive for normalization, thus use binCount config['PyRadiomics']['binWidth'] = 'None' config['PyRadiomics']['force2D'] = 'False' config['PyRadiomics']['force2Ddimension'] = '0' # axial slices, for coronal slices, use dimension 1 and for sagittal, dimension 2. config['PyRadiomics']['voxelArrayShift'] = '300' config['PyRadiomics']['Original'] = 'True' config['PyRadiomics']['Wavelet'] = 'False' config['PyRadiomics']['LoG'] = 'False' if config['General']['Segmentix'] == 'True': config['PyRadiomics']['label'] = '1' else: config['PyRadiomics']['label'] = '255' # Enabled PyRadiomics features config['PyRadiomics']['extract_firstorder'] = 'False' config['PyRadiomics']['extract_shape'] = 'True' config['PyRadiomics']['texture_GLCM'] = 'False' config['PyRadiomics']['texture_GLRLM'] = 'True' config['PyRadiomics']['texture_GLSZM'] = 'True' config['PyRadiomics']['texture_GLDM'] = 'True' config['PyRadiomics']['texture_NGTDM'] = 'True' # ComBat Feature Harmonization config['ComBat'] = dict() config['ComBat']['language'] = 'python' config['ComBat']['batch'] = 'Hospital' config['ComBat']['mod'] = '[]' config['ComBat']['par'] = '1' config['ComBat']['eb'] = '1' config['ComBat']['per_feature'] = '0' config['ComBat']['excluded_features'] = 'sf_, of_, semf_, pf_' config['ComBat']['matlab'] = 'C:\\Program Files\\MATLAB\\R2015b\\bin\\matlab.exe' # Feature OneHotEncoding config['OneHotEncoding'] = dict() config['OneHotEncoding']['Use'] = 'False' config['OneHotEncoding']['feature_labels_tofit'] = '' # Feature imputation config['Imputation'] = dict() config['Imputation']['use'] = 'True' config['Imputation']['strategy'] = 'mean, median, most_frequent, constant, knn' config['Imputation']['n_neighbors'] = '5, 5' # Feature scaling options config['FeatureScaling'] = dict() config['FeatureScaling']['scaling_method'] = 'robust_z_score' config['FeatureScaling']['skip_features'] = 'semf_, pf_' # Feature preprocessing before the whole HyperOptimization config['FeatPreProcess'] = dict() config['FeatPreProcess']['Use'] = 'False' config['FeatPreProcess']['Combine'] = 'False' config['FeatPreProcess']['Combine_method'] = 'mean' # Feature selection config['Featsel'] = dict() config['Featsel']['Variance'] = '1.0' config['Featsel']['GroupwiseSearch'] = 'True' config['Featsel']['SelectFromModel'] = '0.275' config['Featsel']['SelectFromModel_estimator'] = 'Lasso, LR, RF' config['Featsel']['SelectFromModel_lasso_alpha'] = '0.1, 1.4' config['Featsel']['SelectFromModel_n_trees'] = '10, 90' config['Featsel']['UsePCA'] = '0.275' config['Featsel']['PCAType'] = '95variance, 10, 50, 100' config['Featsel']['StatisticalTestUse'] = '0.275' config['Featsel']['StatisticalTestMetric'] = 'MannWhitneyU' config['Featsel']['StatisticalTestThreshold'] = '-3, 2.5' config['Featsel']['ReliefUse'] = '0.275' config['Featsel']['ReliefNN'] = '2, 4' config['Featsel']['ReliefSampleSize'] = '0.75, 0.2' config['Featsel']['ReliefDistanceP'] = '1, 3' config['Featsel']['ReliefNumFeatures'] = '10, 40' # Groupwise Featureselection options config['SelectFeatGroup'] = dict() config['SelectFeatGroup']['shape_features'] = 'True, False' config['SelectFeatGroup']['histogram_features'] = 'True, False' config['SelectFeatGroup']['orientation_features'] = 'True, False' config['SelectFeatGroup']['texture_Gabor_features'] = 'True, False' config['SelectFeatGroup']['texture_GLCM_features'] = 'True, False' config['SelectFeatGroup']['texture_GLDM_features'] = 'True, False' config['SelectFeatGroup']['texture_GLCMMS_features'] = 'True, False' config['SelectFeatGroup']['texture_GLRLM_features'] = 'True, False' config['SelectFeatGroup']['texture_GLSZM_features'] = 'True, False' config['SelectFeatGroup']['texture_GLDZM_features'] = 'True, False' config['SelectFeatGroup']['texture_NGTDM_features'] = 'True, False' config['SelectFeatGroup']['texture_NGLDM_features'] = 'True, False' config['SelectFeatGroup']['texture_LBP_features'] = 'True, False' config['SelectFeatGroup']['dicom_features'] = 'False' config['SelectFeatGroup']['semantic_features'] = 'False' config['SelectFeatGroup']['coliage_features'] = 'False' config['SelectFeatGroup']['vessel_features'] = 'True, False' config['SelectFeatGroup']['phase_features'] = 'True, False' config['SelectFeatGroup']['fractal_features'] = 'True, False' config['SelectFeatGroup']['location_features'] = 'True, False' config['SelectFeatGroup']['rgrd_features'] = 'True, False' # Select features per toolbox, or simply all config['SelectFeatGroup']['toolbox'] = 'All, PREDICT, PyRadiomics' # Select original features, or after transformation of feature space config['SelectFeatGroup']['original_features'] = 'True' config['SelectFeatGroup']['wavelet_features'] = 'True, False' config['SelectFeatGroup']['log_features'] = 'True, False' # Resampling options config['Resampling'] = dict() config['Resampling']['Use'] = '0.20' config['Resampling']['Method'] =\ 'RandomUnderSampling, RandomOverSampling, NearMiss, ' +\ 'NeighbourhoodCleaningRule, ADASYN, BorderlineSMOTE, SMOTE, ' +\ 'SMOTEENN, SMOTETomek' config['Resampling']['sampling_strategy'] = 'auto, majority, minority, not minority, not majority, all' config['Resampling']['n_neighbors'] = '3, 12' config['Resampling']['k_neighbors'] = '5, 15' config['Resampling']['threshold_cleaning'] = '0.25, 0.5' # Classification config['Classification'] = dict() config['Classification']['fastr'] = 'True' config['Classification']['fastr_plugin'] = self.fastr_plugin config['Classification']['classifiers'] =\ 'SVM, RF, LR, LDA, QDA, GaussianNB, ' +\ 'AdaBoostClassifier, ' +\ 'XGBClassifier' config['Classification']['max_iter'] = '100000' config['Classification']['SVMKernel'] = 'linear, poly, rbf' config['Classification']['SVMC'] = '0, 6' config['Classification']['SVMdegree'] = '1, 6' config['Classification']['SVMcoef0'] = '0, 1' config['Classification']['SVMgamma'] = '-5, 5' config['Classification']['RFn_estimators'] = '10, 90' config['Classification']['RFmin_samples_split'] = '2, 3' config['Classification']['RFmax_depth'] = '5, 5' config['Classification']['LRpenalty'] = 'l1, l2, elasticnet' config['Classification']['LRC'] = '0.01, 0.99' config['Classification']['LR_solver'] = 'lbfgs, saga' config['Classification']['LR_l1_ratio'] = '0, 1' config['Classification']['LDA_solver'] = 'svd, lsqr, eigen' config['Classification']['LDA_shrinkage'] = '-5, 5' config['Classification']['QDA_reg_param'] = '-5, 5' config['Classification']['ElasticNet_alpha'] = '-5, 5' config['Classification']['ElasticNet_l1_ratio'] = '0, 1' config['Classification']['SGD_alpha'] = '-5, 5' config['Classification']['SGD_l1_ratio'] = '0, 1' config['Classification']['SGD_loss'] = 'squared_loss, huber, epsilon_insensitive, squared_epsilon_insensitive' config['Classification']['SGD_penalty'] = 'none, l2, l1' config['Classification']['CNB_alpha'] = '0, 1' config['Classification']['AdaBoost_n_estimators'] = config['Classification']['RFn_estimators'] config['Classification']['AdaBoost_learning_rate'] = '0.01, 0.99' # Based on https://towardsdatascience.com/doing-xgboost-hyper-parameter-tuning-the-smart-way-part-1-of-2-f6d255a45dde # and https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/ # and https://medium.com/data-design/xgboost-hi-im-gamma-what-can-i-do-for-you-and-the-tuning-of-regularization-a42ea17e6ab6 config['Classification']['XGB_boosting_rounds'] = config['Classification']['RFn_estimators'] config['Classification']['XGB_max_depth'] = '3, 12' config['Classification']['XGB_learning_rate'] = config['Classification']['AdaBoost_learning_rate'] config['Classification']['XGB_gamma'] = '0.01, 9.99' config['Classification']['XGB_min_child_weight'] = '1, 6' config['Classification']['XGB_colsample_bytree'] = '0.3, 0.7' # CrossValidation config['CrossValidation'] = dict() config['CrossValidation']['Type'] = 'random_split' config['CrossValidation']['N_iterations'] = '100' config['CrossValidation']['test_size'] = '0.2' config['CrossValidation']['fixed_seed'] = 'False' # Hyperparameter optimization options config['HyperOptimization'] = dict() config['HyperOptimization']['scoring_method'] = 'f1_weighted' config['HyperOptimization']['test_size'] = '0.2' config['HyperOptimization']['n_splits'] = '5' config['HyperOptimization']['N_iterations'] = '1000' config['HyperOptimization']['n_jobspercore'] = '200' # only relevant when using fastr in classification config['HyperOptimization']['maxlen'] = '100' config['HyperOptimization']['ranking_score'] = 'test_score' config['HyperOptimization']['memory'] = '3G' config['HyperOptimization']['refit_workflows'] = 'False' # Ensemble options config['Ensemble'] = dict() config['Ensemble']['Use'] = '100' config['Ensemble']['Metric'] = 'Default' # Evaluation options config['Evaluation'] = dict() config['Evaluation']['OverfitScaler'] = 'False' # Bootstrap options config['Bootstrap'] = dict() config['Bootstrap']['Use'] = 'False' config['Bootstrap']['N_iterations'] = '1000' return config def add_tools(self): """Add several tools to the WORC object.""" self.Tools = Tools() def build(self, wtype='training'): """Build the network based on the given attributes. Parameters ---------- wtype: string, default 'training' Specify the WORC execution type. - testing: use if you have a trained classifier and want to train it on some new images. - training: use if you want to train a classifier from a dataset. """ self.wtype = wtype if wtype == 'training': self.build_training() elif wtype == 'testing': self.build_testing() def build_training(self): """Build the training network based on the given attributes.""" # We either need images or features for Radiomics if self.images_test or self.features_test: self.TrainTest = True if self.images_train or self.features_train: print('Building training network...') # We currently require labels for supervised learning if self.labels_train: if not self.configs: print("No configuration given, assuming default") if self.images_train: self.configs = [self.defaultconfig()] * len(self.images_train) else: self.configs = [self.defaultconfig()] * len(self.features_train) self.network = fastr.create_network(self.name) # BUG: We currently use the first configuration as general config image_types = list() for c in range(len(self.configs)): if type(self.configs[c]) == str: # Probably, c is a configuration file self.configs[c] = config_io.load_config(self.configs[c]) image_types.append(self.configs[c]['ImageFeatures']['image_type']) # Create config source self.source_class_config = self.network.create_source('ParameterFile', id='config_classification_source', node_group='conf', step_id='general_sources') # Classification tool and label source self.source_patientclass_train = self.network.create_source('PatientInfoFile', id='patientclass_train', node_group='pctrain', step_id='train_sources') if self.labels_test: self.source_patientclass_test = self.network.create_source('PatientInfoFile', id='patientclass_test', node_group='pctest', step_id='test_sources') memory = self.fastr_memory_parameters['Classification'] self.classify = self.network.create_node('worc/TrainClassifier:1.0', tool_version='1.0', id='classify', resources=ResourceLimit(memory=memory), step_id='WorkflowOptimization') if self.fixedsplits: self.fixedsplits_node = self.network.create_source('CSVFile', id='fixedsplits_source', node_group='conf', step_id='general_sources') self.classify.inputs['fixedsplits'] = self.fixedsplits_node.output self.source_Ensemble =\ self.network.create_constant('String', [self.configs[0]['Ensemble']['Use']], id='Ensemble', step_id='Evaluation') self.source_LabelType =\ self.network.create_constant('String', [self.configs[0]['Labels']['label_names']], id='LabelType', step_id='Evaluation') memory = self.fastr_memory_parameters['PlotEstimator'] self.plot_estimator =\ self.network.create_node('worc/PlotEstimator:1.0', tool_version='1.0', id='plot_Estimator', resources=ResourceLimit(memory=memory), step_id='Evaluation') # Outputs self.sink_classification = self.network.create_sink('HDF5', id='classification', step_id='general_sinks') self.sink_performance = self.network.create_sink('JsonFile', id='performance', step_id='general_sinks') self.sink_class_config = self.network.create_sink('ParameterFile', id='config_classification_sink', node_group='conf', step_id='general_sinks') # Links self.sink_class_config.input = self.source_class_config.output self.link_class_1 = self.network.create_link(self.source_class_config.output, self.classify.inputs['config']) self.link_class_2 = self.network.create_link(self.source_patientclass_train.output, self.classify.inputs['patientclass_train']) self.link_class_1.collapse = 'conf' self.link_class_2.collapse = 'pctrain' self.plot_estimator.inputs['ensemble'] = self.source_Ensemble.output self.plot_estimator.inputs['label_type'] = self.source_LabelType.output if self.labels_test: pinfo = self.source_patientclass_test.output else: pinfo = self.source_patientclass_train.output self.plot_estimator.inputs['prediction'] = self.classify.outputs['classification'] self.plot_estimator.inputs['pinfo'] = pinfo if self.TrainTest: # FIXME: the naming here is ugly self.link_class_3 = self.network.create_link(self.source_patientclass_test.output, self.classify.inputs['patientclass_test']) self.link_class_3.collapse = 'pctest' self.sink_classification.input = self.classify.outputs['classification'] self.sink_performance.input = self.plot_estimator.outputs['output_json'] if self.masks_normalize_train: self.sources_masks_normalize_train = dict() if self.masks_normalize_test: self.sources_masks_normalize_test = dict() # ----------------------------------------------------- # Optionally, add ComBat Harmonization. Currently done # on full dataset, not in a cross-validation if self.configs[0]['General']['ComBat'] == 'True': self.add_ComBat() if not self.features_train: # Create nodes to compute features # General self.sources_parameters = dict() self.source_config_pyradiomics = dict() self.source_toolbox_name = dict() # Training only self.calcfeatures_train = dict() self.featureconverter_train = dict() self.preprocessing_train = dict() self.sources_images_train = dict() self.sinks_features_train = dict() self.converters_im_train = dict() self.converters_seg_train = dict() self.links_C1_train = dict() self.featurecalculators = dict() if self.TrainTest: # A test set is supplied, for which nodes also need to be created self.calcfeatures_test = dict() self.featureconverter_test = dict() self.preprocessing_test = dict() self.sources_images_test = dict() self.sinks_features_test = dict() self.converters_im_test = dict() self.converters_seg_test = dict() self.links_C1_test = dict() # Check which nodes are necessary if not self.segmentations_train: message = "No automatic segmentation method is yet implemented." raise WORCexceptions.WORCNotImplementedError(message) elif len(self.segmentations_train) == len(image_types): # Segmentations provided self.sources_segmentations_train = dict() self.sources_segmentations_test = dict() self.segmode = 'Provided' elif len(self.segmentations_train) == 1: # Assume segmentations need to be registered to other modalities print('\t - Adding Elastix node for image registration.') self.add_elastix_sourcesandsinks() pass else: nseg = len(self.segmentations_train) nim = len(image_types) m = f'Length of segmentations for training is ' +\ f'{nseg}: should be equal to number of images' +\ f' ({nim}) or 1 when using registration.' raise WORCexceptions.WORCValueError(m) # BUG: We assume that first type defines if we use segmentix if self.configs[0]['General']['Segmentix'] == 'True': # Use the segmentix toolbox for segmentation processing print('\t - Adding segmentix node for segmentation preprocessing.') self.sinks_segmentations_segmentix_train = dict() self.sources_masks_train = dict() self.converters_masks_train = dict() self.nodes_segmentix_train = dict() if self.TrainTest: # Also use segmentix on the tes set self.sinks_segmentations_segmentix_test = dict() self.sources_masks_test = dict() self.converters_masks_test = dict() self.nodes_segmentix_test = dict() if self.semantics_train: # Semantic features are supplied self.sources_semantics_train = dict() if self.metadata_train: # Metadata to extract patient features from is supplied self.sources_metadata_train = dict() if self.semantics_test: # Semantic features are supplied self.sources_semantics_test = dict() if self.metadata_test: # Metadata to extract patient features from is supplied self.sources_metadata_test = dict() # Create a part of the pipeline for each modality self.modlabels = list() for nmod, mod in enumerate(image_types): # Create label for each modality/image num = 0 label = mod + '_' + str(num) while label in self.calcfeatures_train.keys(): # if label already exists, add number to label num += 1 label = mod + '_' + str(num) self.modlabels.append(label) # Create required sources and sinks self.sources_parameters[label] = self.network.create_source('ParameterFile', id='config_' + label, step_id='general_sources') self.sources_images_train[label] = self.network.create_source('ITKImageFile', id='images_train_' + label, node_group='train', step_id='train_sources') if self.TrainTest: self.sources_images_test[label] = self.network.create_source('ITKImageFile', id='images_test_' + label, node_group='test', step_id='test_sources') if self.metadata_train and len(self.metadata_train) >= nmod + 1: self.sources_metadata_train[label] = self.network.create_source('DicomImageFile', id='metadata_train_' + label, node_group='train', step_id='train_sources') if self.metadata_test and len(self.metadata_test) >= nmod + 1: self.sources_metadata_test[label] = self.network.create_source('DicomImageFile', id='metadata_test_' + label, node_group='test', step_id='test_sources') if self.masks_train and len(self.masks_train) >= nmod + 1: # Create mask source and convert self.sources_masks_train[label] = self.network.create_source('ITKImageFile', id='mask_train_' + label, node_group='train', step_id='train_sources') memory = self.fastr_memory_parameters['WORCCastConvert'] self.converters_masks_train[label] =\ self.network.create_node('worc/WORCCastConvert:0.3.2', tool_version='0.1', id='convert_mask_train_' + label, node_group='train', resources=ResourceLimit(memory=memory), step_id='FileConversion') self.converters_masks_train[label].inputs['image'] = self.sources_masks_train[label].output if self.masks_test and len(self.masks_test) >= nmod + 1: # Create mask source and convert self.sources_masks_test[label] = self.network.create_source('ITKImageFile', id='mask_test_' + label, node_group='test', step_id='test_sources') memory = self.fastr_memory_parameters['WORCCastConvert'] self.converters_masks_test[label] =\ self.network.create_node('worc/WORCCastConvert:0.3.2', tool_version='0.1', id='convert_mask_test_' + label, node_group='test', resources=ResourceLimit(memory=memory), step_id='FileConversion') self.converters_masks_test[label].inputs['image'] = self.sources_masks_test[label].output # First convert the images if any(modality in mod for modality in ['MR', 'CT', 'MG', 'PET']): # Use WORC PXCastConvet for converting image formats memory = self.fastr_memory_parameters['WORCCastConvert'] self.converters_im_train[label] =\ self.network.create_node('worc/WORCCastConvert:0.3.2', tool_version='0.1', id='convert_im_train_' + label, resources=ResourceLimit(memory=memory), step_id='FileConversion') if self.TrainTest: self.converters_im_test[label] =\ self.network.create_node('worc/WORCCastConvert:0.3.2', tool_version='0.1', id='convert_im_test_' + label, resources=ResourceLimit(memory=memory), step_id='FileConversion') else: raise WORCexceptions.WORCTypeError(('No valid image type for modality {}: {} provided.').format(str(nmod), mod)) # Create required links self.converters_im_train[label].inputs['image'] = self.sources_images_train[label].output if self.TrainTest: self.converters_im_test[label].inputs['image'] = self.sources_images_test[label].output # ----------------------------------------------------- # Preprocessing preprocess_node = str(self.configs[nmod]['General']['Preprocessing']) print('\t - Adding preprocessing node for image preprocessing.') self.add_preprocessing(preprocess_node, label, nmod) # ----------------------------------------------------- # Feature calculation feature_calculators =\ self.configs[nmod]['General']['FeatureCalculators'] feature_calculators = feature_calculators.strip('][').split(', ') self.featurecalculators[label] = [f.split('/')[0] for f in feature_calculators] # Add lists for feature calculation and converter objects self.calcfeatures_train[label] = list() self.featureconverter_train[label] = list() if self.TrainTest: self.calcfeatures_test[label] = list() self.featureconverter_test[label] = list() for f in feature_calculators: print(f'\t - Adding feature calculation node: {f}.') self.add_feature_calculator(f, label, nmod) # ----------------------------------------------------- # Create the neccesary nodes for the segmentation if self.segmode == 'Provided': # Segmentation ---------------------------------------------------- # Use the provided segmantions for each modality memory = self.fastr_memory_parameters['WORCCastConvert'] self.sources_segmentations_train[label] =\ self.network.create_source('ITKImageFile', id='segmentations_train_' + label, node_group='train', step_id='train_sources') self.converters_seg_train[label] =\ self.network.create_node('worc/WORCCastConvert:0.3.2', tool_version='0.1', id='convert_seg_train_' + label, resources=ResourceLimit(memory=memory), step_id='FileConversion') self.converters_seg_train[label].inputs['image'] =\ self.sources_segmentations_train[label].output if self.TrainTest: self.sources_segmentations_test[label] =\ self.network.create_source('ITKImageFile', id='segmentations_test_' + label, node_group='test', step_id='test_sources') self.converters_seg_test[label] =\ self.network.create_node('worc/WORCCastConvert:0.3.2', tool_version='0.1', id='convert_seg_test_' + label, resources=ResourceLimit(memory=memory), step_id='FileConversion') self.converters_seg_test[label].inputs['image'] =\ self.sources_segmentations_test[label].output elif self.segmode == 'Register': # --------------------------------------------- # Registration nodes: Align segmentation of first # modality to others using registration ith Elastix self.add_elastix(label, nmod) # ----------------------------------------------------- # Optionally, add segmentix, the in-house segmentation # processor of WORC if self.configs[nmod]['General']['Segmentix'] == 'True': self.add_segmentix(label, nmod) elif self.configs[nmod]['Preprocessing']['Resampling'] == 'True': raise WORCexceptions.WORCValueError('If you use resampling, ' + 'have to use segmentix to ' + ' make sure the mask is ' + 'also resampled. Please ' + 'set ' + 'config["General"]["Segmentix"]' + 'to "True".') else: # Provide source or elastix segmentations to # feature calculator for i_node in range(len(self.calcfeatures_train[label])): if self.segmode == 'Provided': self.calcfeatures_train[label][i_node].inputs['segmentation'] =\ self.converters_seg_train[label].outputs['image'] elif self.segmode == 'Register': if nmod > 0: self.calcfeatures_train[label][i_node].inputs['segmentation'] =\ self.transformix_seg_nodes_train[label].outputs['image'] else: self.calcfeatures_train[label][i_node].inputs['segmentation'] =\ self.converters_seg_train[label].outputs['image'] if self.TrainTest: if self.segmode == 'Provided': self.calcfeatures_test[label][i_node].inputs['segmentation'] =\ self.converters_seg_test[label].outputs['image'] elif self.segmode == 'Register': if nmod > 0: self.calcfeatures_test[label][i_node].inputs['segmentation'] =\ self.transformix_seg_nodes_test[label].outputs['image'] else: self.calcfeatures_test[label][i_node].inputs['segmentation'] =\ self.converters_seg_test[label].outputs['image'] # ----------------------------------------------------- # Optionally, add ComBat Harmonization if self.configs[0]['General']['ComBat'] == 'True': # Link features to ComBat self.links_Combat1_train[label] = list() for i_node, fname in enumerate(self.featurecalculators[label]): self.links_Combat1_train[label].append(self.ComBat.inputs['features_train'][f'{label}_{self.featurecalculators[label][i_node]}'] << self.featureconverter_train[label][i_node].outputs['feat_out']) self.links_Combat1_train[label][i_node].collapse = 'train' if self.TrainTest: self.links_Combat1_test[label] = list() for i_node, fname in enumerate(self.featurecalculators[label]): self.links_Combat1_test[label].append(self.ComBat.inputs['features_test'][f'{label}_{self.featurecalculators[label][i_node]}'] << self.featureconverter_test[label][i_node].outputs['feat_out']) self.links_Combat1_test[label][i_node].collapse = 'test' # ----------------------------------------------------- # Classification nodes # Add the features from this modality to the classifier node input self.links_C1_train[label] = list() self.sinks_features_train[label] = list() if self.TrainTest: self.links_C1_test[label] = list() self.sinks_features_test[label] = list() for i_node, fname in enumerate(self.featurecalculators[label]): # Create sink for feature outputs self.sinks_features_train[label].append(self.network.create_sink('HDF5', id='features_train_' + label + '_' + fname, step_id='train_sinks')) # Append features to the classification if not self.configs[0]['General']['ComBat'] == 'True': self.links_C1_train[label].append(self.classify.inputs['features_train'][f'{label}_{self.featurecalculators[label][i_node]}'] << self.featureconverter_train[label][i_node].outputs['feat_out']) self.links_C1_train[label][i_node].collapse = 'train' # Save output self.sinks_features_train[label][i_node].input = self.featureconverter_train[label][i_node].outputs['feat_out'] # Similar for testing workflow if self.TrainTest: # Create sink for feature outputs self.sinks_features_test[label].append(self.network.create_sink('HDF5', id='features_test_' + label + '_' + fname, step_id='test_sinks')) # Append features to the classification if not self.configs[0]['General']['ComBat'] == 'True': self.links_C1_test[label].append(self.classify.inputs['features_test'][f'{label}_{self.featurecalculators[label][i_node]}'] << self.featureconverter_test[label][i_node].outputs['feat_out']) self.links_C1_test[label][i_node].collapse = 'test' # Save output self.sinks_features_test[label][i_node].input = self.featureconverter_test[label][i_node].outputs['feat_out'] else: # Features already provided: hence we can skip numerous nodes self.sources_features_train = dict() self.links_C1_train = dict() if self.features_test: self.sources_features_test = dict() self.links_C1_test = dict() # Create label for each modality/image self.modlabels = list() for num, mod in enumerate(image_types): num = 0 label = mod + str(num) while label in self.sources_features_train.keys(): # if label exists, add number to label num += 1 label = mod + str(num) self.modlabels.append(label) # Create a node for the feature computation self.sources_features_train[label] = self.network.create_source('HDF5', id='features_train_' + label, node_group='train', step_id='train_sources') # Add the features from this modality to the classifier node input self.links_C1_train[label] = self.classify.inputs['features_train'][str(label)] << self.sources_features_train[label].output self.links_C1_train[label].collapse = 'train' if self.features_test: # Create a node for the feature computation self.sources_features_test[label] = self.network.create_source('HDF5', id='features_test_' + label, node_group='test', step_id='test_sources') # Add the features from this modality to the classifier node input self.links_C1_test[label] = self.classify.inputs['features_test'][str(label)] << self.sources_features_test[label].output self.links_C1_test[label].collapse = 'test' else: raise WORCexceptions.WORCIOError("Please provide labels.") else: raise WORCexceptions.WORCIOError("Please provide either images or features.") def add_ComBat(self): """Add ComBat harmonization to the network. Note: applied on all objects, not in a train-test or cross-val setting. """ memory = self.fastr_memory_parameters['ComBat'] self.ComBat =\ self.network.create_node('combat/ComBat:1.0', tool_version='1.0', id='ComBat', resources=ResourceLimit(memory=memory), step_id='ComBat') # Create sink for ComBat output self.sinks_features_train_ComBat = self.network.create_sink('HDF5', id='features_train_ComBat', step_id='ComBat') # Create links for inputs self.link_combat_1 = self.network.create_link(self.source_class_config.output, self.ComBat.inputs['config']) self.link_combat_2 = self.network.create_link(self.source_patientclass_train.output, self.ComBat.inputs['patientclass_train']) self.link_combat_1.collapse = 'conf' self.link_combat_2.collapse = 'pctrain' self.links_Combat1_train = dict() self.links_Combat1_test = dict() # Link Combat output to both sink and classify node self.links_Combat_out_train = self.network.create_link(self.ComBat.outputs['features_train_out'], self.classify.inputs['features_train']) self.links_Combat_out_train.collapse = 'ComBat' self.sinks_features_train_ComBat.input = self.ComBat.outputs['features_train_out'] if self.TrainTest: # Create sink for ComBat output self.sinks_features_test_ComBat = self.network.create_sink('HDF5', id='features_test_ComBat', step_id='ComBat') # Create links for inputs self.link_combat_3 = self.network.create_link(self.source_patientclass_test.output, self.ComBat.inputs['patientclass_test']) self.link_combat_3.collapse = 'pctest' # Link Combat output to both sink and classify node self.links_Combat_out_test = self.network.create_link(self.ComBat.outputs['features_test_out'], self.classify.inputs['features_test']) self.links_Combat_out_test.collapse = 'ComBat' self.sinks_features_test_ComBat.input = self.ComBat.outputs['features_test_out'] def add_preprocessing(self, preprocess_node, label, nmod): """Add nodes required for preprocessing of images.""" memory = self.fastr_memory_parameters['Preprocessing'] self.preprocessing_train[label] = self.network.create_node(preprocess_node, tool_version='1.0', id='preprocessing_train_' + label, resources=ResourceLimit(memory=memory), step_id='Preprocessing') if self.TrainTest: self.preprocessing_test[label] = self.network.create_node(preprocess_node, tool_version='1.0', id='preprocessing_test_' + label, resources=ResourceLimit(memory=memory), step_id='Preprocessing') # Create required links self.preprocessing_train[label].inputs['parameters'] = self.sources_parameters[label].output self.preprocessing_train[label].inputs['image'] = self.converters_im_train[label].outputs['image'] if self.TrainTest: self.preprocessing_test[label].inputs['parameters'] = self.sources_parameters[label].output self.preprocessing_test[label].inputs['image'] = self.converters_im_test[label].outputs['image'] if self.metadata_train and len(self.metadata_train) >= nmod + 1: self.preprocessing_train[label].inputs['metadata'] = self.sources_metadata_train[label].output if self.metadata_test and len(self.metadata_test) >= nmod + 1: self.preprocessing_test[label].inputs['metadata'] = self.sources_metadata_test[label].output # If there are masks to use in normalization, add them here if self.masks_normalize_train: self.sources_masks_normalize_train[label] = self.network.create_source('ITKImageFile', id='masks_normalize_train_' + label, node_group='train', step_id='Preprocessing') self.preprocessing_train[label].inputs['mask'] = self.sources_masks_normalize_train[label].output if self.masks_normalize_test: self.sources_masks_normalize_test[label] = self.network.create_source('ITKImageFile', id='masks_normalize_test_' + label, node_group='test', step_id='Preprocessing') self.preprocessing_test[label].inputs['mask'] = self.sources_masks_normalize_test[label].output def add_feature_calculator(self, calcfeat_node, label, nmod): """Add a feature calculation node to the network.""" # Name of fastr node has to exclude some specific symbols, which # are used in the node name node_ID = '_'.join([calcfeat_node.replace(':', '_').replace('.', '_').replace('/', '_'), label]) memory = self.fastr_memory_parameters['FeatureCalculator'] node_train =\ self.network.create_node(calcfeat_node, tool_version='1.0', id='calcfeatures_train_' + node_ID, resources=ResourceLimit(memory=memory), step_id='Feature_Extraction') if self.TrainTest: node_test =\ self.network.create_node(calcfeat_node, tool_version='1.0', id='calcfeatures_test_' + node_ID, resources=ResourceLimit(memory=memory), step_id='Feature_Extraction') # Check if we need to add pyradiomics specific sources if 'pyradiomics' in calcfeat_node.lower(): # Add a config source self.source_config_pyradiomics[label] =\ self.network.create_source('YamlFile', id='config_pyradiomics_' + label, node_group='train', step_id='Feature_Extraction') # Add a format source, which we are going to set to a constant # And attach to the tool node self.source_format_pyradiomics =\ self.network.create_constant('String', 'csv', id='format_pyradiomics_' + label, node_group='train', step_id='Feature_Extraction') node_train.inputs['format'] =\ self.source_format_pyradiomics.output if self.TrainTest: node_test.inputs['format'] =\ self.source_format_pyradiomics.output # Create required links # We can have a different config for different tools if 'pyradiomics' in calcfeat_node.lower(): node_train.inputs['parameters'] =\ self.source_config_pyradiomics[label].output else: node_train.inputs['parameters'] =\ self.sources_parameters[label].output node_train.inputs['image'] =\ self.preprocessing_train[label].outputs['image'] if self.TrainTest: if 'pyradiomics' in calcfeat_node.lower(): node_test.inputs['parameters'] =\ self.source_config_pyradiomics[label].output else: node_test.inputs['parameters'] =\ self.sources_parameters[label].output node_test.inputs['image'] =\ self.preprocessing_test[label].outputs['image'] # PREDICT can extract semantic and metadata features if 'predict' in calcfeat_node.lower(): if self.metadata_train and len(self.metadata_train) >= nmod + 1: node_train.inputs['metadata'] =\ self.sources_metadata_train[label].output if self.metadata_test and len(self.metadata_test) >= nmod + 1: node_test.inputs['metadata'] =\ self.sources_metadata_test[label].output # If a semantics file is provided, connect to feature extraction tool if self.semantics_train and len(self.semantics_train) >= nmod + 1: self.sources_semantics_train[label] =\ self.network.create_source('CSVFile', id='semantics_train_' + label, step_id='train_sources') node_train.inputs['semantics'] =\ self.sources_semantics_train[label].output if self.semantics_test and len(self.semantics_test) >= nmod + 1: self.sources_semantics_test[label] =\ self.network.create_source('CSVFile', id='semantics_test_' + label, step_id='test_sources') node_test.inputs['semantics'] =\ self.sources_semantics_test[label].output # Add feature converter to make features WORC compatible conv_train =\ self.network.create_node('worc/FeatureConverter:1.0', tool_version='1.0', id='featureconverter_train_' + node_ID, resources=ResourceLimit(memory='4G'), step_id='Feature_Extraction') conv_train.inputs['feat_in'] = node_train.outputs['features'] # Add source to tell converter which toolbox we use if 'pyradiomics' in calcfeat_node.lower(): toolbox = 'PyRadiomics' elif 'predict' in calcfeat_node.lower(): toolbox = 'PREDICT' else: message = f'Toolbox {calcfeat_node} not recognized!' raise WORCexceptions.WORCKeyError(message) self.source_toolbox_name[label] =\ self.network.create_constant('String', toolbox, id=f'toolbox_name_{toolbox}_{label}', step_id='Feature_Extraction') conv_train.inputs['toolbox'] = self.source_toolbox_name[label].output conv_train.inputs['config'] = self.sources_parameters[label].output if self.TrainTest: conv_test =\ self.network.create_node('worc/FeatureConverter:1.0', tool_version='1.0', id='featureconverter_test_' + node_ID, resources=ResourceLimit(memory='4G'), step_id='Feature_Extraction') conv_test.inputs['feat_in'] = node_test.outputs['features'] conv_test.inputs['toolbox'] = self.source_toolbox_name[label].output conv_test.inputs['config'] = self.sources_parameters[label].output # Append to nodes to list self.calcfeatures_train[label].append(node_train) self.featureconverter_train[label].append(conv_train) if self.TrainTest: self.calcfeatures_test[label].append(node_test) self.featureconverter_test[label].append(conv_test) def add_elastix_sourcesandsinks(self): """Add sources and sinks required for image registration.""" self.sources_segmentation = dict() self.segmode = 'Register' self.source_Elastix_Parameters = dict() self.elastix_nodes_train = dict() self.transformix_seg_nodes_train = dict() self.sources_segmentations_train = dict() self.sinks_transformations_train = dict() self.sinks_segmentations_elastix_train = dict() self.sinks_images_elastix_train = dict() self.converters_seg_train = dict() self.edittransformfile_nodes_train = dict() self.transformix_im_nodes_train = dict() self.elastix_nodes_test = dict() self.transformix_seg_nodes_test = dict() self.sources_segmentations_test = dict() self.sinks_transformations_test = dict() self.sinks_segmentations_elastix_test = dict() self.sinks_images_elastix_test = dict() self.converters_seg_test = dict() self.edittransformfile_nodes_test = dict() self.transformix_im_nodes_test = dict() def add_elastix(self, label, nmod): """ Add image registration through elastix to network.""" # Create sources and converter for only for the given segmentation, # which should be on the first modality if nmod == 0: memory = self.fastr_memory_parameters['WORCCastConvert'] self.sources_segmentations_train[label] =\ self.network.create_source('ITKImageFile', id='segmentations_train_' + label, node_group='input', step_id='train_sources') self.converters_seg_train[label] =\ self.network.create_node('worc/WORCCastConvert:0.3.2', tool_version='0.1', id='convert_seg_train_' + label, resources=ResourceLimit(memory=memory), step_id='FileConversion') self.converters_seg_train[label].inputs['image'] =\ self.sources_segmentations_train[label].output if self.TrainTest: self.sources_segmentations_test[label] =\ self.network.create_source('ITKImageFile', id='segmentations_test_' + label, node_group='input', step_id='test_sources') self.converters_seg_test[label] =\ self.network.create_node('worc/WORCCastConvert:0.3.2', tool_version='0.1', id='convert_seg_test_' + label, resources=ResourceLimit(memory=memory), step_id='FileConversion') self.converters_seg_test[label].inputs['image'] =\ self.sources_segmentations_test[label].output # Assume provided segmentation is on first modality if nmod > 0: # Use elastix and transformix for registration # NOTE: Assume elastix node type is on first configuration elastix_node =\ str(self.configs[0]['General']['RegistrationNode']) transformix_node =\ str(self.configs[0]['General']['TransformationNode']) memory_elastix = self.fastr_memory_parameters['Elastix'] self.elastix_nodes_train[label] =\ self.network.create_node(elastix_node, tool_version='0.2', id='elastix_train_' + label, resources=ResourceLimit(memory=memory_elastix), step_id='Image_Registration') memory_transformix = self.fastr_memory_parameters['Elastix'] self.transformix_seg_nodes_train[label] =\ self.network.create_node(transformix_node, tool_version='0.2', id='transformix_seg_train_' + label, resources=ResourceLimit(memory=memory_transformix), step_id='Image_Registration') self.transformix_im_nodes_train[label] =\ self.network.create_node(transformix_node, tool_version='0.2', id='transformix_im_train_' + label, resources=ResourceLimit(memory=memory_transformix), step_id='Image_Registration') if self.TrainTest: self.elastix_nodes_test[label] =\ self.network.create_node(elastix_node, tool_version='0.2', id='elastix_test_' + label, resources=ResourceLimit(memory=memory_elastix), step_id='Image_Registration') self.transformix_seg_nodes_test[label] =\ self.network.create_node(transformix_node, tool_version='0.2', id='transformix_seg_test_' + label, resources=ResourceLimit(memory=memory_transformix), step_id='Image_Registration') self.transformix_im_nodes_test[label] =\ self.network.create_node(transformix_node, tool_version='0.2', id='transformix_im_test_' + label, resources=ResourceLimit(memory=memory_transformix), step_id='Image_Registration') # Create sources_segmentation # M1 = moving, others = fixed self.elastix_nodes_train[label].inputs['fixed_image'] =\ self.converters_im_train[label].outputs['image'] self.elastix_nodes_train[label].inputs['moving_image'] =\ self.converters_im_train[self.modlabels[0]].outputs['image'] # Add node that copies metadata from the image to the # segmentation if required if self.CopyMetadata: # Copy metadata from the image which was registered to # the segmentation, if it is not created yet if not hasattr(self, "copymetadata_nodes_train"): # NOTE: Do this for first modality, as we assume # the segmentation is on that one self.copymetadata_nodes_train = dict() self.copymetadata_nodes_train[self.modlabels[0]] =\ self.network.create_node('itktools/0.3.2/CopyMetadata:1.0', tool_version='1.0', id='CopyMetadata_train_' + self.modlabels[0], step_id='Image_Registration') self.copymetadata_nodes_train[self.modlabels[0]].inputs["source"] =\ self.converters_im_train[self.modlabels[0]].outputs['image'] self.copymetadata_nodes_train[self.modlabels[0]].inputs["destination"] =\ self.converters_seg_train[self.modlabels[0]].outputs['image'] self.transformix_seg_nodes_train[label].inputs['image'] =\ self.copymetadata_nodes_train[self.modlabels[0]].outputs['output'] else: self.transformix_seg_nodes_train[label].inputs['image'] =\ self.converters_seg_train[self.modlabels[0]].outputs['image'] if self.TrainTest: self.elastix_nodes_test[label].inputs['fixed_image'] =\ self.converters_im_test[label].outputs['image'] self.elastix_nodes_test[label].inputs['moving_image'] =\ self.converters_im_test[self.modlabels[0]].outputs['image'] if self.CopyMetadata: # Copy metadata from the image which was registered # to the segmentation if not hasattr(self, "copymetadata_nodes_test"): # NOTE: Do this for first modality, as we assume # the segmentation is on that one self.copymetadata_nodes_test = dict() self.copymetadata_nodes_test[self.modlabels[0]] =\ self.network.create_node('itktools/0.3.2/CopyMetadata:1.0', tool_version='1.0', id='CopyMetadata_test_' + self.modlabels[0], step_id='Image_Registration') self.copymetadata_nodes_test[self.modlabels[0]].inputs["source"] =\ self.converters_im_test[self.modlabels[0]].outputs['image'] self.copymetadata_nodes_test[self.modlabels[0]].inputs["destination"] =\ self.converters_seg_test[self.modlabels[0]].outputs['image'] self.transformix_seg_nodes_test[label].inputs['image'] =\ self.copymetadata_nodes_test[self.modlabels[0]].outputs['output'] else: self.transformix_seg_nodes_test[label].inputs['image'] =\ self.converters_seg_test[self.modlabels[0]].outputs['image'] # Apply registration to input modalities self.source_Elastix_Parameters[label] =\ self.network.create_source('ElastixParameterFile', id='Elastix_Para_' + label, node_group='elpara', step_id='Image_Registration') self.link_elparam_train =\ self.network.create_link(self.source_Elastix_Parameters[label].output, self.elastix_nodes_train[label].inputs['parameters']) self.link_elparam_train.collapse = 'elpara' if self.TrainTest: self.link_elparam_test =\ self.network.create_link(self.source_Elastix_Parameters[label].output, self.elastix_nodes_test[label].inputs['parameters']) self.link_elparam_test.collapse = 'elpara' if self.masks_train: self.elastix_nodes_train[label].inputs['fixed_mask'] =\ self.converters_masks_train[label].outputs['image'] self.elastix_nodes_train[label].inputs['moving_mask'] =\ self.converters_masks_train[self.modlabels[0]].outputs['image'] if self.TrainTest: if self.masks_test: self.elastix_nodes_test[label].inputs['fixed_mask'] =\ self.converters_masks_test[label].outputs['image'] self.elastix_nodes_test[label].inputs['moving_mask'] =\ self.converters_masks_test[self.modlabels[0]].outputs['image'] # Change the FinalBSpline Interpolation order to 0 as required for binarie images: see https://github.com/SuperElastix/elastix/wiki/FAQ self.edittransformfile_nodes_train[label] =\ self.network.create_node('elastixtools/EditElastixTransformFile:0.1', tool_version='0.1', id='EditElastixTransformFile_train_' + label, step_id='Image_Registration') self.edittransformfile_nodes_train[label].inputs['set'] =\ ["FinalBSplineInterpolationOrder=0"] self.edittransformfile_nodes_train[label].inputs['transform'] =\ self.elastix_nodes_train[label].outputs['transform'][-1] if self.TrainTest: self.edittransformfile_nodes_test[label] =\ self.network.create_node('elastixtools/EditElastixTransformFile:0.1', tool_version='0.1', id='EditElastixTransformFile_test_' + label, step_id='Image_Registration') self.edittransformfile_nodes_test[label].inputs['set'] =\ ["FinalBSplineInterpolationOrder=0"] self.edittransformfile_nodes_test[label].inputs['transform'] =\ self.elastix_nodes_test[label].outputs['transform'][-1] # Link data and transformation to transformix and source self.transformix_seg_nodes_train[label].inputs['transform'] =\ self.edittransformfile_nodes_train[label].outputs['transform'] self.transformix_im_nodes_train[label].inputs['transform'] =\ self.elastix_nodes_train[label].outputs['transform'][-1] self.transformix_im_nodes_train[label].inputs['image'] =\ self.converters_im_train[self.modlabels[0]].outputs['image'] if self.TrainTest: self.transformix_seg_nodes_test[label].inputs['transform'] =\ self.edittransformfile_nodes_test[label].outputs['transform'] self.transformix_im_nodes_test[label].inputs['transform'] =\ self.elastix_nodes_test[label].outputs['transform'][-1] self.transformix_im_nodes_test[label].inputs['image'] =\ self.converters_im_test[self.modlabels[0]].outputs['image'] if self.configs[nmod]['General']['Segmentix'] != 'True': # These segmentations serve as input for the feature calculation for i_node in range(len(self.calcfeatures_train[label])): self.calcfeatures_train[label][i_node].inputs['segmentation'] =\ self.transformix_seg_nodes_train[label].outputs['image'] if self.TrainTest: self.calcfeatures_test[label][i_node].inputs['segmentation'] =\ self.transformix_seg_nodes_test[label].outputs['image'] # Save outputfor the training set self.sinks_transformations_train[label] =\ self.network.create_sink('ElastixTransformFile', id='transformations_train_' + label, step_id='train_sinks') self.sinks_segmentations_elastix_train[label] =\ self.network.create_sink('ITKImageFile', id='segmentations_out_elastix_train_' + label, step_id='train_sinks') self.sinks_images_elastix_train[label] =\ self.network.create_sink('ITKImageFile', id='images_out_elastix_train_' + label, step_id='train_sinks') self.sinks_transformations_train[label].input =\ self.elastix_nodes_train[label].outputs['transform'] self.sinks_segmentations_elastix_train[label].input =\ self.transformix_seg_nodes_train[label].outputs['image'] self.sinks_images_elastix_train[label].input =\ self.transformix_im_nodes_train[label].outputs['image'] # Save output for the test set if self.TrainTest: self.sinks_transformations_test[label] =\ self.network.create_sink('ElastixTransformFile', id='transformations_test_' + label, step_id='test_sinks') self.sinks_segmentations_elastix_test[label] =\ self.network.create_sink('ITKImageFile', id='segmentations_out_elastix_test_' + label, step_id='test_sinks') self.sinks_images_elastix_test[label] =\ self.network.create_sink('ITKImageFile', id='images_out_elastix_test_' + label, step_id='test_sinks') self.sinks_transformations_test[label].input =\ self.elastix_nodes_test[label].outputs['transform'] self.sinks_segmentations_elastix_test[label].input =\ self.transformix_seg_nodes_test[label].outputs['image'] self.sinks_images_elastix_test[label].input =\ self.transformix_im_nodes_test[label].outputs['image'] def add_segmentix(self, label, nmod): """Add segmentix to the network.""" # Segmentix nodes ------------------------------------------------- # Use segmentix node to convert input segmentation into # correct contour if label not in self.sinks_segmentations_segmentix_train: self.sinks_segmentations_segmentix_train[label] =\ self.network.create_sink('ITKImageFile', id='segmentations_out_segmentix_train_' + label, step_id='train_sinks') memory = self.fastr_memory_parameters['Segmentix'] self.nodes_segmentix_train[label] =\ self.network.create_node('segmentix/Segmentix:1.0', tool_version='1.0', id='segmentix_train_' + label, resources=ResourceLimit(memory=memory), step_id='Preprocessing') # Input the image self.nodes_segmentix_train[label].inputs['image'] =\ self.converters_im_train[label].outputs['image'] # Input the metadata if self.metadata_train and len(self.metadata_train) >= nmod + 1: self.nodes_segmentix_train[label].inputs['metadata'] = self.sources_metadata_train[label].output # Input the segmentation if hasattr(self, 'transformix_seg_nodes_train'): if label in self.transformix_seg_nodes_train.keys(): # Use output of registration in segmentix self.nodes_segmentix_train[label].inputs['segmentation_in'] =\ self.transformix_seg_nodes_train[label].outputs['image'] else: # Use original segmentation self.nodes_segmentix_train[label].inputs['segmentation_in'] =\ self.converters_seg_train[label].outputs['image'] else: # Use original segmentation self.nodes_segmentix_train[label].inputs['segmentation_in'] =\ self.converters_seg_train[label].outputs['image'] # Input the parameters self.nodes_segmentix_train[label].inputs['parameters'] =\ self.sources_parameters[label].output self.sinks_segmentations_segmentix_train[label].input =\ self.nodes_segmentix_train[label].outputs['segmentation_out'] if self.TrainTest: self.sinks_segmentations_segmentix_test[label] =\ self.network.create_sink('ITKImageFile', id='segmentations_out_segmentix_test_' + label, step_id='test_sinks') self.nodes_segmentix_test[label] =\ self.network.create_node('segmentix/Segmentix:1.0', tool_version='1.0', id='segmentix_test_' + label, resources=ResourceLimit(memory=memory), step_id='Preprocessing') # Input the image self.nodes_segmentix_test[label].inputs['image'] =\ self.converters_im_test[label].outputs['image'] # Input the metadata if self.metadata_test and len(self.metadata_test) >= nmod + 1: self.nodes_segmentix_test[label].inputs['metadata'] = self.sources_metadata_test[label].output if hasattr(self, 'transformix_seg_nodes_test'): if label in self.transformix_seg_nodes_test.keys(): # Use output of registration in segmentix self.nodes_segmentix_test[label].inputs['segmentation_in'] =\ self.transformix_seg_nodes_test[label].outputs['image'] else: # Use original segmentation self.nodes_segmentix_test[label].inputs['segmentation_in'] =\ self.converters_seg_test[label].outputs['image'] else: # Use original segmentation self.nodes_segmentix_test[label].inputs['segmentation_in'] =\ self.converters_seg_test[label].outputs['image'] self.nodes_segmentix_test[label].inputs['parameters'] =\ self.sources_parameters[label].output self.sinks_segmentations_segmentix_test[label].input =\ self.nodes_segmentix_test[label].outputs['segmentation_out'] for i_node in range(len(self.calcfeatures_train[label])): self.calcfeatures_train[label][i_node].inputs['segmentation'] =\ self.nodes_segmentix_train[label].outputs['segmentation_out'] if self.TrainTest: self.calcfeatures_test[label][i_node].inputs['segmentation'] =\ self.nodes_segmentix_test[label].outputs['segmentation_out'] if self.masks_train and len(self.masks_train) >= nmod + 1: # Use masks self.nodes_segmentix_train[label].inputs['mask'] =\ self.converters_masks_train[label].outputs['image'] if self.masks_test and len(self.masks_test) >= nmod + 1: # Use masks self.nodes_segmentix_test[label].inputs['mask'] =\ self.converters_masks_test[label].outputs['image'] def set(self): """Set the FASTR source and sink data based on the given attributes.""" self.fastrconfigs = list() self.source_data = dict() self.sink_data = dict() # Save the configurations as files self.save_config() # fixed splits if self.fixedsplits: self.source_data['fixedsplits_source'] = self.fixedsplits # Generate gridsearch parameter files if required self.source_data['config_classification_source'] = self.fastrconfigs[0] # Set source and sink data self.source_data['patientclass_train'] = self.labels_train self.source_data['patientclass_test'] = self.labels_test self.sink_data['classification'] = ("vfs://output/{}/estimator_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name) self.sink_data['performance'] = ("vfs://output/{}/performance_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name) self.sink_data['config_classification_sink'] = ("vfs://output/{}/config_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name) self.sink_data['features_train_ComBat'] = ("vfs://output/{}/ComBat/features_ComBat_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name) self.sink_data['features_test_ComBat'] = ("vfs://output/{}/ComBat/features_ComBat_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name) # Set the source data from the WORC objects you created for num, label in enumerate(self.modlabels): self.source_data['config_' + label] = self.fastrconfigs[num] if self.pyradiomics_configs: self.source_data['config_pyradiomics_' + label] = self.pyradiomics_configs[num] # Add train data sources if self.images_train and len(self.images_train) - 1 >= num: self.source_data['images_train_' + label] = self.images_train[num] if self.masks_train and len(self.masks_train) - 1 >= num: self.source_data['mask_train_' + label] = self.masks_train[num] if self.masks_normalize_train and len(self.masks_normalize_train) - 1 >= num: self.source_data['masks_normalize_train_' + label] = self.masks_normalize_train[num] if self.metadata_train and len(self.metadata_train) - 1 >= num: self.source_data['metadata_train_' + label] = self.metadata_train[num] if self.segmentations_train and len(self.segmentations_train) - 1 >= num: self.source_data['segmentations_train_' + label] = self.segmentations_train[num] if self.semantics_train and len(self.semantics_train) - 1 >= num: self.source_data['semantics_train_' + label] = self.semantics_train[num] if self.features_train and len(self.features_train) - 1 >= num: self.source_data['features_train_' + label] = self.features_train[num] if self.Elastix_Para: # First modality does not need to be registered if num > 0: if len(self.Elastix_Para) > 1: # Each modality has its own registration parameters self.source_data['Elastix_Para_' + label] = self.Elastix_Para[num] else: # Use one fileset for all modalities self.source_data['Elastix_Para_' + label] = self.Elastix_Para[0] # Add test data sources if self.images_test and len(self.images_test) - 1 >= num: self.source_data['images_test_' + label] = self.images_test[num] if self.masks_test and len(self.masks_test) - 1 >= num: self.source_data['mask_test_' + label] = self.masks_test[num] if self.masks_normalize_test and len(self.masks_normalize_test) - 1 >= num: self.source_data['masks_normalize_test_' + label] = self.masks_normalize_test[num] if self.metadata_test and len(self.metadata_test) - 1 >= num: self.source_data['metadata_test_' + label] = self.metadata_test[num] if self.segmentations_test and len(self.segmentations_test) - 1 >= num: self.source_data['segmentations_test_' + label] = self.segmentations_test[num] if self.semantics_test and len(self.semantics_test) - 1 >= num: self.source_data['semantics_test_' + label] = self.semantics_test[num] if self.features_test and len(self.features_test) - 1 >= num: self.source_data['features_test_' + label] = self.features_test[num] self.sink_data['segmentations_out_segmentix_train_' + label] = ("vfs://output/{}/Segmentations/seg_{}_segmentix_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) self.sink_data['segmentations_out_elastix_train_' + label] = ("vfs://output/{}/Elastix/seg_{}_elastix_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) self.sink_data['images_out_elastix_train_' + label] = ("vfs://output/{}/Elastix/im_{}_elastix_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) if hasattr(self, 'featurecalculators'): for f in self.featurecalculators[label]: self.sink_data['features_train_' + label + '_' + f] = ("vfs://output/{}/Features/features_{}_{}_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, f, label) if self.labels_test: self.sink_data['segmentations_out_segmentix_test_' + label] = ("vfs://output/{}/Segmentations/seg_{}_segmentix_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) self.sink_data['segmentations_out_elastix_test_' + label] = ("vfs://output/{}/Elastix/seg_{}_elastix_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) self.sink_data['images_out_elastix_test_' + label] = ("vfs://output/{}/Images/im_{}_elastix_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) if hasattr(self, 'featurecalculators'): for f in self.featurecalculators[label]: self.sink_data['features_test_' + label + '_' + f] = ("vfs://output/{}/Features/features_{}_{}_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, f, label) # Add elastix sinks if used if self.segmode: # Segmode is only non-empty if segmentations are provided if self.segmode == 'Register': self.sink_data['transformations_train_' + label] = ("vfs://output/{}/Elastix/transformation_{}_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) if self.TrainTest: self.sink_data['transformations_test_' + label] = ("vfs://output/{}/Elastix/transformation_{}_{{sample_id}}_{{cardinality}}{{ext}}").format(self.name, label) if self._add_evaluation: self.Evaluate.set() def execute(self): """Execute the network through the fastr.network.execute command.""" # Draw and execute nwtwork try: self.network.draw(file_path=self.network.id + '.svg', draw_dimensions=True) except graphviz.backend.ExecutableNotFound: print('[WORC WARNING] Graphviz executable not found: not drawing network diagram. Make sure the Graphviz executables are on your systems PATH.') except graphviz.backend.CalledProcessError as e: print(f'[WORC WARNING] Graphviz executable gave an error: not drawing network diagram. Original error: {e}') # export hyper param. search space to LaTeX table for config in self.fastrconfigs: config_path = Path(url2pathname(urlparse(config).path)) tex_path = f'{config_path.parent.absolute() / config_path.stem}_hyperparams_space.tex' export_hyper_params_to_latex(config_path, tex_path) if DebugDetector().do_detection(): print("Source Data:") for k in self.source_data.keys(): print(f"\t {k}: {self.source_data[k]}.") print("\n Sink Data:") for k in self.sink_data.keys(): print(f"\t {k}: {self.sink_data[k]}.") # When debugging, set the tempdir to the default of fastr + name self.fastr_tmpdir = os.path.join(fastr.config.mounts['tmp'], self.name) self.network.execute(self.source_data, self.sink_data, execution_plugin=self.fastr_plugin, tmpdir=self.fastr_tmpdir) def add_evaluation(self, label_type, modus='binary_classification'): """Add branch for evaluation of performance to network. Note: should be done after build, before set: WORC.build() WORC.add_evaluation(label_type) WORC.set() WORC.execute() """ self.Evaluate =\ Evaluate(label_type=label_type, parent=self, modus=modus) self._add_evaluation = True def save_config(self): """Save the config files to physical files and add to network.""" # If the configuration files are confiparse objects, write to file self.pyradiomics_configs = list() # Make sure we can dump blank values for PyRadiomics yaml.SafeDumper.add_representer(type(None), lambda dumper, value: dumper.represent_scalar(u'tag:yaml.org,2002:null', '')) for num, c in enumerate(self.configs): if type(c) != configparser.ConfigParser: # A filepath (not a fastr source) is provided. Hence we read # the config file and convert it to a configparser object config = configparser.ConfigParser() config.read(c) c = config cfile = os.path.join(self.fastr_tmpdir, f"config_{self.name}_{num}.ini") if not os.path.exists(os.path.dirname(cfile)): os.makedirs(os.path.dirname(cfile)) with open(cfile, 'w') as configfile: c.write(configfile) # If PyRadiomics is used, also write a config for PyRadiomics if 'pyradiomics' in c['General']['FeatureCalculators']: cfile_pyradiomics = os.path.join(self.fastr_tmpdir, f"config_pyradiomics_{self.name}_{num}.yaml") config_pyradiomics = io.convert_config_pyradiomics(c) with open(cfile_pyradiomics, 'w') as file: yaml.safe_dump(config_pyradiomics, file) cfile_pyradiomics = Path(self.fastr_tmpdir) / f"config_pyradiomics_{self.name}_{num}.yaml" self.pyradiomics_configs.append(cfile_pyradiomics.as_uri().replace('%20', ' ')) # BUG: Make path with pathlib to create windows double slashes cfile = Path(self.fastr_tmpdir) / f"config_{self.name}_{num}.ini" self.fastrconfigs.append(cfile.as_uri().replace('%20', ' ')) class Tools(object): """ Create other pipelines besides the default radiomics executions. Currently includes: 1. Registration pipeline 2. Evaluation pipeline 3. Slicer pipeline, to create pngs of middle slice of images. """ def __init__(self): """Initialize object with all pipelines.""" self.Elastix = Elastix() self.Evaluate = Evaluate() self.Slicer = Slicer()

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null

0

null

0

1

0

8d86c9a6526d8d524710fa780972b087a3f46ac3

7,715

py

Python

causal_rl/environments/multi_typed.py

vluzko/causal_rl

92ee221bdf1932fa83955441baabb5e28b78ab9d

[ "MIT" ]

2

2021-04-02T12:06:13.000Z

2022-02-09T06:57:26.000Z

causal_rl/environments/multi_typed.py

vluzko/causal_rl

92ee221bdf1932fa83955441baabb5e28b78ab9d

[ "MIT" ]

11

2020-12-28T14:51:31.000Z

2021-03-29T19:53:24.000Z

causal_rl/environments/multi_typed.py

vluzko/causal_rl

92ee221bdf1932fa83955441baabb5e28b78ab9d

[ "MIT" ]

null

import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from gym import Env from scipy.spatial import distance from typing import Optional, Tuple, Any from causal_rl.environments import CausalEnv class MultiTyped(CausalEnv): """A simulation of balls bouncing with gravity and elastic collisions. Attributes: num_obj (int): Number of balls in the simulation. obj_dim (int): The dimension of the balls. Will always be 2 * dimension_of_space masses (np.ndarray): The masses of the balls. radii (np.ndarray): The radii of the balls. space (pymunk.Space): The actual simulation space. """ cls_name = 'multi_typed' def __init__(self, num_obj: int=5, mass: float=10, radii: float=10, width: float=400): self.num_obj = 2 * num_obj self.obj_dim = 4 self.mass = mass self.radius = radii self.masses = mass * np.ones(self.num_obj) self.radii = radii * np.ones(self.num_obj) self.width = width self.location_indices = (0, 1) @property def name(self) -> str: return '{}_{}_{}_{}'.format(self.cls_name, self.mass, self.radius, self.width) def reset(self): import pymunk self.space = pymunk.Space() self.space.gravity = (0.0, 0.0) self.objects = [] x_pos = np.random.rand(self.num_obj, 1) * (self.width - 40) + 20 y_pos = np.random.rand(self.num_obj, 1) * (self.width - 40) + 20 x_vel = np.random.rand(self.num_obj, 1) * 300 - 150 y_vel = np.random.rand(self.num_obj, 1) * 300 - 150 # Create circles for i in range(self.num_obj): mass = self.masses[i] radius = self.radii[i] moment = pymunk.moment_for_circle(mass, 0, radius, (0, 0)) body = pymunk.Body(mass, moment) body.position = (x_pos[i], y_pos[i]) body.velocity = (x_vel[i], y_vel[i]) shape = pymunk.Circle(body, radius, (0, 0)) shape.elasticity = 1.0 self.space.add(body, shape) self.objects.append(body) # Create squares for i in range(self.num_obj): mass = self.masses[i] * 6 radius = self.radii[i] * 1.2 size = (radius, radius) moment = pymunk.moment_for_box(mass, size) body = pymunk.Body(mass, moment) body.position = (x_pos[i], y_pos[i]) body.velocity = (x_vel[i], y_vel[i]) shape = pymunk.Poly.create_box(body, size) shape.elasticity = 1.0 self.space.add(body, shape) self.objects.append(body) static_lines = [ pymunk.Segment(self.space.static_body, (0.0, 0.0), (0.0, self.width), 0), pymunk.Segment(self.space.static_body, (0.0, 0.0), (self.width, 0.0), 0), pymunk.Segment(self.space.static_body, (self.width, 0.0), (self.width, self.width), 0), pymunk.Segment(self.space.static_body, (0.0, self.width), (self.width, self.width), 0) ] for line in static_lines: line.elasticity = 1. self.space.add(static_lines) return self.get_state(), 0, False, None def get_state(self) -> np.ndarray: """Get the current state. Returns: A tensor representing the state. Each row is a single ball, columns are [*position, *velocity] """ state = np.zeros((self.num_obj, 4)) for i in range(self.num_obj): state[i, :2] = np.array([self.objects[i].position[0], self.objects[i].position[1]]) state[i, 2:] = np.array([self.objects[i].velocity[0], self.objects[i].velocity[1]]) return state def step(self, dt=0.01) -> Tuple[np.ndarray, float, bool, Any]: self.space.step(dt) return self.get_state(), 0, False, None def generate_data(self, epochs: int=10000, dt: float=0.01) -> Tuple[np.ndarray, np.ndarray]: states = np.zeros((epochs, self.num_obj, 4)) rewards = np.zeros((epochs, 1)) self.reset() for t in range(epochs): states[t] = self.get_state() if t > 0: states[t, :, 2:] = (states[t, :, :2] - states[t - 1, :, :2]) / dt self.step(dt=dt) return states, rewards def visualize(self, state: np.ndarray, save_path: Optional[str]=None): """Visualize a single state. Args: state: The full save_path: Path to save the image to. """ colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k'] pos = state[:, :2] momenta = state[:, 2:] fig, ax = plt.subplots(figsize=(6, 6)) box = plt.Rectangle((0, 0), self.width, self.width, linewidth=5, edgecolor='k', facecolor='none') ax.add_patch(box) for i in range(self.num_obj // 2): circle = plt.Circle((pos[i, 0], pos[i, 1]), radius=self.radii[i], edgecolor='b') label = ax.annotate('{}'.format(i), xy=(pos[i, 0], pos[i, 1]), fontsize=8, ha='center') # Plot the momentum plt.arrow(pos[i, 0], pos[i, 1], momenta[i, 0], momenta[i, 1]) ax.add_patch(circle) for i in range(self.num_obj // 2, self.num_obj): circle = plt.Rectangle((pos[i, 0], pos[i, 1]), self.radii[i], self.radii[i], edgecolor='b') label = ax.annotate('{}'.format(i), xy=(pos[i, 0], pos[i, 1]), fontsize=8, ha='center') # Plot the momentum plt.arrow(pos[i, 0], pos[i, 1], momenta[i, 0], momenta[i, 1]) ax.add_patch(circle) plt.axis([0, self.width, 0, self.width]) plt.axis('off') if save_path is not None: plt.savefig(save_path) else: plt.show() plt.close() def detect_collisions(self, trajectories: np.ndarray) -> np.ndarray: n = trajectories.shape[0] k = self.num_obj radii = np.copy(self.radii) radii[self.num_obj // 2:] = radii[self.num_obj // 2:] * np.sqrt(2) min_dist = radii.reshape(k, 1) + radii.reshape(1, k) np.fill_diagonal(min_dist, 0) collisions = np.zeros((n, k, k)) for i in range(1, n): # The (x, y) coordinates of all balls at t=i locs = trajectories[i, :, :2] distances = distance.squareform(distance.pdist(locs)) collided = np.nonzero(distances < min_dist) collisions[i-1][collided] = 1 collisions[i][collided] = 1 return collisions def wall_collisions(self, states: np.ndarray) -> np.ndarray: min_coord = 0 + self.radius max_coord = self.width - self.radius # Just the position coordinates locs = states[:, :, :2] has_collision = (locs < min_coord) | (locs > max_coord) return has_collision class WithTypes(MultiTyped): """Include the type of the object in the state.""" cls_name = 'with_types' def __init__(self, num_obj=5, mass: float=10, radii: float=10, width: float=400): super().__init__(num_obj, mass, radii, width) self.obj_dim = 5 self.location_indices = (0, 1) def generate_data(self, epochs: int=10000, dt: float=0.01) -> Tuple[np.ndarray, np.ndarray]: states, rewards = super().generate_data(epochs, dt) with_types = np.zeros((epochs, self.num_obj, self.obj_dim)) with_types[:, :, :-1] = states with_types[:, self.num_obj//2:, -1] = 1 return with_types, rewards def detect_collisions(self, trajectories: np.ndarray) -> np.ndarray: return super().detect_collisions(trajectories[:, :, :-1])

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7,715

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null

0

null

0

1

0

8d88e96d4a71ca08ce8d66eee14e65dd7c02396c

3,189

py

Python

bin/makeReport.py

oxfordmmm/SARS-CoV2_workflows

a84cb0a7142684414b2f285dd27cc2ea287eecb9

[ "MIT" ]

null

bin/makeReport.py

oxfordmmm/SARS-CoV2_workflows

a84cb0a7142684414b2f285dd27cc2ea287eecb9

[ "MIT" ]

null

bin/makeReport.py

oxfordmmm/SARS-CoV2_workflows

a84cb0a7142684414b2f285dd27cc2ea287eecb9

[ "MIT" ]

null

#!/usr/bin/env python3 import pandas as pd import sys import json from Bio import SeqIO sample_name=sys.argv[1] pango=pd.read_csv('pango.csv') nextclade=pd.read_csv('nextclade.tsv', sep='\t') aln2type=pd.read_csv('aln2type.csv') pango['sampleName']=sample_name nextclade['sampleName']=sample_name aln2type['sampleName']=sample_name df=pango.merge(nextclade, on='sampleName', how='left', suffixes=("_pango","_nextclade")) df=df.merge(aln2type, on='sampleName', how='left', suffixes=(None,"_aln2type")) # versions wf=open('workflow_commit.txt').read() df['workflowCommit']=str(wf).strip() df['manifestVersion']=sys.argv[2] nextclade_version=open('nextclade_files/version.txt').read() df['nextcladeVersion']=str(nextclade_version).strip() aln2type_variant_commit=open('variant_definitions/aln2type_variant_git_commit.txt').read() aln2type_variant_version=open('variant_definitions/aln2type_variant_version.txt').read() aln2type_source_commit=open('variant_definitions/aln2type_commit.txt').read() df['aln2typeVariantCommit']=str(aln2type_variant_commit).strip() df['aln2typeVariantVersion']=str(aln2type_variant_version).strip() df['aln2typeSourceVommit']=str(aln2type_source_commit).strip() df.to_csv('{0}_report.tsv'.format(sys.argv[1]), sep='\t', index=False) ### convert to json pango['program']='pango' pango.set_index('program',inplace=True) p=pango.to_dict(orient='index') nextclade['program']='nextclade' nextclade['nextcladeVersion']=str(nextclade_version).strip() nextclade.set_index('program',inplace=True) n=nextclade.to_dict(orient='index') with open('nextclade.json','rt', encoding= 'utf-8') as inf: nj=json.load(inf) n['nextcladeOutputJson']=nj aln2type['program']='aln2type' aln2type['label']=aln2type['phe-label'] aln2type['aln2typeVariantCommit']=str(aln2type_variant_commit).strip() aln2type['aln2typeSourceCommit']=str(aln2type_source_commit).strip() aln2type.set_index(['program','phe-label'],inplace=True) a={level: aln2type.xs(level).to_dict('index') for level in aln2type.index.levels[0]} w={'WorkflowInformation':{}} w['WorkflowInformation']['workflowCommit']=str(wf).strip() w['WorkflowInformation']['manifestVersion']=sys.argv[2] w['WorkflowInformation']['sampleIdentifier']=sample_name # add fasta to json record = SeqIO.read('ref.fasta', "fasta") w['WorkflowInformation']['referenceIdentifier']=record.id #f={'FastaRecord':{'SeqId':record.id, # 'SeqDescription': record.description, # 'Sequence':str(record.seq), # 'sampleName':sample_name}} def completeness(nextcladeOutputJson): ref_len = 29903 total_missing = nextcladeOutputJson['results'][0]['qc']['missingData']['totalMissing'] completeness_prop = (ref_len - total_missing) / ref_len completeness_pc = round(completeness_prop * 100, 1) return completeness_pc s={'summary':{}} s['summary']['completeness']=completeness(n['nextcladeOutputJson']) d={sample_name:{}} d[sample_name].update(p) d[sample_name].update(n) d[sample_name].update(a) d[sample_name].update(w) #d[sample_name].update(f) d[sample_name].update(s) with open('{0}_report.json'.format(sample_name), 'w', encoding='utf-8') as f: json.dump(d, f, indent=4, sort_keys=True, ensure_ascii=False)

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0.043515

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0

0.017028

0.060834

3,189

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36.238636

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0.077575

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1

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false

0

0.063492

0

0.095238

0

null

0

null

0

1

0

8d8a5d72d65e690dc4c82341ed975187662e4c48

1,484

py

Python

webhooks/statuscake/alerta_statuscake.py

frekel/alerta-contrib

d8f5c93a4ea735085b3689c2c852ecae94924d08

[ "MIT" ]

114

2015-02-05T00:22:16.000Z

2021-11-25T13:02:44.000Z

webhooks/statuscake/alerta_statuscake.py

NeilOrley/alerta-contrib

69d271ef9fe6542727ec4aa39fc8e0f797f1e8b1

[ "MIT" ]

245

2016-01-09T22:29:09.000Z

2022-03-16T10:37:02.000Z

webhooks/statuscake/alerta_statuscake.py

NeilOrley/alerta-contrib

69d271ef9fe6542727ec4aa39fc8e0f797f1e8b1

[ "MIT" ]

193

2015-01-30T21:22:49.000Z

2022-03-28T05:37:14.000Z

from alerta.models.alert import Alert from alerta.webhooks import WebhookBase from alerta.exceptions import RejectException import os import hashlib class StatusCakeWebhook(WebhookBase): def incoming(self, query_string, payload): alert_severity = os.environ.get('STATUSCAKE_DEFAULT_ALERT_SEVERITY', 'major') # If the statuscake username and apikey are provided # We can validate that the webhook call is valid statuscake_username = os.environ.get('STATUSCAKE_USERNAME') statuscake_apikey = os.environ.get('STATUSCAKE_APIKEY') if statuscake_username and statuscake_apikey: decoded_token = statuscake_username + statuscake_apikey statuscake_token = hashlib.md5(decoded_token.encode()).hexdigest() if statuscake_token != payload['Token']: raise RejectException("Provided Token couldn't be verified") if payload['Status'] == 'UP': severity = 'normal' else: severity = alert_severity return Alert( resource=payload['Name'], event='AppDown', environment='Production', severity=severity, service=['StatusCake'], group='Application', value=payload['StatusCode'], text="%s is down" % payload['URL'], tags=payload['Tags'].split(','), origin='statuscake', raw_data=str(payload) )

35.333333

85

0.624663

147

1,484

6.176871

0.517007

0.099119

0.039648

0.072687

0

0.000937

0.280997

1,484

41

86

36.195122

0.850047

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0

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0.023844

0

1

0.03125

false

0

0.15625

0

0.25

0

null

0

null

0

1

0

8d8b51eaca246cacfde939fcbc4a16b39dba720e

3,738

py

Python

ironic_discoverd/main.py

enovance/ironic-discoverd

d3df6178ca5c95943c93ff80723c86b7080bca0b

[ "Apache-2.0" ]

null

ironic_discoverd/main.py

enovance/ironic-discoverd

d3df6178ca5c95943c93ff80723c86b7080bca0b

[ "Apache-2.0" ]

null

ironic_discoverd/main.py

enovance/ironic-discoverd

d3df6178ca5c95943c93ff80723c86b7080bca0b

[ "Apache-2.0" ]

null

# 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 eventlet eventlet.monkey_patch(thread=False) import json import logging import sys from flask import Flask, request # noqa from keystoneclient import exceptions from ironic_discoverd import conf from ironic_discoverd import discoverd from ironic_discoverd import firewall from ironic_discoverd import node_cache from ironic_discoverd import utils app = Flask(__name__) LOG = discoverd.LOG @app.route('/v1/continue', methods=['POST']) def post_continue(): data = request.get_json(force=True) LOG.debug("Got JSON %s, going into processing thread", data) try: res = discoverd.process(data) except utils.DiscoveryFailed as exc: return str(exc), exc.http_code else: return json.dumps(res), 200, {'Content-Type': 'applications/json'} @app.route('/v1/discover', methods=['POST']) def post_discover(): if conf.getboolean('discoverd', 'authenticate'): if not request.headers.get('X-Auth-Token'): LOG.debug("No X-Auth-Token header, rejecting") return 'Authentication required', 401 try: utils.get_keystone(token=request.headers['X-Auth-Token']) except exceptions.Unauthorized: LOG.debug("Keystone denied access, rejecting") return 'Access denied', 403 # TODO(dtanstur): check for admin role data = request.get_json(force=True) LOG.debug("Got JSON %s", data) try: discoverd.discover(data) except utils.DiscoveryFailed as exc: return str(exc), exc.http_code else: return "", 202 def periodic_update(period): while True: LOG.debug('Running periodic update of filters') try: firewall.update_filters() except Exception: LOG.exception('Periodic update failed') eventlet.greenthread.sleep(period) def periodic_clean_up(period): while True: LOG.debug('Running periodic clean up of timed out nodes') try: if node_cache.clean_up(): firewall.update_filters() except Exception: LOG.exception('Periodic clean up failed') eventlet.greenthread.sleep(period) def main(): if len(sys.argv) < 2: sys.exit("Usage: %s config-file" % sys.argv[0]) conf.read(sys.argv[1]) debug = conf.getboolean('discoverd', 'debug') logging.basicConfig(level=logging.DEBUG if debug else logging.INFO) logging.getLogger('urllib3.connectionpool').setLevel(logging.WARNING) logging.getLogger('requests.packages.urllib3.connectionpool').setLevel( logging.WARNING) if not conf.getboolean('discoverd', 'authenticate'): LOG.warning('Starting unauthenticated, please check configuration') node_cache.init() firewall.init() utils.check_ironic_available() period = conf.getint('discoverd', 'firewall_update_period') eventlet.greenthread.spawn_n(periodic_update, period) period = conf.getint('discoverd', 'clean_up_period') eventlet.greenthread.spawn_n(periodic_clean_up, period) app.run(debug=debug, host=conf.get('discoverd', 'listen_address'), port=conf.getint('discoverd', 'listen_port'))

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0.688604

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0.04931

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0

null

0

null

0

1

0

8d8c7b2102958e3a921b5b5a1f32ed6750cd5ff4

964

py

Python

config_translator.py

Charahiro-tan/Jurubot_Translator

d0d0db137f3ddfe06d7cd9457d22c418bdeff94c

[ "MIT" ]

1

2021-07-26T11:14:05.000Z

config_translator.py

Charahiro-tan/Jurubot_Translator

d0d0db137f3ddfe06d7cd9457d22c418bdeff94c

[ "MIT" ]

null

config_translator.py

Charahiro-tan/Jurubot_Translator

d0d0db137f3ddfe06d7cd9457d22c418bdeff94c

[ "MIT" ]

null

################################################## # 翻訳の設定 # 変更した設定は次回起動時から適用されます ################################################## # []でくくってある項目は""でくくって,で区切ることでいくつも設定できます。 # 無視するユーザー ignore_user = ["Nightbot","Streamelements","Moobot"] # 翻訳する前に削除するワード。正規表現対応。 # URLや同じ言葉の繰り返しなどはデフォルトで削除してますので足りなかったら追加してください。 del_word = ["88+","８８+"] # 無視する言語。 # 言語コードは https://cloud.google.com/translate/docs/languages 参照 ignore_lang = ["",""] # 配信者が使用している言語。あらゆる言語がこの言語に翻訳されます。 home_lang = "ja" # 上のhome_langで投稿された時の翻訳先 default_to_lang = "en" # translate.googleのURLのサフィックス。日本の方ならこのままで。 url_suffix = "co.jp" # 翻訳結果に発言者の名前を入れる場合はTrue、入れない場合はFalse sender = True # 上がTrueの場合に表示する名前 # "displayname" でディスプレイネーム # "loginid" でログインID sender_name = "displayname" # 翻訳結果に言語情報(en ⇒ ja)を付ける場合はTrue、付けない場合はFalse language = True # Google Apps Scriptで作成したAPIを使用するときはTrue、しないときはFalse # Google Apps Scriptを使用するときは必ずReadmeを読んでください。 gas = False # Google Apps Scriptで作成したURL gas_url = ""

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1

0

false

0

null

0

null

0

1

0

8d8cd77924dc533eeabb54595050045f0fb725d3

1,489

py

Python

wxcloudrun/dao.py

lubupang/resume_flask1

1ea18e88c0b667e92710096f57973a77d19e8fc6

[ "MIT" ]

null

wxcloudrun/dao.py

lubupang/resume_flask1

1ea18e88c0b667e92710096f57973a77d19e8fc6

[ "MIT" ]

null

wxcloudrun/dao.py

lubupang/resume_flask1

1ea18e88c0b667e92710096f57973a77d19e8fc6

[ "MIT" ]

null

import logging from sqlalchemy.exc import OperationalError from wxcloudrun import db from wxcloudrun.model import Counters # 初始化日志 logger = logging.getLogger('log') logger.info("aaaaaaa") def query_counterbyid(id): """ 根据ID查询Counter实体 :param id: Counter的ID :return: Counter实体 """ logger.info("bbbbbbbbb") try: return Counters.query.filter(Counters.id == id).first() except OperationalError as e: logger.info("query_counterbyid errorMsg= {} ".format(e)) return None def delete_counterbyid(id): """ 根据ID删除Counter实体 :param id: Counter的ID """ try: counter = Counters.query.get(id) if counter is None: return db.session.delete(counter) db.session.commit() except OperationalError as e: logger.info("delete_counterbyid errorMsg= {} ".format(e)) def insert_counter(counter): """ 插入一个Counter实体 :param counter: Counters实体 """ try: db.session.add(counter) db.session.commit() except OperationalError as e: logger.info("insert_counter errorMsg= {} ".format(e)) def update_counterbyid(counter): """ 根据ID更新counter的值 :param counter实体 """ try: counter = query_counterbyid(counter.id) if counter is None: return db.session.flush() db.session.commit() except OperationalError as e: logger.info("update_counterbyid errorMsg= {} ".format(e))

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65

0.633983

158

1,489

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1,489

65

66

22.907692

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0.324324

0

null

0

null

0

1

0

8d8db8eca4cacfeb8ce07aa8011f8a4b558400b4

7,411

py

Python

src/bpp/tests/tests_legacy/test_views/test_raporty.py

iplweb/django-bpp

85f183a99d8d5027ae4772efac1e4a9f21675849

[ "BSD-3-Clause" ]

1

2017-04-27T19:50:02.000Z

src/bpp/tests/tests_legacy/test_views/test_raporty.py

mpasternak/django-bpp

434338821d5ad1aaee598f6327151aba0af66f5e

[ "BSD-3-Clause" ]

41

2019-11-07T00:07:02.000Z

2022-02-27T22:09:39.000Z

src/bpp/tests/tests_legacy/test_views/test_raporty.py

iplweb/bpp

f027415cc3faf1ca79082bf7bacd4be35b1a6fdf

[ "BSD-3-Clause" ]

null

# -*- encoding: utf-8 -*- import os import sys import uuid import pytest from django.apps import apps from django.contrib.auth.models import Group from django.core.files.base import ContentFile try: from django.core.urlresolvers import reverse except ImportError: from django.urls import reverse from django.db import transaction from django.http import Http404 from django.test.utils import override_settings from django.utils import timezone from model_mommy import mommy from bpp.models import Typ_KBN, Jezyk, Charakter_Formalny, Typ_Odpowiedzialnosci from bpp.tests.tests_legacy.testutil import UserTestCase, UserTransactionTestCase from bpp.tests.util import any_jednostka, any_autor, any_ciagle from bpp.util import rebuild_contenttypes from bpp.views.raporty import RaportSelector, PodgladRaportu, KasowanieRaportu from celeryui.models import Report class TestRaportSelector(UserTestCase): def test_raportselector(self): p = RaportSelector() p.request = self.factory.get('/') p.get_context_data() def test_raportselector_with_reports(self): for x, kiedy_ukonczono in enumerate([timezone.now(), None]): mommy.make( Report, arguments={}, file=None, finished_on=kiedy_ukonczono) self.client.get(reverse('bpp:raporty')) def test_tytuly_raportow_kronika_uczelni(self): any_ciagle(rok=2000) rep = Report.objects.create( ordered_by=self.user, function="kronika-uczelni", arguments={"rok": "2000"}) res = self.client.get(reverse('bpp:raporty')) self.assertContains( res, "Kronika Uczelni dla roku 2000", status_code=200) def test_tytuly_raportow_raport_dla_komisji_centralnej(self): a = any_autor("Kowalski", "Jan") rep = Report.objects.create( ordered_by=self.user, function="raport-dla-komisji-centralnej", arguments={"autor": a.pk}) res = self.client.get(reverse('bpp:raporty')) self.assertContains( res, "Raport dla Komisji Centralnej - %s" % str(a), status_code=200) class RaportMixin: def zrob_raport(self): r = mommy.make( Report, file=None, function="kronika-uczelni", arguments='{"rok":"2013"}') return r class TestPobranieRaportu(RaportMixin, UserTestCase): def setUp(self): UserTestCase.setUp(self) self.r = self.zrob_raport() error_class = OSError if sys.platform.startswith('win'): error_class = WindowsError try: os.unlink( os.path.join(settings.MEDIA_ROOT, 'raport', 'test_raport')) except error_class: pass self.r.file.save("test_raport", ContentFile("hej ho")) def test_pobranie_nginx(self): # Raport musi byc zakonczony, ineczej nie ma pobrania self.r.finished_on = timezone.now() self.r.save() with override_settings(SENDFILE_BACKEND='sendfile.backends.nginx'): url = reverse('bpp:pobranie-raportu', kwargs=dict(uid=self.r.uid)) resp = self.client.get(url) self.assertEqual(resp.status_code, 200) self.assertIn('x-accel-redirect', resp._headers) class TestPodgladRaportu(RaportMixin, UserTestCase): def setUp(self): UserTestCase.setUp(self) self.r = self.zrob_raport() def test_podgladraportu(self): p = PodgladRaportu() p.kwargs = {} p.kwargs['uid'] = self.r.uid self.assertEqual(p.get_object(), self.r) p.kwargs['uid'] = str(uuid.uuid4()) self.assertRaises(Http404, p.get_object) def test_podgladraportu_client(self): url = reverse('bpp:podglad-raportu', kwargs=dict(uid=self.r.uid)) resp = self.client.get(url) self.assertContains(resp, 'Kronika Uczelni', status_code=200) class KasowanieRaportuMixin: def setUp(self): self.r = self.zrob_raport() self.r.ordered_by = self.user self.r.save() class TestKasowanieRaportu(KasowanieRaportuMixin, RaportMixin, UserTestCase): def setUp(self): UserTestCase.setUp(self) KasowanieRaportuMixin.setUp(self) def test_kasowanieraportu(self): k = KasowanieRaportu() k.kwargs = dict(uid=self.r.uid) class FakeRequest: user = self.user k.request = FakeRequest() k.request.user = None self.assertRaises(Http404, k.get_object) k.request.user = self.user self.assertEqual(k.get_object(), self.r) def test_kasowanieraportu_client(self): self.assertEqual(Report.objects.count(), 1) url = reverse('bpp:kasowanie-raportu', kwargs=dict(uid=self.r.uid)) resp = self.client.get(url) self.assertRedirects(resp, reverse("bpp:raporty")) self.assertEqual(Report.objects.count(), 0) from django.conf import settings class TestWidokiRaportJednostek2012(UserTestCase): # fixtures = ['charakter_formalny.json', # 'jezyk.json', # 'typ_kbn.json', # 'typ_odpowiedzialnosci.json'] def setUp(self): UserTestCase.setUp(self) self.j = any_jednostka() Typ_KBN.objects.get_or_create(skrot="PW", nazwa="Praca wieloośrodkowa") Jezyk.objects.get_or_create(skrot='pol.', nazwa='polski') Charakter_Formalny.objects.get_or_create(skrot='KSZ', nazwa='Książka w języku obcym') Charakter_Formalny.objects.get_or_create(skrot='KSP', nazwa='Książka w języku polskim') Charakter_Formalny.objects.get_or_create(skrot='KS', nazwa='Książka') Charakter_Formalny.objects.get_or_create(skrot='ROZ', nazwa='Rozdział książki') Group.objects.get_or_create(name="wprowadzanie danych") def test_jeden_rok(self): url = reverse("bpp:raport-jednostek-rok-min-max", args=(self.j.pk, 2010, 2013)) res = self.client.get(url) self.assertContains( res, "Dane o publikacjach za okres 2010 - 2013", status_code=200) def test_zakres_lat(self): url = reverse("bpp:raport-jednostek", args=(self.j.pk, 2013)) res = self.client.get(url) self.assertContains( res, "Dane o publikacjach za rok 2013", status_code=200) class TestRankingAutorow(UserTestCase): def setUp(self): UserTestCase.setUp(self) rebuild_contenttypes() Typ_Odpowiedzialnosci.objects.get_or_create(skrot='aut.', nazwa='autor') Group.objects.get_or_create(name="wprowadzanie danych") j = any_jednostka() a = any_autor(nazwisko="Kowalski") c = any_ciagle(impact_factor=200, rok=2000) c.dodaj_autora(a, j) def test_renderowanie(self): url = reverse("bpp:ranking-autorow", args=(2000, 2000)) res = self.client.get(url) self.assertContains( res, "Ranking autorów", status_code=200) self.assertContains(res, "Kowalski") def test_renderowanie_csv(self): url = reverse("bpp:ranking-autorow", args=(2000, 2000)) res = self.client.get(url, data={"_export": "csv"}) self.assertContains( res, '"Kowalski Jan Maria, dr",Jednostka')

32.221739

95

0.643233

868

7,411

5.366359

0.268433

0.017175

0.027909

0.034779

0.335552

0.284242

0.256548

0.198583

0.162731

0.14255

0

0.018029

0.244097

7,411

229

96

32.362445

0.813459

0.028336

0

0.296512

0

0.105088

0.014596

0

0.098837

1

0.116279

false

0.005814

0.127907

0

0.302326

0

null

0

null

0

1

0

8d8ebb77655b687ce95045239bb38a91c19a2901

1,192

py

Python

manager_app/serializers/carousel_serializers.py

syz247179876/e_mall

f94e39e091e098242342f532ae371b8ff127542f

[ "Apache-2.0" ]

7

2021-04-10T13:20:56.000Z

2022-03-29T15:00:29.000Z

manager_app/serializers/carousel_serializers.py

syz247179876/E_mall

f94e39e091e098242342f532ae371b8ff127542f

[ "Apache-2.0" ]

9

2021-05-11T03:53:31.000Z

2022-03-12T00:58:03.000Z

manager_app/serializers/carousel_serializers.py

syz247179876/E_mall

f94e39e091e098242342f532ae371b8ff127542f

[ "Apache-2.0" ]

2

2020-11-24T08:59:22.000Z

2020-11-24T14:10:59.000Z

# -*- coding: utf-8 -*- # @Time : 2021/4/6 下午9:21 # @Author : 司云中 # @File : carousel_serializers.py # @Software: Pycharm from rest_framework import serializers from Emall.exceptions import DataFormatError from shop_app.models.commodity_models import Carousel class ManagerCarouselSerializer(serializers.ModelSerializer): """管理轮播图序列化器""" class Meta: model = Carousel fields = ('pk', 'picture', 'url', 'sort', 'type') read_only_fields = ('pk',) def add(self): """增加轮播图""" self.Meta.model.objects.create(**self.validated_data) def modify(self): """修改轮播图""" pk = self.context.get('request').data.get('pk') if not pk: raise DataFormatError('缺少数据') return self.Meta.model.objects.filter(pk=pk).update(**self.validated_data) class DeleteCarouselSerializer(serializers.ModelSerializer): pk_list = serializers.ListField(child=serializers.IntegerField(), allow_empty=False) class Meta: model = Carousel fields = ('pk_list',) def delete(self): """删除轮播图""" return self.Meta.model.objects.filter(pk__in=self.validated_data.pop('pk_list')).delete()

27.090909

97

0.654362

136

1,192

5.625

0.544118

0.058824

0.05098

0.078431

0.16732

0.088889

0

0.010549

0.204698

1,192

43

98

27.72093

0.796414

0.11745

0

0.181818

0

0.047758

0

1

0.136364

false

0

0.136364

0

0.590909

0

null

0

null

0

1

0

8d9135e1864bf2b1336ddc05e72617edb4057d7b

7,312

py

Python

xfbin/structure/nud.py

SutandoTsukai181/xfbin_lib

8e2c56f354bfd868f9162f816cc528e6f830cdbc

[ "MIT" ]

3

2021-07-20T09:13:13.000Z

2021-09-06T18:08:15.000Z

xfbin/structure/nud.py

SutandoTsukai181/xfbin_lib

8e2c56f354bfd868f9162f816cc528e6f830cdbc

[ "MIT" ]

1

2021-09-06T18:07:48.000Z

xfbin/structure/nud.py

SutandoTsukai181/xfbin_lib

8e2c56f354bfd868f9162f816cc528e6f830cdbc

[ "MIT" ]

null

from itertools import chain from typing import List, Tuple from .br.br_nud import * class Nud: name: str # chunk name mesh_groups: List['NudMeshGroup'] def init_data(self, name, br_nud: BrNud): self.name = name self.bounding_sphere = br_nud.boundingSphere self.mesh_groups = list() for br_mesh_group in br_nud.meshGroups: mesh_group = NudMeshGroup() mesh_group.init_data(br_mesh_group) self.mesh_groups.append(mesh_group) def get_bone_range(self) -> Tuple[int, int]: if not (self.mesh_groups and self.mesh_groups[0].meshes and self.mesh_groups[0].meshes[0].bone_type != NudBoneType.NoBones): return (0, 0) lower = 0xFF_FF higher = 0 for mesh in [m for m in self.mesh_groups[0].meshes if m.vertices and m.vertices[0].bone_ids]: lower = min(lower, min(chain(*map(lambda x: x.bone_ids, mesh.vertices)))) higher = max(higher, max(chain(*map(lambda x: x.bone_ids, mesh.vertices)))) if lower > higher: return (0, 0) return (lower, higher) class NudMeshGroup: name: str meshes: List['NudMesh'] def init_data(self, br_mesh_group: BrNudMeshGroup): self.name = br_mesh_group.name self.bone_flags = br_mesh_group.boneFlags self.bounding_sphere = br_mesh_group.boundingSphere self.meshes = list() for br_mesh in br_mesh_group.meshes: mesh = NudMesh() mesh.init_data(br_mesh) self.meshes.append(mesh) class NudMesh: MAX_VERTICES = 32_767 MAX_FACES = 16_383 vertices: List['NudVertex'] faces: List[Tuple[int, int, int]] materials: List['NudMaterial'] vertex_type: NudVertexType bone_type: NudBoneType uv_type: NudUvType def init_data(self, br_mesh: BrNudMesh): self.add_vertices(br_mesh.vertices) self.add_faces(br_mesh.faces, br_mesh.faceSize) self.add_materials(br_mesh.materials) self.vertex_type = NudVertexType(br_mesh.vertexSize & 0x0F) self.bone_type = NudBoneType(br_mesh.vertexSize & 0xF0) self.uv_type = NudUvType(br_mesh.uvSize & 0x0F) self.face_flag = br_mesh.faceFlag def has_bones(self): return bool(self.vertices and self.vertices[0].bone_ids) def has_color(self): return bool(self.vertices and self.vertices[0].color) def get_uv_channel_count(self): return len(self.vertices[0].uv) if bool(self.vertices and self.vertices[0].uv) else 0 def add_vertices(self, vertices: List[BrNudVertex]): self.vertices = list() for br_vertex in vertices: vertex = NudVertex() vertex.init_data(br_vertex) self.vertices.append(vertex) def add_faces(self, faces: List[int], faceSize: int): faces = iter(faces) if faceSize & 0x40: # 0x40 format does not have -1 indices nor changing directions self.faces = zip(faces, faces, faces) return self.faces = list() start_dir = 1 f1 = next(faces) f2 = next(faces) face_dir = start_dir try: while True: f3 = next(faces) if f3 == -1: f1 = next(faces) f2 = next(faces) face_dir = start_dir else: face_dir = -face_dir if f1 != f2 != f3: if face_dir > 0: self.faces.append((f3, f2, f1)) else: self.faces.append((f2, f3, f1)) f1 = f2 f2 = f3 except StopIteration: pass def add_materials(self, materials: List[BrNudMaterial]): self.materials = list() for br_material in materials: material = NudMaterial() material.init_data(br_material) self.materials.append(material) class NudVertex: position: Tuple[float, float, float] normal: Tuple[float, float, float] bitangent: Tuple[float, float, float] tangent: Tuple[float, float, float] color: Tuple[int, int, int, int] uv: List[Tuple[float, float]] bone_ids: Tuple[int, int, int, int] bone_weights: Tuple[float, float, float, float] def init_data(self, br_vertex: BrNudVertex): self.position = br_vertex.position self.normal = br_vertex.normals self.bitangent = br_vertex.biTangents if br_vertex.biTangents else None self.tangent = br_vertex.tangents if br_vertex.tangents else None self.color = tuple(map(lambda x: int(x), br_vertex.color)) if br_vertex.color else None self.uv = br_vertex.uv self.bone_ids = br_vertex.boneIds self.bone_weights = br_vertex.boneWeights def __eq__(self, o: 'NudVertex') -> bool: return all(map(lambda x, y: x == y, self.position, o.position)) \ and all(map(lambda x, y: x == y, self.normal, o.normal)) \ and all(map(lambda x, y: all(map(lambda a, b: a == b, x, y)), self.uv, o.uv)) \ and all(map(lambda x, y: x == y, self.tangent, o.tangent)) \ and all(map(lambda x, y: x == y, self.bitangent, o.bitangent)) \ and all(map(lambda x, y: x == y, self.color, o.color)) \ and all(map(lambda x, y: x == y, self.bone_ids, o.bone_ids)) \ and all(map(lambda x, y: x == y, self.bone_weights, o.bone_weights)) def __hash__(self) -> int: return hash(tuple(self.position)) ^ hash(tuple(self.normal)) ^ hash(tuple(self.color)) ^ hash(tuple(self.uv)) class NudMaterial: def init_data(self, material: BrNudMaterial): self.flags = material.flags self.sourceFactor = material.sourceFactor self.destFactor = material.destFactor self.alphaTest = material.alphaTest self.alphaFunction = material.alphaFunction self.refAlpha = material.refAlpha self.cullMode = material.cullMode self.unk1 = material.unk1 self.unk2 = material.unk2 self.zBufferOffset = material.zBufferOffset self.textures = list() for br_texture in material.textures: texture = NudMaterialTexture() texture.init_data(br_texture) self.textures.append(texture) self.properties = list() for br_property in [p for p in material.properties if p.name]: property = NudMaterialProperty() property.init_data(br_property) self.properties.append(property) class NudMaterialTexture: def init_data(self, texture: BrNudMaterialTexture): self.unk0 = texture.unk0 self.mapMode = texture.mapMode self.wrapModeS = texture.wrapModeS self.wrapModeT = texture.wrapModeT self.minFilter = texture.minFilter self.magFilter = texture.magFilter self.mipDetail = texture.mipDetail self.unk1 = texture.unk1 self.unk2 = texture.unk2 class NudMaterialProperty: def init_data(self, property: BrNudMaterialProperty): self.name = property.name self.values: List[float] = property.values

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117

0.607358

922

7,312

4.67679

0.172451

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0.02551

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0.14077

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0.013725

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0.005952

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0.029762

0.327381

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null

0

null

0

1

0

8d93c9fb2121a519402ceb1deef23ae520c7fdfe

1,717

py

Python

utils/event_store_rebuilder_for_segments.py

initialed85/eds-cctv-system

fcdb7e7e23327bf3a901d23d506b3915833027d1

[ "MIT" ]

null

utils/event_store_rebuilder_for_segments.py

initialed85/eds-cctv-system

fcdb7e7e23327bf3a901d23d506b3915833027d1

[ "MIT" ]

null

utils/event_store_rebuilder_for_segments.py

initialed85/eds-cctv-system

fcdb7e7e23327bf3a901d23d506b3915833027d1

[ "MIT" ]

null

import datetime from pathlib import Path from typing import Optional, Tuple from .common import _IMAGE_SUFFIXES, _PERMITTED_EXTENSIONS, PathDetails, rebuild_event_store def parse_path(path: Path, tzinfo: datetime.tzinfo) -> Optional[PathDetails]: if path.suffix.lower() not in _PERMITTED_EXTENSIONS: return None if path.name.lower().startswith("event"): raise ValueError("cannot process events; only segments") parts = path.name.split('_') timestamp = datetime.datetime.strptime(f'{parts[1]}_{parts[2]}', "%Y-%m-%d_%H-%M-%S") timestamp = timestamp.replace(tzinfo=tzinfo) camera_name = parts[3].split('.')[0] if camera_name.endswith('-lowres'): camera_name = camera_name.split('-lowres')[0] return PathDetails( path=path, event_id=None, camera_id=None, timestamp=timestamp, camera_name=camera_name, is_image=path.suffix.lower() in _IMAGE_SUFFIXES, is_lowres="-lowres" in path.name.lower(), ) def _get_key(path_details: PathDetails) -> Tuple[str, str]: return ( path_details.camera_name, path_details.timestamp.strftime("%Y-%m-%d %H:%M:%S") ) if __name__ == "__main__": import argparse from dateutil.tz import tzoffset parser = argparse.ArgumentParser() parser.add_argument("-r", "--root-path", type=str, required=True) parser.add_argument("-j", "--json-path", type=str, required=True) args = parser.parse_args() rebuild_event_store( root_path=args.root_path, tzinfo=tzoffset(name="WST-8", offset=8 * 60 * 60), json_path=args.json_path, parse_method=parse_path, get_key_methods=[_get_key] )

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92

0.663366

220

1,717

4.936364

0.381818

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0.031308

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0.053407

0.01105

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0.008035

0.202679

1,717

59

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29.101695

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0.023256

0.255814

0

null

0

null

0

1

0

8d94db8d2bb9acc8dbec349c6766ca408545196a

599

py

Python

python/distance/HaversineDistanceInMiles.py

jigneshoo7/AlgoBook

8aecc9698447c0ee561a1c90d5c5ab87c4a07b79

[ "MIT" ]

191

2020-09-28T10:00:20.000Z

2022-03-06T14:36:55.000Z

python/distance/HaversineDistanceInMiles.py

jigneshoo7/AlgoBook

8aecc9698447c0ee561a1c90d5c5ab87c4a07b79

[ "MIT" ]

210

2020-09-28T10:06:36.000Z

2022-03-05T03:44:24.000Z

python/distance/HaversineDistanceInMiles.py

jigneshoo7/AlgoBook

8aecc9698447c0ee561a1c90d5c5ab87c4a07b79

[ "MIT" ]

320

2020-09-28T09:56:14.000Z

2022-02-12T16:45:57.000Z

import math def distanceInMilesOrKilos(milesOrKilos,originLat,originLon,destinationLat,destinationLon): radius = 3959 if milesOrKilos == "miles" else 6371 lat1 = originLat lat2 = destinationLat lon1 = originLon lon2 = destinationLon dlat = math.radians(lat2 - lat1) dlon = math.radians(lon2 - lon1) a = (math.sin(dlat / 2) * math.sin(dlat / 2) + math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) * math.sin(dlon / 2) * math.sin(dlon / 2)) c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a)) distance = radius * c return distance

33.277778

153

0.651085

77

599

5.064935

0.415584

0.112821

0.076923

0.061538

0.071795

0

0.053533

0.220367

599

18

154

33.277778

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false

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0.076923

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0.230769

0

null

0

null

0

1

0

8d95a5da0117840ab07b75457380a92375c5347d

8,837

py

Python

i2i/util.py

thorwhalen/i2i

f967aaba28793029e3fe643c5e17ae9bc7a77732

[ "Apache-2.0" ]

1

2019-08-29T01:35:12.000Z

i2i/util.py

thorwhalen/i2i

f967aaba28793029e3fe643c5e17ae9bc7a77732

[ "Apache-2.0" ]

null

i2i/util.py

thorwhalen/i2i

f967aaba28793029e3fe643c5e17ae9bc7a77732

[ "Apache-2.0" ]

null

from __future__ import division import inspect import types from functools import wraps function_type = type(lambda x: x) # using this instead of callable() because classes are callable, for instance class NoDefault(object): def __repr__(self): return 'no_default' no_default = NoDefault() class imdict(dict): def __hash__(self): return id(self) def _immutable(self, *args, **kws): raise TypeError('object is immutable') __setitem__ = _immutable __delitem__ = _immutable clear = _immutable update = _immutable setdefault = _immutable pop = _immutable popitem = _immutable def inject_method(self, method_function, method_name=None): """ method_function could be: * a function * a {method_name: function, ...} dict (for multiple injections) * a list of functions or (function, method_name) pairs """ if isinstance(method_function, function_type): if method_name is None: method_name = method_function.__name__ setattr(self, method_name, types.MethodType(method_function, self)) else: if isinstance(method_function, dict): method_function = [(func, func_name) for func_name, func in method_function.items()] for method in method_function: if isinstance(method, tuple) and len(method) == 2: self = inject_method(self, method[0], method[1]) else: self = inject_method(self, method) return self def transform_args(**trans_func_for_arg): """ Make a decorator that transforms function arguments before calling the function. For example: * original argument: a relative path --> used argument: a full path * original argument: a pickle filepath --> used argument: the loaded object :param rootdir: rootdir to be used for all name arguments of target function :param name_arg: the position (int) or argument name of the argument containing the name :return: a decorator >>> def f(a, b, c): ... return "a={a}, b={b}, c={c}".format(a=a, b=b, c=c) >>> >>> print(f('foo', 'bar', 3)) a=foo, b=bar, c=3 >>> ff = transform_args()(f) >>> print(ff('foo', 'bar', 3)) a=foo, b=bar, c=3 >>> ff = transform_args(a=lambda x: 'ROOT/' + x)(f) >>> print(ff('foo', 'bar', 3)) a=ROOT/foo, b=bar, c=3 >>> ff = transform_args(b=lambda x: 'ROOT/' + x)(f) >>> print(ff('foo', 'bar', 3)) a=foo, b=ROOT/bar, c=3 >>> ff = transform_args(a=lambda x: 'ROOT/' + x, b=lambda x: 'ROOT/' + x)(f) >>> print(ff('foo', b='bar', c=3)) a=ROOT/foo, b=ROOT/bar, c=3 """ def transform_args_decorator(func): if len(trans_func_for_arg) == 0: # if no transformations were specified... return func # just return the function itself else: @wraps(func) def transform_args_wrapper(*args, **kwargs): # get a {argname: argval, ...} dict from *args and **kwargs # Note: Didn't really need an if/else here but... # Note: ... assuming getcallargs gives us an overhead that can be avoided if there's only keyword args. if len(args) > 0: val_of_argname = inspect.getcallargs(func, *args, **kwargs) else: val_of_argname = kwargs # apply transform functions to argument values for argname, trans_func in trans_func_for_arg.items(): val_of_argname[argname] = trans_func(val_of_argname[argname]) # call the function with transformed values return func(**val_of_argname) return transform_args_wrapper return transform_args_decorator def resolve_filepath_of_name(name_arg=None, rootdir=''): """ Make a decorator that applies a function to an argument before using it. For example: * original argument: a relative path --> used argument: a full path * original argument: a pickle filepath --> used argument: the loaded object :param rootdir: rootdir to be used for all name arguments of target function :param name_arg: the position (int) or argument name of the argument containing the name :return: a decorator >>> def f(a, b, c): ... return "a={a}, b={b}, c={c}".format(a=a, b=b, c=c) >>> >>> print(f('foo', 'bar', 3)) a=foo, b=bar, c=3 >>> ff = resolve_filepath_of_name()(f) >>> print(ff('foo', 'bar', 3)) a=foo, b=bar, c=3 >>> ff = resolve_filepath_of_name('a', 'ROOT')(f) >>> print(ff('foo', 'bar', 3)) a=ROOT/foo, b=bar, c=3 >>> ff = resolve_filepath_of_name('b', 'ROOT')(f) >>> print(ff('foo', 'bar', 3)) a=foo, b=ROOT/bar, c=3 """ if name_arg is not None: return transform_args(**{name_arg: lambda x: os.path.join(rootdir, x)}) else: return lambda x: x def arg_dflt_dict_of_callable(f): """ Get a {arg_name: default_val, ...} dict from a callable. See also :py:mint_of_callable: :param f: A callable (function, method, ...) :return: """ argspec = inspect.getfullargspec(f) args = argspec.args or [] defaults = argspec.defaults or [] return {arg: dflt for arg, dflt in zip(args, [no_default] * (len(args) - len(defaults)) + list(defaults))} def add_self_as_first_argument(func): @wraps(func) def wrapped_func(self, *args, **kwargs): return func(*args, **kwargs) return wrapped_func def add_cls_as_first_argument(func): @wraps(func) def wrapped_func(cls, *args, **kwargs): return func(*args, **kwargs) return wrapped_func def infer_if_function_might_be_intended_as_a_classmethod_or_staticmethod(func): """ Tries to infer if the input function is a 'classmethod' or 'staticmethod' (or just 'normal') When is that? When: * the function's first argument is called 'cls' and has no default: 'classmethod' * the function's first argument is called 'self' and has no default: 'staticmethod' * otherwise: 'normal' >>> def a_normal_func(x, y=None): ... pass >>> def a_func_that_is_probably_a_classmethod(cls, y=None): ... pass >>> def a_func_that_is_probably_a_staticmethod(self, y=None): ... pass >>> def a_func_that_is_probably_a_classmethod_but_is_not(cls=3, y=None): ... pass >>> def a_func_that_is_probably_a_staticmethod_but_is_not(self=None, y=None): ... pass >>> list_of_functions = [ ... a_normal_func, ... a_func_that_is_probably_a_classmethod, ... a_func_that_is_probably_a_staticmethod, ... a_func_that_is_probably_a_classmethod_but_is_not, ... a_func_that_is_probably_a_staticmethod_but_is_not, ... ] >>> >>> for func in list_of_functions: ... print("{}: {}".format(func.__name__, ... infer_if_function_might_be_intended_as_a_classmethod_or_staticmethod(func))) ... a_normal_func: normal a_func_that_is_probably_a_classmethod: classmethod a_func_that_is_probably_a_staticmethod: staticmethod a_func_that_is_probably_a_classmethod_but_is_not: normal_with_cls a_func_that_is_probably_a_staticmethod_but_is_not: normal_with_self """ argsspec = inspect.getfullargspec(func) if len(argsspec.args) > 0: first_element_has_no_defaults = bool(len(argsspec.args) > len(argsspec.defaults)) if argsspec.args[0] == 'cls': if first_element_has_no_defaults: return 'classmethod' else: return 'normal_with_cls' elif argsspec.args[0] == 'self': if first_element_has_no_defaults: return 'staticmethod' else: return 'normal_with_self' return 'normal' def decorate_as_staticmethod_or_classmethod_if_needed(func): type_of_func = infer_if_function_might_be_intended_as_a_classmethod_or_staticmethod(func) if type_of_func == 'classmethod': return classmethod(func) elif type_of_func == 'staticmethod': return staticmethod(func) elif type_of_func == 'normal': return func if __name__ == '__main__': import os import re key_file_re = re.compile('setup.py') def dir_is_a_pip_installable_dir(dirpath): return any(filter(key_file_re.match, os.listdir(dirpath))) rootdir = '/D/Dropbox/dev/py/proj' cumul = list() for f in filter(lambda x: not x.startswith('.'), os.listdir(rootdir)): filepath = os.path.join(rootdir, f) if os.path.isdir(filepath): if dir_is_a_pip_installable_dir(filepath): cumul.append(filepath) for f in cumul: print(f)

34.928854

119

0.629965

1,203

8,837

4.362427

0.176226

0.011433

0.020579

0.025152

0.420732

0.393293

0.38205

0.34718

0.329078

0.308308

0

0.004092

0.253367

8,837

252

120

35.06746

0.7913

0.452303

0

0.162162

0

0.036962

0.004958

0

1

0.144144

false

0

0.054054

0.045045

0.486486

0.009009

0

null

0

null

0

1

0

8d99f51b98aee394d6e4b4f62dcc6cdca1b6db1f

10,131

py

Python

tutorials/seq2seq_sated/seq2seq_sated_meminf.py

rizwandel/ml_privacy_meter

5dc4c300eadccceadd0e664a7e46099f65728628

[ "MIT" ]

294

2020-04-13T18:32:45.000Z

2022-03-31T10:32:34.000Z

tutorials/seq2seq_sated/seq2seq_sated_meminf.py

kypomon/ml_privacy_meter

c0324e8f74cbd0cde0643a7854fa66eab47bbe53

[ "MIT" ]

26

2020-04-29T19:56:21.000Z

2022-03-31T10:42:24.000Z

tutorials/seq2seq_sated/seq2seq_sated_meminf.py

kypomon/ml_privacy_meter

c0324e8f74cbd0cde0643a7854fa66eab47bbe53

[ "MIT" ]

50

2020-04-16T02:16:24.000Z

2022-03-16T00:37:40.000Z

import os import sys from collections import defaultdict import tensorflow as tf import tensorflow.keras.backend as K import numpy as np import scipy.stats as ss import matplotlib.pyplot as plt from sklearn.metrics import roc_curve from sklearn.linear_model import LogisticRegression from utils import process_texts, load_texts, load_users, load_sated_data_by_user, \ build_nmt_model, words_to_indices, \ SATED_TRAIN_USER, SATED_TRAIN_FR, SATED_TRAIN_ENG MODEL_PATH = 'checkpoints/model/' OUTPUT_PATH = 'checkpoints/output/' tf.compat.v1.disable_eager_execution() # ================================ GENERATE RANKS ================================ # # Code adapted from https://github.com/csong27/auditing-text-generation def load_train_users_heldout_data(train_users, src_vocabs, trg_vocabs, user_data_ratio=0.5): src_users = load_users(SATED_TRAIN_USER) train_src_texts = load_texts(SATED_TRAIN_ENG) train_trg_texts = load_texts(SATED_TRAIN_FR) user_src_texts = defaultdict(list) user_trg_texts = defaultdict(list) for u, s, t in zip(src_users, train_src_texts, train_trg_texts): if u in train_users: user_src_texts[u].append(s) user_trg_texts[u].append(t) assert 0. < user_data_ratio < 1. # Hold out some fraction of data for testing for u in user_src_texts: l = len(user_src_texts[u]) l = int(l * user_data_ratio) user_src_texts[u] = user_src_texts[u][l:] user_trg_texts[u] = user_trg_texts[u][l:] for u in train_users: process_texts(user_src_texts[u], src_vocabs) process_texts(user_trg_texts[u], trg_vocabs) return user_src_texts, user_trg_texts def rank_lists(lists): ranks = np.empty_like(lists) for i, l in enumerate(lists): ranks[i] = ss.rankdata(l, method='min') - 1 return ranks def get_ranks(user_src_data, user_trg_data, pred_fn, save_probs=False): indices = np.arange(len(user_src_data)) """ Get ranks from prediction vectors. """ ranks = [] labels = [] probs = [] for idx in indices: src_text = np.asarray(user_src_data[idx], dtype=np.float32).reshape(1, -1) trg_text = np.asarray(user_trg_data[idx], dtype=np.float32) trg_input = trg_text[:-1].reshape(1, -1) trg_label = trg_text[1:].reshape(1, -1) prob = pred_fn([src_text, trg_input, trg_label, 0])[0][0] if save_probs: probs.append(prob) all_ranks = rank_lists(-prob) sent_ranks = all_ranks[np.arange(len(all_ranks)), trg_label.flatten().astype(int)] ranks.append(sent_ranks) labels.append(trg_label.flatten()) if save_probs: return probs return ranks, labels def save_users_rank_results(users, user_src_texts, user_trg_texts, src_vocabs, trg_vocabs, prob_fn, save_dir, member_label=1, cross_domain=False, save_probs=False, mask=False, rerun=False): """ Save user ranks in the appropriate format for attacks. """ for i, u in enumerate(users): save_path = save_dir + 'rank_u{}_y{}{}.npz'.format(i, member_label, '_cd' if cross_domain else '') prob_path = save_dir + 'prob_u{}_y{}{}.npz'.format(i, member_label, '_cd' if cross_domain else '') if os.path.exists(save_path) and not save_probs and not rerun: continue user_src_data = words_to_indices(user_src_texts[u], src_vocabs, mask=mask) user_trg_data = words_to_indices(user_trg_texts[u], trg_vocabs, mask=mask) rtn = get_ranks(user_src_data, user_trg_data, prob_fn, save_probs=save_probs) if save_probs: probs = rtn np.savez(prob_path, probs) else: ranks, labels = rtn[0], rtn[1] np.savez(save_path, ranks, labels) if (i + 1) % 500 == 0: sys.stderr.write('Finishing saving ranks for {} users'.format(i + 1)) def get_target_ranks(num_users=200, num_words=5000, mask=False, h=128, emb_h=128, user_data_ratio=0., tied=False, save_probs=False): """ Get ranks of target machine translation model. """ user_src_texts, user_trg_texts, test_user_src_texts, test_user_trg_texts, src_vocabs, trg_vocabs \ = load_sated_data_by_user(num_users, num_words, test_on_user=True, user_data_ratio=user_data_ratio) train_users = sorted(user_src_texts.keys()) test_users = sorted(test_user_src_texts.keys()) # Get model save_dir = OUTPUT_PATH + 'target_{}{}/'.format(num_users, '_dr' if 0. < user_data_ratio < 1. else '') if not os.path.exists(save_dir): os.mkdir(save_dir) model_path = 'sated_nmt'.format(num_users) if 0. < user_data_ratio < 1.: model_path += '_dr{}'.format(user_data_ratio) heldout_src_texts, heldout_trg_texts = load_train_users_heldout_data(train_users, src_vocabs, trg_vocabs) for u in train_users: user_src_texts[u] += heldout_src_texts[u] user_trg_texts[u] += heldout_trg_texts[u] model = build_nmt_model(Vs=num_words, Vt=num_words, mask=mask, drop_p=0., h=h, demb=emb_h, tied=tied) model.load_weights(MODEL_PATH + '{}_{}.h5'.format(model_path, num_users)) src_input_var, trg_input_var = model.inputs prediction = model.output trg_label_var = K.placeholder((None, None), dtype='float32') # Get predictions prediction = K.softmax(prediction) prob_fn = K.function([src_input_var, trg_input_var, trg_label_var, K.learning_phase()], [prediction]) # Save user ranks for train and test dataset save_users_rank_results(users=train_users, save_probs=save_probs, user_src_texts=user_src_texts, user_trg_texts=user_trg_texts, src_vocabs=src_vocabs, trg_vocabs=trg_vocabs, cross_domain=False, prob_fn=prob_fn, save_dir=save_dir, member_label=1) save_users_rank_results(users=test_users, save_probs=save_probs, user_src_texts=test_user_src_texts, user_trg_texts=test_user_trg_texts, src_vocabs=src_vocabs, trg_vocabs=trg_vocabs, cross_domain=False, prob_fn=prob_fn, save_dir=save_dir, member_label=0) # ================================ ATTACK ================================ # def avg_rank_feats(ranks): """ Averages ranks to get features for deciding the threshold for membership inference. """ avg_ranks = [] for r in ranks: avg = np.mean(np.concatenate(r)) avg_ranks.append(avg) return avg_ranks def load_ranks_by_label(save_dir, num_users=300, cross_domain=False, label=1): """ Helper method to load ranks by train/test dataset. If label = 1, train set ranks are loaded. If label = 0, test set ranks are loaded. Ranks are generated by running sated_nmt_ranks.py. """ ranks = [] labels = [] y = [] for i in range(num_users): save_path = save_dir + 'rank_u{}_y{}{}.npz'.format(i, label, '_cd' if cross_domain else '') if os.path.exists(save_path): f = np.load(save_path, allow_pickle=True) train_rs, train_ls = f['arr_0'], f['arr_1'] ranks.append(train_rs) labels.append(train_ls) y.append(label) return ranks, labels, y def load_all_ranks(save_dir, num_users=5000, cross_domain=False): """ Loads all ranks generated by the target model. Ranks are generated by running sated_nmt_ranks.py. """ ranks = [] labels = [] y = [] # Load train ranks train_label = 1 train_ranks, train_labels, train_y = load_ranks_by_label(save_dir, num_users, cross_domain, train_label) ranks = ranks + train_ranks labels = labels + train_labels y = y + train_y # Load test ranks test_label = 0 test_ranks, test_labels, test_y = load_ranks_by_label(save_dir, num_users, cross_domain, test_label) ranks = ranks + test_ranks labels = labels + test_labels y = y + test_y return ranks, labels, np.asarray(y) def run_average_rank_thresholding(num_users=300, dim=100, prop=1.0, user_data_ratio=0., top_words=5000, cross_domain=False, rerun=False): """ Runs average rank thresholding attack on the target model. """ result_path = OUTPUT_PATH if dim > top_words: dim = top_words attack1_results_save_path = result_path + 'mi_data_dim{}_prop{}_{}{}_attack1.npz'.format( dim, prop, num_users, '_cd' if cross_domain else '') if not rerun and os.path.exists(attack1_results_save_path): f = np.load(attack1_results_save_path) X, y = [f['arr_{}'.format(i)] for i in range(4)] else: save_dir = result_path + 'target_{}{}/'.format(num_users, '_dr' if 0. < user_data_ratio < 1. else '') # Load ranks train_ranks, _, train_y = load_ranks_by_label(save_dir, num_users, label=1) test_ranks, _, test_y = load_ranks_by_label(save_dir, num_users, label=0) # Convert to average rank features train_feat = avg_rank_feats(train_ranks) test_feat = avg_rank_feats(test_ranks) # Create dataset X, y = np.concatenate([train_feat, test_feat]), np.concatenate([train_y, test_y]) np.savez(attack1_results_save_path, X, y) # print(X.shape, y.shape) # Find threshold using ROC clf = LogisticRegression() clf.fit(X.reshape(-1, 1), y) probs = clf.predict_proba(X.reshape(-1, 1)) fpr, tpr, thresholds = roc_curve(y, probs[:, 1]) plt.figure(1) plt.plot(fpr, tpr, label='Attack 1') plt.xlabel('False positive rate') plt.ylabel('True positive rate') plt.title('ROC curve') plt.savefig('sateduser_attack1_roc_curve.png') if __name__ == '__main__': num_users = 300 save_probs = False rerun = True print("Getting target ranks...") get_target_ranks(num_users=num_users, save_probs=save_probs) print("Running average rank thresholding attack...") run_average_rank_thresholding(num_users=num_users, rerun=True)

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null

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8d9d264830cab7159205ed06b41898abec3b84f4

2,685

py

Python

app/recipe/tests/test_tags_api.py

MohamedAbdelmagid/django-recipe-api

229d3a7cff483b3cad76c70aefde6a51250b9bc8

[ "MIT" ]

null

app/recipe/tests/test_tags_api.py

MohamedAbdelmagid/django-recipe-api

229d3a7cff483b3cad76c70aefde6a51250b9bc8

[ "MIT" ]

null

app/recipe/tests/test_tags_api.py

MohamedAbdelmagid/django-recipe-api

229d3a7cff483b3cad76c70aefde6a51250b9bc8

[ "MIT" ]

null

from django.test import TestCase from django.urls import reverse from django.contrib.auth import get_user_model from rest_framework.test import APIClient from rest_framework import status from core.models import Tag from recipe.serializers import TagSerializer TAGS_URL = reverse("recipe:tag-list") class PublicTagsApiTests(TestCase): """ Test the publicly available tags API """ def setUp(self): self.client = APIClient() def test_login_required(self): """ Test that login is required for retrieving tags """ response = self.client.get(TAGS_URL) self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateTagsApiTests(TestCase): """ Test the authorized user tags API """ def setUp(self): self.user = get_user_model().objects.create_user( "test@gmail.com", "testpassword" ) self.client = APIClient() self.client.force_authenticate(self.user) def test_retrieve_tags(self): """ Test retrieving tags """ Tag.objects.create(user=self.user, name="Dessert") Tag.objects.create(user=self.user, name="Salad") response = self.client.get(TAGS_URL) tags = Tag.objects.all().order_by("-name") serializer = TagSerializer(tags, many=True) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(response.data, serializer.data) def test_tags_limited_to_user(self): """ Test that tags returned are for the authenticated user """ user2 = get_user_model().objects.create_user( "test2@gmail.com", "test2password" ) Tag.objects.create(user=user2, name="Candied Yams") tag = Tag.objects.create(user=self.user, name="Soul Food") response = self.client.get(TAGS_URL) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(len(response.data), 1) self.assertEqual(response.data[0]["name"], tag.name) def test_create_tag_successful(self): """ Test creating a new tag """ payload = {'name': 'Test Tag Name'} notExists = Tag.objects.filter(user=self.user, name=payload['name']).exists() self.client.post(TAGS_URL, payload) exists = Tag.objects.filter(user=self.user, name=payload['name']).exists() self.assertTrue(exists) self.assertFalse(notExists) def test_create_tag_invalid(self): """ Test creating a new tag with invalid payload """ payload = {'name': ''} response = self.client.post(TAGS_URL, payload) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)

32.349398

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0

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2,685

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0

null

0

null

0

1

0

8d9d7d5c7ee0f28e0c8877291fb904e2d8ace2db

5,736

py

Python

dtlpy/entities/annotation_definitions/cube_3d.py

dataloop-ai/dtlpy

2c73831da54686e047ab6aefd8f12a8e53ea97c2

[ "Apache-2.0" ]

10

2020-05-21T06:25:35.000Z

2022-01-07T20:34:03.000Z

dtlpy/entities/annotation_definitions/cube_3d.py

dataloop-ai/dtlpy

2c73831da54686e047ab6aefd8f12a8e53ea97c2

[ "Apache-2.0" ]

22

2019-11-17T17:25:16.000Z

2022-03-10T15:14:28.000Z

dtlpy/entities/annotation_definitions/cube_3d.py

dataloop-ai/dtlpy

2c73831da54686e047ab6aefd8f12a8e53ea97c2

[ "Apache-2.0" ]

8

2020-03-05T16:23:55.000Z

2021-12-27T11:10:42.000Z

import numpy as np # import open3d as o3d from . import BaseAnnotationDefinition # from scipy.spatial.transform import Rotation as R import logging logger = logging.getLogger(name=__name__) class Cube3d(BaseAnnotationDefinition): """ Cube annotation object """ type = "cube_3d" def __init__(self, label, position, scale, rotation, attributes=None, description=None): """ :param label: :param position: the XYZ position of the ‘center’ of the annotation. :param scale: the scale of the object by each axis (XYZ). :param rotation: an euler representation of the object rotation on each axis (with rotation order ‘XYZ’). (rotation in radians) :param attributes: :param description: """ super().__init__(description=description, attributes=attributes) self.position = position self.scale = scale self.rotation = rotation self.label = label def _translate(self, points, translate_x, translate_y, translate_z): translation_matrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [translate_x, translate_y, translate_z, 1]]) matrix = [(list(i) + [1]) for i in points] pts2 = np.dot(matrix, translation_matrix) return [pt[:3] for pt in pts2] # def make_points(self): # simple = [ # [self.scale[0] / 2, self.scale[1] / 2, self.scale[2] / 2], # [-self.scale[0] / 2, self.scale[1] / 2, self.scale[2] / 2], # [self.scale[0] / 2, -self.scale[1] / 2, self.scale[2] / 2], # [self.scale[0] / 2, self.scale[1] / 2, -self.scale[2] / 2], # [-self.scale[0] / 2, -self.scale[1] / 2, self.scale[2] / 2], # [self.scale[0] / 2, -self.scale[1] / 2, -self.scale[2] / 2], # [-self.scale[0] / 2, self.scale[1] / 2, -self.scale[2] / 2], # [-self.scale[0] / 2, -self.scale[1] / 2, -self.scale[2] / 2], # ] # # # matrix = R.from_euler('xyz', self.rotation, degrees=False) # # vecs = [np.array(p) for p in simple] # rotated = matrix.apply(vecs) # translation = np.array(self.position) # dX = translation[0] # dY = translation[1] # dZ = translation[2] # points = self._translate(rotated, dX, dY, dZ) # return points @property def geo(self): return np.asarray([ list(self.position), list(self.scale), list(self.rotation) ]) def show(self, image, thickness, with_text, height, width, annotation_format, color): """ Show annotation as ndarray :param image: empty or image to draw on :param thickness: :param with_text: not required :param height: item height :param width: item width :param annotation_format: options: list(dl.ViewAnnotationOptions) :param color: color :return: ndarray """ try: import cv2 except (ImportError, ModuleNotFoundError): self.logger.error( 'Import Error! Cant import cv2. Annotations operations will be limited. import manually and fix errors') raise points = self.make_points() front_bl = points[0] front_br = points[1] front_tr = points[2] front_tl = points[3] back_bl = points[4] back_br = points[5] back_tr = points[6] back_tl = points[7] logger.warning('the show for 3d_cube is not supported.') return image # image = np.zeros((100, 100, 100), dtype=np.uint8) # pcd = o3d.io.read_point_cloud(r"C:\Users\97250\PycharmProjects\tt\qw\3D\D34049418_0000635.las.pcd") # # o3d.visualization.draw_geometries([pcd]) # # points = [[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [1, 0, 1], # # [0, 1, 1], [1, 1, 1]] # lines = [[0, 1], [0, 2], [1, 3], [2, 3], [4, 5], [4, 6], [5, 7], [6, 7], # [0, 4], [1, 5], [2, 6], [3, 7]] # colors = [[1, 0, 0] for i in range(len(lines))] # points = [back_bl, back_br, back_tl, back_tr, front_bl, front_br, front_tl, front_tr] # line_set = o3d.geometry.LineSet() # line_set.points = o3d.utility.Vector3dVector(points) # line_set.lines = o3d.utility.Vector2iVector(lines) # line_set.colors = o3d.utility.Vector3dVector(colors) # o3d.visualization.draw_geometries([line_set]) # return image def to_coordinates(self, color=None): keys = ["position", "scale", "rotation"] coordinates = {keys[idx]: {"x": float(x), "y": float(y), "z": float(z)} for idx, [x, y, z] in enumerate(self.geo)} return coordinates @staticmethod def from_coordinates(coordinates): geo = list() for key, pt in coordinates.items(): geo.append([pt["x"], pt["y"], pt["z"]]) return np.asarray(geo) @classmethod def from_json(cls, _json): if "coordinates" in _json: key = "coordinates" elif "data" in _json: key = "data" else: raise ValueError('can not find "coordinates" or "data" in annotation. id: {}'.format(_json["id"])) return cls( position=list(_json[key]['position'].values()), scale=list(_json[key]['scale'].values()), rotation=list(_json[key]['rotation'].values()), label=_json["label"], attributes=_json.get("attributes", None) )

38.496644

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0.544107

712

5,736

4.286517

0.259831

0.076671

0.07536

0.028834

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0.112385

0.092726

0.086501

0

0.044337

0.31189

5,736

148

136

38.756757

0.728908

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0

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1

0.097222

false

0

0.083333

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0.291667

0

null

0

null

0

1

0

8d9e1079bef17b6514de9131ede3ab7099ea53a4

3,702

py

Python

my_module/tools.py

roki18d/sphinx_autogen-apidoc

67ad9c716c909d89bcd813a5fa871df8850e4fd5

[ "Apache-2.0" ]

null

my_module/tools.py

roki18d/sphinx_autogen-apidoc

67ad9c716c909d89bcd813a5fa871df8850e4fd5

[ "Apache-2.0" ]

null

my_module/tools.py

roki18d/sphinx_autogen-apidoc

67ad9c716c909d89bcd813a5fa871df8850e4fd5

[ "Apache-2.0" ]

null

#!/usr/bin/env python # coding: utf-8 from my_module.exceptions import InvalidArgumentsError class SimpleCalculator(object): """SimpleCalculator SimpleCalculator is a simple calculator. Attributes: operator (str): String that represents operation type. Acceptable values are: {"add": addition, "sub": subtraction "mul": multiplication, "div": divide} response (dict): Response for API execution. This contains conditions (such as operands) and execution results. """ def __init__(self, operator: str) -> None: """Initialize instance Args: operator (str): """ valid_operators = ["add", "sub", "mul", "div"] if operator not in valid_operators: msg = f"Invalid operator '{operator}' was given, choose from {valid_operators}." raise InvalidArgumentsError(msg) else: self.operator = operator self.response = dict() def __add(self, num1: int, num2: int) -> None: self.response['results'] = {"sum": num1 + num2} return None def __sub(self, num1: int, num2: int) -> None: self.response['results'] = {"difference": num1 - num2} return None def __mul(self, num1: int, num2: int) -> None: self.response['results'] = {"product": num1 * num2} return None def __div(self, num1: int, num2: int) -> None: self.response['results'] = {"quotient": num1//num2, "remainder": num1%num2} return None def __handle_exceptions(self, e) -> None: self.response['results'] = {"error_message": e} return None def execute(self, num1: int, num2: int): """ Interface to execute caluculation. Args: num1 (int): 1st operand. num2 (int): 2nd operand. Returns: dict: self.response Raises: InvalidArgumentsError: Examples: >>> my_adder = SimpleCalculator(operator="add") >>> my_adder.execute(4, 2) {'operands': {'num1': 4, 'num2': 2}, 'results': {'sum': 6}} """ try: operands = {"num1": num1, "num2": num2} self.response['operands'] = operands if (not isinstance(num1, int)) or (not isinstance(num2, int)): msg = f"All operands should be integer, given: {operands}." raise InvalidArgumentsError(msg) except Exception as e: _ = self.__handle_exceptions(e) try: if self.operator == "add": _ = self.__add(num1, num2) elif self.operator == "sub": _ = self.__sub(num1, num2) elif self.operator == "mul": _ = self.__mul(num1, num2) elif self.operator == "div": _ = self.__div(num1, num2) except Exception as e: _ = self.__handle_exceptions(e) return self.response if __name__ == "__main__": my_adder = SimpleCalculator(operator="add") print('Case01:', my_adder.execute(4, 2)) print('Case02:', my_adder.execute(5, "a")) my_subtractor = SimpleCalculator(operator="sub") print('Case03:', my_subtractor.execute(3, 5)) my_multiplier = SimpleCalculator(operator="mul") print('Case04:', my_multiplier.execute(2, 7)) my_divider = SimpleCalculator(operator="div") print('Case05:', my_divider.execute(17, 5)) print('Case06:', my_divider.execute(6, 0)) print('Case07:') my_unknown = SimpleCalculator(operator="unknown") import sys; sys.exit(0)

30.85

92

0.562939

387

3,702

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0.335917

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0

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0.308752

3,702

119

93

31.109244

0.762016

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0

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0

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0

1

0.116667

false

0

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0

0.266667

0.116667

0

null

0

null

0

1

0

8da38969800ff2540723920b2ba94670badb3561

12,114

py

Python

PCA_ResNet50.py

liuyingbin19222/HSI_svm_pca_resNet50

cd95d21c81e93f8b873183f10f52416f71a93d07

[ "Apache-2.0" ]

12

2020-03-13T02:39:53.000Z

2022-02-21T03:28:33.000Z

PCA_ResNet50.py

liuyingbin19222/HSI_svm_pca_resNet50

cd95d21c81e93f8b873183f10f52416f71a93d07

[ "Apache-2.0" ]

14

2020-02-17T12:31:08.000Z

2022-02-10T01:07:05.000Z

PCA_ResNet50.py

liuyingbin19222/HSI_svm_pca_resNet50

cd95d21c81e93f8b873183f10f52416f71a93d07

[ "Apache-2.0" ]

3

2020-09-06T08:19:15.000Z

2021-03-08T10:15:40.000Z

import keras from keras.layers import Conv2D, Conv3D, Flatten, Dense, Reshape, BatchNormalization from keras.layers import Dropout, Input from keras.models import Model from keras.optimizers import Adam from keras.callbacks import ModelCheckpoint from keras.utils import np_utils from sklearn.decomposition import PCA from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score from operator import truediv from plotly.offline import init_notebook_mode import numpy as np import tensorflow as tf from keras import layers from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D from keras.models import Model, load_model from keras.preprocessing import image from keras.utils import layer_utils from keras.utils.data_utils import get_file from keras.applications.imagenet_utils import preprocess_input from keras.utils.vis_utils import model_to_dot from keras.utils import plot_model from keras.initializers import glorot_uniform import pydot from IPython.display import SVG import scipy.misc from matplotlib.pyplot import imshow import keras.backend as K K.set_image_data_format('channels_last') K.set_learning_phase(1) from keras.utils import to_categorical import numpy as np import matplotlib.pyplot as plt import scipy.io as sio import os import spectral ## GLOBAL VARIABLES dataset = 'IP' test_ratio = 0.8 windowSize = 25 def loadData(name): data_path = os.path.join(os.getcwd(),'data') if name == 'IP': data = sio.loadmat(os.path.join(data_path, 'Indian_pines_corrected.mat'))['indian_pines_corrected'] labels = sio.loadmat(os.path.join(data_path, 'Indian_pines_gt.mat'))['indian_pines_gt'] elif name == 'SA': data = sio.loadmat(os.path.join(data_path, 'Salinas_corrected.mat'))['salinas_corrected'] labels = sio.loadmat(os.path.join(data_path, 'Salinas_gt.mat'))['salinas_gt'] elif name == 'PU': data = sio.loadmat(os.path.join(data_path, 'PaviaU.mat'))['paviaU'] labels = sio.loadmat(os.path.join(data_path, 'PaviaU_gt.mat'))['paviaU_gt'] return data, labels def splitTrainTestSet(X, y, testRatio, randomState=345): X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testRatio, random_state=randomState, stratify=y) return X_train, X_test, y_train, y_test def applyPCA(X, numComponents=75): newX = np.reshape(X, (-1, X.shape[2])) pca = PCA(n_components=numComponents, whiten=True) newX = pca.fit_transform(newX) newX = np.reshape(newX, (X.shape[0],X.shape[1], numComponents)) return newX, pca def padWithZeros(X, margin=2): newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2* margin, X.shape[2])) x_offset = margin y_offset = margin newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X return newX # 去零 def createImageCubes(X, y, windowSize=5, removeZeroLabels = True): margin = int((windowSize - 1) / 2) zeroPaddedX = padWithZeros(X, margin=margin) # split patches patchesData = np.zeros((X.shape[0] * X.shape[1], windowSize, windowSize, X.shape[2])) patchesLabels = np.zeros((X.shape[0] * X.shape[1])) patchIndex = 0 for r in range(margin, zeroPaddedX.shape[0] - margin): for c in range(margin, zeroPaddedX.shape[1] - margin): patch = zeroPaddedX[r - margin:r + margin + 1, c - margin:c + margin + 1] patchesData[patchIndex, :, :, :] = patch patchesLabels[patchIndex] = y[r-margin, c-margin] patchIndex = patchIndex + 1 if removeZeroLabels: patchesData = patchesData[patchesLabels>0,:,:,:] patchesLabels = patchesLabels[patchesLabels>0] patchesLabels -= 1 return patchesData, patchesLabels X, y = loadData(dataset) K = 30 if dataset == 'IP' else 15 X,pca = applyPCA(X,numComponents=K) X, y = createImageCubes(X, y, windowSize=windowSize) ## Xtrain, Xtest, ytrain, ytest = splitTrainTestSet(X, y, test_ratio) # print("Xtrain.shape:",Xtrain.shape) # print("ytrain.shape:",ytrain.shape) # print("ytrain:",ytrain) def convert_one_hot(labels,classes=16): return to_categorical(labels,num_classes=classes) ytrain = convert_one_hot(ytrain,16) ytest = convert_one_hot(ytest,16) # print("ytrain.shape:",ytrain.shape) # ResNet50 网络; def identity_block(X, f, filters, stage, block): """ 实现图3的恒等块参数： X - 输入的tensor类型的数据，维度为( m, n_H_prev, n_W_prev, n_H_prev ) f - 整数，指定主路径中间的CONV窗口的维度 filters - 整数列表，定义了主路径每层的卷积层的过滤器数量 stage - 整数，根据每层的位置来命名每一层，与block参数一起使用。 block - 字符串，据每层的位置来命名每一层，与stage参数一起使用。返回： X - 恒等块的输出，tensor类型，维度为(n_H, n_W, n_C) """ #定义命名规则 conv_name_base = "res" + str(stage) + block + "_branch" bn_name_base = "bn" + str(stage) + block + "_branch" #获取过滤器 F1, F2, F3 = filters #保存输入数据，将会用于为主路径添加捷径 X_shortcut = X #主路径的第一部分 ##卷积层 X = Conv2D(filters=F1, kernel_size=(1,1), strides=(1,1) ,padding="valid", name=conv_name_base+"2a", kernel_initializer=glorot_uniform(seed=0))(X) ##归一化 X = BatchNormalization(axis=3,name=bn_name_base+"2a")(X) ##使用ReLU激活函数 X = Activation("relu")(X) #主路径的第二部分 ##卷积层 X = Conv2D(filters=F2, kernel_size=(f,f),strides=(1,1), padding="same", name=conv_name_base+"2b", kernel_initializer=glorot_uniform(seed=0))(X) ##归一化 X = BatchNormalization(axis=3,name=bn_name_base+"2b")(X) ##使用ReLU激活函数 X = Activation("relu")(X) #主路径的第三部分 ##卷积层 X = Conv2D(filters=F3, kernel_size=(1,1), strides=(1,1), padding="valid", name=conv_name_base+"2c", kernel_initializer=glorot_uniform(seed=0))(X) ##归一化 X = BatchNormalization(axis=3,name=bn_name_base+"2c")(X) ##没有ReLU激活函数 #最后一步： ##将捷径与输入加在一起 X = Add()([X,X_shortcut]) ##使用ReLU激活函数 X = Activation("relu")(X) return X def convolutional_block(X, f, filters, stage, block, s=2): """ 实现图5的卷积块参数： X - 输入的tensor类型的变量，维度为( m, n_H_prev, n_W_prev, n_C_prev) f - 整数，指定主路径中间的CONV窗口的维度 filters - 整数列表，定义了主路径每层的卷积层的过滤器数量 stage - 整数，根据每层的位置来命名每一层，与block参数一起使用。 block - 字符串，据每层的位置来命名每一层，与stage参数一起使用。 s - 整数，指定要使用的步幅返回： X - 卷积块的输出，tensor类型，维度为(n_H, n_W, n_C) """ #定义命名规则 conv_name_base = "res" + str(stage) + block + "_branch" bn_name_base = "bn" + str(stage) + block + "_branch" #获取过滤器数量 F1, F2, F3 = filters #保存输入数据 X_shortcut = X #主路径 ##主路径第一部分 X = Conv2D(filters=F1, kernel_size=(1,1), strides=(s,s), padding="valid", name=conv_name_base+"2a", kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3,name=bn_name_base+"2a")(X) X = Activation("relu")(X) ##主路径第二部分 X = Conv2D(filters=F2, kernel_size=(f,f), strides=(1,1), padding="same", name=conv_name_base+"2b", kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3,name=bn_name_base+"2b")(X) X = Activation("relu")(X) ##主路径第三部分 X = Conv2D(filters=F3, kernel_size=(1,1), strides=(1,1), padding="valid", name=conv_name_base+"2c", kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3,name=bn_name_base+"2c")(X) #捷径 X_shortcut = Conv2D(filters=F3, kernel_size=(1,1), strides=(s,s), padding="valid", name=conv_name_base+"1", kernel_initializer=glorot_uniform(seed=0))(X_shortcut) X_shortcut = BatchNormalization(axis=3,name=bn_name_base+"1")(X_shortcut) #最后一步 X = Add()([X,X_shortcut]) X = Activation("relu")(X) return X def ResNet50(input_shape=(25,25,30),classes=16): """ 实现ResNet50 CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3 -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER 参数： input_shape - 图像数据集的维度 classes - 整数，分类数返回： model - Keras框架的模型 """ #定义tensor类型的输入数据 X_input = Input(input_shape) #0填充 X = ZeroPadding2D((3,3))(X_input) #stage1 X = Conv2D(filters=64, kernel_size=(7,7), strides=(2,2), name="conv1", kernel_initializer=glorot_uniform(seed=0))(X) X = BatchNormalization(axis=3, name="bn_conv1")(X) X = Activation("relu")(X) X = MaxPooling2D(pool_size=(3,3), strides=(2,2))(X) #stage2 X = convolutional_block(X, f=3, filters=[64,64,256], stage=2, block="a", s=1) X = identity_block(X, f=3, filters=[64,64,256], stage=2, block="b") X = identity_block(X, f=3, filters=[64,64,256], stage=2, block="c") #stage3 X = convolutional_block(X, f=3, filters=[128,128,512], stage=3, block="a", s=2) X = identity_block(X, f=3, filters=[128,128,512], stage=3, block="b") X = identity_block(X, f=3, filters=[128,128,512], stage=3, block="c") X = identity_block(X, f=3, filters=[128,128,512], stage=3, block="d") #stage4 X = convolutional_block(X, f=3, filters=[256,256,1024], stage=4, block="a", s=2) X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block="b") X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block="c") X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block="d") X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block="e") X = identity_block(X, f=3, filters=[256,256,1024], stage=4, block="f") #stage5 X = convolutional_block(X, f=3, filters=[512,512,2048], stage=5, block="a", s=2) X = identity_block(X, f=3, filters=[512,512,2048], stage=5, block="b") X = identity_block(X, f=3, filters=[512,512,2048], stage=5, block="c") #均值池化层 X = AveragePooling2D(pool_size=(2,2),padding="same")(X) #输出层 X = Flatten()(X) X = Dense(classes, activation="softmax", name="fc"+str(classes), kernel_initializer=glorot_uniform(seed=0))(X) #创建模型 model = Model(inputs=X_input, outputs=X, name="ResNet50") return model # # x_train : (3074,25,25,30) y_train: (3074) # model = ResNet50(input_shape=(25,25,30),classes=16) # model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]) # # # model.fit(Xtrain,ytrain,epochs=2,batch_size=25) # preds = model.evaluate(Xtest,ytest) # # print("误差率:",str(preds[0])) # print("准确率:",str(preds[1])) def main(): # x_train : (3074,25,25,30) y_train: (3074) model = ResNet50(input_shape=(25, 25, 30), classes=16) model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]) history = model.fit(Xtrain, ytrain, epochs=100, batch_size=25) preds = model.evaluate(Xtest, ytest) plt.figure(figsize=(5,5)) plt.ylim(0,1.1) plt.grid() plt.plot(history.history['accuracy']) #plt.plot(history.history['val_acc']) plt.ylabel( dataset+' _Accuracy') plt.xlabel('Epochs') plt.legend(['Training','Validation']) plt.savefig("acc_curve.jpg") plt.show() plt.figure(figsize=(7,7)) plt.grid() plt.plot(history.history['loss']) #plt.plot(history.history['val_loss']) plt.ylabel(dataset+' _Loss') plt.xlabel('Epochs') plt.legend(['Training','Validation'], loc='upper right') plt.savefig("loss_curve.jpg") plt.show() print("误差率:", str(preds[0])) print("准确率:", str(preds[1])) if __name__ == "__main__": main()

34.123944

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null

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null

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1

0

8da4e24daba79cfc5a237fbfd0bd61228b6bdc1d

754

py

Python

tests/test_data/utest/setup.py

gordonmessmer/pyp2rpm

60145ba6fa49ad5bb29eeffa5765e10ba8417f03

[ "MIT" ]

114

2015-07-13T12:38:27.000Z

2022-03-23T15:05:11.000Z

tests/test_data/utest/setup.py

gordonmessmer/pyp2rpm

60145ba6fa49ad5bb29eeffa5765e10ba8417f03

[ "MIT" ]

426

2015-07-13T12:09:38.000Z

2022-01-07T16:41:32.000Z

tests/test_data/utest/setup.py

Mattlk13/pyp2rpm

f9ced95877d88c96b77b2b8c510dc4ceaa10504a

[ "MIT" ]

51

2015-07-14T13:11:29.000Z

2022-03-31T07:27:32.000Z

#!/usr/bin/env python3 from setuptools import setup, find_packages requirements = ["pyp2rpm~=3.3.1"] setup( name="utest", version="0.1.0", description="Micro test module", license="GPLv2+", author="pyp2rpm Developers", author_email='bkabrda@redhat.com, rkuska@redhat.com, mcyprian@redhat.com, ishcherb@redhat.com', url='https://github.com/fedora-python/pyp2rpm', install_requires=requirements, include_package_data=True, packages=find_packages(exclude=["test"]), classifiers=( "License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)", "Operating System :: POSIX :: Linux", "Programming Language :: Python", "Programming Language :: Python :: 3", ), )

30.16

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0

0.022876

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754

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0.05

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null

0

null

0

1

0

8da621c7d046b3bbba97fe0075833d24a4276a49

4,235

py

Python

abstract_nas/train/preprocess.py

dumpmemory/google-research

bc87d010ab9086b6e92c3f075410fa6e1f27251b

[ "Apache-2.0" ]

null

abstract_nas/train/preprocess.py

dumpmemory/google-research

bc87d010ab9086b6e92c3f075410fa6e1f27251b

[ "Apache-2.0" ]

null

abstract_nas/train/preprocess.py

dumpmemory/google-research

bc87d010ab9086b6e92c3f075410fa6e1f27251b

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2022 The Google Research Authors. # # 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. """Data preprocessing for ImageNet2012 and CIFAR-10.""" from typing import Any, Callable # pylint: disable=unused-import from big_vision.pp import ops_general from big_vision.pp import ops_image # pylint: enable=unused-import from big_vision.pp import utils from big_vision.pp.builder import get_preprocess_fn as _get_preprocess_fn from big_vision.pp.registry import Registry import tensorflow as tf CIFAR_MEAN = [0.4914, 0.4822, 0.4465] CIFAR_STD = [0.247, 0.243, 0.261] @Registry.register("preprocess_ops.random_crop_with_pad") @utils.InKeyOutKey() def get_random_crop_with_pad(crop_size, padding): """Makes a random crop of a given size. Args: crop_size: either an integer H, where H is both the height and width of the random crop, or a list or tuple [H, W] of integers, where H and W are height and width of the random crop respectively. padding: how much to pad before cropping. Returns: A function, that applies random crop. """ crop_size = utils.maybe_repeat(crop_size, 2) padding = utils.maybe_repeat(padding, 2) def _crop(image): image = tf.image.resize_with_crop_or_pad(image, crop_size[0] + padding[0], crop_size[1] + padding[1]) return tf.image.random_crop(image, [crop_size[0], crop_size[1], image.shape[-1]]) return _crop def preprocess_cifar(split, **_): """Preprocessing functions for CIFAR-10 training.""" mean_str = ",".join([str(m) for m in CIFAR_MEAN]) std_str = ",".join([str(m) for m in CIFAR_STD]) if split == "train": pp = ("decode|" "value_range(0,1)|" "random_crop_with_pad(32,4)|" "flip_lr|" f"vgg_value_range(({mean_str}),({std_str}))|" "onehot(10, key='label', key_result='labels')|" "keep('image', 'labels')") else: pp = ("decode|" "value_range(0,1)|" "central_crop(32)|" f"vgg_value_range(({mean_str}),({std_str}))|" "onehot(10, key='label', key_result='labels')|" "keep('image', 'labels')") return _get_preprocess_fn(pp) def preprocess_imagenet(split, autoaugment = False, label_smoothing = 0.0, **_): """Preprocessing functions for ImageNet training.""" if split == "train": pp = ("decode_jpeg_and_inception_crop(224)|" "flip_lr|") if autoaugment: pp += "randaug(2,10)|" pp += "value_range(-1,1)|" if label_smoothing: confidence = 1.0 - label_smoothing low_confidence = (1.0 - confidence) / (1000 - 1) pp += ("onehot(1000, key='label', key_result='labels', " f"on_value={confidence}, off_value={low_confidence})|") else: pp += "onehot(1000, key='label', key_result='labels')|" pp += "keep('image', 'labels')" else: pp = ("decode|" "resize_small(256)|" "central_crop(224)|" "value_range(-1,1)|" "onehot(1000, key='label', key_result='labels')|" "keep('image', 'labels')") return _get_preprocess_fn(pp) PREPROCESS = { "cifar10": preprocess_cifar, "imagenet2012": preprocess_imagenet, } def get_preprocess_fn(dataset, split, **preprocess_kwargs): """Makes a preprocessing function.""" preprocess_fn_by_split = PREPROCESS.get(dataset, lambda _: (lambda x: x)) split = "train" if "train" in split else "val" preprocess_fn = preprocess_fn_by_split(split, **preprocess_kwargs) return preprocess_fn

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0.025223

0.029104

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0

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0

0.220779

0

null

0

null

0

1

0

8da70610f3402c8b44d3fbdf21a05f4f563b016b

488

py

Python

hidb/wrapper.py

sk-ip/hidb

1394000992c016607e7af15095f058cd9cce007b

[ "MIT" ]

null

hidb/wrapper.py

sk-ip/hidb

1394000992c016607e7af15095f058cd9cce007b

[ "MIT" ]

null

hidb/wrapper.py

sk-ip/hidb

1394000992c016607e7af15095f058cd9cce007b

[ "MIT" ]

null

from datetime import datetime class fileWrapper(object): def __init__(self): self.data = {} self.keys = set() # JSON data size 16KB in Bytes self.max_data_size = 16384 # Max database size 1GB in Bytes self.max_database_size = 1073741824 self.current_database_size = 0 class dataWrapper: def __init__(self, data, ttl): self.data = data self.timestamp = datetime.today().timestamp() self.ttl = ttl

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null

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1

0

8da8f86888f2ee041a3f2312c9709ef180e420d0

4,504

py

Python

ion-channel-models/compare.py

sanmitraghosh/fickleheart-method-tutorials

d5ee910258a2656951201d4ada2a412804013bd5

[ "BSD-3-Clause" ]

null

ion-channel-models/compare.py

sanmitraghosh/fickleheart-method-tutorials

d5ee910258a2656951201d4ada2a412804013bd5

[ "BSD-3-Clause" ]

null

ion-channel-models/compare.py

sanmitraghosh/fickleheart-method-tutorials

d5ee910258a2656951201d4ada2a412804013bd5

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 from __future__ import print_function import sys sys.path.append('./method') import os import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import model as m """ Run fit. """ predict_list = ['sinewave', 'staircase', 'activation', 'ap'] try: which_predict = sys.argv[1] except: print('Usage: python %s [str:which_predict]' % os.path.basename(__file__)) sys.exit() if which_predict not in predict_list: raise ValueError('Input data %s is not available in the predict list' \ % which_predict) # Get all input variables import importlib sys.path.append('./mmt-model-files') info_id_a = 'model_A' info_a = importlib.import_module(info_id_a) info_id_b = 'model_B' info_b = importlib.import_module(info_id_b) data_dir = './data' savedir = './fig/compare' if not os.path.isdir(savedir): os.makedirs(savedir) data_file_name = 'data-%s.csv' % which_predict print('Predicting ', data_file_name) saveas = 'compare-sinewave-' + which_predict # Protocol protocol = np.loadtxt('./protocol-time-series/%s.csv' % which_predict, skiprows=1, delimiter=',') protocol_times = protocol[:, 0] protocol = protocol[:, 1] # Load data data = np.loadtxt(data_dir + '/' + data_file_name, delimiter=',', skiprows=1) # headers times = data[:, 0] data = data[:, 1] # Model model_a = m.Model(info_a.model_file, variables=info_a.parameters, current_readout=info_a.current_list, set_ion=info_a.ions_conc, transform=None, temperature=273.15 + info_a.temperature, # K ) model_b = m.Model(info_b.model_file, variables=info_b.parameters, current_readout=info_b.current_list, set_ion=info_b.ions_conc, transform=None, temperature=273.15 + info_b.temperature, # K ) # Update protocol model_a.set_fixed_form_voltage_protocol(protocol, protocol_times) model_b.set_fixed_form_voltage_protocol(protocol, protocol_times) # Load calibrated parameters load_seed = 542811797 fix_idx = [1] calloaddir_a = './out/' + info_id_a calloaddir_b = './out/' + info_id_b cal_params_a = [] cal_params_b = [] for i in fix_idx: cal_params_a.append(np.loadtxt('%s/%s-solution-%s-%s.txt' % \ (calloaddir_a, 'sinewave', load_seed, i))) cal_params_b.append(np.loadtxt('%s/%s-solution-%s-%s.txt' % \ (calloaddir_b, 'sinewave', load_seed, i))) # Predict predictions_a = [] for p in cal_params_a: predictions_a.append(model_a.simulate(p, times)) predictions_b = [] for p in cal_params_b: predictions_b.append(model_b.simulate(p, times)) # Plot fig, axes = plt.subplots(2, 1, sharex=True, figsize=(10, 4), gridspec_kw={'height_ratios': [1, 3]}) is_predict = ' prediction' if which_predict != 'sinewave' else '' sim_protocol = model_a.voltage(times) # model_b should give the same thing axes[0].plot(times, sim_protocol, c='#7f7f7f') axes[0].set_ylabel('Voltage\n(mV)', fontsize=16) axes[1].plot(times, data, alpha=0.5, label='Data') for i, p in zip(fix_idx, predictions_a): axes[1].plot(times, p, label='Model A' + is_predict) for i, p in zip(fix_idx, predictions_b): axes[1].plot(times, p, label='Model B' + is_predict) # Zooms from mpl_toolkits.axes_grid1.inset_locator import inset_axes, mark_inset sys.path.append('./protocol-time-series') zoom = importlib.import_module(which_predict + '_to_zoom') axes[1].set_ylim(zoom.set_ylim) for i_zoom, (w, h, loc) in enumerate(zoom.inset_setup): axins = inset_axes(axes[1], width=w, height=h, loc=loc, axes_kwargs={"facecolor" : "#f0f0f0"}) axins.plot(times, data, alpha=0.5) for i, p in zip(fix_idx, predictions_a): axins.plot(times, p) for i, p in zip(fix_idx, predictions_b): axins.plot(times, p) axins.set_xlim(zoom.set_xlim_ins[i_zoom]) axins.set_ylim(zoom.set_ylim_ins[i_zoom]) #axins.yaxis.get_major_locator().set_params(nbins=3) #axins.xaxis.get_major_locator().set_params(nbins=3) axins.set_xticklabels([]) axins.set_yticklabels([]) pp, p1, p2 = mark_inset(axes[1], axins, loc1=zoom.mark_setup[i_zoom][0], loc2=zoom.mark_setup[i_zoom][1], fc="none", lw=0.75, ec='k') pp.set_fill(True); pp.set_facecolor("#f0f0f0") axes[1].legend() axes[1].set_ylabel('Current (pA)', fontsize=16) axes[1].set_xlabel('Time (ms)', fontsize=16) plt.subplots_adjust(hspace=0) plt.savefig('%s/%s' % (savedir, saveas), bbox_inches='tight', dpi=200) plt.close()

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78

0.690941

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4,504

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0.287554

0.036672

0.006791

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null

0

null

0

1

0

8da906c8ad76ecde7a1bd94e5017709b02a7ce8e

7,752

py

Python

examples/services/classifier_service.py

bbbdragon/python-pype

f0618150cb4d2fae1f959127453fb6eca8db84e5

[ "MIT" ]

8

2019-07-12T03:28:10.000Z

2019-07-19T20:34:45.000Z

examples/services/classifier_service.py

bbbdragon/python-pype

f0618150cb4d2fae1f959127453fb6eca8db84e5

[ "MIT" ]

null

examples/services/classifier_service.py

bbbdragon/python-pype

f0618150cb4d2fae1f959127453fb6eca8db84e5

[ "MIT" ]

null

''' python3 classifier_service.py data.csv This service runs a scikit-learn classifier on data provided by the csv file data.csv. The idea of this is a simple spam detector. In the file, you will see a number, 1 or -1, followed by a pipe, followed by a piece of text. The text is designed to be a subject email, and the number its label: 1 for spam and -1 for not spam. The service loads the csv file, trains the classifier, and then waits for you to send it a list of texts via the 'classify' route. This service can be tested using: ./test_classifier_service.sh ''' from flask import Flask,request,jsonify from pype import pype as p from pype import _,_0,_1,_p from pype import _assoc as _a from pype import _dissoc as _d from pype import _do from statistics import mean,stdev from pype.vals import lenf from sklearn.ensemble import RandomForestClassifier as Classifier from sklearn.feature_extraction.text import TfidfVectorizer as Vectorizer import sys import csv ''' We have to use lambda to define the read function because pype functions can't yet deal with keyword args. ''' read=lambda f: csv.reader(f,delimiter='|') def train_classifier(texts,y): ''' Here is a perfect example of the "feel it ... func it" philosophy: The pype call uses the function arguments and function body to specify three variables, texts, a list of strings, y, a list of floats, and vectorizer, a scikit-learn object that vectorizes text. This reiterates the adivce that you should use the function body and function arguments to declare your scope, whenever you can. Line-by-line, here we go: {'vectorizer':vectorizer.fit, 'X':vectorizer.transform}, We build a dict, the first element of which is the fit vectorizer. Luckily, the 'fit' function returns an instance of the trained vectorizer, so we do not need to use _do. This vectorizer is then assigned to 'vectorizer'. Because iterating through dictionaries in Python3.6 preserves the order of the keys in which they were declared, we can apply the fit function to the vectorizer on the texts, assign that to the 'vectorizer' key. We need this instance of the vectorizer to run the classifier for unknown texts. After this, we apply the 'transform' to convert the texts into a training matrix keyed by 'X', whose rows are texts and whose columns are words. _a('classifier',(Classifier().fit,_['X'],y)), Finally, we can build a classifier. _a, or _assoc, means we are adding a key-value pair to the previous dictionary. This will be a new instance of our Classifier, which is trained through the fit function on the text-word matrix 'X' and the labels vector y. _d('X'), Since we don't need the X matrix anymore, we delete it from the returned JSON, which now only contains 'vectorizer' and 'classifier', the two things we will need to classify unknown texts. ''' vectorizer=Vectorizer() return p( texts, {'vectorizer':vectorizer.fit, 'X':vectorizer.transform}, _a('classifier',(Classifier().fit,_['X'],y)), _d('X'), ) ''' We train the model in a global variable containing our vectorizer and classifier. This use of global variables is only used for microservices, by the way. Here is a line-by-line description: sys.argv[1], open, Open the file. read, We build a csv reader with the above-defined 'read' function, which builds a csv reader with a '|' delimiter. I chose this delimeter because the texts often have commas. list, Because csv.reader is a generator, it cannot be accessed twice, so I cast it to a list. This list is a list of 2-element lists, of the form [label,text], where label is a string for the label ('1' or '-1'), and text is a string for the training text. So an example of this would be ['1','free herbal viagra buy now']. (train,[_1],[(float,[_0])]) This is a lambda which calls the 'train' function on two arguments, the first being a list of texts, the second being a list of numerical labels. We know that the incoming argument is a list of 2-element lists, so [_1] is a map, which goes through this list - [] - and builds a new list containing only the second element of each 2-element list, referenced by _1. With the first elements of the 2-element lists, we must extract the first element and cast it to a float. In [(float,[_0])], the [] specifies a map over the list of 2-element lists. (float,_0) specifies we are accessing the first element of the 2-element list ('1' or '-1'), and calls the float function on it, to cast it to a float. If we do not cast it to a float, sklearn will not be able to process it as a label. ''' MODEL=p( sys.argv[1], open, read, list, (train_classifier,[_1],[(float,_0)]), ) app = Flask(__name__) @app.route('/classify',methods=['POST']) def classify(): ''' This is the function that is run on a JSON containing one field, 'texts', which is a list of strings. This function will return a list of JSON's containing the label for that text given by the classifier (1 or -1), and the original text. Notice that, in this routing, we need access to 'texts' in (zip,_,texts). Line-by-line: global MODEL We need this to refer to the model we trained at the initialization of the microservice. texts=request.get_json(force=True)['texts'] This extracts the 'texts' list from the json embedded in the request. MODEL['vectorizer'].transform, This uses the vectorizer to convert the list of strings in texts to a text-word matrix that can be fed into the classifier. MODEL['classifier'].predict, This runs the prediction on the text-word matrix, producing an array of 1's and -1's, where 1 indicates that the classification is positive (it is spam), and -1 indicates that the classification is negative (it is not spam). (zip,_,texts), We know that the n-th label produced by the classifier is for the n-th string in texts, so we zip them together to produce an iterable of tuples (label,text). [{'label':_0, 'text':_1, 'description':{_0 == 1: 'not spam', 'else':'spam'}}], Here, we are performing a mapping over the (label,text) tuples produced by the zip. For each tuple, we build a dictionary with three items. The first is the label, which is numberic, either 1.0 or -1.0. The second is the actual text string. However, to help the user, we also include a description of what the label means: 'description':{_0 == 1: 'not spam', 'else':'spam'} The value is a switch dict. Since _0 is a Getter object, it overrides the == operator to produce a LamTup, which Python will accept, but which the pype interpreter will run as an expression. _0 == 1 simply means, "the first element of the (label,text) tuple, label, is 1. If this is true, 'description is set to 'not spam'. Otherwise, it is set to 'spam'. jsonify This just turns the resulting JSON, a list of dicitonaries, into something that can be returned to the client over HTTP. ''' global MODEL texts=request.get_json(force=True)['texts'] return p( texts, MODEL['vectorizer'].transform, MODEL['classifier'].predict, (zip,_,texts), [{'label':_0, 'text':_1, 'description':{_0 == 1: 'not spam', 'else':'spam'}}], jsonify) if __name__=='__main__': app.run(host='0.0.0.0',port=10004,debug=True)

36.739336

172

0.68434

1,238

7,752

4.238288

0.267367

0.007433

0.013341

0.006861

0.112255

0.080046

0.041166

0.014866

0

0.010645

0.236584

7,752

210

173

36.914286

0.875972

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0.081237

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0.046512

false

0

0.27907

0

0.372093

0

null

0

null

0

1

0

8da9192128d87d058ba7b763d377c653bfe2eb10

2,657

py

Python

ida_plugin/uefi_analyser.py

fengjixuchui/UEFI_RETool

72c5d54c1dab9f58a48294196bca5ce957f6fb24

[ "MIT" ]

240

2019-03-12T21:28:06.000Z

2021-02-09T16:20:09.000Z

ida_plugin/uefi_analyser.py

fengjixuchui/UEFI_RETool

72c5d54c1dab9f58a48294196bca5ce957f6fb24

[ "MIT" ]

10

2019-09-09T08:38:35.000Z

2020-11-30T15:19:30.000Z

ida_plugin/uefi_analyser.py

fengjixuchui/UEFI_RETool

72c5d54c1dab9f58a48294196bca5ce957f6fb24

[ "MIT" ]

53

2019-03-16T06:54:18.000Z

2020-12-23T06:16:38.000Z

# SPDX-License-Identifier: MIT import os import idaapi import idautils from PyQt5 import QtWidgets from uefi_analyser import dep_browser, dep_graph, prot_explorer, ui AUTHOR = "yeggor" VERSION = "1.2.0" NAME = "UEFI_RETool" WANTED_HOTKEY = "Ctrl+Alt+U" HELP = "This plugin performs automatic analysis of the input UEFI module" class UefiAnalyserPlugin(idaapi.plugin_t): flags = idaapi.PLUGIN_MOD | idaapi.PLUGIN_PROC | idaapi.PLUGIN_FIX comment = HELP help = HELP wanted_name = NAME wanted_hotkey = WANTED_HOTKEY def init(self): self._last_directory = idautils.GetIdbDir() ui.init_menu(MenuHandler(self)) self._welcome() return idaapi.PLUGIN_KEEP def run(self, arg): try: self._analyse_all() except Exception as err: import traceback print(f"[{NAME} error] {str(err)}\n{traceback.format_exc()}") def term(self): pass def load_json_log(self): print(f"[{NAME}] try to parse JSON log file") log_name = self._select_log() print(f"[{NAME}] log name: {log_name}") dep_browser.run(log_name) dep_graph.run(log_name) def _select_log(self): file_dialog = QtWidgets.QFileDialog() file_dialog.setFileMode(QtWidgets.QFileDialog.ExistingFiles) filename = None try: filename, _ = file_dialog.getOpenFileName( file_dialog, f"Select the {NAME} log file", self._last_directory, "Results files (*.json)", ) except Exception as e: print(f"[{NAME} error] {str(e)}") if filename: self._last_directory = os.path.dirname(filename) return filename @staticmethod def _welcome(): print(f"\n{NAME} plugin by {AUTHOR} ({VERSION})") print(f"{NAME} shortcut key is {WANTED_HOTKEY}\n") @staticmethod def _analyse_all(): prot_explorer.run() class MenuHandler(idaapi.action_handler_t): def __init__(self, plugin): idaapi.action_handler_t.__init__(self) self.plugin = plugin def activate(self, ctx): try: self.plugin.load_json_log() except Exception as err: import traceback print(f"[{NAME} error] {str(err)}\n{traceback.format_exc()}") return True def update(self, ctx): return idaapi.AST_ENABLE_ALWAYS def PLUGIN_ENTRY(): try: return UefiAnalyserPlugin() except Exception as err: import traceback print(f"[{NAME} error] {str(err)}\n{traceback.format_exc()}")

26.04902

73

0.616861

320

2,657

4.915625

0.359375

0.030515

0.044501

0.038144

0.154482

0.143039

0

0.002087

0.27851

2,657

101

74

26.306931

0.818466

0.010538

0

0.194805

0

0.176247

0.041112

0

1

0.142857

false

0.012987

0.103896

0.012987

0.402597

0.103896

0

null

0

null

0

1

0

8daa3414a09b9f3c7c95225a1a7fdf929b8d3dfe

440

py

Python

BPt/default/options/samplers.py

sahahn/ABCD_ML

a8b1c48c33f3fdc046c8922964f1c456273238da

[ "MIT" ]

1

2019-09-25T23:23:49.000Z

BPt/default/options/samplers.py

sahahn/ABCD_ML

a8b1c48c33f3fdc046c8922964f1c456273238da

[ "MIT" ]

1

2020-04-20T20:53:27.000Z

BPt/default/options/samplers.py

sahahn/ABCD_ML

a8b1c48c33f3fdc046c8922964f1c456273238da

[ "MIT" ]

1

2019-06-21T14:44:40.000Z

from ..helpers import get_obj_and_params, all_from_objects from ...extensions.samplers import OverSampler SAMPLERS = { 'oversample': (OverSampler, ['default']), } def get_sampler_and_params(obj_str, extra_params, params, **kwargs): obj, extra_obj_params, obj_params =\ get_obj_and_params(obj_str, SAMPLERS, extra_params, params) return obj(**extra_obj_params), obj_params all_obj_keys = all_from_objects(SAMPLERS)

25.882353

68

0.756818

60

440

5.133333

0.35

0.116883

0.058442

0.097403

0.168831

0

0.138636

440

17

69

25.882353

0.812665

0

0.038549

0

1

0.1

false

0

0.2

0

0.4

0

null

0

null

0

1

0

a5c0fa60cac177d2865547e53143112bdfdc7111

1,008

py

Python

testing.py

madjabal/morphine

2c76b10a7276936042913d609ad773fbc08b0887

[ "MIT" ]

15

2017-03-11T18:25:04.000Z

2022-03-31T19:54:31.000Z

testing.py

madjabal/morphine

2c76b10a7276936042913d609ad773fbc08b0887

[ "MIT" ]

2

2018-10-17T15:08:36.000Z

2021-06-08T13:34:56.000Z

testing.py

madjabal/morphine

2c76b10a7276936042913d609ad773fbc08b0887

[ "MIT" ]

2

2018-07-25T15:15:54.000Z

2019-06-14T11:16:41.000Z

# Python modules import time from datetime import timedelta def consistency(func, args, expected, n=10**4): """Analyze and report on the consistency of a function.""" print('\n[CONSISTENCY TEST] {0}'.format(func.__doc__.format(*args))) def show(num, den, t, p, end='\r'): print('{3}|{4:.3f}: {0}/{1} = {2}'.format(num, den, num/den, str(timedelta(seconds=t)), p), end=end) start = time.time() interval = start tally = 0 for i in range(n): isCorrect = func(*args) == expected tally += (1 if isCorrect else 0) diff = time.time() - interval if diff > 0.01: interval = time.time() show(tally, (i+1), time.time() - start, (i+1)/n) show(tally, n, time.time() - start, (i+1)/n, '\n') def max_over(n, func, args=None): """Compute the maximum value returned by func(args) in n runs.""" m = 0 for i in range(n): v = func(*args) if args else func() if v > m: m = v return m

30.545455

108

0.558532

154

1,008

3.623377

0.422078

0.071685

0.057348

0.02509

0.103943

0

0.027211

0.270833

1,008

33

109

30.545455

0.731973

0.126984

0

0.083333

0

0.062069

0

1

0.125

false

0

0.083333

0

0.25

0.083333

0

null

0

null

0

1

0

a5c112fb1800922ae32e15c8c2c3119937a66895

520

py

Python

misc/python/fibonacci.py

saranshbht/codes-and-more-codes

0bd2e46ca613b3b81e1196d393902e86a43aa353

[ "MIT" ]

null

misc/python/fibonacci.py

saranshbht/codes-and-more-codes

0bd2e46ca613b3b81e1196d393902e86a43aa353

[ "MIT" ]

null

misc/python/fibonacci.py

saranshbht/codes-and-more-codes

0bd2e46ca613b3b81e1196d393902e86a43aa353

[ "MIT" ]

null

from itertools import permutations from collections import Counter import time print(time.time()) s=["dgajkhdjkjfkl","ahfjkh","jfskoj","hfakljfio","fjfjir","jiosj","jiojf","jriosj","jiorjf","jhhhhaskgasjdfljjriof"] t=10 while t>0: S=s[10-t] c=dict(Counter(S)) Cperm=list(permutations(c.values())) flag= False for i in Cperm: for j in range(2,len(i)): if i[j]==i[j-1]+i[j-2]: print("Dynamic") flag= True break if flag==True: break else: print("Not") t=t-1 print(time.time())

18.571429

117

0.646154

82

520

4.097561

0.536585

0.017857

0.077381

0

0.020785

0.167308

520

27

118

19.259259

0.755196

0

0.173913

0

0.178846

0.040385

0

1

0

false

0

0.130435

0

0.130435

0.173913

0

null

0

null

0

1

0

a5c6922a61844f38e222e52aacc04701fb1c3022

4,953

py

Python

main.py

rodrigobercinimartins/export-import-por-mesorregiao-brasil

73b8126e593eec63ae29eb81a2967f566ec93bc9

[ "MIT" ]

1

2020-04-06T17:55:04.000Z

main.py

rodrigobercini/export-import-por-mesorregiao-brasil

73b8126e593eec63ae29eb81a2967f566ec93bc9

[ "MIT" ]

null

main.py

rodrigobercini/export-import-por-mesorregiao-brasil

73b8126e593eec63ae29eb81a2967f566ec93bc9

[ "MIT" ]

null

import pandas as pd import os import ssl # I'm getting SSL certificates issues when downloading files from MDIC. # The code below is a hack to get around this issue. try: _create_unverified_https_context = ssl._create_unverified_context except AttributeError: pass else: ssl._create_default_https_context = _create_unverified_https_context class ExportsByMesoregion: def __init__(self , start_year:int , end_year:int = None , transaction_type:str='exports'): self.start_year = start_year if end_year is not None: self.end_year = end_year else: self.end_year = start_year self.TRANSACTION_TYPES = { 'exports':'EXP' , 'imports':'IMP' } if transaction_type in self.TRANSACTION_TYPES: self.transaction_type = transaction_type else: raise ValueError(f"Invalid transaction type. Valid values are: {''.join(self.TRANSACTION_TYPES)}") self.BASE_URL = 'https://balanca.economia.gov.br/balanca/bd/comexstat-bd/mun/' self.REPO_FOLDER_PATH = os.path.dirname(os.path.abspath(__file__)) self.MUN_FOLDER_PATH = os.path.join(self.REPO_FOLDER_PATH, 'data', 'municipalities',"") self.MESO_FOLDER_PATH = os.path.join(self.REPO_FOLDER_PATH, 'data', 'mesoregions',"") self.MUN_LOOKUP_FILENAME = os.path.join(self.REPO_FOLDER_PATH, 'municipalities_lookup.xlsx') def create_folder_if_not_exists(self, folder_path): if not os.path.exists(folder_path): os.makedirs(folder_path) def get_file_name(self, transaction_type, year, division_type): return f'{self.TRANSACTION_TYPES[transaction_type]}_{year}_{division_type}.csv' def download_mun_data(self): self.create_folder_if_not_exists(self.MUN_FOLDER_PATH) for year in range(self.start_year, self.end_year+1): file_name = self.get_file_name(self.transaction_type, year, 'MUN') file_path = f'{self.MUN_FOLDER_PATH}{file_name}' if os.path.isfile(file_path): print(f'{year} - Mun {self.transaction_type} already exists. Skipping download...') continue url = f'{self.BASE_URL}{file_name}' pd.read_csv(url, sep=';', encoding='UTF-8').to_csv(file_path, sep=';', encoding='UTF-8') print(f'{year} - Municipalities {self.transaction_type} finished downloading') def add_meso_to_mun_data(self, year): mun_exp_filename = self.get_file_name(self.transaction_type, year, 'MUN') mun_exports = pd.read_csv(f'{self.MUN_FOLDER_PATH}{mun_exp_filename}', sep=';') municip_codes = pd.read_excel(self.MUN_LOOKUP_FILENAME) mun_with_meso = mun_exports.merge(municip_codes, left_on= 'CO_MUN', right_on='Código Município Completo (MDIC)') mun_with_meso.drop(['Município', 'CO_MUN', 'Nome_Microrregião', 'Microrregião Geográfica', 'Código Município Completo (MDIC)'], axis=1, inplace=True) print(f'{year} - Mesoregions info added to municipalities data') return mun_with_meso def aggregate_by_mesoregion(self, year, mun_with_meso): meso_aggregated = mun_with_meso.groupby(['CO_ANO','Nome_Mesorregião','CD_GEOCME', 'CO_MES', 'CO_PAIS', 'SH4'],as_index=False).sum() # Consolida dados por mesorregião meso_aggregated.drop(['UF', 'Mesorregião Geográfica', 'Código Município Completo (IBGE)'], axis=1, inplace=True) print(f'{year} - Mesoregions data aggregated') return meso_aggregated def download_data_and_aggregate_by_meso(self): self.create_folder_if_not_exists(self.MESO_FOLDER_PATH) self.download_mun_data() for year in (range(self.start_year, self.end_year+1)): mun_with_meso = self.add_meso_to_mun_data(year) meso_aggregated = self.aggregate_by_mesoregion(year, mun_with_meso) meso_exp_filename = self.get_file_name(self.transaction_type, year, 'MESO') meso_aggregated.to_csv(f'{self.MESO_FOLDER_PATH}{meso_exp_filename}', encoding='UTF-8') print(f'{year} - Mesoregions data saved') def download_data_and_add_meso_info(self): self.create_folder_if_not_exists(self.MUN_FOLDER_PATH) self.download_mun_data() final_df = pd.DataFrame() for year in (range(self.start_year, self.end_year+1)): mun_with_meso = self.add_meso_to_mun_data(year) final_df = final_df.append(mun_with_meso) return final_df if __name__ == '__main__': ExportsObject = ExportsByMesoregion(start_year=2020, end_year=2020, transaction_type='imports') ExportsObject.download_data_and_aggregate_by_meso()

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null

0

null

0

1

0

a5ca6ea7872c55e908f6afc4233961e95a90159a

1,366

py

Python

sendUAV/recevier.py

RobEn-AAST/AI-UAVC

732683fd5821d492b772cc5f966e86aed164a68c

[ "MIT" ]

16

2022-02-05T15:51:13.000Z

2022-02-05T17:38:54.000Z

sendUAV/recevier.py

RobEn-AAST/AI-UAVC

732683fd5821d492b772cc5f966e86aed164a68c

[ "MIT" ]

null

sendUAV/recevier.py

RobEn-AAST/AI-UAVC

732683fd5821d492b772cc5f966e86aed164a68c

[ "MIT" ]

null

from socket import socket, AF_INET, SOCK_STREAM, IPPROTO_TCP import struct import pickle class ServerSock(socket): def __init__(self, PORT): super().__init__(AF_INET, SOCK_STREAM, IPPROTO_TCP) self.bind(("", PORT)) self.listen() def getMessage(self): payload_size = struct.calcsize(">L") conn, _ = self.accept() conn.settimeout(5) while True: try: string = b"" while len(string) < payload_size: bits = conn.recv(4096) string += bits packed_msg_size = string[:payload_size] data = string[payload_size:] msg_size = struct.unpack(">L", packed_msg_size)[0] while len(data) < msg_size: bits = conn.recv(4096) data += bits frame_data = data[:msg_size] data = data[msg_size:] msg = pickle.loads(frame_data, fix_imports=True, encoding="bytes") # if msg start then get it's len from the header return msg except Exception: conn.close() return self.getMessage() if __name__ == "__main__": server = ServerSock(5500) while True: print(server.getMessage())

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null

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null

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a5cb7a30978758aaea2edade994cdb342894093c

21,620

py

Python

pedal/questions/loader.py

acbart/python-analysis

3cd2cc22d50a414ae6b62c74d2643be4742238d4

[ "MIT" ]

14

2019-08-22T03:40:23.000Z

2022-03-13T00:30:53.000Z

pedal/questions/loader.py

pedal-edu/pedal

3cd2cc22d50a414ae6b62c74d2643be4742238d4

[ "MIT" ]

74

2019-09-12T04:35:56.000Z

2022-01-26T19:21:32.000Z

pedal/questions/loader.py

acbart/python-analysis

3cd2cc22d50a414ae6b62c74d2643be4742238d4

[ "MIT" ]

2

2021-01-11T06:34:00.000Z

2021-07-21T12:48:07.000Z

""" instructions: blah blah blah settings: tifa: enabled: True unit test by function (bool): Whether to test each function entirely before moving onto the next one, or to first check that all functions have been defined, and then checking their parameters, etc. Defaults to True. show case details (bool): Whether to show the specific args/inputs that caused a test case to fail. rubric: functions: total: 100 definition: 10 signature: 10 cases: 80 global: variables: name: type: value: inputs: prints: # Sandbox, type checking functions: documentation: "any" or "google" coverage: 100% tests: int name: do_complicated_stuff arity: int signature: int, int -> float signature: int, int, list[int], (int->str), dict[str:list[int]] -> list[int] parameters: name: banana exactly: regex: includes: within: type: int cases: - arguments (list): 5, 4 inputs (list): returns (Any): equals: 27.3 is: is not: _1 name (str): Meaningful name for tracking purposes? Or possibly separate into label/id/code hint (str): Message to display to user prints: exactly: regex: startswith: endswith: plots: # Cait syntax: prevent: ___ + ___ # Override any of our default feedback messages messages: FUNCTION_NOT_DEFINED: "Oops you missed a function" """ from pedal.core.commands import set_success, give_partial from pedal.core.feedback_category import FeedbackCategory from pedal.questions.constants import TOOL_NAME from pedal.sandbox.commands import get_sandbox from pedal.utilities.comparisons import equality_test SETTING_SHOW_CASE_DETAILS = "show case details" DEFAULT_SETTINGS = { SETTING_SHOW_CASE_DETAILS: True } EXAMPLE_DATA = { 'functions': [{ 'name': 'do_complicated_stuff', 'signature': 'int, int, [int] -> list[int]', 'cases': [ {'arguments': "5, 4, 3", 'returns': "12"}, ] }] } class FeedbackException(Exception): """ """ def __init__(self, category, label, **fields): self.category = category self.label = label self.fields = fields def as_message(self): """ Returns: """ return FEEDBACK_MESSAGES[self.category][self.label].format(**self.fields) def check_function_defined(function, function_definitions, settings=None): """ Args: function: function_definitions: settings: Returns: """ # 1. Is the function defined syntactically? # 1.1. With the right name? function_name = function['name'] if function_name not in function_definitions: raise FeedbackException(FeedbackCategory.SPECIFICATION, 'missing_function', function_name=function_name) definition = function_definitions[function_name] return definition def check_function_signature(function, definition, settings=None): """ Args: function: definition: settings: Returns: """ function_name = function['name'] # 1.2. With the right parameters and return type? # 1.2.1 'arity' style - simply checks number of parameters if 'arity' in function or 'parameters' in function: expected_arity = function['arity'] if 'arity' in function else len(function['parameters']) actual_arity = len(definition.args.args) if actual_arity < expected_arity: raise FeedbackException(FeedbackCategory.SPECIFICATION, 'insufficient_args', function_name=function_name, expected_arity=expected_arity, actual_arity=actual_arity) elif actual_arity > expected_arity: raise FeedbackException(FeedbackCategory.SPECIFICATION, 'excessive_args', function_name=function_name, expected_arity=expected_arity, actual_arity=actual_arity) # 1.2.2 'parameters' style - checks each parameter's name and type if 'parameters' in function: expected_parameters = function['parameters'] actual_parameters = definition.args.args for expected_parameter, actual_parameter in zip(expected_parameters, actual_parameters): actual_parameter_name = get_arg_name(actual_parameter) if 'name' in expected_parameter: if actual_parameter_name != expected_parameter['name']: raise FeedbackException(FeedbackCategory.SPECIFICATION, 'wrong_parameter_name', function_name=function_name, expected_parameter_name=expected_parameter['name'], actual_parameter_name=actual_parameter_name ) if 'type' in expected_parameter: actual_parameter_type = parse_type(actual_parameter) # TODO: Handle non-string expected_parameter types (dict) expected_parameter_type = parse_type_value(expected_parameter['type'], True) if not type_check(expected_parameter_type, actual_parameter_type): raise FeedbackException(FeedbackCategory.SPECIFICATION, 'wrong_parameter_type', function_name=function_name, parameter_name=actual_parameter_name, expected_parameter_type=expected_parameter_type, actual_parameter_type=actual_parameter_type) # 1.2.3. 'returns' style - checks the return type explicitly if 'returns' in function: expected_returns = parse_type_value(function['returns'], True) actual_returns = parse_type(definition.returns) if actual_returns != "None": if not type_check(expected_returns, actual_returns): raise FeedbackException(FeedbackCategory.SPECIFICATION, "wrong_returns", function_name=function_name, expected_returns=expected_returns, actual_returns=actual_returns) elif expected_returns != "None": raise FeedbackException(FeedbackCategory.SPECIFICATION, "missing_returns", function_name=function_name, expected_returns=expected_returns) # 1.2.4. 'signature' style - shortcut for specifying the types if 'signature' in function: expected_signature = function['signature'] actual_returns = parse_type(definition.returns) actual_parameters = ", ".join(parse_type(actual_parameter.annotation) for actual_parameter in definition.args.args) actual_signature = "{} -> {}".format(actual_parameters, actual_returns) if not type_check(expected_signature, actual_signature): raise FeedbackException(FeedbackCategory.SPECIFICATION, "wrong_signature", function_name=function_name, expected_signature=expected_signature, actual_signature=actual_signature) # All good here! return True def check_function_value(function, values, settings): """ 2. Does the function exist in the data? :param function: :param values: :param settings: :return: """ function_name = function['name'] # 2.1. Does the name exist in the values? if function_name not in values: raise FeedbackException(FeedbackCategory.SPECIFICATION, "function_not_available", function_name=function_name) function_value = values[function_name] # 2.2. Is the name bound to a callable? if not callable(function_value): raise FeedbackException(FeedbackCategory.SPECIFICATION, "name_is_not_function", function_name=function_name) # All good here return function_value class TestCase: """ """ CASE_COUNT = 0 def __init__(self, function_name, case_name): self.function_name = function_name if case_name is None: self.case_name = str(TestCase.CASE_COUNT) TestCase.CASE_COUNT += 1 else: self.case_name = case_name self.arguments, self.has_arguments = [], False self.inputs, self.has_inputs = [], False self.error, self.has_error = None, False self.message, self.has_message = None, False self.expected_prints, self.has_expected_prints = None, False self.expected_returns, self.has_expected_returns = None, False self.prints = [] self.returns = None self.success = True def add_message(self, message): """ Args: message: """ self.message = message self.has_message = True def add_inputs(self, inputs): """ Args: inputs: """ if not isinstance(inputs, list): inputs = [inputs] self.inputs = inputs self.has_inputs = True def add_arguments(self, arguments): """ Args: arguments: """ if not isinstance(arguments, list): arguments = [arguments] self.arguments = arguments self.has_arguments = True def add_error(self, error): """ Args: error: """ self.error = error self.has_error = True self.success = False def add_expected_prints(self, prints): """ Args: prints: """ self.expected_prints = prints self.has_expected_prints = True def add_expected_returns(self, returns): """ Args: returns: """ self.expected_returns = returns self.has_expected_returns = True def add_prints_returns(self, prints, returns): """ Args: prints: returns: """ self.prints = prints self.returns = returns def fail(self): """ """ self.success = False def check_case(function, case, student_function): """ :param function: :param case: :param student_function: :return: status, arg, input, error, output, return, message """ function_name = function['name'] test_case = TestCase(function_name, case.get('name')) # Get callable sandbox = get_sandbox(MAIN_REPORT) sandbox.clear_output() # Potential bonus message if 'message' in case: test_case.add_message(case['message']) # Queue up the the inputs if 'inputs' in case: test_case.add_inputs(case['inputs']) sandbox.set_input(test_case.inputs) else: sandbox.clear_input() # Pass in the arguments and call the function if 'arguments' in case: test_case.add_arguments(case['arguments']) result = sandbox.call(function_name, *test_case.arguments) # Store actual values test_case.add_prints_returns(sandbox.output, result) # Check for errors if sandbox.exception: test_case.add_error(sandbox.exception) # 4. Check out the output if 'prints' in case: test_case.add_expected_prints(case['prints']) if not output_test(sandbox.output, case['prints'], False, .0001): test_case.fail() # 5. Check the return value if 'returns' in case: test_case.add_expected_returns(case['returns']) if not equality_test(result, case['returns'], True, .0001): test_case.fail() # TODO: Check the plots # Return results return test_case # TODO: blockpy-feedback-unit => pedal-test-cases in BlockPy Client TEST_TABLE_TEMPLATE = """<table class='pedal-test-cases table table-sm table-bordered table-hover'> <tr class='table-active'> <th></th> <th>Arguments</th> <th>Expected</th> <th>Returned</th> </tr> {body} </table>""" TEST_TABLE_FOOTER = "</table>" TEST_TABLE_ROW_HEADER = "<tr class='table-active'>" TEST_TABLE_ROW_NORMAL = "<tr>" TEST_TABLE_ROW_FOOTER = "</tr>" TEST_TABLE_ROW_INFO = "<tr class='table-info'>" GREEN_CHECK = " <td class='green-check-mark'>✔</td>" RED_X = " <td>❌</td>" CODE_CELL = " <td><code>{}</code></td>" COLUMN_TITLES = ["", "Arguments", "Inputs", "Errors", "Expected", "Expected", "Returned", "Printed"] def make_table(cases): """ Args: cases: Returns: """ body = [] for case in cases: body.append(" <tr>") body.append(GREEN_CHECK if case.success else RED_X) body.append(CODE_CELL.format(", ".join(repr(arg) for arg in case.arguments))) if case.has_error: body.append(" <td colspan='2'>Error: <code>{}</code></td>".format(str(case.error))) else: body.append(CODE_CELL.format(repr(case.expected_returns))) body.append(CODE_CELL.format(repr(case.returns))) if not case.success and case.has_message: body.append(" </tr><tr><td colspan='4'>{}</td>".format(case.message)) body.append(" </tr>") body = "\n".join(body) return TEST_TABLE_TEMPLATE.format(body=body) #if ((any(args) and any(inputs)) or # (any(expected_outputs) and any(expected_returns)) or # (any(actual_outputs) and any(actual_returns))): # # Complex cells # pass #else: # Simple table # Make header # row_mask = [True, any(args), any(inputs), False, # any("returns" in reason for reason in reasons), # any("prints" in reason for reason in reasons), # any("returns" in reason for reason in reasons), # any("prints" in reason for reason in reasons)] # header_cells = "".join("<th>{}</th>".format(title) for use, title in zip(row_mask, COLUMN_TITLES) if use) # body = [TEST_TABLE_ROW_HEADER.format(header_cells)] # for case in zip( # statuses, args, inputs, errors, actual_outputs, actual_returns, # expected_outputs, expected_returns): # status, case = case[0], case[1:] # print(row_mask[1:], case) # def make_code(values): # if values == None: # return "<code>None</code>" # elif isinstance(values, int): # return "<code>{!r}</code>".format(values) # else: # return ", ".join("<code>{}</code>".format(repr(value)) for value in values) # body.append( # TEST_TABLE_ROW_NORMAL+ # (GREEN_CHECK if case[0] else RED_X)+ # "\n".join(" <td>{}</td>".format(make_code(values)) # for use, values in zip(row_mask[1:], case) if use)+ # "</tr>\n" # ) # # Make each row # table = "{}\n{}\n{}".format(TEST_TABLE_HEADER, "\n ".join(body), TEST_TABLE_FOOTER) # return table def check_cases(function, student_function, settings): """ Args: function: student_function: settings: """ function_name = function['name'] if 'cases' in function: cases = function['cases'] test_cases = [check_case(function, case, student_function) for case in cases] success_cases = sum(test.success for test in test_cases) if success_cases < len(cases): if settings[SETTING_SHOW_CASE_DETAILS]: table = make_table(test_cases) raise FeedbackException(FeedbackCategory.SPECIFICATION, "failed_test_cases", function_name=function_name, cases_count=len(cases), failure_count=len(cases)-success_cases, table=table) else: raise FeedbackException(FeedbackCategory.SPECIFICATION, "failed_test_cases_count", function_name=function_name, cases_count=len(cases), failure_count=len(cases) - success_cases) def get_arg_name(node): """ Args: node: Returns: """ name = node.id if name is None: return node.arg else: return name def load_question(data): """ :param data: :return: """ ast = parse_program() student_data = commands.get_student_data() # Check that there aren't any invalid syntactical structures # Get all of the function ASTs in a dictionary function_definitions = {definition._name: definition for definition in ast.find_all("FunctionDef")} settings = DEFAULT_SETTINGS.copy() settings.update(data.get('settings', {})) rubric = settings.get('rubric', {}) function_points = 0 if 'functions' in data: function_rubric = rubric.get('functions', {}) successes = [] for function in data['functions']: success = False try: definition = check_function_defined(function, function_definitions, settings) function_points += function_rubric.get('definition', 10) check_function_signature(function, definition, settings) function_points += function_rubric.get('signature', 10) student_function = check_function_value(function, student_data, settings) function_points += function_rubric.get('value', 0) except FeedbackException as fe: yield fe.as_message(), fe.label else: try: check_cases(function, student_function, settings) except FeedbackException as fe: success_ratio = (1.0 - fe.fields['failure_count'] / fe.fields['cases_count']) function_points += function_rubric.get('cases', 80*success_ratio) yield fe.as_message(), fe.label else: function_points += function_rubric.get('cases', 80) success = True successes.append(success) function_points /= len(data['functions']) if all(successes): set_success() else: give_partial(function_points, tool=TOOL_NAME, justification="Passed some but not all unit tests") def check_question(data): """ Args: data: """ results = list(load_question(data)) if results: message, label = results[0] gently(message, label=label) def check_pool(questions): """ Args: questions: """ pass def load_file(filename): """ Args: filename: """ pass FEEDBACK_MESSAGES = { FeedbackCategory.SPECIFICATION: { "missing_function": "No function named `{function_name}` was found.", "insufficient_args": ("The function named `{function_name}` " "has fewer parameters ({actual_arity}) " "than expected ({expected_arity})."), "excessive_args": ("The function named `{function_name}` " "has more parameters ({actual_arity}) " "than expected ({expected_arity})."), # TODO: missing_parameter that checks if parameter name exists, but is in the wrong place "wrong_parameter_name": ("Error in definition of `{function_name}`. " "Expected a parameter named `{expected_parameter_name}`, " "instead found `{actual_parameter_name}`."), "wrong_parameter_type": ("Error in definition of function `{function_name}` " "parameter `{parameter_name}`. Expected `{expected_parameter_type}`, " "instead found `{actual_parameter_type}`."), "missing_returns": ("Error in definition of function `{function_name}` return type. " "Expected `{expected_returns}`, but there was no return type specified."), "wrong_returns": ("Error in definition of function `{function_name}` return type. " "Expected `{expected_returns}`, instead found `{actual_returns}`."), "wrong_signature": ("Error in definition of function `{function_name}` signature. " "Expected `{expected_signature}`, instead found `{actual_signature}`."), "name_is_not_function": "You defined `{function_name}`, but did not define it as a function.", "function_not_available": ("You defined `{function_name}` somewhere in your code, " "but it was not available in the top-level scope to be called. " "Perhaps you defined it inside another function or scope?"), "failed_test_cases": ("I ran your function <code>{function_name}</code> on my own test cases. " "It failed {failure_count}/{cases_count} of my tests.\n{table}"), "failed_test_cases_count": ("I ran your function <code>{function_name}</code> on my own test cases. " "It failed {failure_count}/{cases_count} of my tests."), } }

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null

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1

0

a5cc7ebfb0f671bb1d1aeac6021cc68675439a1a

8,732

py

Python

VM/fetchLoop.py

djtech-dev/PyVM

1edda436ce7073d0cecbf16f5cab2509895d953c

[ "MIT" ]

75

2017-09-22T22:36:13.000Z

2022-03-20T16:18:27.000Z

VM/fetchLoop.py

djtech-dev/PyVM

1edda436ce7073d0cecbf16f5cab2509895d953c

[ "MIT" ]

7

2019-05-10T19:15:08.000Z

2021-08-24T16:03:34.000Z

VM/fetchLoop.py

djtech-dev/PyVM

1edda436ce7073d0cecbf16f5cab2509895d953c

[ "MIT" ]

14

2018-07-02T02:49:46.000Z

2022-02-22T15:24:47.000Z

import enum from .ELF import ELF32, enums from .util import SegmentRegs, MissingOpcodeError from .CPU import CPU32 import logging logger = logging.getLogger(__name__) class FetchLoopMixin: _attrs_ = 'eip', 'mem', 'reg.ebx', 'fmt', 'instr', 'sizes', 'default_mode' def execute_opcode(self: CPU32) -> None: self.eip += 1 off = 1 if self.opcode == 0x0F: op = self.mem.get_eip(self.eip, 1) self.eip += 1 self.opcode = (self.opcode << 8) | op off += 1 if __debug__: logger.debug(self.fmt, self.eip - off, self.opcode) try: impls = self.instr[self.opcode] except KeyError: ... # could not find opcode else: for impl in impls: if impl(): return # opcode executed # could not find suitable implementation # read one more byte op = self.mem.get_eip(self.eip, 1) self.eip += 1 self.opcode = (self.opcode << 8) | op try: impls = self.instr[self.opcode] except KeyError: raise MissingOpcodeError(f'Opcode {self.opcode:x} is not recognized yet (at 0x{self.eip - off - 1:08x})') else: for impl in impls: if impl(): return # opcode executed # could not find suitable implementation raise NotImplementedError(f'No suitable implementation found for opcode {self.opcode:x} (@0x{self.eip - off - 1:02x})') def run(self: CPU32) -> int: """ Implements the basic CPU instruction cycle (https://en.wikipedia.org/wiki/Instruction_cycle) :param self: passed implicitly :param offset: location of the first opcode :return: None """ # opcode perfixes pref_segments = { 0x2E: SegmentRegs.CS, 0x36: SegmentRegs.SS, 0x3E: SegmentRegs.DS, 0x26: SegmentRegs.ES, 0x64: SegmentRegs.FS, 0x65: SegmentRegs.GS } pref_op_size_override = {0x66, 0x67} pref_lock = {0xf0} rep = {0xf3} prefixes = set(pref_segments) | pref_op_size_override | pref_lock | rep self.running = True while self.running and self.eip + 1 < self.mem.size: overrides = [] self.opcode = self.mem.get(self.eip, 1) while self.opcode in prefixes: overrides.append(self.opcode) self.eip += 1 self.opcode = self.mem.get(self.eip, 1) # apply overrides size_override_active = False for ov in overrides: if ov == 0x66: if not size_override_active: self.current_mode = not self.current_mode size_override_active = True old_operand_size = self.operand_size self.operand_size = self.sizes[self.current_mode] logger.debug( 'Operand size override: %d -> %d', old_operand_size, self.operand_size ) elif ov == 0x67: if not size_override_active: self.current_mode = not self.current_mode size_override_active = True old_address_size = self.address_size self.address_size = self.sizes[self.current_mode] logger.debug( 'Address size override: %d -> %d', old_address_size, self.address_size ) elif ov in pref_segments: is_special = ov >> 6 if is_special: sreg_number = 4 + (ov & 1) # FS or GS else: sreg_number = (ov >> 3) & 0b11 self.mem.segment_override = sreg_number logger.debug('Segment override: %s', self.mem.segment_override) elif ov == 0xf0: # LOCK prefix logger.debug('LOCK prefix') # do nothing; all operations are atomic anyway. Right? elif ov == 0xf3: # REP prefix self.opcode = ov self.eip -= 1 # repeat the previous opcode self.execute_opcode() # undo all overrides for ov in overrides: if ov == 0x66: self.current_mode = self.default_mode self.operand_size = self.sizes[self.current_mode] elif ov == 0x67: self.current_mode = self.default_mode self.address_size = self.sizes[self.current_mode] elif ov in pref_segments: self.mem.segment_override = SegmentRegs.DS return self.reg.eax class ExecutionStrategy(enum.Enum): BYTES = 1 FLAT = 2 ELF = 3 class ExecutionMixin(FetchLoopMixin): def execute(self, *args, **kwargs): return NotImplemented class ExecuteBytes(ExecutionMixin): _attrs_ = 'eip', 'mem', 'code_segment_end' _funcs_ = 'run', def execute(self: CPU32, data: bytes, offset=0): l = len(data) self.mem.set_bytes(offset, l, data) self.eip = offset self.code_segment_end = self.eip + l - 1 self.mem.program_break = self.code_segment_end return self.run() class ExecuteFlat(ExecutionMixin): _attrs_ = 'eip', 'mem', 'code_segment_end' _funcs_ = 'run', def execute(self: CPU32, fname: str, offset=0): with open(fname, 'rb') as f: data = f.read() l = len(data) self.mem.set_bytes(offset, l, data) self.eip = offset self.code_segment_end = self.eip + l - 1 self.mem.program_break = self.code_segment_end return self.run() class ExecuteELF(ExecutionMixin): _attrs_ = 'eip', 'mem', 'reg', 'code_segment_end' _funcs_ = 'run', 'stack_init', 'stack_push' def execute(self: CPU32, fname: str, args=()): with ELF32(fname) as elf: if elf.hdr.e_type != enums.e_type.ET_EXEC: raise ValueError(f'ELF file {elf.fname!r} is not executable (type: {elf.hdr.e_type})') max_memsz = max( phdr.p_vaddr + phdr.p_memsz for phdr in elf.phdrs if phdr.p_type == enums.p_type.PT_LOAD ) if self.mem.size < max_memsz * 2: self.mem.size = max_memsz * 2 self.stack_init() for phdr in elf.phdrs: if phdr.p_type not in (enums.p_type.PT_LOAD, enums.p_type.PT_GNU_EH_FRAME): continue logger.info(f'LOAD {phdr.p_memsz:10,d} bytes at address 0x{phdr.p_vaddr:09_x}') elf.file.seek(phdr.p_offset) data = elf.file.read(phdr.p_filesz) self.mem.set_bytes(phdr.p_vaddr, len(data), data) self.mem.set_bytes(phdr.p_vaddr + phdr.p_filesz, phdr.p_memsz - phdr.p_filesz, bytearray(phdr.p_memsz - phdr.p_filesz)) self.eip = elf.hdr.e_entry self.code_segment_end = self.eip + max_memsz - 1 self.mem.program_break = self.code_segment_end # INITIALIZE STACK LAYOUT: # http://asm.sourceforge.net/articles/startup.html # https://lwn.net/Articles/631631/ environment = ["USER=ForceBru"] args = [fname] + list(args) arg_addresses, env_addresses = [], [] for arg in args: arg = arg.encode() + b'\0' l = len(arg) self.mem.set_bytes(self.reg.esp - l, l, arg) self.reg.esp -= l arg_addresses.append(self.reg.esp) for env in environment: env = env.encode() + b'\0' l = len(env) self.mem.set_bytes(self.reg.esp - l, l, env) self.reg.esp -= l env_addresses.append(self.reg.esp) # auxiliary vector (just NULL) self.stack_push(0) # environment (array of pointers + NULL) self.stack_push(0) for addr in env_addresses[::-1]: self.stack_push(addr) # argv self.stack_push(0) # end of argv for addr in arg_addresses[::-1]: self.stack_push(addr) # argc self.stack_push(len(args)) logger.info(f'EXEC at 0x{self.eip:09_x}') # logger.debug(f'Stack start at 0x{self.reg.esp:08x}') # logger.debug(f'Stack end at 0x{self.reg.ebp:08x}') return self.run()

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a5cef8d918f7406a1dd78059cb13a600f918323a

5,897

py

Python

mlpy/regression/logistic_regression.py

SNUDerek/MLPy

0d47a8ef8522a663716cda6a831855e6482069ba

[ "MIT" ]

1

2019-05-10T10:39:12.000Z

mlpy/regression/logistic_regression.py

SNUDerek/MLPy

0d47a8ef8522a663716cda6a831855e6482069ba

[ "MIT" ]

null

mlpy/regression/logistic_regression.py

SNUDerek/MLPy

0d47a8ef8522a663716cda6a831855e6482069ba

[ "MIT" ]

null

import numpy as np from ..tools import batchGenerator # LOGISTIC REGRESSION # for (binary) categorical data class LogisticRegression(): ''' Logistic regression with Gradient Descent binary Logistic regression Parameters ---------- epochs : int maximum epochs of gradient descent lr : float learning rate lmb : float (L2) regularization parameter lambda sgd : int batch size for stochastic gradient descent (0 = gradient descent) tol : float tolerance for convergence weights : array weights (coefficients) of linear model Attributes ------- ''' def __init__(self, epochs=1000, intercept=False, lmb=0.0, lr=0.01, sgd=0, tol=1e-5): self.epochs = epochs self.intercept = intercept self.lmb=lmb self.lr = lr self.sgd = sgd self.tol = tol self.weights = np.array([]) self.costs = [] # internal function for sigmoid def _sigmoid(self, estimates): sigmoid = 1 / (1 + np.exp(-estimates)) return sigmoid # internal function for making hypothesis and getting cost def _getestimate(self, x_data, y_data, weights): # get hypothesis 'scores' (features by weights) scores = x_data.dot(weights).flatten() # sigmoid these scores for predictions (0~1) y_hat = self._sigmoid(scores) # get the difference between the trues and the hypothesis difference = y_data.flatten() - y_hat # calculate cost function J (log-likelihood) # loglik = sum y_i theta.T x_i - log( 1 + e^b.T x_i ) nloglik = -np.sum(y_data*scores - np.log(1 + np.exp(scores))) return y_hat, difference, nloglik # fit ("train") the function to the training data # inputs : x and y data as np.arrays (x is array of x-dim arrays where x = features) # params : verbose : Boolean - whether to print out detailed information # outputs : none def fit(self, x_data, y_data, verbose=False, print_iters=100): # STEP 1: ADD X_0 TERM FOR BIAS (IF INTERCEPT==TRUE) # add an 'x0' = 1.0 to our x data so we can treat intercept as a weight # use numpy.hstack (horizontal stack) to add a column of ones: if self.intercept: x_data = np.hstack((np.ones((x_data.shape[0], 1)), x_data)) # STEP 2: INIT WEIGHT COEFFICIENTS # one weight per feature (+ intercept) # you can init the weights randomly: # weights = np.random.randn(x_data.shape[1]) # or you can use zeroes with np.zeros(): weights = np.zeros(x_data.shape[1]) # STEP 3: INIT REGULARIZATION TERM LAMBDA # make as array with bias = 0 so don't regularize bias # then we can element-wise multiply with weights # this is the second term in the ( 1 - lambda/m ) lmbda = np.array([self.lmb/x_data.shape[0] for i in range(x_data.shape[1])]) if self.intercept: lmbda[0] = 0.0 iters = 0 # choose between iterations of sgd and epochs if self.sgd==0: maxiters = self.epochs else: maxiters = self.epochs * int(len(y_data)/self.sgd) minibatch = batchGenerator(x_data, y_data, self.sgd) for epoch in range(maxiters): # make an estimate, calculate the difference and the cost # gradient_ll = X.T(y - y_hat) # GRADIENT DESCENT: # get gradient over ~all~ training instances each iteration if self.sgd==0: y_hat, difference, cost = self._getestimate(x_data, y_data, weights) gradient = -np.dot(x_data.T, difference) # STOCHASTIC (minibatch) GRADIENT DESCENT # get gradient over random minibatch each iteration # for "true" sgd, this should be sgd=1 # though minibatches of power of 2 are more efficient (2, 4, 8, 16, 32, etc) else: x_batch, y_batch = next(minibatch) y_hat, difference, cost = self._getestimate(x_batch, y_batch, weights) gradient = -np.dot(x_data.T, difference) # get new predicted weights by stepping "backwards' along gradient # use lambda parameter for regularization (calculated above) new_weights = (weights - lmbda) - gradient * self.lr # check stopping condition if np.sum(abs(new_weights - weights)) < self.tol: if verbose: print("converged after {0} iterations".format(iters)) break # update weight values, save cost weights = new_weights self.costs.append(cost) iters += 1 # print diagnostics if verbose and iters % print_iters == 0: print("iteration {0}: cost: {1}".format(iters, cost)) # update final weights self.weights = weights return self.costs # predict probas on the test data # inputs : x data as np.array # outputs : y probabilities as list def predict_proba(self, x_data): # STEP 1: ADD X_0 TERM FOR BIAS (IF INTERCEPT==TRUE) if self.intercept: x_data = np.hstack((np.ones((x_data.shape[0], 1)), x_data)) # STEP 2: PREDICT USING THE y_hat EQN scores = x_data.dot(self.weights).flatten() y_hat = self._sigmoid(scores) return y_hat # predict on the test data # inputs : x data as np.array # outputs : y preds as list def predict(self, x_data): y_hat = self.predict_proba(x_data) # ROUND TO 0, 1 preds = [] for p in y_hat: if p > 0.5: preds.append(1.0) else: preds.append(0.0) return preds

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null

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1

0

a5d2df25221764ec5395b74a6c3cb30a216ee3ff

12,269

py

Python

server.py

satriabw/Tugas_Sisdis

b1e152f35834e52806071b9b1424b114dce65148

[ "MIT" ]

null

server.py

satriabw/Tugas_Sisdis

b1e152f35834e52806071b9b1424b114dce65148

[ "MIT" ]

null

server.py

satriabw/Tugas_Sisdis

b1e152f35834e52806071b9b1424b114dce65148

[ "MIT" ]

null

# coding: utf-8 from random import randint from urllib.parse import parse_qs import socket import sys import json import traceback import os import base64 import yaml import datetime import requests import re class Route: def __init__(self): self._route = [] def route(self, method, path, handler): self._route.append({"method": method, "path": path, "handler": handler}) def dispatch(self, path, method): pattern = re.compile(r'/api/plusone/[0-9]*[0-9]$') match = re.match(pattern, path) if match != None: path = "/api/plusone/<:digit>" for item in self._route: if item["path"] == path and item["method"] == method: return item["handler"] return None def findPath(self, path): for item in self._route: if item["path"] == path: return True return False route = Route() class HTTPRequest: def __init__(self, request): self._raw_request = request self._build_header() self._build_body() def _build_header(self): raw_head = self._split_request()[0] head = raw_head.split("\n") # Get method, path, and http version temp = head[0].split(" ") self.header = { "method" : temp[0], "path" : temp[1], "http_version" : temp[2], } # Get Content-type and Content-length for info in head: if "Content-Type" in info: self.header["content_type"] = info.split(" ")[1] continue if "Content-Length" in info: self.header["content_length"] = info.split(" ")[1] def _build_body(self): self._raw_body = self._split_request()[1] def _split_request(self): return self._raw_request.decode( "utf-8").replace("\r", "").split("\n\n") def body_json(self): return json.loads('[{}]'.format(self._raw_body)) def body_query(self, query): return parse_qs(self._raw_body)[query] def validation(func): def func_wrapper(conn, request): if (request.header["http_version"] not in "HTTP/1.0") and (request.header["http_version"] not in "HTTP/1.1"): badRequest(conn, request) else: func(conn, request) return func_wrapper @validation def getRoot(conn, request): debugger = "Hooray getRoot end point is hitted\n" print(debugger) status = "302 Found" loc = "/hello-world" c_type = "text/plain; charset=UTF-8" data = '302 Found: Location: /hello-world' msgSuccess = renderMessage(status, str(21+len(loc)), loc, None, c_type, data) writeResponse(conn, msgSuccess) @validation def getHelloWorld(conn, request): with open("./hello-world.html", "r") as f: html = f.read() data = html.replace("__HELLO__", "World") status = "200 OK" c_type = "text/html" msgSuccess = renderMessage(status, str(len(data)), None, None, c_type, data) writeResponse(conn, msgSuccess) @validation def getStyle(conn, request): with open("./style.css", "r") as f: css = f.read() status = "200 OK" c_type = "text/css" msgSuccess = renderMessage(status, str(len(css)), None, None, c_type, css) writeResponse(conn, msgSuccess) @validation def getBackground(conn, request): with open("./background.jpg", "rb") as f: img = f.read() status = "200 OK" c_type = "image/jpeg" enc = "base64" msgSuccess = renderMessage(status, str(len(img)), None, enc, c_type, "") msgSuccess = msgSuccess + img writeResponse(conn, msgSuccess) @validation def getSpesifikasi(conn, request): with open("./spesifikasi.yaml", "r") as f: yaml = f.read() status = "200 OK" c_type = "text/plain; charset=UTF-8" msgSuccess = renderMessage(status, str(len(yaml)), None, None, c_type, yaml) writeResponse(conn, msgSuccess) @validation def getInfo(conn, request): query = request.header["path"].split("?") data = "No Data" try: tipe = exctractUrl(query[1], "type") if tipe == "time": data = "{}".format(datetime.datetime.now()) elif tipe == "random": data = "{}".format(randint(111111,999999)) except (IndexError, ValueError) as e: pass status = "200 OK" c_type = "text/plain; charset=UTF-8" msgSuccess = renderMessage(status, str(len(data)), None, None, c_type, data) writeResponse(conn, msgSuccess) def notFound(conn, request): if "/api" in request.header["path"]: notFoundJson(conn) status = "404 Not Found" c_type = "text/plain; charset=UTF-8" msgErr = renderMessage(status, str(len(status)), None, None, c_type, status) writeResponse(conn, msgErr) def notImplemented(conn, request): status = "501 Not Implemented" c_type = "text/plain; charset=UTF-8" msgErr = renderMessage(status, str(len(status)), None, None, c_type, status) writeResponse(conn, msgErr) def badRequest(conn, request): if "/api" in request.header["path"]: badRequestJson(conn, "Please use proper http version") status = "400 Bad Request" c_type = "text/plain; charset=UTF-8" msgErr = renderMessage(status, str(len(status)), None, None, c_type, status) writeResponse(conn, msgErr) @validation def postHelloWorld(conn, request): debugger = "Hooray postHelloWorld end point is hitted\n" print(debugger) try: if request.header["content_type"] == "application/x-www-form-urlencoded": name = request.body_query("name")[0] with open("./hello-world.html", "r") as f: html = f.read() data = html.replace("__HELLO__", str(name)) status = "200 OK" c_type = "text/html; charset=UTF-8" msgSuccess = renderMessage(status, str(len(data)), None, None, c_type, data) writeResponse(conn, msgSuccess) else: raise ValueError("Cannot parse the request") except (IndexError, KeyError, ValueError) as e: badRequest(conn, request) def validateHelloAPI(func): def func_wrapper(conn, request): if (request.header["http_version"] not in "HTTP/1.0") and (request.header["http_version"] not in "HTTP/1.1"): badRequestJson(conn, "Please use proper http version") elif request.header["method"] != "POST": methodNotAllowedJson(conn, "Method is not allowed, please use POST method") elif request.header["content_type"] != "application/json": methodNotAllowedJson(conn, "please use application/json") else: func(conn, request) return func_wrapper @validateHelloAPI def helloAPI(conn, request): req = requests.get(url='http://172.22.0.222:5000') data = req.json() current_visit = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%fZ") try: name = request.body_json()[0]["request"] count = getCounter() + 1 writeCounter(count) res = "Good {}, {}".format(data["state"], name) json_http_ok(conn, count=count, currentvisit=current_visit, response=res) except KeyError: badRequestJson(conn, "'request' is a required property") @validation def plusOneAPI(conn, request): val = int(request.header["path"].split("/")[-1]) json_http_ok(conn, plusoneret=val+1) def getTime(t_raw): t = datetime.datetime.strptime(t_raw, "%Y-%m-%d %H:%M:%S") return t.strftime("%Y-%m-%dT%H:%M:%S.%fZ") def getCounter(): with open('counter.json', 'r') as json_file: data = json.load(json_file) return data["count"] def writeCounter(c): count = {"count": c} with open('counter.json', 'w') as json_file: data = json.dump(count, json_file) def getApiVersion(): with open('./spesifikasi.yaml', 'r') as f: doc = yaml.load(f) return doc["info"]["version"] def notFoundJson(conn): detail = "The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again." status = "404" title = "Not Found" json_http_error(conn, detail, status, title) def methodNotAllowedJson(conn, d): detail = d status = "405" title = "Method Not Allowed" json_http_error(conn, detail, status, title) def badRequestJson(conn, d): detail = d status = "400" title = "Bad Request" json_http_error(conn, detail, status, title) def json_http_ok(conn, **kwargs): res_dict = {'apiversion': getApiVersion()} for key, value in kwargs.items(): res_dict[key] = value data = json.dumps(res_dict) # Build Response status = "200 OK" c_type = "application/json; charset=UTF-8" msgErr = renderMessage(status, str(len(data)), None, None, c_type, data) writeResponse(conn, msgErr) def json_http_error(conn, detail, status, title): res_dict = {'detail': detail, 'status': status, 'title': title} data = json.dumps(res_dict) status = "{} {}".format(status, title) c_type = "application/json; charset=UTF-8" msgErr = renderMessage(status, str(len(data)), None, None, c_type, data) writeResponse(conn, msgErr) def main(): # HOST = socket.gethostbyname(socket.gethostname()) HOST = "0.0.0.0" PORT = int(sys.argv[1]) #Get method route.route("GET", "/", getRoot) route.route("GET", "/hello-world", getHelloWorld) route.route("GET", "/style", getStyle) route.route("GET", "/background", getBackground) route.route("GET", "/info", getInfo) route.route("GET", "/api/hello", helloAPI) route.route("GET", "/api/plusone/<:digit>", plusOneAPI) route.route("GET", "/api/spesifikasi.yaml", getSpesifikasi) #Post Method route.route("POST", "/api/hello", helloAPI) route.route("POST", "/hello-world", postHelloWorld) # PUT route.route("PUT", "/api/hello", helloAPI) #PATCH route.route("PATCH", "/api/hello", helloAPI) #DELETE route.route("DELETE", "/api/hello", helloAPI) #HEAD route.route("HEAD", "/api/hello", helloAPI) # Serve the connection connect(HOST, PORT) def handler(conn, req): try: debugger = "=== Got Request ===\n{}\n===Got Header====\n{}\n".format(req._raw_request, req.header) print(debugger) route.dispatch(cleanURL(req.header["path"]), req.header["method"])(conn, req) except TypeError as e: print(traceback.format_exc()) if route.findPath(cleanURL(req.header["path"])): notImplemented(conn, req) return notFound(conn, req) return def cleanURL(url): return url.split("?")[0] def writeResponse(conn, message): debugger = "=== Got Message ===\n{}\n".format(message) print(debugger) conn.sendall(message) def renderMessage(stat, c_length, location, encoding, c_type, data): msg = "" if stat != None: status = "HTTP/1.1 {}\r\n".format(stat) msg = msg + status msg = msg + "Connection: close\r\n" if c_length != None: content_length = "Content-Length: {}\r\n".format(c_length) msg = msg + content_length if location != None: loc = "Location: {}\r\n".format(location) msg = msg + loc if encoding != None: enc = "Content-Transfer-Encoding: {}\r\n".format(encoding) msg = msg + enc if c_type != None: content_type = "Content-Type: {}\r\n".format(c_type) msg = msg + content_type if data != None: msg = msg + "\r\n" + data return bytes(msg, "utf-8") def exctractUrl(url, query): return parse_qs(url)[query][0] def connect(host, port): s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.bind((host, port)) s.listen() while True: try: conn, addr = s.accept() data = conn.recv(1024) req = HTTPRequest(data) handler(conn, req) conn.shutdown(socket.SHUT_WR) conn.close() except Exception: print(traceback.format_exc()) continue main()

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0

a5d9baaf2337daeafdfe9b9a22db73d38a684f6f

576

py

Python

functions-and-keda/src/python-function-publisher/QueueTrigger/__init__.py

emctl/samples

569f81035a6c214d4cda3687173e24003f17f95e

[ "MIT" ]

3

2021-11-16T11:24:27.000Z

2021-11-21T17:11:24.000Z

functions-and-keda/src/python-function-publisher/QueueTrigger/__init__.py

emctl/samples

569f81035a6c214d4cda3687173e24003f17f95e

[ "MIT" ]

7

2021-09-01T06:50:41.000Z

2021-09-03T23:12:07.000Z

functions-and-keda/src/python-function-publisher/QueueTrigger/__init__.py

emctl/samples

569f81035a6c214d4cda3687173e24003f17f95e

[ "MIT" ]

4

2021-02-05T17:30:28.000Z

2021-08-16T21:26:55.000Z

import logging import requests import json import azure.functions as func dapr_url = "http://localhost:3500/v1.0" def main(msg: func.QueueMessage): logging.info(f"Python queue-triggered function received a message!") message = msg.get_body().decode('utf-8') logging.info(f"Message: {message}") # Publish an event url = f'{dapr_url}/publish/myTopic' content = { "message": message } logging.info(f'POST to {url} with content {json.dumps(content)}') p = requests.post(url, json=content) logging.info(f'Got response code {p.status_code}')

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null

0

null

0

1

0

a5dac3d6ca2b3d760f8736d068bcd1c838b5581c

2,618

py

Python

tests/unit/test_upstream_dataset.py

ianbakst/tamr-client

ae7a6190a2251d31f973f9d5a7170ac5dc097f97

[ "Apache-2.0" ]

9

2019-08-13T11:07:06.000Z

2022-01-14T18:15:13.000Z

tests/unit/test_upstream_dataset.py

ianbakst/tamr-client

ae7a6190a2251d31f973f9d5a7170ac5dc097f97

[ "Apache-2.0" ]

166

2019-08-09T18:51:05.000Z

2021-12-02T15:24:15.000Z

tests/unit/test_upstream_dataset.py

ianbakst/tamr-client

ae7a6190a2251d31f973f9d5a7170ac5dc097f97

[ "Apache-2.0" ]

21

2019-08-12T15:37:31.000Z

2021-06-15T14:06:23.000Z

import responses from tamr_unify_client import Client from tamr_unify_client.auth import UsernamePasswordAuth @responses.activate def test_upstream_dataset(): dataset_json = { "id": "unify://unified-data/v1/datasets/12", "name": "Project_1_unified_dataset_dedup_features", "description": "Features for all the rows and values in the source dataset. Used in Dedup Workflow.", "version": "543", "keyAttributeNames": ["entityId"], "tags": [], "created": { "username": "admin", "time": "2019-06-05T18:31:59.327Z", "version": "212", }, "lastModified": { "username": "admin", "time": "2019-07-18T14:19:28.133Z", "version": "22225", }, "relativeId": "datasets/12", "upstreamDatasetIds": ["unify://unified-data/v1/datasets/8"], "externalId": "Project_1_unified_dataset_dedup_features", } upstream_json = ["unify://unified-data/v1/datasets/8"] upstream_ds_json = { "id": "unify://unified-data/v1/datasets/8", "name": "Project_1_unified_dataset", "description": "", "version": "529", "keyAttributeNames": ["tamr_id"], "tags": [], "created": { "username": "admin", "time": "2019-06-05T16:28:11.639Z", "version": "83", }, "lastModified": { "username": "admin", "time": "2019-07-22T20:31:23.968Z", "version": "23146", }, "relativeId": "datasets/8", "upstreamDatasetIds": ["unify://unified-data/v1/datasets/6"], "externalId": "Project_1_unified_dataset", "resourceId": "8", } tamr = Client(UsernamePasswordAuth("username", "password")) url_prefix = "http://localhost:9100/api/versioned/v1/" dataset_url = url_prefix + "datasets/12" upstream_url = url_prefix + "datasets/12/upstreamDatasets" upstream_ds_url = url_prefix + "datasets/8" responses.add(responses.GET, dataset_url, json=dataset_json) responses.add(responses.GET, upstream_url, json=upstream_json) responses.add(responses.GET, upstream_ds_url, json=upstream_ds_json) project_ds = tamr.datasets.by_relative_id("datasets/12") actual_upstream_ds = project_ds.upstream_datasets() uri_dataset = actual_upstream_ds[0].dataset() assert actual_upstream_ds[0].relative_id == upstream_ds_json["relativeId"] assert actual_upstream_ds[0].resource_id == upstream_ds_json["resourceId"] assert uri_dataset.name == upstream_ds_json["name"]

34.906667

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0.063492

0

null

0

null

0

1

0

a5daeaca530d32aa4078eb1a40a959857dd7e442

14,531

py

Python

pmaf/sequence/_multiple/_multiple.py

mmtechslv/PhyloMAF

bab43dd4a4d2812951b1fdf4f1abb83edb79ea88

[ "BSD-3-Clause" ]

1

2021-07-02T06:24:17.000Z

pmaf/sequence/_multiple/_multiple.py

mmtechslv/PhyloMAF

bab43dd4a4d2812951b1fdf4f1abb83edb79ea88

[ "BSD-3-Clause" ]

1

2021-06-28T12:02:46.000Z

pmaf/sequence/_multiple/_multiple.py

mmtechslv/PhyloMAF

bab43dd4a4d2812951b1fdf4f1abb83edb79ea88

[ "BSD-3-Clause" ]

null

import warnings warnings.simplefilter("ignore", category=FutureWarning) from skbio import TabularMSA from skbio.sequence import GrammaredSequence from io import StringIO, IOBase from shutil import copyfileobj import copy import numpy as np from pmaf.internal.io._seq import SequenceIO from pmaf.sequence._sequence._nucleotide import Nucleotide from pmaf.sequence._metakit import MultiSequenceMetabase, NucleotideMetabase from pmaf.sequence._shared import validate_seq_mode from typing import Union, Optional, Any, Sequence, Generator from pmaf.internal._typing import AnyGenericIdentifier class MultiSequence(MultiSequenceMetabase): """Class responsible for handling multiple sequences.""" def __init__( self, sequences: Any, name: Optional[str] = None, mode: Optional[str] = None, metadata: Optional[dict] = None, aligned: bool = False, **kwargs: Any ): """Constructor for :class:`.MultiSequence` Parameters ---------- sequences Anything that can be parsed as multiple sequences. name Name of the multi-sequence instance mode Mode of the sequences. All sequences must have same mode/type. Otherwise error will be raised metadata Metadata of the multi-sequence instance aligned True if sequences are aligned. Default is False kwargs Compatibility """ if name is None or np.isscalar(name): tmp_name = name else: raise TypeError("`name` can be any scalar") if isinstance(metadata, dict): tmp_metadata = metadata elif metadata is None: tmp_metadata = {} else: raise TypeError("`metadata` can be dict or None") if mode is not None: if validate_seq_mode(mode): tmp_mode = mode.lower() else: raise ValueError("`mode` is invalid.") else: tmp_mode = mode tmp_sequences = [] if isinstance(sequences, list): if all( [isinstance(sequence, NucleotideMetabase) for sequence in sequences] ): tmp_sequences = sequences elif all( [isinstance(sequence, GrammaredSequence) for sequence in sequences] ): tmp_sequences = [ Nucleotide(skbio_seq, mode=None, **kwargs) for skbio_seq in sequences ] else: raise ValueError( "`sequences` must have same type when provided as list." ) else: if tmp_mode is not None: seq_gen = SequenceIO(sequences, upper=True).pull_parser( parser="simple", id=True, description=True, sequence=True ) for sid, desc, seq_str in seq_gen: tmp_sequences.append( Nucleotide( seq_str, name=sid, mode=tmp_mode, metadata={"description": desc}, **kwargs ) ) else: raise ValueError("`mode` cannot be None if raw read is performed.") if aligned: if len(set([sequence.length for sequence in tmp_sequences])) != 1: raise ValueError("`sequences` must be all of the length if aligned.") tmp_indices = [sequence.name for sequence in tmp_sequences] if len(tmp_indices) != len(set(tmp_indices)): raise ValueError("`sequences` must have unique names.") tmp_modes = set([sequence.mode for sequence in tmp_sequences]) if len(tmp_modes) > 1: raise ValueError("`sequences` cannot have different modes.") if tmp_mode is not None: if tmp_mode not in tmp_modes: raise ValueError("`mode` must match modes of sequences.") else: tmp_mode = tmp_modes.pop() tmp_internal_id = kwargs.get("internal_id", None) if tmp_internal_id is not None: for sequence in tmp_sequences: if tmp_internal_id not in sequence.metadata.keys(): raise ValueError( "Metadata of all sequences must contain same internal_id." ) self.__indices = np.asarray([seq.name for seq in tmp_sequences]) self.__sequences = tmp_sequences self.__metadata = tmp_metadata self.__aligned = bool(aligned) self.__internal_id = tmp_internal_id self.__skbio_mode = tmp_sequences[0].skbio_mode self.__mode = tmp_mode self.__name = tmp_name self.__buckled = bool(kwargs.get("buckled", None)) def __repr__(self): class_name = self.__class__.__name__ name = self.__name if self.__name is not None else "N/A" count = len(self.__sequences) metadata_state = "Present" if len(self.__metadata) > 0 else "N/A" aligned = "Yes" if self.__aligned else "No" mode = self.__mode.upper() if self.__mode is not None else "N/A" repr_str = ( "<{}:[{}], Name:[{}], Mode:[{}], Aligned: [{}], Metadata:[{}]>".format( class_name, count, name, mode, aligned, metadata_state ) ) return repr_str def to_skbio_msa( self, indices: Optional[AnyGenericIdentifier] = None ) -> TabularMSA: """Convert to :mod:`skbio` :class:`~skbio.alignment.TabularMSA` instance. Parameters ---------- indices List of target sequences to select. Default is None for all sequences. Returns ------- Instance of :class:`skbio.alignment.TabularMSA` """ if self.__aligned: tmp_sequences = self.__get_seqs_by_index(indices) return TabularMSA([sequence.skbio for sequence in tmp_sequences]) else: raise RuntimeError("TabularMSA can only be retrieved for alignment.") def __get_seqs_by_index(self, ids: Optional[AnyGenericIdentifier]): """Get sequences by indices/ids.""" if ids is not None: target_ids = np.asarray(ids) else: target_ids = self.__indices if np.isin(self.__indices, target_ids).sum() == len(target_ids): return [seq for seq in self.__sequences if seq.name in target_ids] else: raise ValueError("Invalid indices are provided.") def get_consensus( self, indices: Optional[AnyGenericIdentifier] = None ) -> Nucleotide: """If sequence are aligned, estimate consensus sequence from the :term:`MSA` Parameters ---------- indices List of target sequences to select. Default is None for all sequences. Returns ------- Consensus sequence. """ if self.__aligned: tmp_msa = self.to_skbio_msa(indices) return Nucleotide( tmp_msa.consensus(), name=self.__name, metadata=self.__metadata, mode=self.__mode, ) else: raise RuntimeError("Consensus can be retrieved only from alignment.") def get_subset( self, indices: Optional[AnyGenericIdentifier] = None ) -> "MultiSequence": """Get subset of the mutli-sequence instance. Parameters ---------- indices Indices to subset for. Returns ------- Subset instance of :class:`.MultiSequence` """ return type(self)( self.__get_seqs_by_index(indices), name=self.__name, metadata=self.__metadata, mode=self.__mode, aligned=self.__aligned, ) def buckle_for_alignment(self) -> dict: """Buckle individual sequences for alignment. Returns ------ Packed metadata of all sequences. """ if not self.__buckled: from collections import defaultdict from random import random if self.__internal_id is None: self.__internal_id = round(random() * 100000, None) packed_metadata = { "master-metadata": self.__metadata, "__name": self.__name, "__internal_id": self.__internal_id, } children_metadata = defaultdict(dict) for tmp_uid, sequence in enumerate(self.__sequences): tmp_uid_str = "TMP_ID_{}".format(str(tmp_uid)) children_metadata[tmp_uid_str] = sequence.buckle_by_uid(tmp_uid_str) packed_metadata.update({"children-metadata": dict(children_metadata)}) self.__buckled = True return packed_metadata else: raise RuntimeError("MultiSequence instance is already buckled.") def restore_buckle(self, buckled_pack: dict) -> None: """Restore the buckled :class:`MultiSequence` instance. Parameters ---------- buckled_pack Backed up packed metadata of all individual sequences Returns ------- None if success or raise error """ if self.__buckled: self.__metadata = buckled_pack["master-metadata"] self.__name = buckled_pack["__name"] self.__internal_id = buckled_pack["__internal_id"] for sequence in self.__sequences: tmp_uid = sequence.unbuckle_uid() child_packed_metadata = buckled_pack["children-metadata"][tmp_uid] sequence.restore_buckle(child_packed_metadata) self.__indices = np.asarray([seq.name for seq in self.__sequences]) else: raise RuntimeError("MultiSequence instance is not buckled.") def get_iter(self, method: str = "asis") -> Generator: """Get generator for the idividual sequences. Parameters ---------- method Method indicate how generator must yield the sequence data Returns ------- Generator for the sequences. Depending on `method` result can yield on of following: - 'asis' - (name[str], sequence[Instance]) - 'string' - (name[str], sequence[str]) - 'skbio' - (name[str], sequence[skbio]) """ def make_generator(): for sequence in self.__sequences: if method == "asis": yield sequence.name, sequence elif method == "string": yield sequence.name, sequence.text elif method == "skbio": yield sequence.name, sequence.skbio else: raise ValueError("`method` is invalid.") return make_generator() def copy(self): """Copy current instance.""" return copy.deepcopy(self) def write(self, file: Union[str, IOBase], mode: str = "w", **kwargs: Any) -> None: """Write the sequence data into the file. Parameters ---------- file File path or IO stream to write into mode File write mode such as "w" or "a" or "w+" kwargs Compatibility. """ buffer_io = self.__make_fasta_io(**kwargs) if isinstance(file, IOBase): file_handle = file elif isinstance(file, str): file_handle = open(file, mode=mode) else: raise TypeError("`file` has invalid type.") copyfileobj(buffer_io, file_handle) buffer_io.close() def get_string_as(self, **kwargs): """Get string of all sequences. Parameters ---------- kwargs Compatibility. Will be passed to :meth:`pmaf.sequence.Nucleotide.write` method. Returns ------- String with formatted sequence data """ buffer_io = self.__make_fasta_io(**kwargs) ret = buffer_io.getvalue() buffer_io.close() return ret def __make_fasta_io(self, **kwargs): """Make a FASTA file IO stream.""" buffer_io = StringIO() for sequence in self.__sequences: sequence.write(buffer_io, mode="a", **kwargs) buffer_io.seek(0) return buffer_io @classmethod def from_buckled( cls, sequences: Any, buckled_pack: dict, **kwargs: Any ) -> "MultiSequence": """Factory method to create :class:`.MultiSequence` using packed metadata from buckling. Parameters ---------- sequences Sequences that will be passed to constructor buckled_pack Packed metadata produced during buckling kwargs Compatibility Returns ------- New instance of :class:`.MultiSequence` """ if not isinstance(buckled_pack, dict): raise TypeError("`buckled_pack` must have dict type.") tmp_multiseq = cls(sequences, buckled=True, **kwargs) tmp_multiseq.restore_buckle(buckled_pack) return tmp_multiseq @property def count(self): """Total number of sequences.""" return len(self.__sequences) @property def metadata(self): """Instance metadata.""" return self.__metadata @property def mode(self): """Mode/type of the sequences.""" return self.__mode @property def skbio_mode(self): """The :mod:`skbio` mode of the sequence.""" return self.__skbio_mode @property def sequences(self): """List of individual sequence instances.""" return self.__sequences @property def name(self): """Name of the instance.""" return self.__name @property def is_alignment(self): """Is mutli-sequence is aligned or not.""" return self.__aligned @property def is_buckled(self): """Is mulit-sequence instance is buckled or not.""" return self.__buckled @property def index(self): """Indices of the internals sequences.""" return self.__indices

33.871795

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5.228383

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14,531

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0

0.229885

0

null

0

null

0

1

0

a5db8882e50338e2cfe3830ff393ba99f5232ba1

1,498

py

Python

arvore_derivacao.py

rjribeiro/trabalho-formais

358de668cc256c696fdc4b426a69cf5a3d17b511

[ "MIT" ]

3

2018-04-28T15:55:50.000Z

2018-05-11T22:57:20.000Z

arvore_derivacao.py

rjribeiro/trabalho-formais

358de668cc256c696fdc4b426a69cf5a3d17b511

[ "MIT" ]

null

arvore_derivacao.py

rjribeiro/trabalho-formais

358de668cc256c696fdc4b426a69cf5a3d17b511

[ "MIT" ]

null

class ArvoreDerivacao: def __init__(self, conteudo, esquerda=None, direita=None): self._conteudo = conteudo self._esquerda = esquerda self._direita = direita self.children = [self._esquerda, self._direita] @property def conteudo(self): return self._conteudo def print_arvore(self, nivel=1): """ Objetivo: imprimir toda a árvore, cuja raíz tem o nivel fornecido. :param nivel: :type nivel: int :rtype: None """ print("Nível {espacos}: {:>{espacos}}".format(self._conteudo, espacos=nivel)) if self._direita: self._direita.print_arvore(nivel + 1) if self._esquerda: self._esquerda.print_arvore(nivel + 1) def palavra_gerada(self): """ Objetivo: Obter a palavra gerada pela árvore de derivação. :return: Palavra derivada. :rtype: str """ if not self._esquerda and not self._direita: return self._conteudo if self._esquerda: prefixo = self._esquerda.palavra_gerada() else: prefixo = "" if self._direita: sufixo = self._direita.palavra_gerada() else: sufixo = "" return prefixo + sufixo if __name__ == '__main__': a = ArvoreDerivacao('a') b = ArvoreDerivacao('b') A = ArvoreDerivacao('A', a) B = ArvoreDerivacao('B', b) S = ArvoreDerivacao('S', A, B) S.print_arvore()

27.740741

85

0.579439

160

1,498

5.19375

0.31875

0.101083

0.045728

0.040915

0

0.002918

0.313752

1,498

53

86

28.264151

0.805447

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0.235294

0

0.035774

0

1

0.117647

false

0

0.029412

0.235294

0.147059

0

null

0

null

0

1

0

a5dca4db049c83c9e0aaf82c2743e38347886e01

1,404

py

Python

src/test.py

biqar/hypergraph-study

04b54117eb8f684a72259b27b03162efb4c18cd0

[ "MIT" ]

2

2021-12-24T12:02:48.000Z

2021-12-25T00:00:22.000Z

src/test.py

biqar/hypergraph-study

04b54117eb8f684a72259b27b03162efb4c18cd0

[ "MIT" ]

null

src/test.py

biqar/hypergraph-study

04b54117eb8f684a72259b27b03162efb4c18cd0

[ "MIT" ]

1

2021-07-19T02:05:13.000Z

import re import sys from operator import add from pyspark.sql import SparkSession def computeContribs(urls, rank): """Calculates URL contributions to the rank of other URLs.""" num_urls = len(urls) for url in urls: yield (url, rank / num_urls) def parseNeighbors(urls): """Parses a urls pair string into urls pair.""" parts = re.split(r'\s+', urls) for i in range(len(parts)): for j in range(i,len(parts)): if i!=j: yield parts[i],parts[j] if __name__ == "__main__": if len(sys.argv) != 3: print("Usage: pagerank <file> <iterations>", file=sys.stderr) sys.exit(-1) print("WARN: This is a naive implementation of PageRank and is given as an example!\n" + "Please refer to PageRank implementation provided by graphx", file=sys.stderr) # Initialize the spark context. spark = SparkSession\ .builder\ .appName("PythonPageRank")\ .getOrCreate() # Loads in input file. It should be in format of: # URL neighbor URL # URL neighbor URL # URL neighbor URL # ... lines = spark.read.text(sys.argv[1]).rdd.map(lambda r: r[0]) print("ALL LINKS",lines.collect()) links = lines.flatMap(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache() print("ALL LINKS",links.collect())

29.25

92

0.608262

185

1,404

4.562162

0.513514

0.0391

0.049763

0.040284

0.049763

0

0.003906

0.270655

1,404

47

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false

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0.137931

0

null

0

null

0

1

0

a5df0a5e25ad5c8a611b093330f6ecc81a28362f

1,312

py

Python

wagtail_lightadmin/wagtail_hooks.py

leukeleu/wagtail_lightadmin

6aa465e2673f4eb8865f7b4dc6cd2c7c41ed71a5

[ "MIT" ]

4

2019-02-22T14:07:26.000Z

2020-04-20T05:33:39.000Z

wagtail_lightadmin/wagtail_hooks.py

leukeleu/wagtail_lightadmin

6aa465e2673f4eb8865f7b4dc6cd2c7c41ed71a5

[ "MIT" ]

1

2019-05-18T08:04:32.000Z

2019-05-20T13:39:14.000Z

wagtail_lightadmin/wagtail_hooks.py

leukeleu/wagtail_lightadmin

6aa465e2673f4eb8865f7b4dc6cd2c7c41ed71a5

[ "MIT" ]

2

2017-06-06T09:34:53.000Z

2019-09-10T16:16:12.000Z

from __future__ import absolute_import, unicode_literals from django.conf import settings from django.templatetags.static import static from django.utils.html import format_html from django.utils.module_loading import import_string from wagtail.core import hooks @hooks.register('insert_editor_css') def editor_css(): return format_html( '<link rel="stylesheet" href="{}">', static('css/admin_editor.css') ) @hooks.register('insert_editor_js') def editor_js(): return format_html( """ <script type="text/javascript" src="{0}"></script> <script type="text/javascript" src="{1}"></script> """, static('js/wagtailadmin/admin_link_widget.js'), static('wagtailadmin/js/page-chooser-modal.js'), ) @hooks.register('insert_editor_js') def editor_js_hallo(): """ We need an extra JS file for Wagtail<1.12.x """ import wagtail _, version, _, = wagtail.__version__.split('.') if int(version) < 12: # Use our custom hallo-bootstrap js = static('js/wagtailadmin/lighter-hallo-bootstrap.js') else: js = static('wagtailadmin/js/hallo-bootstrap.js') return format_html( """ <script type="text/javascript" src="{0}"></script> """, js )

26.24

65

0.641768

159

1,312

5.100629

0.396226

0.049322

0.070284

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false

0

0.225806

0.064516

0.419355

0

null

0

null

0

1

0

a5e4666915212b8f6b0b15dc2449a686ce496e42

5,633

py

Python

stackdriver/restapi.py

MarkMarine/stackdriver-client-python

7e5e5806d02fcf4b8633d19adbce6d64f3082083

[ "Apache-2.0" ]

null

stackdriver/restapi.py

MarkMarine/stackdriver-client-python

7e5e5806d02fcf4b8633d19adbce6d64f3082083

[ "Apache-2.0" ]

null

stackdriver/restapi.py

MarkMarine/stackdriver-client-python

7e5e5806d02fcf4b8633d19adbce6d64f3082083

[ "Apache-2.0" ]

null

""" restapi - base for calling rest resources Stackdriver Public API, Copyright Stackdriver 2014 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. """ import requests import copy import types import json import logging logger = logging.getLogger(__name__) def _wrap_transport_decorator(transport_func, wrapper, userdata): def inner(*args, **kwargs): return wrapper(transport_func, userdata=userdata, func_args=args, func_kwargs=kwargs) return inner def transport_func(func): """ Decorates each of the transport functions that can get wrapped by a transport_controller """ func._is_transport_func = True return func class RestApi(object): def __init__(self, entrypoint_uri, version=None, apikey=None, username=None, password=None, useragent=None, transport_controller=None, transport_userdata=None): """ Base class for accessing REST services :param entrypoint_path: The http or https uri to the api :param version: version of the api we support :param apikey: the stackdriver apikey to use for authentication :param username: username for basic auth - this is here for completeness but for the stackdriver apis auth should be done using the apikey :param password: password for basic auth - this is here for completeness but for the stackdriver apis auth should be done using the apikey :param transport_controller: if defined run this function before each network call :param transport_userdata: data to send to the transport_controller """ # always end with a slash entrypoint_uri = entrypoint_uri.strip() if entrypoint_uri[-1] != '/': entrypoint_uri += '/' self._entrypoint_uri = entrypoint_uri self._apikey = apikey self._username = username self._password = password self._version = version self._useragent = useragent if transport_controller: self._decorate_transport_funcs(transport_controller, transport_userdata) def _decorate_transport_funcs(self, controller, userdata): """ decorate all methods that have an attribute of _is_transport_func set to True skip any methods that start with an underscore (_) SEE @transport_func decorator """ for method_name in dir(self): if method_name.startswith('_'): continue method = getattr(self, method_name, None) if isinstance(method, types.MethodType): setattr(self, method_name, _wrap_transport_decorator(method, controller, userdata)) def _merge_headers(self, extra, is_post=False): headers = {} if extra is not None: headers = copy.copy(extra) if self._apikey: headers['x-stackdriver-apikey'] = self._apikey headers['x-stackdriver-version'] = self._version if is_post: headers['accept'] = 'application/json, text/plain, */*' headers['content-type'] = 'application/json' if self._useragent: headers['user-agent'] = self._useragent return headers def _gen_full_endpoint(self, endpoint_path): if endpoint_path.startswith('/'): endpoint_path = endpoint_path[1:] return '%s%s' % (self._entrypoint_uri, endpoint_path) @transport_func def get(self, endpoint, params=None, headers=None): headers = self._merge_headers(headers) uri = self._gen_full_endpoint(endpoint) logger.debug('GET %s', uri, extra={'params': params}) r = requests.get(uri, params=params, headers=headers) r.raise_for_status() return r.json() @transport_func def post(self, endpoint, data=None, headers=None): headers = self._merge_headers(headers, is_post=True) uri = self._gen_full_endpoint(endpoint) logger.debug('POST %s', uri, extra={'data': data}) r = requests.post(uri, data=json.dumps(data), headers=headers) r.raise_for_status() return r.json() @transport_func def put(self, endpoint, data=None, headers=None): headers = self._merge_headers(headers, is_post=True) uri = self._gen_full_endpoint(endpoint) logger.debug('PUT %s', uri, extra={'data': data}) r = requests.put(uri, data=json.dumps(data), headers=headers) r.raise_for_status() return r.json() @transport_func def delete(self, endpoint, headers=None): headers = self._merge_headers(headers, is_post=True) uri = self._gen_full_endpoint(endpoint) logger.debug('DELETE %s', uri) r = requests.delete(uri, headers=headers) r.raise_for_status() return r.json() @property def api_version(self): return self._version @property def entrypoint(self): return self._entrypoint_uri

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null

0

null

0

1

0

a5e7acf2b322f72151a720e8d6b6a7577bf377de

13,896

py

Python

ventana_perceptron.py

musicbiker/ANNT

301f1090925c8937f0fd3b4955ec68ff772022ce

[ "MIT" ]

null

ventana_perceptron.py

musicbiker/ANNT

301f1090925c8937f0fd3b4955ec68ff772022ce

[ "MIT" ]

null

ventana_perceptron.py

musicbiker/ANNT

301f1090925c8937f0fd3b4955ec68ff772022ce

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Wed Dec 11 15:05:41 2019 @author: jrodriguez119 """ import tkinter as tk from tkinter import ttk import crearcapas import perceptron_multicapa from threading import Thread import sys from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk from matplotlib.figure import Figure from tkinter import filedialog as fd from tkinter import messagebox as mb import menu import sklearn class Display(tk.Frame): def __init__(self,parent=0): tk.Frame.__init__(self,parent) self.output = tk.Text(self, width=80, height=15) self.output.pack(padx = 30, pady = 5,) sys.stdout = self self.pack() def flush(self): pass def write(self, txt): self.output.insert(tk.END,str(txt)) self.output.see("end") self.update_idletasks() #Función que genera la ventana de parámetros del Perceptron multicapa def Ventana_perceptron(ventana_seleccion,X_train,Y_train,X_test,Y_test,ventana_inicio): #Crear ventana ventana_perceptron = tk.Toplevel(ventana_seleccion) ventana_perceptron.geometry('725x600+500+200') #Insertar menu menu.menu(ventana_perceptron,ventana_inicio) #Esconder ventana previa ventana_seleccion.withdraw() #Título labeltitulo = ttk.Label(ventana_perceptron,text = "Parámetros necesarios para el Perceptrón", foreground = "#054FAA",font=("Arial Bold", 15)) labeltitulo.pack(pady=10) #Frame donde alojar los widget de entrada lframe = ttk.Frame(ventana_perceptron) lframe.pack() #------------------------ entrada de datos --------------------------------- #Tamaño de lote tamlot = tk.IntVar() lbtamlote = ttk.Label(lframe,text = "Tamaño lote: ", foreground = "#054FAA",font=("Arial Bold", 12)) lbtamlote.grid(column=0, row=0 ,pady=5,sticky=tk.W) etamlot = ttk.Entry(lframe,width=5, textvariable = tamlot) etamlot.grid(column=1, row=0,pady=5,sticky=tk.E) #Optimizador opt =tk.StringVar() lbopt = ttk.Label(lframe, text="Optimizador: ", foreground = "#054FAA",font=("Arial Bold", 12)) lbopt.grid(column=0, row=1,pady=5,sticky=tk.W) cbopt=ttk.Combobox(lframe,width=9,state="readonly",textvariable = opt) cbopt["values"] = ["SGD", "RMSProp","Adam","Adagrad"] cbopt.grid(column = 1 ,row = 1,pady=5,columnspan=2) cbopt.current(0) #Proporción de validación pv = tk.DoubleVar() pv.set(0.2) lbpv = ttk.Label(lframe,text = "Proporción de Validación :", foreground = "#054FAA",font=("Arial Bold", 12)) lbpv.grid(column=0, row=2 ,pady=5,sticky=tk.W) epv = ttk.Entry(lframe,width=5, textvariable = pv) epv.grid(column=1, row=2,pady=5,sticky=tk.E) #Número de capas ocultas nco = tk.IntVar() lbnco = ttk.Label(lframe,text = "Número capas ocultas :", foreground = "#054FAA",font=("Arial Bold", 12)) lbnco.grid(column=0, row=3 ,pady=5,sticky=tk.W) enco = ttk.Entry(lframe,width=5, textvariable = nco) enco.grid(column=1, row=3,pady=5,sticky=tk.E) #Función Loss fl =tk.StringVar() lbfl = ttk.Label(lframe, text="Función Loss: ", foreground = "#054FAA",font=("Arial Bold", 12)) lbfl.grid(column=0, row=4,pady=5,sticky=tk.W) cbfl=ttk.Combobox(lframe,width=21,state="readonly",textvariable = fl) cbfl["values"] = ["kullback_leibler_divergence","mean_squared_error", "categorical_hinge", "categorical_crossentropy","binary_crossentropy","poisson","cosine_proximity"] cbfl.grid(column = 1 ,row = 4,pady=5,columnspan=2,sticky=tk.E) cbfl.current(3) #Método de parada labeltitulo1 = ttk.Label(ventana_perceptron,text = "Método de parada", foreground = "#054FAA",font=("Arial Bold", 15)) labeltitulo1.pack(pady=10) lframe1 = ttk.Frame(ventana_perceptron) lframe1.pack() #Tipo de parada #Parada por número de iteraciones mp=tk.IntVar() bat1= ttk.Radiobutton(lframe1, value=0,variable=mp) bat1.grid(column=0, row=0) nui=tk.IntVar() lbnui = ttk.Label(lframe1, text="Número de iteraciones: ", foreground = "#054FAA",font=("Arial Bold", 12)) lbnui.grid(column=1, row=0,pady=5,sticky=tk.W) enui = ttk.Entry(lframe1,width=5, textvariable = nui) enui.grid(column=2, row=0,pady=5,sticky=tk.E) #Parada por control de un parámetro bat2 = ttk.Radiobutton(lframe1, value=1,variable=mp) bat2.grid(column=0, row=1) lbparada = ttk.Label(lframe1, text="Parada temprana: ", foreground = "#054FAA",font=("Arial Bold", 12)) lbparada.grid(column = 1, row = 1,sticky=tk.W ) #Parámetro a controlar lbcon = ttk.Label(lframe1, text=" Parámetro a controlar: ", foreground = "#054FAA",font=("Arial Bold", 12)) lbcon.grid(column = 1, row = 2,pady=5,sticky=tk.W ) con =tk.StringVar() cbcon=ttk.Combobox(lframe1,width=9,state="readonly",textvariable = con) cbcon["values"] = ["loss","val_loss", "acc","val_acc"] cbcon.grid(column = 2 ,row = 2,pady=5,sticky=tk.E) cbcon.current(0) #Delta mínima de evolución delt =tk.DoubleVar() delt.set(0.001) lbdelt = ttk.Label(lframe1, text=" Delta min: ", foreground = "#054FAA",font=("Arial Bold", 12)) lbdelt.grid(column=1, row=3,pady=5,sticky=tk.W) edelt = ttk.Entry(lframe1,width=5, textvariable = delt) edelt.grid(column=2, row=3,pady=5,sticky=tk.E) #Paciencia para realizar la parada pat =tk.IntVar() pat.set(3) lbpat = ttk.Label(lframe1, text=" Paciencia: ", foreground = "#054FAA",font=("Arial Bold", 12)) lbpat.grid(column=1, row=4,pady=5,sticky=tk.W) epat = ttk.Entry(lframe1,width=5, textvariable = pat) epat.grid(column=2, row=4,pady=5,sticky=tk.E) #Función que abre una ventana externa y nos permite crear nuestro modelo editando las capas ocultas def crearmodelo(): global NO,AC,BA,DR,numero_capas numero_capas = int(nco.get()) NO,AC,BA,DR = crearcapas.capas(numero_capas, ventana_perceptron) btnmodelo = ttk.Button(ventana_perceptron, text = "Crear modelo",style='my.TButton', command=crearmodelo) btnmodelo.pack(pady=50) lframe2 = ttk.Frame(ventana_perceptron) lframe2.pack(side= "bottom") def entrenar(): lote = tamlot.get() optimizador = opt.get() prop_val = pv.get() numero_capas_ocultas = int(nco.get()) loss = fl.get() parada = mp.get() iteraciones = nui.get() control = con.get() delta = delt.get() paciencia = pat.get() #Excepciones if lote == 0: mb.showerror("Error", "Variable tamaño del lote = 0 ") return if prop_val == 0: mb.showerror("Error", "El algoritmo necesita una parte del conjunto de entrenamiento para su validación ") return if prop_val > 1: mb.showerror("Error", "Proporción de validación no válida ") return if numero_capas_ocultas == 0: mb.showerror("Error", "Variable numero de capas ocultas = 0 ") return if parada == 0 and iteraciones==0: mb.showerror("Error", "No se ha indicado el número de iteraciones requeridas ") return if parada == 1 and delta==0.0: mb.showerror("Error", "No se ha indicado el mínimo delta para controlar la evolución ") return while True: try: NO break except NameError: mb.showerror("Error", "No se ha creado el modelo, haga click en crear modelo ") return for i in range(numero_capas_ocultas) : if NO[i].get()==0: mb.showerror("Error", "No es posible tener capas con 0 neuronas, asegurese de haber creado el modelo correctamente ") return for i in range(numero_capas_ocultas) : if DR[i].get() > 1: mb.showerror("Error", "Valor Dropout no válido ") return #Ventana donde aparece el proceso y los botones para guardar el modelo ventana_display = tk.Toplevel(ventana_perceptron) labeltitulo1 = ttk.Label(ventana_display,text = "Entrenamiento", foreground = "#054FAA",font=("Arial Bold", 15)) labeltitulo1.pack(pady=5) #Funcion que representa la evolución del entrenamiento def plot(): ventana_plot = tk.Toplevel(ventana_perceptron) ventana_plot.geometry('900x600') f = Figure(figsize = (5,5),dpi = 100) a = f.add_subplot(121) b = f.add_subplot(122) #Resumimos e imprimimos esos datos a.plot(entrenamiento.history['acc']) a.plot(entrenamiento.history['val_acc']) a.set_title('Precisión del modelo') a.set_ylabel('Precisión') a.set_xlabel('Iteraciones') a.legend(['Entrenamiento', 'Validación'], loc='upper left') # summarize history for loss b.plot(entrenamiento.history['loss']) b.plot(entrenamiento.history['val_loss']) b.set_title('Loss del modelo') b.set_ylabel('Loss') b.set_xlabel('Iteraciones') b.legend(['Entrenamiento', 'Validación'], loc='upper left') canvas1 = FigureCanvasTkAgg(f,ventana_plot) canvas1.get_tk_widget().pack(side = tk.TOP,fill = tk.BOTH, expand = True) toolbar = NavigationToolbar2Tk(canvas1,ventana_plot) toolbar.update() canvas1._tkcanvas.pack(side = tk.TOP,fill = tk.BOTH, expand = True) def guardarcompl(): nombrearch=fd.asksaveasfilename(initialdir = "/",title = "Guardar como",defaultextension = 'h5') model.save(nombrearch) mb.showinfo("Información", "Los datos fueron guardados.") def guardarpesos(): nombrearch=fd.asksaveasfilename(initialdir = "/",title = "Guardar como",defaultextension = 'h5') model.save_weights(nombrearch) mb.showinfo("Información", "Los datos fueron guardados.") def atras(): ventana_display.destroy() framebotones = ttk.Frame(ventana_display) framebotones.pack(side= "bottom") btnguardarcompl = ttk.Button(framebotones, text="Modelo completo", command=guardarcompl,style='my.TButton',width = 15) btnguardarcompl.grid(row = 0, column = 0, padx = 10, pady = 5,sticky=tk.W) btnguardarpesos = ttk.Button(framebotones, text="Pesos", command=guardarpesos,style='my.TButton',width = 15) btnguardarpesos.grid(row = 0, column = 1, padx = 10, pady = 5,sticky=tk.W) btnplot = ttk.Button(framebotones, text="Plot", command=plot,style='my.TButton',width = 15) btnplot.grid(row = 1, column = 0, padx = 10, pady = 5,sticky=tk.W) btnatras = ttk.Button(framebotones, text="Atrás", command=atras,style='my.TButton',width = 15) btnatras.grid(row = 1, column = 1, padx = 10, pady = 5,sticky=tk.W) def pantalla(): global Display Display(ventana_display) def run(): global model, entrenamiento while True: try: model, entrenamiento = perceptron_multicapa.Perceptron_multicapa(ventana_perceptron,ventana_display,X_train,Y_train,X_test,Y_test, lote,optimizador,prop_val,numero_capas_ocultas,loss, parada,iteraciones,control,delta,paciencia,NO,AC,BA,DR) break except tk.TclError: mb.showerror("Error desconocido", "Por favor vuelva a intentarlo ") ventana_display.destroy() return except RuntimeError: mb.showerror("Error desconocido", "Por favor reinicie la aplicación ") ventana_display.destroy() return except sklearn.metrics.classification.UndefinedMetricWarning: mb.showerror("Error ", "Algo salió mal con los datos, reinicie la aplicación y vuelva a intentarlo ") ventana_display.destroy() return t1=Thread(target=pantalla) t2=Thread(target=run) t1.start() t2.start() btntrain = ttk.Button(lframe2, text = "Entrenar",style='my.TButton', command=entrenar) btntrain.grid(row = 0, column = 1, padx = 20, pady=15) def atras(): ventana_perceptron.destroy() ventana_seleccion.deiconify() btnatras = ttk.Button(lframe2, text = "Atras",style='my.TButton', command=atras) btnatras.grid(row=0,column=0, padx = 20, pady=15)

40.750733

151

0.573258

1,594

13,896

4.937265

0.235257

0.015248

0.027954

0.033037

0.310165

0.250445

0.149301

0.115121

0.10953

0.020839

0

0.031192

0.307858

13,896

340

152

40.870588

0.787066

0.062608

0

0.172691

0

0.143286

0.004031

0

0.002941

0

1

0.052209

false

0.004016

0.048193

0

0.15261

0

null

0

null

0

1

0

a5ea06e0a07718613f62378639588110228f7035

728

py

Python

secu/tests/user_post_test.py

wancy86/tornado-seed

bea842f4ba6b23dda53ec9ae9f1349e1d2b54fd3

[ "MIT" ]

null

secu/tests/user_post_test.py

wancy86/tornado-seed

bea842f4ba6b23dda53ec9ae9f1349e1d2b54fd3

[ "MIT" ]

null

secu/tests/user_post_test.py

wancy86/tornado-seed

bea842f4ba6b23dda53ec9ae9f1349e1d2b54fd3

[ "MIT" ]

null

import requests from ..base.test import BaseTestCase, AuthorizedTestCase import uuid import common class T(AuthorizedTestCase): @property def path(self): return '/service/secu/user' def setUp(self): super().setUp() self.data = { 'username': 'myao', 'email': '1343030803@qq.com', 'mobile': '18665369920', 'pwd': '123456', 'fullname': '姚贯伟', 'roles': '' } def test_by_correct_info(self): response = requests.post(self.url, json=self.data) self.assertNotEqual(response.text, '', '返回值为空！') resp = response.json() self.assertEqual('000', resp['code'])

26.962963

59

0.542582

70

728

5.6

0.7

0.045918

0

0.060484

0.318681

728

27

60

26.962963

0.729839

0

0.152205

0

0.086957

1

0.130435

false

0

0.173913

0.043478

0.391304

0

null

0

null

0

1

0

a5ef7047358651b5620e1896751f01c69ce61941

6,404

py

Python

products_and_services_client/models/monthly_price.py

pitzer42/opbk-br-quickstart

b3f86b2e5f82a6090aaefb563614e174a452383c

[ "MIT" ]

2

2021-02-07T23:58:36.000Z

2021-02-08T01:03:25.000Z

products_and_services_client/models/monthly_price.py

pitzer42/opbk-br-quickstart

b3f86b2e5f82a6090aaefb563614e174a452383c

[ "MIT" ]

null

products_and_services_client/models/monthly_price.py

pitzer42/opbk-br-quickstart

b3f86b2e5f82a6090aaefb563614e174a452383c

[ "MIT" ]

null

# coding: utf-8 """ API's OpenData do Open Banking Brasil As API's descritas neste documento são referentes as API's da fase OpenData do Open Banking Brasil. # noqa: E501 OpenAPI spec version: 1.0.0-rc5.2 Contact: apiteam@swagger.io Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class MonthlyPrice(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'interval': 'PriceIntervals', 'monthly_fee': 'str', 'currency': 'Currency', 'customers': 'Customer' } attribute_map = { 'interval': 'interval', 'monthly_fee': 'monthlyFee', 'currency': 'currency', 'customers': 'customers' } def __init__(self, interval=None, monthly_fee=None, currency=None, customers=None): # noqa: E501 """MonthlyPrice - a model defined in Swagger""" # noqa: E501 self._interval = None self._monthly_fee = None self._currency = None self._customers = None self.discriminator = None self.interval = interval self.monthly_fee = monthly_fee self.currency = currency self.customers = customers @property def interval(self): """Gets the interval of this MonthlyPrice. # noqa: E501 :return: The interval of this MonthlyPrice. # noqa: E501 :rtype: PriceIntervals """ return self._interval @interval.setter def interval(self, interval): """Sets the interval of this MonthlyPrice. :param interval: The interval of this MonthlyPrice. # noqa: E501 :type: PriceIntervals """ if interval is None: raise ValueError("Invalid value for `interval`, must not be `None`") # noqa: E501 self._interval = interval @property def monthly_fee(self): """Gets the monthly_fee of this MonthlyPrice. # noqa: E501 Valor da mediana de cada faixa relativa ao serviço ofertado, informado no período, conforme Res nº 32 BCB, 2020. p.ex. ''45.00'' (representa um valor monetário. p.ex: 1547368.92. Este valor, considerando que a moeda seja BRL, significa R$ 1.547.368,92. O único separador presente deve ser o ''.'' (ponto) para indicar a casa decimal. Não deve haver separador de milhar) # noqa: E501 :return: The monthly_fee of this MonthlyPrice. # noqa: E501 :rtype: str """ return self._monthly_fee @monthly_fee.setter def monthly_fee(self, monthly_fee): """Sets the monthly_fee of this MonthlyPrice. Valor da mediana de cada faixa relativa ao serviço ofertado, informado no período, conforme Res nº 32 BCB, 2020. p.ex. ''45.00'' (representa um valor monetário. p.ex: 1547368.92. Este valor, considerando que a moeda seja BRL, significa R$ 1.547.368,92. O único separador presente deve ser o ''.'' (ponto) para indicar a casa decimal. Não deve haver separador de milhar) # noqa: E501 :param monthly_fee: The monthly_fee of this MonthlyPrice. # noqa: E501 :type: str """ if monthly_fee is None: raise ValueError("Invalid value for `monthly_fee`, must not be `None`") # noqa: E501 self._monthly_fee = monthly_fee @property def currency(self): """Gets the currency of this MonthlyPrice. # noqa: E501 :return: The currency of this MonthlyPrice. # noqa: E501 :rtype: Currency """ return self._currency @currency.setter def currency(self, currency): """Sets the currency of this MonthlyPrice. :param currency: The currency of this MonthlyPrice. # noqa: E501 :type: Currency """ if currency is None: raise ValueError("Invalid value for `currency`, must not be `None`") # noqa: E501 self._currency = currency @property def customers(self): """Gets the customers of this MonthlyPrice. # noqa: E501 :return: The customers of this MonthlyPrice. # noqa: E501 :rtype: Customer """ return self._customers @customers.setter def customers(self, customers): """Sets the customers of this MonthlyPrice. :param customers: The customers of this MonthlyPrice. # noqa: E501 :type: Customer """ if customers is None: raise ValueError("Invalid value for `customers`, must not be `None`") # noqa: E501 self._customers = customers def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(MonthlyPrice, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, MonthlyPrice): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

32.841026

392

0.600562

767

6,404

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null

0

null

0

1

0

a5f31b512ae3b988c292e1211f6d15cfb61624fc

839

py

Python

suppy/simulator/atomics/divergence_atomic.py

bmaris98/suppy

8450c6d25ffa492cdedfbbb4c111d22e7f2788a7

[ "BSD-3-Clause" ]

null

suppy/simulator/atomics/divergence_atomic.py

bmaris98/suppy

8450c6d25ffa492cdedfbbb4c111d22e7f2788a7

[ "BSD-3-Clause" ]

null

suppy/simulator/atomics/divergence_atomic.py

bmaris98/suppy

8450c6d25ffa492cdedfbbb4c111d22e7f2788a7

[ "BSD-3-Clause" ]

null

from suppy.utils.stats_constants import DIVERGENCE, TYPE from typing import Any, Dict from suppy.simulator.atomics.atomic import Atomic class DivergenceAtomic(Atomic): def __init__(self, uid: str, seh, name: str): Atomic.__init__(self, uid, seh, name, 0, 0) def get_stats(self) -> Dict[str, Any]: stats = Atomic.get_stats(self) stats[TYPE] = DIVERGENCE return stats def _all_output_clear(self) -> bool: for output_stream in self._output_streams: if not output_stream.has_input: return True return False def _do_process(self) -> None: resource = self._loaded_input[0] for output_stream in self._output_streams: if not output_stream.has_input: output_stream.try_load(resource) return

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839

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839

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0.571429

0

null

0

null

0

1

0

a5f7a4ecfa05bf78a585981771c76de8e093cf7a

5,180

py

Python

database/BuildDatabase.py

chanzuckerberg/scoreboard

7ebf783819d0f5b4dd54092201f709b8644c85a4

[ "MIT" ]

8

2017-11-28T22:36:37.000Z

2020-10-20T06:46:19.000Z

database/BuildDatabase.py

chanzuckerberg/scoreboard

7ebf783819d0f5b4dd54092201f709b8644c85a4

[ "MIT" ]

25

2017-12-27T19:05:41.000Z

2022-03-15T18:35:22.000Z

database/BuildDatabase.py

chanzuckerberg/scoreboard

7ebf783819d0f5b4dd54092201f709b8644c85a4

[ "MIT" ]

1

2018-04-23T11:16:41.000Z

from sqlalchemy import create_engine from sqlalchemy import Column, Integer, Boolean, String, DateTime, ForeignKey from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.dialects.postgresql import JSONB from sqlalchemy.orm import sessionmaker from sqlalchemy.sql import func import datetime import os import json database = { 'pg_user': os.environ['SCOREBOARD_PG_USERNAME'], 'pg_pass': os.environ['SCOREBOARD_PG_PASSWORD'], 'pg_host': os.environ.get('SCOREBOARD_PG_HOST', 'localhost'), 'pg_port': os.environ.get('SCOREBOARD_PG_PORT', 5432), 'pg_database': os.environ.get('SCOREBOARD_PG_DATABASE', 'scoreboard') } # Build database engine = create_engine( "postgresql://{pg_user}:{pg_pass}@{pg_host}:{pg_port}/{pg_database}".format(**database)) Base = declarative_base() class User(Base): __tablename__ = 'users' __table_args__ = {'extend_existing': True} id = Column(Integer, primary_key=True, nullable=False) github_username = Column(String, nullable=False) name = Column(String) email = Column(String) is_admin = Column(Boolean, nullable=False) create_date = Column(DateTime, nullable=False, server_default=func.now()) class Challenge(Base): __tablename__ = 'challenges' __table_args__ = {'extend_existing': True} id = Column(Integer, primary_key=True, nullable=False) name = Column(String, nullable=False) description = Column(String) docker_container = Column(String, nullable=False) image = Column(String) data_path = Column(String) data_size = Column(String) color = Column(String) about = Column(String) example_file = Column(String) submission_header = Column(JSONB) submission_separator = Column(String, default=",") scores = Column(JSONB) subscores = Column(JSONB) start_date = Column(DateTime, nullable=False, server_default=func.now()) end_date = Column(DateTime) is_open = Column(Boolean, nullable=False, default=True) create_date = Column(DateTime, nullable=False, server_default=func.now()) class Dataset(Base): __tablename__ = 'datasets' __table_args__ = {'extend_existing': True} id = Column(Integer, primary_key=True, nullable=False) name = Column(String, nullable=False) description = Column(String) tree = Column(JSONB) challenge_id = Column(Integer, ForeignKey("challenges.id", onupdate="CASCADE", ondelete="CASCADE"), nullable=False) create_date = Column(DateTime, nullable=False, server_default=func.now()) class Submission(Base): __tablename__ = 'submissions' __table_args__ = {'extend_existing': True} id = Column(Integer, primary_key=True) user_id = Column(Integer, ForeignKey("users.id", onupdate="CASCADE", ondelete="CASCADE"), nullable=False) challenge_id = Column(Integer, ForeignKey("challenges.id", onupdate="CASCADE", ondelete="CASCADE"), nullable=False) name = Column(String, nullable=False) repository = Column(String, nullable=False) is_private = Column(Boolean, nullable=False) institution = Column(String) publication = Column(String) is_accepted = Column(Boolean, nullable=False) create_date = Column(DateTime, nullable=False, server_default=func.now()) class Result(Base): __tablename__ = 'results' __table_args__ = {'extend_existing': True} id = Column(Integer, primary_key=True) submission_id = Column(Integer, ForeignKey("submissions.id", onupdate="CASCADE", ondelete="CASCADE"), nullable=False) results_path = Column(String, nullable=False) score_data = Column(JSONB) is_current = Column(Boolean, nullable=False) submission_date = Column(DateTime, nullable=False, server_default=func.now()) create_date = Column(DateTime, nullable=False, server_default=func.now()) class AdminEmailSettings(Base): __tablename__ = 'email_settings' __table_args__ = {'extend_existing': True} id = Column(Integer, primary_key=True, nullable=False) email_provider = Column(String, nullable=False) email_address = Column(String, nullable=False) email_pass = Column(String, nullable=False) Base.metadata.create_all(engine) # Load Data Base.metadata.bind = engine DBSession = sessionmaker(bind=engine) session = DBSession() with open("initialize.json") as fh: initialize_data = json.load(fh) for challenge in initialize_data["challenges"]: datasets = challenge.pop('datasets', []) new_challenge = Challenge(**challenge) session.add(new_challenge) session.flush() session.refresh(new_challenge) challenge_id = new_challenge.id for dataset in datasets: dataset["challenge_id"] = challenge_id new_dataset = Dataset(**dataset) session.add(new_dataset) for admin in initialize_data["admins"]: new_user = User(github_username=admin, is_admin=True) session.add(new_user) email_settings = initialize_data["email_settings"] settings = AdminEmailSettings(email_provider=email_settings["email_provider"], email_address= email_settings["admin_email"], email_pass=email_settings["admin_pass"]) session.add(settings) session.commit()

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null

0

null

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1

0

a5fbd11dbc9a0e80007cdb92a40b5c8dd7191ce7

8,387

py

Python

packages/w3af/w3af/core/data/url/HTTPRequest.py

ZooAtmosphereGroup/HelloPackages

0ccffd33bf927b13d28c8f715ed35004c33465d9

[ "Apache-2.0" ]

null

packages/w3af/w3af/core/data/url/HTTPRequest.py

ZooAtmosphereGroup/HelloPackages

0ccffd33bf927b13d28c8f715ed35004c33465d9

[ "Apache-2.0" ]

null

packages/w3af/w3af/core/data/url/HTTPRequest.py

ZooAtmosphereGroup/HelloPackages

0ccffd33bf927b13d28c8f715ed35004c33465d9

[ "Apache-2.0" ]

null

""" HTTPRequest.py Copyright 2010 Andres Riancho This file is part of w3af, http://w3af.org/ . w3af is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation version 2 of the License. w3af 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 GNU General Public License for more details. You should have received a copy of the GNU General Public License along with w3af; if not, write to the Free Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA """ import copy import socket import urllib2 from w3af.core.data.dc.headers import Headers from w3af.core.data.dc.utils.token import DataToken from w3af.core.data.parsers.doc.url import URL from w3af.core.data.request.request_mixin import RequestMixIn from w3af.core.data.url.constants import MAX_HTTP_RETRIES class HTTPRequest(RequestMixIn, urllib2.Request): def __init__(self, url, data=None, headers=None, origin_req_host=None, unverifiable=False, cookies=True, session=None, cache=False, method=None, error_handling=True, retries=MAX_HTTP_RETRIES, timeout=socket._GLOBAL_DEFAULT_TIMEOUT, new_connection=False, follow_redirects=False, use_basic_auth=True, use_proxy=True, debugging_id=None, binary_response=False): """ This is a simple wrapper around a urllib2 request object which helps with some common tasks like serialization, cache, etc. :param method: None means choose the default (POST if data is not None) :param data: The post_data as a string """ headers = headers or Headers() # # Save some information for later access in an easier way # self.url_object = url self.cookies = cookies self.session = session self.get_from_cache = cache self.error_handling = error_handling self.retries_left = retries self.timeout = timeout self.new_connection = new_connection self.follow_redirects = follow_redirects self.use_basic_auth = use_basic_auth self.use_proxy = use_proxy self.debugging_id = debugging_id self._binary_response = binary_response self.method = method if self.method is None: self.method = 'POST' if data else 'GET' if isinstance(headers, Headers): headers.tokens_to_value() headers = dict(headers) # Call the base class constructor urllib2.Request.__init__(self, url.url_encode(), data, headers, origin_req_host, unverifiable) RequestMixIn.__init__(self) def __eq__(self, other): return (self.get_method() == other.get_method() and self.get_uri() == other.get_uri() and self.get_headers() == other.get_headers() and self.get_data() == other.get_data() and self.get_timeout() == other.get_timeout()) def with_binary_response(self): return self._binary_response def set_data(self, data): self.data = data def add_header(self, key, val): """ Override mostly to avoid having header values of DataToken type :param key: The header name as a string :param val: The header value (a string of a DataToken) :return: None """ if isinstance(val, DataToken): val = val.get_value() self.headers[key.capitalize()] = val def get_method(self): return self.method def set_method(self, method): self.method = method def get_netloc(self): uri = self.get_uri() return '%s:%s' % (uri.get_domain(), uri.get_port()) def get_domain(self): return self.get_uri().get_domain() def get_uri(self): return self.url_object def set_uri(self, url_object): self.url_object = url_object def get_headers(self): headers = Headers(self.headers.items()) headers.update(self.unredirected_hdrs.items()) return headers def set_headers(self, headers): self.headers = dict(headers) def get_timeout(self): return self.timeout def set_timeout(self, timeout): self.timeout = timeout def set_new_connection(self, new_connection): self.new_connection = new_connection def get_new_connection(self): return self.new_connection def to_dict(self): serializable_dict = {} sdict = serializable_dict sdict['method'] = self.get_method() sdict['uri'] = self.get_uri().url_string sdict['headers'] = dict(self.get_headers()) sdict['data'] = self.get_data() sdict['cookies'] = self.cookies sdict['session'] = self.session sdict['cache'] = self.get_from_cache sdict['timeout'] = None if self.timeout is socket._GLOBAL_DEFAULT_TIMEOUT else self.timeout sdict['new_connection'] = self.new_connection sdict['follow_redirects'] = self.follow_redirects sdict['use_basic_auth'] = self.use_basic_auth sdict['use_proxy'] = self.use_proxy sdict['debugging_id'] = self.debugging_id sdict['binary_response'] = self._binary_response return serializable_dict @classmethod def from_fuzzable_request(cls, fuzzable_request): """ :param fuzzable_request: The FuzzableRequest :return: An instance of HTTPRequest with all the information contained in the FuzzableRequest passed as parameter """ host = fuzzable_request.get_url().get_domain() data = fuzzable_request.get_data() headers = fuzzable_request.get_headers() headers.tokens_to_value() return cls(fuzzable_request.get_uri(), data=data, headers=headers, origin_req_host=host) @classmethod def from_dict(cls, unserialized_dict): """ * msgpack is MUCH faster than cPickle, * msgpack can't serialize python objects, * I have to create a dict representation of HTTPRequest to serialize it, * and a from_dict to have the object back :param unserialized_dict: A dict just as returned by to_dict() """ udict = unserialized_dict method, uri = udict['method'], udict['uri'] headers, data = udict['headers'], udict['data'] cookies = udict['cookies'] session = udict['session'] cache = udict['cache'] timeout = socket.getdefaulttimeout() if udict['timeout'] is None else udict['timeout'] new_connection = udict['new_connection'] follow_redirects = udict['follow_redirects'] use_basic_auth = udict['use_basic_auth'] use_proxy = udict['use_proxy'] debugging_id = udict['debugging_id'] binary_response = udict['binary_response'] headers_inst = Headers(headers.items()) url = URL(uri) return cls(url, data=data, headers=headers_inst, cookies=cookies, session=session, cache=cache, method=method, timeout=timeout, new_connection=new_connection, follow_redirects=follow_redirects, use_basic_auth=use_basic_auth, use_proxy=use_proxy, debugging_id=debugging_id, binary_response=binary_response) def copy(self): return copy.deepcopy(self) def __repr__(self): fmt = '<HTTPRequest "%s" (cookies:%s, cache:%s, did:%s, timeout:%.2f, new_connection:%s)>' timeout = 3 if self.timeout is socket._GLOBAL_DEFAULT_TIMEOUT else self.timeout return fmt % (self.url_object.url_string, self.cookies, self.get_from_cache, self.debugging_id, timeout, self.new_connection)

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0

null

0

null

0

1

0

a5ff1935416c4a799dc3631e3b180db7559793bf

817

py

Python

moldesign/_notebooks/nbscripts/gen_toc.py

Autodesk/molecular-design-toolkit

5f45a47fea21d3603899a6366cb163024f0e2ec4

[ "Apache-2.0" ]

147

2016-07-15T18:53:55.000Z

2022-01-30T04:36:39.000Z

moldesign/_notebooks/nbscripts/gen_toc.py

cherishyli/molecular-design-toolkit

5f45a47fea21d3603899a6366cb163024f0e2ec4

[ "Apache-2.0" ]

151

2016-07-15T21:35:11.000Z

2019-10-10T08:57:29.000Z

moldesign/_notebooks/nbscripts/gen_toc.py

cherishyli/molecular-design-toolkit

5f45a47fea21d3603899a6366cb163024f0e2ec4

[ "Apache-2.0" ]

33

2016-08-02T00:04:51.000Z

2021-09-02T10:05:04.000Z

#!/usr/bin/env python from __future__ import print_function import sys, os from nbformat import v4 def parse_line(line): if not line.startswith('#'): return None ilevel = 0 for char in line: if char == '#': ilevel += 1 else: break name = line[ilevel:].strip() return ilevel, name if __name__ == '__main__': with open(sys.argv[1], 'r') as nbfile: nb = v4.reads(nbfile.read()) print('Contents\n=======\n---') for cell in nb.cells: if cell['cell_type'] == 'markdown': for line in cell['source'].splitlines(): header = parse_line(line) if header is None: continue ilevel, name = header print(' '*(ilevel-1) + ' - [%s](#%s)'%(name, name.replace(' ','-')))

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0.130435

0

null

0

null

0

1

0

570168b655cd4c5fe01f67c0408794d1cfd928aa

2,306

py

Python

tests/test_persona.py

holnburger/persine

cb26d1e275f7ed7e1048bc1e6b66b71386c3e602

[ "MIT" ]

84

2020-12-20T20:39:19.000Z

2022-02-02T01:01:12.000Z

tests/test_persona.py

holnburger/persine

cb26d1e275f7ed7e1048bc1e6b66b71386c3e602

[ "MIT" ]

1

2020-12-25T01:07:09.000Z

2020-12-25T04:05:19.000Z

tests/test_persona.py

holnburger/persine

cb26d1e275f7ed7e1048bc1e6b66b71386c3e602

[ "MIT" ]

9

2020-12-23T03:10:35.000Z

2021-09-08T14:44:18.000Z

import pytest from persine import Persona from selenium import webdriver from selenium.webdriver.chrome.options import Options from unittest.mock import Mock @pytest.fixture def engine(): def launch_chrome(user_data_dir): options = Options() options.add_argument("--headless") return webdriver.Chrome(options=options) eng = Mock() eng.data_dir = "/tmp/data_dir" eng.history_path = "/tmp/history.json" eng.launch = launch_chrome eng.run = lambda driver, action: { 'action': action } return eng def test_context(engine): with Persona(engine=engine) as persona: assert persona.driver is not None assert persona.driver is None def test_history(engine): persona = Persona(engine=engine) assert len(persona.history) == 0 persona.update_history( { "key": "test-key-1", "url": "sample", "action": "test:sample", "recommendations": [{"number": 1}, {"number": 2}, {"number": 3}], } ) persona.update_history( { "key": "test-key-2", "url": "sample2", "action": "test:sample", "recommendations": [{"number": 3}, {"number": 2}, {"number": 1}], } ) assert len(persona.history) == 2 assert len(persona.recommendations) == 6 def test_history_notes(engine): persona = Persona(engine=engine) assert len(persona.history) == 0 persona.update_history( { "key": "test-key-1", "url": "sample", "action": "test:sample", "recommendations": [{"number": 1}, {"number": 2}, {"number": 3}], }, { "note_key": "note_value" } ) assert persona.history[-1]['note_key'] == 'note_value' def test_run_notes(engine): with Persona(engine=engine) as persona: persona.run('http://jonathansoma.com', {'note_key': 'note_value'}) assert persona.history[-1]['action'] == 'http://jonathansoma.com' assert persona.history[-1]['note_key'] == 'note_value' def test_startup_shutdown(engine): persona = Persona(engine=engine) assert persona.driver is None persona.launch() assert persona.driver is not None persona.quit() assert persona.driver is None

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0

null

0

null

0

1

0

57018df18d3cbc94d73679782950464b4f793c17

26,556

py

Python

inference.py

QuPengfei/learnable-triangulation-pytorch

861d9ccf8b06bd2f130697cd40b7ac57d7f7d9f2

[ "MIT" ]

null

inference.py

QuPengfei/learnable-triangulation-pytorch

861d9ccf8b06bd2f130697cd40b7ac57d7f7d9f2

[ "MIT" ]

null

inference.py

QuPengfei/learnable-triangulation-pytorch

861d9ccf8b06bd2f130697cd40b7ac57d7f7d9f2

[ "MIT" ]

null

import torch import numpy as np import cv2 import os import h5py from collections import defaultdict from mvn.models.triangulation import RANSACTriangulationNet, AlgebraicTriangulationNet, VolumetricTriangulationNet from mvn.models.loss import KeypointsMSELoss, KeypointsMSESmoothLoss, KeypointsMAELoss, KeypointsL2Loss, VolumetricCELoss from mvn.utils import img, multiview, op, vis, misc, cfg from mvn.utils.img import get_square_bbox, resize_image, crop_image, normalize_image, scale_bbox from mvn.utils.multiview import Camera from mvn.utils.multiview import project_3d_points_to_image_plane_without_distortion as project from mvn.datasets import utils as dataset_utils from mvn.utils.img import image_batch_to_torch retval = { 'subject_names': ['S1', 'S5', 'S6', 'S7', 'S8', 'S9', 'S11'], 'camera_names': ['54138969', '55011271', '58860488', '60457274'], 'action_names': [ 'Directions-1', 'Directions-2', 'Discussion-1', 'Discussion-2', 'Eating-1', 'Eating-2', 'Greeting-1', 'Greeting-2', 'Phoning-1', 'Phoning-2', 'Posing-1', 'Posing-2', 'Purchases-1', 'Purchases-2', 'Sitting-1', 'Sitting-2', 'SittingDown-1', 'SittingDown-2', 'Smoking-1', 'Smoking-2', 'TakingPhoto-1', 'TakingPhoto-2', 'Waiting-1', 'Waiting-2', 'Walking-1', 'Walking-2', 'WalkingDog-1', 'WalkingDog-2', 'WalkingTogether-1', 'WalkingTogether-2'] } h5_file="data/human36m/extra/una-dinosauria-data/h36m/cameras.h5" bbox_file="data/human36m/extra/bboxes-Human36M-GT.npy" def square_the_bbox(bbox): top, left, bottom, right = bbox width = right - left height = bottom - top if height < width: center = (top + bottom) * 0.5 top = int(round(center - width * 0.5)) bottom = top + width else: center = (left + right) * 0.5 left = int(round(center - height * 0.5)) right = left + height return top, left, bottom, right def fill_bbox(bb_file): # Fill bounding boxes TLBR bboxes = np.load(bb_file, allow_pickle=True).item() for subject in bboxes.keys(): for action in bboxes[subject].keys(): for camera, bbox_array in bboxes[subject][action].items(): for frame_idx, bbox in enumerate(bbox_array): bbox[:] = square_the_bbox(bbox) return bboxes def fill_bbox_subject_action(bb_file, subject, action): # Fill bounding boxes TLBR bboxes = np.load(bb_file, allow_pickle=True).item() bboxes_subject_action = bboxes[subject][action] for camera, bbox_array in bboxes_subject_action.items(): for frame_idx, bbox in enumerate(bbox_array): bbox[:] = square_the_bbox(bbox) return bboxes_subject_action def get_bbox_subject_action(bboxes, idx): bbox = {} for (camera_idx, camera) in enumerate(retval['camera_names']): bbox[camera] = bboxes[camera][idx] return bbox def fill_cameras(h5_cameras): info = np.empty( (len(retval['subject_names']), len(retval['camera_names'])), dtype=[ ('R', np.float32, (3,3)), ('t', np.float32, (3,1)), ('K', np.float32, (3,3)), ('dist', np.float32, 5) ] ) cameras_params = h5py.File(h5_cameras, 'r') # Fill retval['cameras'] for subject_idx, subject in enumerate(retval['subject_names']): for camera_idx, camera in enumerate(retval['camera_names']): assert len(cameras_params[subject.replace('S', 'subject')]) == 4 camera_params = cameras_params[subject.replace('S', 'subject')]['camera%d' % (camera_idx+1)] camera_retval = info[subject_idx][camera_idx] def camera_array_to_name(array): return ''.join(chr(int(x[0])) for x in array) assert camera_array_to_name(camera_params['Name']) == camera camera_retval['R'] = np.array(camera_params['R']).T camera_retval['t'] = -camera_retval['R'] @ camera_params['T'] camera_retval['K'] = 0 camera_retval['K'][:2, 2] = camera_params['c'][:, 0] camera_retval['K'][0, 0] = camera_params['f'][0] camera_retval['K'][1, 1] = camera_params['f'][1] camera_retval['K'][2, 2] = 1.0 camera_retval['dist'][:2] = camera_params['k'][:2, 0] camera_retval['dist'][2:4] = camera_params['p'][:, 0] camera_retval['dist'][4] = camera_params['k'][2, 0] return info def fill_cameras_subject(h5_cameras,subject): info = np.empty( len(retval['camera_names']), dtype=[ ('R', np.float32, (3,3)), ('t', np.float32, (3,1)), ('K', np.float32, (3,3)), ('dist', np.float32, 5) ] ) cameras = {} subject_idx = retval['subject_names'].index(subject) cameras_params = h5py.File(h5_cameras, 'r') # Fill retval['cameras'] for camera_idx, camera in enumerate(retval['camera_names']): assert len(cameras_params[subject.replace('S', 'subject')]) == 4 camera_params = cameras_params[subject.replace('S', 'subject')]['camera%d' % (camera_idx+1)] camera_retval = info[camera_idx] def camera_array_to_name(array): return ''.join(chr(int(x[0])) for x in array) assert camera_array_to_name(camera_params['Name']) == camera camera_retval['R'] = np.array(camera_params['R']).T camera_retval['t'] = -camera_retval['R'] @ camera_params['T'] camera_retval['K'] = 0 camera_retval['K'][:2, 2] = camera_params['c'][:, 0] camera_retval['K'][0, 0] = camera_params['f'][0] camera_retval['K'][1, 1] = camera_params['f'][1] camera_retval['K'][2, 2] = 1.0 camera_retval['dist'][:2] = camera_params['k'][:2, 0] camera_retval['dist'][2:4] = camera_params['p'][:, 0] camera_retval['dist'][4] = camera_params['k'][2, 0] cameras[camera] = camera_retval return cameras #retval['bboxes'] = fill_bbox(bbox_file) #retval['cameras'] = fill_cameras(h5_file) class Detector: def __init__(self, config, device = "cuda:0"): super().__init__() self.model = { "ransac": RANSACTriangulationNet, "alg": AlgebraicTriangulationNet, "vol": VolumetricTriangulationNet }[config.model.name](config, device=device).to(device) if config.model.init_weights: state_dict = torch.load(config.model.checkpoint) for key in list(state_dict.keys()): new_key = key.replace("module.", "") state_dict[new_key] = state_dict.pop(key) state_dict = torch.load(config.model.checkpoint) self.model.load_state_dict(state_dict, strict=True) print("Successfully loaded pretrained weights for whole model") def infer(self, batch, model_type, device, config): """ For a single image inference """ outputBatch = {} inputBatch = {} images_batch = [] image_batch = image_batch_to_torch(batch['images']) image_batch = image_batch.to(device) images_batch.append(image_batch) images_batch = torch.stack(images_batch, dim=0) #proj_matricies_batch = [torch.from_numpy(camera.projection).float().to(device) for camera in batch['cameras']] proj_matricies_batch = torch.stack([torch.from_numpy(camera.projection) for camera in batch['cameras']], dim=0) proj_matricies_batch = proj_matricies_batch.float().to(device) proj_matricies_batchs = [] # shape (batch_size, n_views, 3, 4) proj_matricies_batchs.append(proj_matricies_batch) proj_matricies_batchs = torch.stack(proj_matricies_batchs,dim=0) #print(proj_matricies_batchs,proj_matricies_batchs.shape,len(batch),images_batch.shape) keypoints_2d_pred, cuboids_pred, base_points_pred, volumes_pred, coord_volumes_pred = None, None, None, None, None if model_type == "alg" or model_type == "ransac": keypoints_3d_pred, keypoints_2d_pred, heatmaps_pred, confidences_pred = self.model(images_batch, proj_matricies_batchs, batch) elif model_type == "vol": keypoints_3d_pred, heatmaps_pred, volumes_pred, confidences_pred, cuboids_pred, coord_volumes_pred, base_points_pred = self.model(images_batch, proj_matricies_batchs, batch) outputBatch["keypoints_3d_pred"] = keypoints_3d_pred outputBatch["heatmaps_pred"] = heatmaps_pred outputBatch["volumes_pred"] = volumes_pred outputBatch["confidences_pred"] = confidences_pred outputBatch["cuboids_pred"] = confidences_pred outputBatch["coord_volumes_pred"] = coord_volumes_pred outputBatch["base_points_pred"] = base_points_pred inputBatch["images_batch"] = images_batch return outputBatch, inputBatch def inferHuman36Data(self, batch, model_type, device, config, randomize_n_views, min_n_views, max_n_views): """ For batch inferences """ outputBatch = {} inputBatch = {} collatFunction = dataset_utils.make_collate_fn(randomize_n_views, min_n_views, max_n_views) batch = collatFunction(batch) images_batch, keypoints_3d_gt, keypoints_3d_validity_gt, proj_matricies_batch = dataset_utils.prepare_batch(batch, device, config) #print(proj_matricies_batch,proj_matricies_batch.shape,len(batch),images_batch.shape) keypoints_2d_pred, cuboids_pred, base_points_pred, volumes_pred, coord_volumes_pred = None, None, None, None, None if model_type == "alg" or model_type == "ransac": keypoints_3d_pred, keypoints_2d_pred, heatmaps_pred, confidences_pred = self.model(images_batch, proj_matricies_batch, batch) elif model_type == "vol": keypoints_3d_pred, heatmaps_pred, volumes_pred, confidences_pred, cuboids_pred, coord_volumes_pred, base_points_pred = self.model(images_batch, proj_matricies_batch, batch) outputBatch["keypoints_3d_pred"] = keypoints_3d_pred outputBatch["heatmaps_pred"] = heatmaps_pred outputBatch["volumes_pred"] = volumes_pred outputBatch["confidences_pred"] = confidences_pred outputBatch["cuboids_pred"] = confidences_pred outputBatch["coord_volumes_pred"] = coord_volumes_pred outputBatch["base_points_pred"] = base_points_pred inputBatch["images_batch"] = images_batch inputBatch["proj_matricies_batch"] = proj_matricies_batch return outputBatch, inputBatch def viewSample(sample,idx=0): camera_idx = 0 image = sample['images'][camera_idx] camera = sample['cameras'][camera_idx] subject = sample['subject'][camera_idx] action = sample['action'][camera_idx] display = image.copy() keypoints_2d = project(camera.projection, sample['keypoints_3d'][:, :3]) for i,(x,y) in enumerate(keypoints_2d): cv2.circle(display, (int(x), int(y)), 3, (0,0,255), -1) file = f"./result/{subject}-{action}-{camera.name}-{idx}.png" cv2.imwrite(file, display) def viewHeatmaps(sample,idx,prediction,config): # TODO get the visualization done images_batch = [] for image_batch in sample['images']: images_batch.append(image_batch) heatmaps_vis = vis.visualize_heatmaps( inputBatch["images_batch"], prediction["heatmaps_pred"], kind=config.kind, batch_index=0, size=5, max_n_rows=10, max_n_cols=10) heatmaps_vis = heatmaps_vis.transpose(2, 0, 1) for i in range(0,4): cv2.imwrite(f"./result/heatmaps_test_{idx}_{i}.png", heatmaps_vis[i,:,:]) def viewVideo(sample): displays = [] # Project and draw keypoints on images for camera_idx in range(len(sample['cameras'])): #camera_indexes_to_show: # import ipdb; ipdb.set_trace() display = sample['images'][camera_idx] cv2.putText(display, f"Cam {camera_idx:02}", (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0)) displays.append(display) # Fancy stacked images for j, display in enumerate(displays): if j == 0: combined = display else: combined = np.concatenate((combined, display), axis=1) return combined def viewVideoResult(sample,idx, prediction,config,size=(384,384)): displays = [] keypoints3d_pred = prediction['keypoints_3d_pred'].cpu() keypoints_3d_pred = keypoints3d_pred[0,:, :3].detach().numpy() # Project and draw keypoints on images for camera_idx in range(len(sample['cameras'])): #camera_indexes_to_show: camera = sample['cameras'][camera_idx] keypoints_2d_pred = project(camera.projection, keypoints_3d_pred) # import ipdb; ipdb.set_trace() img = sample['images'][camera_idx] pred_kind = config.pred_kind if hasattr(config, "pred_kind") else config.kind display = vis.draw_2d_pose_cv2(keypoints_2d_pred, img, kind=pred_kind) cv2.putText(display, f"Cam {camera_idx:02}", (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0)) displays.append(display) display3 = vis.draw_3d_pose_image(keypoints_3d_pred,kind=pred_kind,radius=450) display3 = cv2.cvtColor(display3,cv2.COLOR_RGBA2RGB) display3 = cv2.resize(display3, size, interpolation=cv2.INTER_AREA) cv2.putText(display3, f"3D prediction", (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0)) displays.append(display3) # Fancy stacked images for j, display in enumerate(displays): if j == 0: combined = display else: combined = np.concatenate((combined, display), axis=1) return combined def viewResult(sample,idx,prediction,config,save_images_instead=1,size=(384,384)): displays = [] camera_idx = 0 camera = sample['cameras'][camera_idx] subject = sample['subject'][camera_idx] action = sample['action'][camera_idx] keypoints3d_pred = prediction['keypoints_3d_pred'].cpu() keypoints_3d_pred = keypoints3d_pred[0,:, :3].detach().numpy() keypoints_3d_gt = sample['keypoints_3d'][:, :3] # Project and draw keypoints on images for camera_idx in range(len(sample['cameras'])): #camera_indexes_to_show: camera = sample['cameras'][camera_idx] keypoints_2d_pred = project(camera.projection, keypoints_3d_pred) keypoints_2d_gt = project(camera.projection, keypoints_3d_gt) # import ipdb; ipdb.set_trace() img = sample['images'][camera_idx] pred_kind = config.pred_kind if hasattr(config, "pred_kind") else config.kind display = vis.draw_2d_pose_cv2(keypoints_2d_pred, img, kind=pred_kind) #display = vis.draw_2d_pose_cv2(keypoints_2d_gt, img, kind=config.kind) cv2.putText(display, f"Cam {camera_idx:02}", (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0)) displays.append(display) display3 = vis.draw_3d_pose_image(keypoints_3d_pred,kind=pred_kind,radius=450) display3 = cv2.cvtColor(display3,cv2.COLOR_RGBA2RGB) display3 = cv2.resize(display3, size, interpolation=cv2.INTER_AREA) cv2.putText(display3, f"3D prediction", (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0)) displays.append(display3) display3_gt = vis.draw_3d_pose_image(sample['keypoints_3d'][:, :3],kind=pred_kind,radius=450) display3_gt = cv2.cvtColor(display3_gt,cv2.COLOR_RGBA2RGB) display3_gt = cv2.resize(display3_gt, size, interpolation=cv2.INTER_AREA) cv2.putText(display3_gt, f"3D GT", (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0)) displays.append(display3_gt) # Fancy stacked images for j, display in enumerate(displays): if j == 0: combined = display else: combined = np.concatenate((combined, display), axis=1) # Load if save_images_instead: file = f"./result/result-{subject}-{action}-{camera.name}-{idx}.png" cv2.imwrite(file, combined) else: cv2.imshow('w', combined) cv2.setWindowTitle('w', f"Index {idx}") c = cv2.waitKey(0) % 256 if c == ord('q') or c == 27: print('Quitting...') cv2.destroyAllWindows() def prepareSample(idx, labels, human36mRoot, keyPoint3d = None , imageShape = None, scaleBox = 1.0, crop = True, normImage = False): sample = defaultdict(list) # return value shot = labels['table'][idx] subject = labels['subject_names'][shot['subject_idx']] action = labels['action_names'][shot['action_idx']] frame_idx = shot['frame_idx'] for camera_idx, camera_name in enumerate(labels['camera_names']): bbox = shot['bbox_by_camera_tlbr'][camera_idx][[1,0,3,2]] # TLBR to LTRB bbox_height = bbox[2] - bbox[0] if bbox_height == 0: # convention: if the bbox is empty, then this view is missing continue # scale the bounding box bbox = scale_bbox(bbox, scaleBox) # load image image_path = os.path.join(human36mRoot, subject, action, 'imageSequence', camera_name, 'img_%06d.jpg' % (frame_idx+1)) assert os.path.isfile(image_path), '%s doesn\'t exist' % image_path image = cv2.imread(image_path) # load camera shot_camera = labels['cameras'][shot['subject_idx'], camera_idx] #print(shot_camera) retval_camera = Camera(shot_camera['R'], shot_camera['t'], shot_camera['K'], shot_camera['dist'], camera_name) if crop: # crop image image = crop_image(image, bbox) retval_camera.update_after_crop(bbox) if imageShape is not None: # resize image_shape_before_resize = image.shape[:2] image = resize_image(image, imageShape) retval_camera.update_after_resize(image_shape_before_resize, imageShape) sample['image_shapes_before_resize'].append(image_shape_before_resize) if normImage: image = normalize_image(image) sample['images'].append(image) sample['detections'].append(bbox + (1.0,)) # TODO add real confidences sample['cameras'].append(retval_camera) sample['proj_matrices'].append(retval_camera.projection) sample["action"].append(action) sample["subject"].append(subject) sample["frameId"].append(frame_idx) # 3D keypoints # add dummy confidences sample['keypoints_3d'] = np.pad( shot['keypoints'][:17], ((0,0), (0,1)), 'constant', constant_values=1.0) # build cuboid # base_point = sample['keypoints_3d'][6, :3] # sides = np.array([self.cuboid_side, self.cuboid_side, self.cuboid_side]) # position = base_point - sides / 2 # sample['cuboids'] = volumetric.Cuboid3D(position, sides) # save sample's index sample['indexes'] = idx if keyPoint3d is not None: sample['pred_keypoints_3d'] = keyPoint3d[idx] sample.default_factory = None return sample def prepareVideoSample(info, images, cameras, bboxes, subject = 'S1', imageShape = [384, 384], scaleBox = 1.0, crop = True, normImage = False): sample = defaultdict(list) # return value subject_idx = info['subject_names'].index(subject) for camera_idx, camera_name in enumerate(info['camera_names']): bbox = bboxes[camera_name][[1,0,3,2]] # TLBR to LTRB bbox_height = bbox[2] - bbox[0] if bbox_height == 0: # convention: if the bbox is empty, then this view is missing continue # scale the bounding box bbox = scale_bbox(bbox, scaleBox) # load camera shot_camera = cameras[camera_name] image = images[camera_name] #print(shot_camera) retval_camera = Camera(shot_camera['R'], shot_camera['t'], shot_camera['K'], shot_camera['dist'], camera_name) if crop: # crop image image = crop_image(image, bbox) retval_camera.update_after_crop(bbox) if imageShape is not None: # resize image_shape_before_resize = image.shape[:2] image = resize_image(image, imageShape) retval_camera.update_after_resize(image_shape_before_resize, imageShape) sample['images'].append(image) sample['cameras'].append(retval_camera) sample['proj_matrices'].append(retval_camera.projection) # projection matricies #print(sample['proj_matrices']) sample.default_factory = None return sample def loadHuman36mLabel(path,train = True, withDamageAction=True, retain_every_n_frames_in_test=1): """ this load the label, including bouding box, camera matrices """ test = not train labels = np.load(path, allow_pickle=True).item() train_subjects = ['S1', 'S5', 'S6', 'S7', 'S8'] test_subjects = ['S9', 'S11'] train_subjects = list(labels['subject_names'].index(x) for x in train_subjects) test_subjects = list(labels['subject_names'].index(x) for x in test_subjects) indices = [] if train: mask = np.isin(labels['table']['subject_idx'], train_subjects, assume_unique=True) indices.append(np.nonzero(mask)[0]) if test: mask = np.isin(labels['table']['subject_idx'], test_subjects, assume_unique=True) if not withDamageAction: mask_S9 = labels['table']['subject_idx'] == labels['subject_names'].index('S9') damaged_actions = 'Greeting-2', 'SittingDown-2', 'Waiting-1' damaged_actions = [labels['action_names'].index(x) for x in damaged_actions] mask_damaged_actions = np.isin(labels['table']['action_idx'], damaged_actions) mask &= ~(mask_S9 & mask_damaged_actions) indices.append(np.nonzero(mask)[0][::retain_every_n_frames_in_test]) labels['table'] = labels['table'][np.concatenate(indices)] return labels def loadPrePelvis(path): pred_results = np.load(path, allow_pickle=True) keypoints_3d_pred = pred_results['keypoints_3d'][np.argsort(pred_results['indexes'])] return keypoints_3d_pred def infer(model_type="alg",max_num=5, save_images_instead=1, crop=True): if model_type == "alg": config = cfg.load_config("./experiments/human36m/train/human36m_alg.yaml") elif model_type == "vol": config = cfg.load_config("./experiments/human36m/train/human36m_vol_softmax.yaml") pelvis3d = loadPrePelvis(config.dataset.train.pred_results_path) device = torch.device(0) labels = loadHuman36mLabel(config.dataset.train.labels_path) detector = Detector(config, device=device) for idx in range(max_num): sample = [prepareSample(100+idx, labels, config.dataset.train.h36m_root, keyPoint3d=None, crop=crop, imageShape=config.image_shape)] viewSample(sample[0],idx) prediction, inputBatch = detector.inferHuman36Data(sample, model_type, device, config, randomize_n_views=config.dataset.val.randomize_n_views, min_n_views=config.dataset.val.min_n_views, max_n_views=config.dataset.val.max_n_views) viewResult(sample[0],idx,prediction,config,save_images_instead=save_images_instead) def infer_videos(model_type="alg",subject="S1", action="Sitting-1", max_num=5, save_images_instead=True, crop=True): if model_type == "alg": config = cfg.load_config("./experiments/human36m/train/human36m_alg.yaml") elif model_type == "vol": config = cfg.load_config("./experiments/human36m/train/human36m_vol_softmax.yaml") pelvis3d = loadPrePelvis(config.dataset.train.pred_results_path) device = torch.device(0) detector = Detector(config, device=device) bboxes = fill_bbox_subject_action(bbox_file, subject, action) cameras = fill_cameras_subject(h5_file,subject) cap = {} wri = None human36mRoot = "/dataset/experiment-dataset/extracted/" video_path = os.path.join(human36mRoot, subject, 'Videos') for (camera_idx, camera) in enumerate(retval['camera_names']): video_name = video_path+'/'+action.replace("-"," ")+'.'+camera+'.mp4' assert os.path.isfile(video_name), '%s doesn\'t exist' % video_name cap[camera] = cv2.VideoCapture(video_name) size = (int(cap[camera].get(cv2.CAP_PROP_FRAME_WIDTH)),int(cap[camera].get(cv2.CAP_PROP_FRAME_HEIGHT))) if save_images_instead: wri = cv2.VideoWriter( f'./result/result-{subject}-{action}.mp4',cv2.VideoWriter_fourcc('m','p','4','v'), 30,(1920,384)) idx = 0 #while True: while True: frames = {} for (camera_idx, camera) in enumerate(retval['camera_names']): success,frames[camera] = cap[camera].read() if success != True: break bbox = get_bbox_subject_action(bboxes,idx) sample = prepareVideoSample(info=retval, images=frames, cameras=cameras, bboxes=bbox, subject = subject, imageShape = [384, 384], scaleBox = 1.0, crop = True, normImage = False) prediction, inputBatch = detector.infer(sample, model_type, device, config) combined = viewVideoResult(sample,idx, prediction,config) #combined = viewVideo(sample) idx = idx + 1 if save_images_instead: if idx < max_num: #file = f"./result/result-video-{subject}-{action}-{camera}-{idx}.png" #cv2.imwrite(file, combined) wri.write(combined) else: break else: cv2.imshow('w', combined) cv2.setWindowTitle('w', f"Index {idx}") c = cv2.waitKey(0) % 256 if c == ord('q') or c == 27: print('Quitting...') break; cv2.destroyAllWindows() for (camera_idx, camera) in enumerate(retval['camera_names']): cap[camera].release() if save_images_instead: wri.release() if __name__ == "__main__": #infer("alg",max_num=2, crop=True) infer_videos("alg",max_num=1000, save_images_instead=False, crop=True)

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57028ca06deb47d996805621dce315dc63a9dc8f

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Python

survol/sources_types/Linux/tcp_sockets.py

AugustinMascarelli/survol

7a822900e82d1e6f016dba014af5741558b78f15

[ "BSD-3-Clause" ]

null

survol/sources_types/Linux/tcp_sockets.py

AugustinMascarelli/survol

7a822900e82d1e6f016dba014af5741558b78f15

[ "BSD-3-Clause" ]

null

survol/sources_types/Linux/tcp_sockets.py

AugustinMascarelli/survol

7a822900e82d1e6f016dba014af5741558b78f15

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python """ TCP Linux sockets with netstat """ import re import sys import socket import lib_util import lib_common from lib_properties import pc from sources_types import addr as survol_addr # Many advantages compared to psutil: # The Python module psutil is not needed # psutil gives only sockets if the process is accessible. # It is much faster. # On the other it is necessary to run netstat in the shell. # $ netstat -aptn # (Not all processes could be identified, non-owned process info # will not be shown, you would have to be root to see it all.) # Active Internet connections (servers and established) # Proto Recv-Q Send-Q Local Address Foreign Address State PID/Program name # tcp 0 0 192.168.0.17:8000 0.0.0.0:* LISTEN 25865/python # tcp 0 0 127.0.0.1:427 0.0.0.0:* LISTEN - # tcp 0 0 0.0.0.0:5900 0.0.0.0:* LISTEN 4119/vino-server # tcp 0 0 192.168.122.1:53 0.0.0.0:* LISTEN - # tcp 0 0 192.168.0.17:44634 192.168.0.14:60685 ESTABLISHED 4118/rygel # tcp 0 0 192.168.0.17:22 192.168.0.14:60371 ESTABLISHED - # tcp 0 0 192.168.0.17:44634 192.168.0.14:58478 ESTABLISHED 4118/rygel # tcp 0 0 192.168.0.17:44634 192.168.0.15:38960 TIME_WAIT - # tcp 0 0 192.168.0.17:44634 192.168.0.14:58658 ESTABLISHED 4118/rygel # tcp 0 0 192.168.0.17:44634 192.168.0.14:59694 ESTABLISHED 4118/rygel # tcp 0 0 fedora22:44634 192.168.0.14:58690 ESTABLISHED 4118/rygel # tcp 0 0 fedora22:ssh 192.168.0.14:63599 ESTABLISHED - # tcp 0 0 fedora22:42042 176.103.:universe_suite ESTABLISHED 23512/amule # tcp6 0 0 [::]:wbem-http [::]:* LISTEN - # tcp6 0 0 [::]:wbem-https [::]:* LISTEN - # tcp6 0 0 [::]:mysql [::]:* LISTEN - # tcp6 0 0 [::]:rfb [::]:* LISTEN 4119/vino-server # tcp6 0 0 [::]:50000 [::]:* LISTEN 23512/amule # tcp6 0 0 [::]:43056 [::]:* LISTEN 4125/httpd # tcp6 0 0 [::]:http [::]:* LISTEN - # tcp6 0 0 [::]:ssh [::]:* LISTEN - # tcp6 0 0 localhost:ipp [::]:* LISTEN - # tcp6 0 0 [::]:telnet [::]:* LISTEN - # def Main(): cgiEnv = lib_common.CgiEnv() args = ["netstat", '-aptn', ] p = lib_common.SubProcPOpen(args) grph = cgiEnv.GetGraph() (netstat_last_output, netstat_err) = p.communicate() # Converts to string for Python3. netstat_str = netstat_last_output.decode("utf-8") netstat_lines = netstat_str.split('\n') seenHeader = False for lin in netstat_lines: # By default, consecutive spaces are treated as one. linSplit = lin.split() if len(linSplit) == 0: continue if not seenHeader: if linSplit[0] == "Proto": seenHeader = True continue # TODO: "tcp6" if linSplit[0] != "tcp": continue # sys.stderr.write("tcp_sockets.py lin=%s\n"%lin) sockStatus = linSplit[5] if sockStatus not in ["ESTABLISHED","TIME_WAIT"]: continue addrLocal = linSplit[3] ipLocal, portLocal = survol_addr.SplitAddrPort(addrLocal) # It does not use survol_addr.PsutilAddSocketToGraphOne(node_process,cnt,grph) # because sometimes we do not have the process id. localSocketNode = lib_common.gUriGen.AddrUri( ipLocal, portLocal ) grph.add( ( localSocketNode, pc.property_information, lib_common.NodeLiteral(sockStatus) ) ) addrRemot = linSplit[4] # This is different for IPV6 if addrRemot != "0.0.0.0:*": ipRemot, portRemot = survol_addr.SplitAddrPort(addrRemot) remotSocketNode = lib_common.gUriGen.AddrUri( ipRemot, portRemot ) grph.add( ( localSocketNode, pc.property_socket_end, remotSocketNode ) ) pidCommand = linSplit[6] if pidCommand != "-": procPid, procNam = pidCommand.split("/") procNode = lib_common.gUriGen.PidUri(procPid) grph.add( ( procNode, pc.property_host, lib_common.nodeMachine ) ) grph.add( ( procNode, pc.property_pid, lib_common.NodeLiteral(procPid) ) ) grph.add( ( procNode, pc.property_has_socket, localSocketNode ) ) else: # If the local process is not known, just link the local socket to the local machine. grph.add( ( lib_common.nodeMachine, pc.property_host, localSocketNode ) ) cgiEnv.OutCgiRdf() if __name__ == '__main__': Main()

38.460317

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0.577383

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4,846

4.429952

0.362319

0.031261

0.038168

0.023264

0.211923

0.138495

0.115231

0.08615

0.077063

0

0.12024

0.311804

4,846

125

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38.768

0.704648

0.60648

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0.16

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null

0

null

0

1

0

57033e68edf1bc714421c03684cc8349a3a89d3f

5,832

py

Python

models.py

JiaMingLin/residual_adapters

a3d32b4fb6c3c252f5adc1ad178b026a111c1a08

[ "Apache-2.0" ]

137

2018-03-22T15:45:30.000Z

2022-03-17T09:39:07.000Z

models.py

JiaMingLin/residual_adapters

a3d32b4fb6c3c252f5adc1ad178b026a111c1a08

[ "Apache-2.0" ]

5

2018-09-25T19:44:34.000Z

2020-12-19T11:26:41.000Z

models.py

JiaMingLin/residual_adapters

a3d32b4fb6c3c252f5adc1ad178b026a111c1a08

[ "Apache-2.0" ]

40

2018-04-04T12:36:54.000Z

2022-02-19T05:46:36.000Z

# models.py # created by Sylvestre-Alvise Rebuffi [srebuffi@robots.ox.ac.uk] # Copyright © The University of Oxford, 2017-2020 # This code is made available under the Apache v2.0 licence, see LICENSE.txt for details import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable from torch.nn.parameter import Parameter import config_task import math def conv3x3(in_planes, out_planes, stride=1): return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) def conv1x1_fonc(in_planes, out_planes=None, stride=1, bias=False): if out_planes is None: return nn.Conv2d(in_planes, in_planes, kernel_size=1, stride=stride, padding=0, bias=bias) else: return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=bias) class conv1x1(nn.Module): def __init__(self, planes, out_planes=None, stride=1): super(conv1x1, self).__init__() if config_task.mode == 'series_adapters': self.conv = nn.Sequential(nn.BatchNorm2d(planes), conv1x1_fonc(planes)) elif config_task.mode == 'parallel_adapters': self.conv = conv1x1_fonc(planes, out_planes, stride) else: self.conv = conv1x1_fonc(planes) def forward(self, x): y = self.conv(x) if config_task.mode == 'series_adapters': y += x return y class conv_task(nn.Module): def __init__(self, in_planes, planes, stride=1, nb_tasks=1, is_proj=1, second=0): super(conv_task, self).__init__() self.is_proj = is_proj self.second = second self.conv = conv3x3(in_planes, planes, stride) if config_task.mode == 'series_adapters' and is_proj: self.bns = nn.ModuleList([nn.Sequential(conv1x1(planes), nn.BatchNorm2d(planes)) for i in range(nb_tasks)]) elif config_task.mode == 'parallel_adapters' and is_proj: self.parallel_conv = nn.ModuleList([conv1x1(in_planes, planes, stride) for i in range(nb_tasks)]) self.bns = nn.ModuleList([nn.BatchNorm2d(planes) for i in range(nb_tasks)]) else: self.bns = nn.ModuleList([nn.BatchNorm2d(planes) for i in range(nb_tasks)]) def forward(self, x): task = config_task.task y = self.conv(x) if self.second == 0: if config_task.isdropout1: x = F.dropout2d(x, p=0.5, training = self.training) else: if config_task.isdropout2: x = F.dropout2d(x, p=0.5, training = self.training) if config_task.mode == 'parallel_adapters' and self.is_proj: y = y + self.parallel_conv[task](x) y = self.bns[task](y) return y # No projection: identity shortcut class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_planes, planes, stride=1, shortcut=0, nb_tasks=1): super(BasicBlock, self).__init__() self.conv1 = conv_task(in_planes, planes, stride, nb_tasks, is_proj=int(config_task.proj[0])) self.conv2 = nn.Sequential(nn.ReLU(True), conv_task(planes, planes, 1, nb_tasks, is_proj=int(config_task.proj[1]), second=1)) self.shortcut = shortcut if self.shortcut == 1: self.avgpool = nn.AvgPool2d(2) def forward(self, x): residual = x y = self.conv1(x) y = self.conv2(y) if self.shortcut == 1: residual = self.avgpool(x) residual = torch.cat((residual, residual*0),1) y += residual y = F.relu(y) return y class ResNet(nn.Module): def __init__(self, block, nblocks, num_classes=[10]): super(ResNet, self).__init__() nb_tasks = len(num_classes) blocks = [block, block, block] factor = config_task.factor self.in_planes = int(32*factor) self.pre_layers_conv = conv_task(3,int(32*factor), 1, nb_tasks) self.layer1 = self._make_layer(blocks[0], int(64*factor), nblocks[0], stride=2, nb_tasks=nb_tasks) self.layer2 = self._make_layer(blocks[1], int(128*factor), nblocks[1], stride=2, nb_tasks=nb_tasks) self.layer3 = self._make_layer(blocks[2], int(256*factor), nblocks[2], stride=2, nb_tasks=nb_tasks) self.end_bns = nn.ModuleList([nn.Sequential(nn.BatchNorm2d(int(256*factor)),nn.ReLU(True)) for i in range(nb_tasks)]) self.avgpool = nn.AdaptiveAvgPool2d(1) self.linears = nn.ModuleList([nn.Linear(int(256*factor), num_classes[i]) for i in range(nb_tasks)]) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def _make_layer(self, block, planes, nblocks, stride=1, nb_tasks=1): shortcut = 0 if stride != 1 or self.in_planes != planes * block.expansion: shortcut = 1 layers = [] layers.append(block(self.in_planes, planes, stride, shortcut, nb_tasks=nb_tasks)) self.in_planes = planes * block.expansion for i in range(1, nblocks): layers.append(block(self.in_planes, planes, nb_tasks=nb_tasks)) return nn.Sequential(*layers) def forward(self, x): x = self.pre_layers_conv(x) task = config_task.task x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.end_bns[task](x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.linears[task](x) return x def resnet26(num_classes=10, blocks=BasicBlock): return ResNet(blocks, [4,4,4],num_classes)

39.945205

133

0.627743

837

5,832

4.201912

0.185185

0.045778

0.035826

0.021894

0.372477

0.287745

0.194484

0.141882

0.106625

0.077339

0

0.03175

0.249314

5,832

145

134

40.22069

0.771357

0.041152

0

0.196581

0

0.017192

0

1

0.102564

false

0

0.059829

0.017094

0.282051

0

null

0

null

0

1

0

57053e08159134b657dc6dde4b49efc028c6a0a2

2,196

py

Python

main.py

GauravP2001/courseSniperBot

c3e05d2890f10177ee847a961b957d5e63e7d0ec

[ "BSD-2-Clause-FreeBSD" ]

null

main.py

GauravP2001/courseSniperBot

c3e05d2890f10177ee847a961b957d5e63e7d0ec

[ "BSD-2-Clause-FreeBSD" ]

null

main.py

GauravP2001/courseSniperBot

c3e05d2890f10177ee847a961b957d5e63e7d0ec

[ "BSD-2-Clause-FreeBSD" ]

null

import discord import os import requests import asyncio import psycopg2 import logging from apscheduler.schedulers.asyncio import AsyncIOScheduler from discord.ext import commands logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', datefmt=f"%m/%d/%Y %H:%M:%S %Z") logger = logging.getLogger("Snipe Bot") client = commands.Bot(command_prefix=".") scheduler = AsyncIOScheduler() DATABASE_URL = os.environ.get("DATABASE_URL") conn = psycopg2.connect(DATABASE_URL, sslmode="require") cur = conn.cursor() # with conn: # cur.execute("CREATE TABLE coursesToBeFound (index VARCHAR primary key);") # cur.execute("INSERT INTO coursesToBeFound (index) VALUES (%s)", ("00150",)) # cur.execute("DELETE FROM coursesToBeFound where index = %s", ("00150",)) # cur.execute("SELECT * from coursesToBeFound;") # for row in cur: # print(row[0]) sectionsFound = [] @client.event async def on_ready(): logger.info("Bot is ready") @client.command() async def addCourse(ctx, arg): logger.info(arg) await ctx.send("Successfully Added the Course to Snipe!") with conn: cur.execute("INSERT INTO coursesToBeFound (index) VALUES (%s)", (arg,)) async def check_courses(): logger.info("Searching") url = "https://sis.rutgers.edu/soc/api/openSections.json?year=2022&term=1&campus=NB" try: dataJSON = requests.get(url).json() except Exception as e: logger.error(e) return cur.execute("SELECT * from coursesToBeFound;") for row in cur: logger.info(row) for index in dataJSON: if row[0] == index: sectionsFound.append(index) logger.info(f"Found index: {row[0]}") await client.get_channel(int(os.environ.get("CHANNEL_ID"))).send(f"Found Index: {index}") for index in sectionsFound: cur.execute("DELETE FROM coursesToBeFound where index = %s", (index,)) conn.commit() if __name__ == "__main__": logger.info("Starting") scheduler.add_job(check_courses, "interval", seconds=10) scheduler.start() client.run(os.environ.get("token"))

28.519481

105

0.658015

276

2,196

5.17029

0.460145

0.049054

0.025228

0.199019

0.065872

0

0.012521

0.199909

2,196

76

106

28.894737

0.799659

0.142987

0

0.02

0.235326

0

1

0

false

0

0.16

0

0.18

0

null

0

null

0

1

0

57075fadbef4087df6eac236abcbc48b853a6d54

619

py

Python

Python_Exercicios/Mundo2/Condições em Python (if..elif)/python_038.py

jbauermanncode/Curso_Em_Video_Python

330c207d7bed4e663fe1b9ab433ab57a9828b7f1

[ "MIT" ]

null

Python_Exercicios/Mundo2/Condições em Python (if..elif)/python_038.py

jbauermanncode/Curso_Em_Video_Python

330c207d7bed4e663fe1b9ab433ab57a9828b7f1

[ "MIT" ]

null

Python_Exercicios/Mundo2/Condições em Python (if..elif)/python_038.py

jbauermanncode/Curso_Em_Video_Python

330c207d7bed4e663fe1b9ab433ab57a9828b7f1

[ "MIT" ]

null

''' Escreva um programa que leia dois números inteiros e compare- os, mostrando na tela uma mensagem: - O primeiro valor é maior - O segundo valor é maior - não existe valor maior, os dois são iguais ''' # Ler dois números inteiros n1 = int(input('Informe o primeiro número: ')) n2 = int(input('Informe o segundo número: ')) # Operadores Lógicos n1_maior = n1 > n2 n2_maior = n2 > n1 # Estrutura Condicional if, elif, else. if n1_maior: print('O número {} é o maior!'.format(n1)) elif n2_maior: print('O número {} é o maior!'.format(n2)) else: print('Os números são iguais!')

22.925926

101

0.663974

95

619

4.284211

0.442105

0.054054

0.093366

0.078624

0.14742

0

0.025105

0.227787

619

27

102

22.925926

0.82636

0.455574

0

0.386364

0

1

0

false

0

0.3

0

null

0

null

0

1

0

57088093d1d0b3cfd26c3d3201f0bca2db2decb3

324

py

Python

ABS/ABC085C.py

fumiyanll23/AtCoder

362ca9fcacb5415c1458bc8dee5326ba2cc70b65

[ "MIT" ]

null

ABS/ABC085C.py

fumiyanll23/AtCoder

362ca9fcacb5415c1458bc8dee5326ba2cc70b65

[ "MIT" ]

null

ABS/ABC085C.py

fumiyanll23/AtCoder

362ca9fcacb5415c1458bc8dee5326ba2cc70b65

[ "MIT" ]

null

def main(): # input N, Y = map(int, input().split()) # compute for i in range(N+1): for j in range(N+1): if 10000*i+5000*j+1000*(N-i-j)==Y and N-i-j>=0: print(i, j, N-i-j) exit() # output print(-1, -1, -1) if __name__ == '__main__': main()

18

59

0.435185

53

324

2.509434

0.471698

0.06015

0.067669

0.135338

0

0.093596

0.373457

324

17

60

19.058824

0.561576

0.061728

0

0.026667

0

1

0.1

false

0

0.1

0.2

0

null

0

null

0

1

0

5708df6ade016849aefe1a0044ec7ee2d375c82f

10,853

py

Python

testing/test_pulse_prop.py

ibegleris/w-fopo

e44b83b8ec54d01bb34b89805378a2b0659dfe6f

[ "BSD-3-Clause" ]

null

testing/test_pulse_prop.py

ibegleris/w-fopo

e44b83b8ec54d01bb34b89805378a2b0659dfe6f

[ "BSD-3-Clause" ]

null

testing/test_pulse_prop.py

ibegleris/w-fopo

e44b83b8ec54d01bb34b89805378a2b0659dfe6f

[ "BSD-3-Clause" ]

null

import sys sys.path.append('src') from functions import * import numpy as np from numpy.testing import assert_allclose "-----------------------Full soliton--------------------------------------------" def get_Qs(nm, gama,fv, a_vec, dnerr, index, master_index,lamda, n2): if nm == 1: D = loadmat('loading_data/M1_M2_1m_new.mat') M1_temp, M2 = D['M1'], D['M2'] M2[:, :] -= 1 M1 = np.empty([np.shape(M1_temp)[0]-2, np.shape(M1_temp)[1]], dtype=np.int64) M1[:4] = M1_temp[:4] - 1 Q_large = M1_temp[np.newaxis, 4:6, :] M1[-1] = M1_temp[6, :] - 1 Q_large[:,:,:] = gama / (3*n2*(2*pi/lamda)) else: M1, M2, dump, Q_large = \ fibre_parameter_loader(fv, a_vec, dnerr, index, master_index, filename='step_index_2m', filepath='testing/testing_data/step_index/') print(Q_large.shape) Q_large[0,0,:] = gama / (3*n2*(2*pi/lamda)) * np.array([1,1,0,0,0,0,1,1]) Q_large[0,1,:] = gama / (3*n2*(2*pi/lamda)) * np.array([1,0,0,1,1,0,0,1]) return Q_large, M1, M2 def pulse_propagations(ram, ss, nm, N_sol=1, cython = True, u = None): "SOLITON TEST. IF THIS FAILS GOD HELP YOU!" n2 = 2.5e-20 # n2 for silica [m/W] # 0.0011666666666666668 # loss [dB/m] alphadB = np.array([0 for i in range(nm)]) gama = 1e-3 # w/m "-----------------------------General options------------------------------" maxerr = 1e-13 # maximum tolerable error per step "----------------------------Simulation parameters-------------------------" N = 10 z = np.array([0,70]) # total distance [m] nplot = 10 # number of plots nt = 2**N # number of grid points #dzstep = z/nplot # distance per step dz_less = 1 dz = 1 # starting guess value of the step lam_p1 = 1550 lamda_c = 1550e-9 lamda = lam_p1*1e-9 beta2 = -1e-3 P0_p1 = 1 betas = np.array([0, 0, beta2]) T0 = (N_sol**2 * np.abs(beta2) / (gama * P0_p1))**0.5 TFWHM = (2*np.log(1+2**0.5)) * T0 int_fwm = sim_parameters(n2, nm, alphadB) int_fwm.general_options(maxerr, raman_object, ss, ram) int_fwm.propagation_parameters(N, z, nplot, dz_less, 1) int_fwm.woble_propagate(0) fv, where = fv_creator(lam_p1,lam_p1 + 25,0, 100, int_fwm) #fv, where = fv_creator(lam_p1, , int_fwm, prot_casc=0) sim_wind = sim_window(fv, lamda, lamda_c, int_fwm, fv_idler_int=1) loss = Loss(int_fwm, sim_wind, amax=int_fwm.alphadB) alpha_func = loss.atten_func_full(sim_wind.fv, int_fwm) int_fwm.alphadB = alpha_func int_fwm.alpha = int_fwm.alphadB dnerr = [0] index = 1 master_index = 0 a_vec = [2.2e-6] Q_large,M1,M2 = get_Qs(nm, gama, fv, a_vec, dnerr, index, master_index, lamda, n2) if nm ==1: M1, M2, Q_large= np.array([1]), np.array([1]), Q_large[:,0,0] betas = betas[np.newaxis, :] # sys.exit() Dop = dispersion_operator(betas, int_fwm, sim_wind) print(Dop.shape) integrator = Integrator(int_fwm) integrand = Integrand(int_fwm.nm,ram, ss, cython = False, timing = False) dAdzmm = integrand.dAdzmm RK = integrator.RK45mm dAdzmm = integrand.dAdzmm pulse_pos_dict_or = ('after propagation', "pass WDM2", "pass WDM1 on port2 (remove pump)", 'add more pump', 'out') #M1, M2, Q = Q_matrixes(1, n2, lamda, gama=gama) raman = raman_object(int_fwm.ram, int_fwm.how) raman.raman_load(sim_wind.t, sim_wind.dt, M2, nm) if raman.on == 'on': hf = raman.hf else: hf = None u = np.empty( [ int_fwm.nm, len(sim_wind.t)], dtype='complex128') U = np.empty([int_fwm.nm, len(sim_wind.t)], dtype='complex128') sim_wind.w_tiled = np.tile(sim_wind.w + sim_wind.woffset, (int_fwm.nm, 1)) u[:, :] = ((P0_p1)**0.5 / np.cosh(sim_wind.t/T0)) * \ np.exp(-1j*(sim_wind.woffset)*sim_wind.t) U[:, :] = fftshift(sim_wind.dt*fft(u[:, :])) gam_no_aeff = -1j*int_fwm.n2*2*pi/sim_wind.lamda u, U = pulse_propagation(u, U, int_fwm, M1, M2.astype(np.int64), Q_large[0].astype(np.complex128), sim_wind, hf, Dop[0], dAdzmm, gam_no_aeff,RK) U_start = np.abs(U[ :, :])**2 u[:, :] = u[:, :] * \ np.exp(1j*z[-1]/2)*np.exp(-1j*(sim_wind.woffset)*sim_wind.t) """ fig1 = plt.figure() plt.plot(sim_wind.fv,np.abs(U[1,:])**2) plt.savefig('1.png') fig2 = plt.figure() plt.plot(sim_wind.fv,np.abs(U[1,:])**2) plt.savefig('2.png') fig3 = plt.figure() plt.plot(sim_wind.t,np.abs(u[1,:])**2) plt.xlim(-10*T0, 10*T0) plt.savefig('3.png') fig4 = plt.figure() plt.plot(sim_wind.t,np.abs(u[1,:])**2) plt.xlim(-10*T0, 10*T0) plt.savefig('4.png') fig5 = plt.figure() plt.plot(fftshift(sim_wind.w),(np.abs(U[1,:])**2 - np.abs(U[1,:])**2 )) plt.savefig('error.png') fig6 = plt.figure() plt.plot(sim_wind.t,np.abs(u[1,:])**2 - np.abs(u[1,:])**2) plt.xlim(-10*T0, 10*T0) plt.savefig('error2.png') plt.show() """ return u, U, maxerr class Test_cython_nm2(object): def test_ramoff_s0_nm2(self): u_c, U_c, maxerr = pulse_propagations('off', 0, nm=2, cython = True) u_p, U_p, maxerr = pulse_propagations('off', 0, nm=2, cython = False) a,b = np.sum(np.abs(u_c)**2), np.sum(np.abs(u_p)**2) assert np.allclose(a,b) def test_ramon_s0_nm2(self): u_c, U_c, maxerr = pulse_propagations('on', 0, nm=2, cython = True) u_p, U_p, maxerr = pulse_propagations('on', 0, nm=2, cython = False) a,b = np.sum(np.abs(u_c)**2), np.sum(np.abs(u_p)**2) assert np.allclose(a,b) def test_ramoff_s1_nm2(self): u_c, U_c, maxerr = pulse_propagations('off', 1, nm=2, cython = True) u_p, U_p, maxerr = pulse_propagations('off', 1, nm=2, cython = False) a,b = np.sum(np.abs(u_c)**2), np.sum(np.abs(u_p)**2) assert np.allclose(a,b) def test_ramon_s1_nm2(self): u_c, U_c, maxerr = pulse_propagations('on', 1, nm=2, cython = True) u_p, U_p, maxerr = pulse_propagations('on', 1, nm=2, cython = False) a,b = np.sum(np.abs(u_c)**2), np.sum(np.abs(u_p)**2) assert np.allclose(a,b) class Test_cython_nm1(object): def test_ramoff_s0_nm2(self): u_c, U_c, maxerr = pulse_propagations('off', 0, nm=1, cython = True) u_p, U_p, maxerr = pulse_propagations('off', 0, nm=1, cython = False) a,b = np.sum(np.abs(u_c)**2), np.sum(np.abs(u_p)**2) assert np.allclose(a,b) def test_ramon_s0_nm2(self): u_c, U_c, maxerr = pulse_propagations('on', 0, nm=1, cython = True) u_p, U_p, maxerr = pulse_propagations('on', 0, nm=1, cython = False) a,b = np.sum(np.abs(u_c)**2), np.sum(np.abs(u_p)**2) assert np.allclose(a,b) def test_ramoff_s1_nm2(self): u_c, U_c, maxerr = pulse_propagations('off', 1, nm=1, cython = True) u_p, U_p, maxerr = pulse_propagations('off', 1, nm=1, cython = False) a,b = np.sum(np.abs(u_c)**2), np.sum(np.abs(u_p)**2) assert np.allclose(a,b) def test_ramon_s1_nm2(self): u_c, U_c, maxerr = pulse_propagations('on', 1, nm=1, cython = True) u_p, U_p, maxerr = pulse_propagations('on', 1, nm=1, cython = False) a,b = np.sum(np.abs(u_c)**2), np.sum(np.abs(u_p)**2) assert np.allclose(a,b) class Test_pulse_prop(object): def test_solit_r0_ss0(self): u, U, maxerr = pulse_propagations('off', 0, nm=1) assert_allclose(np.abs(u[:, :])**2, np.abs(u[:, :])**2, atol=9e-4) def test_solit_r0_ss0_2(self): u, U, maxerr = pulse_propagations('off', 0, nm=2) #print(np.linalg.norm(np.abs(u[:, 0])**2 - np.abs(u[:, -1])**2, 2)) assert_allclose(np.abs(u[:, :])**2, np.abs(u[:, :])**2, atol=9e-3) def test_energy_r0_ss0(self): u, U, maxerr = pulse_propagations( 'off', 0, nm=1, N_sol=np.abs(10*np.random.randn())) E = [] for i in range(np.shape(u)[1]): E.append(np.linalg.norm(u[:, i], 2)**2) assert np.all(x == E[0] for x in E) def test_energy_r0_ss1(self): u, U, maxerr = pulse_propagations( 'off', 1, nm=1, N_sol=np.abs(10*np.random.randn())) E = [] for i in range(np.shape(u)[1]): E.append(np.linalg.norm(u[:, i], 2)**2) assert np.all(x == E[0] for x in E) def test_energy_r1_ss0(self): u, U, maxerr = pulse_propagations( 'on', 0, nm=1, N_sol=np.abs(10*np.random.randn())) E = [] for i in range(np.shape(u)[1]): E.append(np.linalg.norm(u[:, i], 2)**2) assert np.all(x == E[0] for x in E) def test_energy_r1_ss1(self): u, U, maxerr = pulse_propagations( 'on', 1, nm=1, N_sol=np.abs(10*np.random.randn())) E = [] for i in range(np.shape(u)[1]): E.append(np.linalg.norm(u[:, i], 2)**2) assert np.all(x == E[0] for x in E) def test_energy_r0_ss0_2(self): u, U, maxerr = pulse_propagations( 'off', 0, nm=2, N_sol=np.abs(10*np.random.randn())) E = [] for i in range(np.shape(u)[1]): E.append(np.linalg.norm(u[:, i], 2)**2) assert np.all(x == E[0] for x in E) def test_energy_r0_ss1_2(self): u, U, maxerr = pulse_propagations( 'off', 1, nm=2, N_sol=np.abs(10*np.random.randn())) E = [] for i in range(np.shape(u)[1]): E.append(np.linalg.norm(u[:, i], 2)**2) assert np.all(x == E[0] for x in E) def test_energy_r1_ss0_2(self): u, U, maxerr = pulse_propagations( 'on', 0, nm=2, N_sol=np.abs(10*np.random.randn())) E = [] for i in range(np.shape(u)[1]): E.append(np.linalg.norm(u[:, i], 2)**2) assert np.all(x == E[0] for x in E) def test_energy_r1_ss1_2(self): u, U, maxerr = pulse_propagations( 'on', 1, nm=2, N_sol=np.abs(10*np.random.randn())) E = [] for i in range(np.shape(u)[1]): E.append(np.linalg.norm(u[:, i], 2)**2) assert np.all(x == E[0] for x in E) def test_bire_pass(): Da = np.random.uniform(0, 2*pi, 100) b = birfeg_variation(Da,2) u = np.random.randn(2, 2**14) + 1j * np.random.randn(2, 2**14) u *= 10 for i in range(100): ut = b.bire_pass(u,i) assert_allclose(np.abs(u)**2, np.abs(ut)**2) u = 1 * ut

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null

0

null

0

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0

570a3a32cbbdc85ab026871552208d720276a1d7

1,089

py

Python

download.py

wujushan/AndroidHeatMap

1d6ecff8d810ffd63ba84f56c1a44ee5e7770c59

[ "Apache-2.0" ]

1

2019-06-13T16:05:36.000Z

download.py

wujushan/AndroidHeatMap

1d6ecff8d810ffd63ba84f56c1a44ee5e7770c59

[ "Apache-2.0" ]

null

download.py

wujushan/AndroidHeatMap

1d6ecff8d810ffd63ba84f56c1a44ee5e7770c59

[ "Apache-2.0" ]

null

import os import requests def download(url): download_path = '/root/AndroidHeatMap/download/' if not os.path.exists(download_path): os.mkdir(download_path) all_content = requests.get(url).text file_line = all_content.split("\n") if file_line[0] != "#EXTM3U": raise BaseException(u"not M3U8link") else: unknow = True for index, line in enumerate(file_line): if "EXTINF" in line: unknow = False pd_url = url.rsplit("/", 1)[0] + "/" + file_line[index + 1] res = requests.get(pd_url) c_fule_name = str(file_line[index + 1]) with open(download_path + "/" + c_fule_name, 'ab') as f: f.write(res.content) f.flush() if unknow: raise BaseException("cannot find link") else: print("finish downloading") if __name__ == '__main__': url = 'https://jjdong5.com/get_file/4/1fa69b06c6276768e95cc0c04d85feec693488a588/13000/13287/13287_360p.m3u8' download(url)

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0.035714

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null

0

null

0

1

0

570a7fbde091be0d15c77144e4caa11f184860d3

4,945

py

Python

tests/watermarks_test.py

yujialuo/erdos

7a631b55895f1a473b0f4d38a0d6053851e65b5d

[ "Apache-2.0" ]

null

tests/watermarks_test.py

yujialuo/erdos

7a631b55895f1a473b0f4d38a0d6053851e65b5d

[ "Apache-2.0" ]

null

tests/watermarks_test.py

yujialuo/erdos

7a631b55895f1a473b0f4d38a0d6053851e65b5d

[ "Apache-2.0" ]

null

from collections import defaultdict from time import sleep from absl import app from absl import flags import erdos.graph from erdos.op import Op from erdos.utils import frequency from erdos.message import Message from erdos.data_stream import DataStream from erdos.timestamp import Timestamp from erdos.message import WatermarkMessage INTEGER_FREQUENCY = 10 # The frequency at which to send the integers. class FirstOperator(Op): """ Source operator that publishes increasing integers at a fixed frequency. The operator also inserts a watermark after a fixed number of messages. """ def __init__(self, name, batch_size): """ Initializes the attributes to be used by the source operator.""" super(FirstOperator, self).__init__(name) self.batch_size = batch_size self.counter = 1 self.batch_number = 1 @staticmethod def setup_streams(input_streams): """ Outputs a single stream where the messages are sent. """ return [DataStream(data_type = int, name = "integer_out")] @frequency(INTEGER_FREQUENCY) def publish_numbers(self): """ Sends an increasing count of numbers to the downstream operators. """ output_msg = Message(self.counter, Timestamp(coordinates = [self.batch_number])) self.get_output_stream("integer_out").send(output_msg) # Decide if the watermark needs to be sent. if self.counter % self.batch_size == 0: # The batch has completed. We need to send a watermark now. watermark_msg = WatermarkMessage(Timestamp(coordinates = [self.batch_number])) self.batch_number += 1 self.get_output_stream("integer_out").send(watermark_msg) # Update the counters. self.counter += 1 def execute(self): """ Execute the publish number loop. """ self.publish_numbers() self.spin() class SecondOperator(Op): """ Second operator that listens in on the numbers and reports their sum when the watermark is received. """ def __init__(self, name): """ Initializes the attributes to be used.""" super(SecondOperator, self).__init__(name) self.windows = defaultdict(list) @staticmethod def setup_streams(input_streams): """ Subscribes all the input streams to the save numbers callback. """ input_streams.add_callback(SecondOperator.save_numbers) input_streams.add_completion_callback(SecondOperator.execute_sum) return [DataStream(data_type = int, name = "sum_out")] def save_numbers(self, message): """ Save all the numbers corresponding to a window. """ batch_number = message.timestamp.coordinates[0] self.windows[batch_number].append(message.data) def execute_sum(self, message): """ Sum all the numbers in this window and send out the aggregate. """ batch_number = message.timestamp.coordinates[0] window_data = self.windows.pop(batch_number, None) #print("Received a watermark for the timestamp: {}".format(batch_number)) #print("The sum of the window {} is {}".format( # window_data, sum(window_data))) output_msg = Message(sum(window_data), Timestamp(coordinates = [batch_number])) self.get_output_stream("sum_out").send(output_msg) def execute(self): """ Execute the spin() loop to continue processing messages. """ self.spin() class ThirdOperator(Op): """ Third operator that listens in on the sum and verifies correctness.""" def __init__(self, name): """Initializes the attributes to be used.""" super(ThirdOperator, self).__init__(name) @staticmethod def setup_streams(input_streams): """ Subscribes all the input streams to the assert callback.""" input_streams.add_callback(ThirdOperator.assert_correctness) return [] def assert_correctness(self, message): """ Assert the correctness of the results.""" batch_number = message.timestamp.coordinates[0] sum_data = sum(range((batch_number - 1) * 10 + 1, batch_number * 10 + 1)) print("Received sum: {} for the batch_number {}, expected {}".format( message.data, batch_number, sum_data)) def main(argv): # Set up the graph. graph = erdos.graph.get_current_graph() # Add the operators. source_op = graph.add(FirstOperator, name = "gen_op", init_args = {'batch_size' : 10}) sum_op = graph.add(SecondOperator, name = "sum_op") assert_op = graph.add(ThirdOperator, name = "assert_op") # Connect the operators. graph.connect([source_op], [sum_op]) graph.connect([sum_op], [assert_op]) # Execute the graph. graph.execute('ray') if __name__ == "__main__": app.run(main)

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0

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0.012987

0

null

0

null

0

1

0

570a9547e24dbd1a28701e76c97396c34016c792

1,436

py

Python

test/test_shop/views.py

blakelockley/django-base-shop

455a2f4465e90cde57719ac29dc090b14f0bd324

[ "MIT" ]

1

2020-01-12T04:05:42.000Z

test/test_shop/views.py

blakelockley/django-base-shop

455a2f4465e90cde57719ac29dc090b14f0bd324

[ "MIT" ]

14

2020-03-24T18:11:07.000Z

2022-03-12T00:15:20.000Z

test/test_shop/views.py

blakelockley/django-base-shop

455a2f4465e90cde57719ac29dc090b14f0bd324

[ "MIT" ]

null

from django.http import HttpResponse from django_base_shop.models import ShippingTag from .models import ConcreteCart, ConcreteProduct def index(request): return HttpResponse(b"Hello world") def check_cart(request): cart = request.cart if not cart.is_persisted: return HttpResponse(b"None") return HttpResponse(cart.cart_token.encode("utf-8")) def check_cart_items(request): cart = request.cart if not cart.is_persisted: return HttpResponse(b"None") body = f"{cart.cart_token}<br /><br />" for item in cart.items.all(): body += f"{item.product.name} {item.quantity}<br />" return HttpResponse(body.encode("utf-8")) def add_cart_item(request, pk): cart = request.cart if ConcreteProduct.objects.count() == 0: ConcreteProduct.objects.create( handle="ANV-001", name="Anvil", price=100.0, shipping_tag=ShippingTag.objects.create( name="Medium", category="Size", order=1 ), ) product = ConcreteProduct.objects.get(pk=pk) cart.add_item(product) return HttpResponse(b"Item added! <a href='/check_cart_items'>Check items</a>") def remove_cart_item(request, pk): cart = request.cart product = ConcreteProduct.objects.get(pk=pk) cart.remove_item(product) return HttpResponse(b"Item removed! <a href='/check_cart_items'>Check items</a>")

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0

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1,436

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false

0

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0.405405

0

null

0

null

0

1

0

570cfc314b92388cc92855fea7600f5e8b1e443e

11,600

py

Python

q3/q3/drivers/ui/pyqt5.py

virtimus/makaronLab

10b9be7d7d65d3da6219f929ea7070dd5fed3a81

[ "0BSD" ]

2

2021-03-16T05:48:36.000Z

2021-10-11T01:55:48.000Z

q3/q3/drivers/ui/pyqt5.py

virtimus/makaronLab

10b9be7d7d65d3da6219f929ea7070dd5fed3a81

[ "0BSD" ]

null

q3/q3/drivers/ui/pyqt5.py

virtimus/makaronLab

10b9be7d7d65d3da6219f929ea7070dd5fed3a81

[ "0BSD" ]

1

2021-03-16T05:48:39.000Z

# PYQT import sys #from ...TabPanel import TabPanel import sip from q3.ui.engine import qtw,qtc,qtg from ... import consts, prop, direction from ...ui import orientation, colors from ...moduletype import ModuleType from ...nodeiotype import NodeIoType from ...q3vector import Q3Vector from ...EventSignal import EventProps from ..driverBase import Q3DriverBase from enum import Enum from ...valuetype import ValueType from .IoLinkView import IoLinkView from .IoNodeView import IoNodeView from .ModuleViewImpl import ModuleViewImpl from .GraphViewImpl import GraphViewImpl #class IoNode: # pass class Q3Scene(qtw.QGraphicsScene): def __init__(self,*args, **kwargs): super(Q3Scene,self).__init__(*args, **kwargs) def contextMenuEvent(self, event): # Check it item exists on event position item = self.itemAt(event.scenePos(),qtg.QTransform()) #.toPoint(),qtg.QTransform.TxNone) if item: # Try run items context if it has one try: item.contextMenuEvent(event) return except: pass menu = qtw.QMenu() action = menu.addAction('ACTION') class DetailWindowBaseImpl(qtw.QWidget): def __init__(self,parent): self._parent=parent super(qtw.QWidget, self).__init__() def resizeEvent(self, event): self._parent.parent().events().detailWindowResized.emit(EventProps({'event':event})) #print(f'WinResizeEV{dir(event)}') def closeEvent(self, event): evs = self._parent.parent().events() if evs.callDetailWindowCloseReq.hasHandlers(): evs.callDetailWindowCloseReq.sync() event.accept() # .checkSyncHandler() #windowDidResize class Q3Driver(Q3DriverBase): def doModuleView_Init(self): if self.s().isRoot():#@s:PackageView::PackageView #sc = qtw.QGraphicsScene(self.pimpl()) sc = Q3Scene(self.pimpl()) #result = qtw.QGraphicsView(sc,self.pimpl()) package = self.s().module().impl() result = GraphViewImpl(sc,self.pimpl(),self.p(), package) #'''EditorFrame''' result._self = self.s() result._scene = sc ''' wheelEvent = getattr(self.s(), "wheelEvent", None) if callable(wheelEvent): result.wheelEvent = wheelEvent drawBackground = getattr(self.s(), "drawBackground", None) if callable(drawBackground): result.drawBackground = drawBackground ''' else: if isinstance(self.pimpl(), qtw.QGraphicsView): #//MODULES FIRST LEVEL result = ModuleViewImpl(None) result._self = self.s() self.pimpl()._scene.addItem(result) el = self.s().module().impl() result.setElement(el) else: result = ModuleViewImpl(self.pimpl()) # next levels result._self = self.s() result._self = self.s() return result; def doModuleView_AfterInit(self): tImpl = self.impl() #tImpl._self = self.s() #tImpl._element = self.s().module().impl() tImpl.setElement(self.s().module().impl()) if self.s().isRoot():#@s:PackageView::PackageView #self.s()._inputsView = self.s().addModuleView('moduleInputs', type=ModuleType.INPUTS) #self.s()._outputsView = self.s().addModuleView('moduleOutputs', type=ModuleType.OUTPUTS) #vec2d m_inputsPosition{ -400.0, 0.0 }; self.s()._inputsView.setProp(prop.PositionX,-400.0) self.s()._inputsView.setProp(prop.PositionY,0.0) self.s()._outputsView.setProp(prop.PositionX,400.0) self.s()._outputsView.setProp(prop.PositionY,0.0) else: #Node::Node tImpl._nameFont.setFamily("Consolas") tImpl._nameFont.setPointSize(8) tImpl.setFlags(qtw.QGraphicsItem.ItemIsMovable | qtw.QGraphicsItem.ItemIsSelectable | qtw.QGraphicsItem.ItemSendsGeometryChanges) tImpl.collapse() tImpl.setGraphView(self.pimpl()) pass #nop self.callAfterInit(tImpl) #if iscallable(tImpl) def doApp_Init(self): result = qtw.QApplication(sys.argv) app = result app.setStyle(qtw.QStyleFactory.create("Fusion")); darkPalette=qtg.QPalette() c1 = qtg.QColor(55, 55, 55); c2 = qtg.QColor(25, 25, 25); c3 = qtg.QColor(45, 130, 220); darkPalette.setColor(qtg.QPalette.Window, c1); darkPalette.setColor(qtg.QPalette.WindowText, qtc.Qt.white); darkPalette.setColor(qtg.QPalette.Base, c2); darkPalette.setColor(qtg.QPalette.AlternateBase, c1); darkPalette.setColor(qtg.QPalette.ToolTipBase, qtc.Qt.white); darkPalette.setColor(qtg.QPalette.ToolTipText, qtc.Qt.white); darkPalette.setColor(qtg.QPalette.Text, qtc.Qt.white); darkPalette.setColor(qtg.QPalette.Button, c1); darkPalette.setColor(qtg.QPalette.ButtonText, qtc.Qt.white); darkPalette.setColor(qtg.QPalette.BrightText, qtc.Qt.red); darkPalette.setColor(qtg.QPalette.Link, c3); darkPalette.setColor(qtg.QPalette.Highlight, c3); darkPalette.setColor(qtg.QPalette.HighlightedText, qtc.Qt.white); app.setPalette(darkPalette); app.setStyleSheet("QToolTip { color: #ffffff; background-color: #2b8bdb; border: 1px solid white; }"); ''' palette = app.palette() palette.setColor(QPalette.Window, QColor(239, 240, 241)) palette.setColor(QPalette.WindowText, QColor(49, 54, 59)) palette.setColor(QPalette.Base, QColor(252, 252, 252)) palette.setColor(QPalette.AlternateBase, QColor(239, 240, 241)) palette.setColor(QPalette.ToolTipBase, QColor(239, 240, 241)) palette.setColor(QPalette.ToolTipText, QColor(49, 54, 59)) palette.setColor(QPalette.Text, QColor(49, 54, 59)) palette.setColor(QPalette.Button, QColor(239, 240, 241)) palette.setColor(QPalette.ButtonText, QColor(49, 54, 59)) palette.setColor(QPalette.BrightText, QColor(255, 255, 255)) palette.setColor(QPalette.Link, QColor(41, 128, 185)) # palette.setColor(QPalette.Highlight, QColor(126, 71, 130)) # palette.setColor(QPalette.HighlightedText, Qt.white) palette.setColor(QPalette.Disabled, QPalette.Light, Qt.white) palette.setColor(QPalette.Disabled, QPalette.Shadow, QColor(234, 234, 234)) app.setPalette(palette) ''' return result def doMainWindow_Init(self): result = qtw.QMainWindow() if 'title' in self._self._kwargs: result.setWindowTitle(self._self._kwargs['title']) #result = qtw.QFrame() result.resize(1400, 980) ''' sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(result.sizePolicy().hasHeightForWidth()) result.setSizePolicy(sizePolicy) ''' showEvent = getattr(self._self, "showEvent", None) if callable(showEvent): result.showEvent = showEvent return result def doMainWindow_Show(self): result = self.impl().show() return result def doMenu_Init(self): if self._impl == None: self._self._qtMenu = qtw.QMenu(self._parent.implObject()) self.s()._menu = self._self._qtMenu pass else: self.s()._menu = self._impl return self._self._menu def doMenu_AddSeparator(self): result = self._self.implObject().addSeparator() return result def doMenu_addAction(self, label,id,helpStr,onClick): if (label == None and consts.ID_EXIT == id): exitAct = qtw.QAction(qtg.QIcon('exit.png'), '&Exit', self._self.implObject()) exitAct.setShortcut('Ctrl+Q') exitAct.setStatusTip('Exit application') exitAct.triggered.connect(qtw.qApp.quit) result = self._self.implObject().addAction(exitAct) else: result = self._self.implObject().addAction(label, onClick) if onClick != None: result.triggered.connect(onClick) #!TODO!result.onClick = onClick return result def doMenuBar_Init(self): return self.pimpl().menuBar() def doMenuBar_AddMenu(self,menuTitle): return self.impl().addMenu(menuTitle) ''' else: result = Menu(self._parent) self._wxMenuBar.Append(result.implObject(),menuTitle) return result ''' def doMdiPanel_Init(self): result = qtw.QMdiArea(self._parent.impl()) return result def doTabPanel_Init(self): result = qtw.QTabWidget(self._parent.impl()) ''' sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding) sizePolicy.setHorizontalStretch(10) sizePolicy.setVerticalStretch(10) sizePolicy.setHeightForWidth(result.sizePolicy().hasHeightForWidth()) result.setSizePolicy(sizePolicy) ''' #result.setMinimumSize(QtCore.QSize(2080, 1630)) result.setTabsClosable(True) return result def doTabPanel_AddTab(self, obj, title): return self.impl().addTab(obj.impl(),title) def doTabPanel_CurrentIndex(self): return self.impl().currentIndex() def doTab_Init(self): result = qtw.QWidget() self._parent.impl().addTab(result,"test") return result def doLayout_Init(self): orient = self.s()._kwargs['orient'] if 'orient' in self.s()._kwargs else None result = qtw.QVBoxLayout() if orient == orientation.VERTICAL else qtw.QHBoxLayout() return result def doLayout_AddElement(self, element): result = self.impl().addWidget(element.impl()) return result def doLayout_Add(self,label, sizerFlags): result = self.impl().addWidget(label.inpl()) return result def doElement_Init(self): result = qtw.QWidget(self.pimpl()) return result def doElement_Resize(self,w,h): result = self.impl().resize(w,h) return result def doElement_SizePolicy(self): result = self.impl().sizePolicy() return result def doElement_SetSizePolicy(self, sizePolicy): result = self.impl().setSizePolicy(sizePolicy) return result def doPanel_Init(self): result = qtw.QFrame(self.pimpl()) return result def doLabel_Init(self): result = qtw.QLabel(self.pimpl()) if 'label' in self.s()._kwargs: result.setText(self.s()._kwargs['label']) return result def doLabel_GetFont(self): result = self.impl.font() return result def doLabel_SetFont(self, font): result = self.impl().setFont(font) return result def doDetailWindow_Init(self): result = DetailWindowBaseImpl(self.s()) result._self = self.s() return result def doDetailWindow_Show(self): result = self.impl().show() return result

34.017595

141

0.617931

1,199

11,600

5.899083

0.253545

0.0205

0.046656

0.055139

0.231019

0.165418

0.146755

0.045808

0.009897

0

0.021635

0.262845

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null

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null

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1

0

570eadcaa613e66d764e81bda74fc4c5ac38c715

2,538

py

Python

2. ExaminingBivariateandMultivariateRelationships/2. scatter_plots.py

michaelbwalker/Data-Cleaning-and-Exploration-with-Machine-Learning

9de44e5ad2e8d197b0a3c1b362b0377339278bd2

[ "MIT" ]

7

2021-10-02T03:19:59.000Z

2022-03-21T21:24:14.000Z

2. ExaminingBivariateandMultivariateRelationships/2. scatter_plots.py

michaelbwalker/Data-Cleaning-and-Exploration-with-Machine-Learning

9de44e5ad2e8d197b0a3c1b362b0377339278bd2

[ "MIT" ]

null

2. ExaminingBivariateandMultivariateRelationships/2. scatter_plots.py

michaelbwalker/Data-Cleaning-and-Exploration-with-Machine-Learning

9de44e5ad2e8d197b0a3c1b362b0377339278bd2

[ "MIT" ]

6

2021-08-30T02:58:02.000Z

2022-02-01T07:46:49.000Z

# import pandas, matplotlib, and seaborn import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns pd.set_option('display.width', 53) pd.set_option('display.max_columns', 5) pd.set_option('display.max_rows', 200) pd.options.display.float_format = '{:,.0f}'.format covidtotals = pd.read_csv("data/covidtotals.csv") covidtotals.set_index("iso_code", inplace=True) landtemps = pd.read_csv("data/landtemps2019avgs.csv") # do a scatterplot of total_cases by total_deaths ax = sns.regplot(x="total_cases_mill", y="total_deaths_mill", data=covidtotals) ax.set(xlabel="Cases Per Million", ylabel="Deaths Per Million", title="Total Covid Cases and Deaths by Country") plt.show() fig, axes = plt.subplots(1,2, sharey=True) sns.regplot(x=covidtotals.aged_65_older, y=covidtotals.total_cases_mill, ax=axes[0]) sns.regplot(x=covidtotals.gdp_per_capita, y=covidtotals.total_cases_mill, ax=axes[1]) axes[0].set_xlabel("Aged 65 or Older") axes[0].set_ylabel("Cases Per Million") axes[1].set_xlabel("GDP Per Capita") axes[1].set_ylabel("") plt.suptitle("Age 65 Plus and GDP with Cases Per Million") plt.tight_layout() fig.subplots_adjust(top=0.92) plt.show() # show the high elevation points in a different color low, high = landtemps.loc[landtemps.elevation<=1000], landtemps.loc[landtemps.elevation>1000] low.shape[0], low.avgtemp.mean() high.shape[0], high.avgtemp.mean() plt.scatter(x="latabs", y="avgtemp", c="blue", data=low) plt.scatter(x="latabs", y="avgtemp", c="red", data=high) plt.legend(('low elevation', 'high elevation')) plt.xlabel("Latitude (N or S)") plt.ylabel("Average Temperature (Celsius)") plt.title("Latitude and Average Temperature in 2019") plt.show() # show scatter plot with different regression lines by elevation group landtemps['elevation_group'] = np.where(landtemps.elevation<=1000,'low','high') sns.lmplot(x="latabs", y="avgtemp", hue="elevation_group", palette=dict(low="blue", high="red"), legend_out=False, data=landtemps) plt.xlabel("Latitude (N or S)") plt.ylabel("Average Temperature") plt.legend(('low elevation', 'high elevation'), loc='lower left') plt.yticks(np.arange(-60, 40, step=20)) plt.title("Latitude and Average Temperature in 2019") plt.tight_layout() plt.show() # show this as a 3D plot fig = plt.figure() plt.suptitle("Latitude, Temperature, and Elevation in 2019") ax = plt.axes(projection='3d') ax.set_xlabel("Elevation") ax.set_ylabel("Latitude") ax.set_zlabel("Avg Temp") ax.scatter3D(landtemps.elevation, landtemps.latabs, landtemps.avgtemp) plt.show()

39.046154

130

0.754137

403

2,538

4.657568

0.330025

0.018647

0.017581

0.028769

0.256793

0.198189

0.161961

0.10016

0.051145

0

0.027214

0.087864

2,538

64

131

39.65625

0.783585

0.090623

0

0.211538

0

0.300608

0.011295

0

1

0

false

0

0.076923

0

0.076923

0

null

0

null

0

1

0

570f7be4fc6a73c331b26ffda6ddfc47a075df88

1,252

py

Python

minifyoperation.py

seece/cbpp

b6771c7933fa07444e660eafda6f06cf60edce01

[ "MIT" ]

null

minifyoperation.py

seece/cbpp

b6771c7933fa07444e660eafda6f06cf60edce01

[ "MIT" ]

null

minifyoperation.py

seece/cbpp

b6771c7933fa07444e660eafda6f06cf60edce01

[ "MIT" ]

null

import re from util import * from operation import Operation, OperationResult class Replacement: def __init__(self, regex, substitution): self.regex = regex self.substitution = substitution class MinifyOperation(Operation): def __init__(self): self.inMultilineComment = False pass def apply(self, line, state): result = OperationResult(line, False) if not state.args.minify: return result l = stripComments(line) strings = scanForStrings(l) commentStart = len(l) stringRegex = r'(("[^"]+")|(|[^"]*?)([^\s]*?))?' comments = r'(?P<comment>(|(\'|//)*$))' def string(s): if not s: return "" return s def replace(m, group): if checkIfInsideString(m.start(group), strings): return string(m.group(0)) return string(m.group(1)) + string(m.group(group)) ops = [] ops.append(Replacement(re.compile(r'' + stringRegex + '\s*(?P<op>[=+\-*/\><,\^]{1,2})\s*'), lambda m: replace(m, "op"))) ops.append(Replacement(re.compile(r'' + stringRegex + r'(?<=\D)(0)(?P<digit>\.\d+)'), lambda m: replace(m, "digit") )) #l = l.lstrip("\t") for o in ops: l = o.regex.sub(o.substitution, l) l = l.rstrip("\r\n") result.line = strInsert(result.line, 0, commentStart-1, l) return result

24.076923

122

0.626997

166

1,252

4.680723

0.379518

0.030888

0.046332

0.105534

0

0.006776

0.17492

1,252

51

123

24.54902

0.745402

0.014377

0

0.055556

0

0.094968

0.073052

0

1

0.138889

false

0.027778

0.083333

0

0.444444

0

null

0

null

0

1

0

570fe23611397bcc46c1ab733771a0e34fdc4ba4

1,302

py

Python

ep004_helper.py

jpch89/effectivepython

97ba297bf987f346219bf8de5198c0817f5146e0

[ "MIT" ]

null

ep004_helper.py

jpch89/effectivepython

97ba297bf987f346219bf8de5198c0817f5146e0

[ "MIT" ]

null

ep004_helper.py

jpch89/effectivepython

97ba297bf987f346219bf8de5198c0817f5146e0

[ "MIT" ]

null

from urllib.parse import parse_qs # 解析查询字符串 query string my_values = parse_qs('red=5&blue=0&green=', keep_blank_values=True) # print(repr(my_values)) # 原书写法 print(my_values) # 返回的是字典，直接这样写就行了 # >>> # {'red': ['5'], 'blue': ['0'], 'green': ['']} # 查询字符串中的参数可能有：多个值和空白 blank 值。 # 有些参数则没有出现。 # 使用 get 方法可以不报错的从字典中取值。 print('Red: ', my_values.get('red')) print('Green: ', my_values.get('green')) print('Opacity: ', my_values.get('opacity')) print('-' * 50) # 需求：当查询的参数没有出现在查询字符串中 # 或者参数的值为空白的时候 # 可以返回 0 # 思路：空值和零值都是 False red = my_values.get('red', [''])[0] or 0 green = my_values.get('green', [''])[0] or 0 opacity = my_values.get('opacity', [''])[0] or 0 print('Red: %r' % red) print('Green: %r' % green) print('Opacity: %r' % opacity) print('-' * 50) # 需求：最后要用到的是整数类型 # 思路：类型转换 red = int(my_values.get('red', [''])[0] or 0) # 这种长表达式的写法看上去很乱！ # 改进1：使用 Python 2.5 添加的三元表达式 red = my_values.get('red', ['']) red = int(red[0]) if red[0] else 0 # 改进2：使用跨行的 if/else 语句 green = my_values.get('green', ['']) if green[0]: green = int(green[0]) else: green = 0 # 改进3：频繁使用的逻辑，需要封装成辅助函数 def get_first_value(values, key, default=0): found = values.get(key, ['']) if found[0]: found = int(found[0]) else: found = default return found

23.25

48

0.609831

192

1,302

4.041667

0.333333

0.123711

0.127577

0.072165

0.275773

0.046392

0

0.029245

0.185868

1,302

55

49

23.672727

0.70283

0.264977

0

0.133333

0

0.130203

0

1

0.033333

false

0

0.033333

0

0.1

0.3

0

null

0

null

0

1

0

5712c5f2bba3745161134c95e4c1fe8d35033684

5,808

py

Python

sc_cost_meter/utils.py

zaro0508/lambda-sc-cost-meter

2e10fa102af983f61a352ae633651fc3eaf64b19

[ "Apache-2.0" ]

null

sc_cost_meter/utils.py

zaro0508/lambda-sc-cost-meter

2e10fa102af983f61a352ae633651fc3eaf64b19

[ "Apache-2.0" ]

null

sc_cost_meter/utils.py

zaro0508/lambda-sc-cost-meter

2e10fa102af983f61a352ae633651fc3eaf64b19

[ "Apache-2.0" ]

null

import boto3 import logging import os from datetime import datetime log = logging.getLogger(__name__) log.setLevel(logging.DEBUG) def get_ec2_client(): return boto3.client('ec2') def get_ssm_client(): return boto3.client('ssm') def get_ce_client(): return boto3.client('ce') def get_meteringmarketplace_client(): return boto3.client('meteringmarketplace') def get_dynamo_client(): return boto3.client('dynamodb') def get_env_var_value(env_var): '''Get the value of an environment variable :param env_var: the environment variable :returns: the environment variable's value, None if env var is not found ''' value = os.getenv(env_var) if not value: log.warning(f'cannot get environment variable: {env_var}') return value def get_marketplace_synapse_ids(): '''Get Synapse IDs from the Marketplace Dynamo DB, these are the Marketplace customers. Assumes that there is a Dynamo DB with a table containing a mapping of Synapse IDs to SC subscriber data :return a list of synapse IDs, otherwise return empty list if no customers are in DB ''' synapse_ids = [] ddb_marketplace_table_name = get_env_var_value('MARKETPLACE_ID_DYNAMO_TABLE_NAME') ddb_marketplace_synapse_user_id_attribute = "SynapseUserId" if ddb_marketplace_table_name: client = get_dynamo_client() response = client.scan( TableName=ddb_marketplace_table_name, ProjectionExpression=ddb_marketplace_synapse_user_id_attribute, ) if "Items" in response.keys(): for item in response["Items"]: synapse_ids.append(item[ddb_marketplace_synapse_user_id_attribute]["S"]) return synapse_ids def get_marketplace_customer_id(synapse_id): '''Get the Service Catalog customer ID from the Marketplace Dynamo DB. Assumes that there is a Dynamo DB with a table containing a mapping of Synapse IDs to SC subscriber data :param synapse_id: synapse user id :return the Marketplace customer ID, otherwise return None if cannot find an associated customer ID ''' customer_id = None ddb_marketplace_table_name = get_env_var_value('MARKETPLACE_ID_DYNAMO_TABLE_NAME') if ddb_marketplace_table_name: ddb_customer_id_attribute = 'MarketplaceCustomerId' client = get_dynamo_client() response = client.get_item( Key={ 'SynapseUserId': { 'S': synapse_id, } }, TableName=ddb_marketplace_table_name, ConsistentRead=True, AttributesToGet=[ ddb_customer_id_attribute ] ) if "Item" in response.keys(): customer_id = response["Item"][ddb_customer_id_attribute]["S"] else: log.info(f'cannot find registration for synapse user: {synapse_id}') return customer_id def get_marketplace_product_code(synapse_id): '''Get the registered Service Catalog customer product code. Assumes that there is a Dynamo DB with a table containing a mapping of Synapse IDs to SC subscriber data :param synapse_id: synapse user id :return the Marketplace product code, None if cannot find customer ID ''' product_code = None ddb_marketplace_table_name = get_env_var_value('MARKETPLACE_ID_DYNAMO_TABLE_NAME') if ddb_marketplace_table_name: ddb_product_code_attribute = 'ProductCode' client = get_dynamo_client() response = client.get_item( Key={ 'SynapseUserId': { 'S': synapse_id, } }, TableName=ddb_marketplace_table_name, ConsistentRead=True, AttributesToGet=[ ddb_product_code_attribute ] ) if "Item" in response.keys(): product_code = response["Item"][ddb_product_code_attribute]["S"] else: log.info(f'cannot find registration for synapse user: {synapse_id}') return product_code def get_customer_cost(customer_id, time_period, granularity): ''' Get the total cost of all resources tagged with the customer_id for a given time_period. The time_period and time granularity must match. :param customer_id: the Marketplace customer ID :param time_period: the cost time period :param granularity: the granularity of time HOURLY|DAILY|MONTHLY :return: the total cost of all resources and the currency unit ''' client = get_ce_client() response = client.get_cost_and_usage( TimePeriod=time_period, Granularity=granularity, Filter={ "Tags": { "Key": "marketplace:customerId", "Values": [ customer_id ] } }, Metrics=["UnblendedCost"] ) results_by_time = response['ResultsByTime'] cost = results_by_time[0]["Total"]["UnblendedCost"]["Amount"] unit = results_by_time[0]["Total"]["UnblendedCost"]["Unit"] return float(cost), unit def report_cost(cost, customer_id, product_code): ''' Report the incurred cost of the customer's resources to the AWS Marketplace :param cost: the cost (as a float value) :param customer_id: the Marketplace customer ID :param product_code: the Marketplace product code ''' cost_accrued_rate = 0.001 # TODO: use mareketplace get_dimension API to get this info quantity = int(cost / cost_accrued_rate) mrktpl_client = get_meteringmarketplace_client() response = mrktpl_client.batch_meter_usage( UsageRecords=[ { 'Timestamp': datetime.utcnow(), 'CustomerIdentifier': customer_id, 'Dimension': 'costs_accrued', 'Quantity': quantity } ], ProductCode=product_code ) log.debug(f'batch_meter_usage response: {response}') results = response["Results"][0] status = results["Status"] if status == 'Success': log.info(f'usage record: {results}') else: # TODO: need to add a retry mechanism for failed reports unprocessed_records = response["UnprocessedRecords"][0] log.error(f'unprocessed record: {unprocessed_records}')

31.394595

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5714de071955ec101c9d0bd2f8b9cad2f55c7b5c

8,000

py

Python

source/metadata.py

sanmik/brain-network-viz

9c881e49c14c94e3f7ef4b7776d98c930716ee91

[ "MIT" ]

5

2017-09-01T14:05:03.000Z

2019-07-13T07:52:49.000Z

source/metadata.py

sanmik/brain-network-viz

9c881e49c14c94e3f7ef4b7776d98c930716ee91

[ "MIT" ]

null

source/metadata.py

sanmik/brain-network-viz

9c881e49c14c94e3f7ef4b7776d98c930716ee91

[ "MIT" ]

1

2017-09-01T14:05:03.000Z

# Library Imports from itertools import islice import csv # Local Module Imports import config class Metadata(object): """ Base class for maintaining metadata (properties and their attributes) about Node and Edge objects. This base class handles parsing and storing the CSV data, and providing accessor methods. The NodeMetadata and EdgeMetadata add some specific methods. The top rows of an input CSV file define metadata and should look like the following example. +-------------+-----------+-----------+-----------+ | Primary Key | Property1 | Property2 | Property3 | ... +-------------------------------------------------+ <-+ | Attribute1 | | | | | +-------------------------------------------------+ | | Attribute2 | | | | | Metadata +-------------------------------------------------+ | | Attribute3 | | | | | +-------------------------------------------------+ <-+ | Item1 | | | | | +-------------------------------------------------+ | | Item2 | | | | | Data +-------------------------------------------------+ | | Item3 | | | | | +-------------+-----------+-----------+-----------+ <-+ Class usage example: m = NodeMetadata('sample_nodes.csv', 3, 'Id') m.get('X', 'MIN_VAL') """ def __init__(self, in_file, num_rows, prime_key): """ Construct a Metadata object Args: in_file: A file handle for an input csv file num_rows: The number of rows of the csv file defining metadata prime_key: The name of the column of primary keys. EG: Attribute names or Item (Node, Edge) IDs. """ # 'data' will contain the property name row + all metadata rows as lists self.data = [] # Lookup table mapping property names to column indices self.prop_indices = {} # Lookup table mapping attribute names to row indices self.attr_indices = {} # Detect and use correct delimiter. Commas and Tabs are supported. dialect = csv.Sniffer().sniff(in_file.read(1024), delimiters=",\t") in_file.seek(0) reader = csv.reader(in_file, dialect) # Populate data structs while reader.line_num < num_rows + 1: row = next(reader) if reader.line_num == 1: for i, name in enumerate(row): self.prop_indices[name] = i self.attr_indices[row[0]] = reader.line_num - 1 self.data.append(row) def get(self, prop, attr): """ Gets the value of a specific property attribute. Treats the CSV matrix shown up top as a 2D array, using prop to lookup the column, and attr to lookup the row. EG: To get the minimum value of a node's x coordinate. m = Metadata('sample_nodes.csv', 3, 'Name') m.get('X', 'MIN_VAL') Args: prop: The metadata property attr: The attribute of that given property Return: The string value of the specified metadata property attribute """ # Get indices into 2D data array j = self.getPropIdx(prop) i = self.getAttrIdx(attr) # Get value return self.data[i][j] def getPropIdx(self, prop): """ Gets the index of a metadata property (Column index). Args: prop: The name of the metadata property Return: Integer column index """ return self.prop_indices[prop] def getAttrIdx(self, attr): """ Gets the index of a metadata attribute (Row index). Args: attr: The name of the metadata attribute Return: Integer row index """ return self.attr_indices[attr] class NodeMetadata(Metadata): """ Subclass to implement Node specific Metadata functionality """ def __init__(self, in_file, num_rows, prime_key): super(NodeMetadata, self).__init__(in_file, num_rows, prime_key) """ A list of dicts for looking up Property names by layer: self.layers[0] => { 'C': (Property Name, CSV column index, min val, max val), 'D': (Property Name, CSV column index, min val, max val), ... }. And to get the property name used for color in layer 2 you would access as: self.layers[0]['C'][0] In the value tuples, Property Name and CSV column index will be None if no such property is specified in the input file. """ self.layers = [{k: None for k in config.NODE_USE_AS_KEYS}] # Populate self.layers row_i = self.attr_indices['USE_AS'] for col_i in range(config.NODE_LAYER_COLS_BEGIN, len(self.data[0])): prop_use_as = self.data[row_i][col_i] assert prop_use_as in config.NODE_USE_AS_KEYS # Find or create the destination layer object and property dest_layer = None for layer in self.layers: if not layer[prop_use_as]: dest_layer = layer break if not dest_layer: dest_layer = {k: None for k in config.NODE_USE_AS_KEYS} self.layers.append(dest_layer) #min_val = self.data[self.getAttrIdx('MIN_VAL')][col_i] min_val = self.data[self.getAttrIdx('MIN_VAL')][col_i] max_val = self.data[self.getAttrIdx('MAX_VAL')][col_i] prop_name = self.data[self.getAttrIdx(prime_key)][col_i] dest_layer[prop_use_as] = (prop_name, col_i, min_val, max_val) """ Fill in any gaps in self.layers. If a layer didn't have property metadata explicitly set - it takes on default metadata values """ for layer_i, layer in enumerate(self.layers): for use_as_key, v in layer.items(): if not v: layer[use_as_key] = config.NODE_DEFAULT_META[use_as_key] def getPropertyName(self, use_as, layer_i): """ Get the Property name associated with the given USE_AS string for the given layer. Args: use_as: A USE_AS value. EG: C, D, etc. layer_i: The layer index Return: The string name of the associated property. None if that property wasn't set in the input file. """ return self.layers[layer_i][use_as][0] def getPropertyIdx(self, use_as, layer_i): """ Get the CSV column of the Property associated with the given USE_AS value for every node's layer i. Return Numeric index of the CSV colum. None that property was not set in the input file. """ return self.layers[layer_i][use_as][1] def getPropertyMinVal(self, use_as, layer_i): """ Get the minimum value of the Property associated with the given USE_AS value for every node's layer i. Return: String minimum value for the property. """ return self.layers[layer_i][use_as][2] def getPropertyMaxVal(self, use_as, layer_i): """ Get the maximum value of the Property associated with the given USE_AS value for every node's layer i. Return String maximum value for the property. """ return self.layers[layer_i][use_as][3] def numLabeledLayers(self): """ Return the number of node layers that a label property explicitly set. """ return len(filter(lambda l: l['L'][0] != None, self.layers)) # TODO: Write Unit Test class EdgeMetadata(Metadata): """ Subclass to implement Node specific Metadata functionality TODO: Consider constructing a lookup table in the same way NodeMetadata does. """ def getPropertyName(self, use_as): """ Get the Property name associated with the given USE_AS string. Args: use_as: A USE_AS value. EG: C, D, etc. Return: The string name of the associated property """ row_i = self.getAttrIdx('USE_AS') use_as_row = self.data[row_i] col_i = 0 for val in use_as_row: if val == use_as: return self.data[0][col_i] col_i += 1

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79

0.58775

1,061

8,000

4.303487

0.223374

0.036137

0.009855

0.024091

0.307709

0.272887

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0

null

0

null

0

1

0

571574f8dd7e9bd961d512815c9fd6535e05f1d8

20,165

py

Python

src/src_python/antares_xpansion/driver.py

pelefebvre/antares-xpansion

c62ed1a982e970325dec6007eb57a9c6288ef0c7

[ "Apache-2.0" ]

null

src/src_python/antares_xpansion/driver.py

pelefebvre/antares-xpansion

c62ed1a982e970325dec6007eb57a9c6288ef0c7

[ "Apache-2.0" ]

null

src/src_python/antares_xpansion/driver.py

pelefebvre/antares-xpansion

c62ed1a982e970325dec6007eb57a9c6288ef0c7

[ "Apache-2.0" ]

1

2021-05-27T13:06:26.000Z

""" Class to control the execution of the optimization session """ import shutil import configparser import glob import os import subprocess import sys from pathlib import Path from antares_xpansion.input_checker import check_candidates_file from antares_xpansion.input_checker import check_settings_file from antares_xpansion.xpansion_utils import read_and_write_mps class XpansionDriver(): """ Class to control the execution of the optimization session """ def __init__(self, config): """ Initialise driver with a given antaresXpansion configuration, the system platform and parses the arguments :param config: configuration to use for the optimization :type config: XpansionConfig object """ self.platform = sys.platform self.config = config self.candidates_list = [] self.check_candidates() self.check_settings() print(self.candidates_list) def exe_path(self, exe): """ prefixes the input exe with the install direcectory containing the binaries :param exe: executable name :return: path to specified executable """ return os.path.normpath(os.path.join(self.config.installDir, exe)) def solver_cmd(self, solver): """ returns a list consisting of the path to the required solver and its launching options """ assert solver in [self.config.MERGE_MPS, self.config.BENDERS_MPI, self.config.BENDERS_SEQUENTIAL] if solver == self.config.MERGE_MPS: return self.exe_path(solver) +" "+ self.config.OPTIONS_TXT elif solver == self.config.BENDERS_MPI: return self.config.MPI_LAUNCHER +" "+\ self.config.MPI_N +" "+ str(self.config.n_mpi)+\ " "+ self.exe_path(solver) +" "+ self.config.OPTIONS_TXT #solver == self.config.BENDERS_SEQUENTIAL: return self.exe_path(solver) +" "+ self.config.OPTIONS_TXT def antares(self): """ returns antares binaries location """ return os.path.normpath(os.path.join(self.config.installDir, self.config.ANTARES)) def general_data(self): """ returns path to general data ini file """ return os.path.normpath(os.path.join(self.data_dir(), self.config.SETTINGS, self.config.GENERAL_DATA_INI)) def settings(self): """ returns path to setting ini file """ return os.path.normpath(os.path.join(self.data_dir(), self.config.USER, self.config.EXPANSION, self.config.SETTINGS_INI)) def candidates(self): """ returns path to candidates ini file """ return os.path.normpath(os.path.join(self.data_dir(), self.config.USER, self.config.EXPANSION, self.config.CANDIDATES_INI)) def capacity_file(self, filename): """ returns path to input capacity file """ return os.path.normpath(os.path.join(self.data_dir(), self.config.USER, self.config.EXPANSION, self.config.CAPADIR, filename)) def weights_file(self, filename): """ returns the path to a yearly-weights file :param filename: name of the yearly-weights file :return: path to input yearly-weights file """ return os.path.normpath(os.path.join(self.data_dir(), self.config.USER, self.config.EXPANSION, filename)) def antares_output(self): """ returns path to antares output data directory """ return os.path.normpath(os.path.join(self.data_dir(), self.config.OUTPUT)) def data_dir(self): """ returns path to the data directory """ return self.config.dataDir def is_accurate(self): """ indicates if method to use is accurate by reading the uc_type in the settings file """ with open(self.settings(), 'r') as file_l: options = dict( {line.strip().split('=')[0].strip(): line.strip().split('=')[1].strip() for line in file_l.readlines()}) uc_type = options.get(self.config.UC_TYPE, self.config.settings_default[self.config.UC_TYPE]) assert uc_type in [self.config.EXPANSION_ACCURATE, self.config.EXPANSION_FAST] return uc_type == self.config.EXPANSION_ACCURATE assert False def is_relaxed(self): """ indicates if method to use is relaxed by reading the relaxation_type from the settings file """ with open(self.settings(), 'r') as file_l: options = dict( {line.strip().split('=')[0].strip(): line.strip().split('=')[1].strip() for line in file_l.readlines()}) relaxation_type = options.get('master', self.config.settings_default["master"]) assert relaxation_type in ['integer', 'relaxed', 'full_integer'] return relaxation_type == 'relaxed' assert False def optimality_gap(self): """ prints and returns the optimality gap read from the settings file :return: gap value or 0 if the gap is set to -Inf """ with open(self.settings(), 'r') as file_l: options = dict( {line.strip().split('=')[0].strip(): line.strip().split('=')[1].strip() for line in file_l.readlines()}) optimality_gap_str = options.get('optimality_gap', self.config.settings_default["optimality_gap"]) assert not '%' in optimality_gap_str print('optimality_gap_str :', optimality_gap_str) return float(optimality_gap_str) if optimality_gap_str != '-Inf' else 0 assert False def max_iterations(self): """ prints and returns the maximum iterations read from the settings file :return: max iterations value or -1 if the parameter is is set to +Inf """ with open(self.settings(), 'r') as file_l: options = dict( {line.strip().split('=')[0].strip(): line.strip().split('=')[1].strip() for line in file_l.readlines()}) max_iterations_str = options.get('max_iteration', self.config.settings_default["max_iteration"]) assert not '%' in max_iterations_str print('max_iterations_str :', max_iterations_str) return float(max_iterations_str) if ( (max_iterations_str != '+Inf') and (max_iterations_str != '+infini') ) else -1 assert False def additional_constraints(self): """ returns path to additional constraints file """ with open(self.settings(), 'r') as file_l: options = dict( {line.strip().split('=')[0].strip(): line.strip().split('=')[1].strip() for line in file_l.readlines()}) additional_constraints_filename = options.get("additional-constraints", self.config.settings_default["additional-constraints"]) if additional_constraints_filename == "" : return "" return os.path.normpath(os.path.join(self.data_dir(), self.config.USER, self.config.EXPANSION, additional_constraints_filename)) def nb_years(self): """ returns the nubyears parameter value read from the general data file """ ini_file = configparser.ConfigParser() ini_file.read(self.general_data()) return float(ini_file['general']['nbyears']) def launch(self): """ launch antares xpansion steps """ self.clear_old_log() if self.config.step == "full": lp_path = self.generate_mps_files() self.launch_optimization(lp_path) elif self.config.step == "antares": self.pre_antares() self.launch_antares() elif self.config.step == "getnames": if self.config.simulationName: self.get_names(self.config.simulationName) else: print("Missing argument simulationName") sys.exit(1) elif self.config.step == "lp": if self.config.simulationName: self.lp_step(self.config.simulationName) output_path = os.path.normpath(os.path.join(self.antares_output(), self.config.simulationName)) self.set_options(output_path) else: print("Missing argument simulationName") sys.exit(1) elif self.config.step == "optim": if self.config.simulationName: lp_path = os.path.normpath(os.path.join(self.antares_output(), self.config.simulationName, 'lp')) self.launch_optimization(lp_path) else: print("Missing argument simulationName") sys.exit(1) else: print("Launching failed") sys.exit(1) def clear_old_log(self): """ clears old log files for antares and the lp_namer """ if (self.config.step in ["full", "antares"]) and (os.path.isfile(self.antares() + '.log')): os.remove(self.antares() + '.log') if (self.config.step in ["full", "lp"])\ and (os.path.isfile(self.exe_path(self.config.LP_NAMER) + '.log')): os.remove(self.exe_path(self.config.LP_NAMER) + '.log') def check_candidates(self): """ checks that candidates file has correct format """ #check file existence if not os.path.isfile(self.candidates()): print('Missing file : %s was not retrieved.' % self.candidates()) sys.exit(1) check_candidates_file(self) def check_settings(self): """ checks that settings file has correct format """ #check file existence if not os.path.isfile(self.settings()): print('Missing file : %s was not retrieved.' % self.settings()) sys.exit(1) check_settings_file(self) def pre_antares(self): """ modifies the general data file to configure antares execution """ ini_file = configparser.ConfigParser() ini_file.read(self.general_data()) ini_file[self.config.OPTIMIZATION][self.config.EXPORT_MPS] = "true" ini_file[self.config.OPTIMIZATION][self.config.EXPORT_STRUCTURE] = "true" ini_file[self.config.OPTIMIZATION][self.config.USE_XPRS] = "false" ini_file.remove_option(self.config.OPTIMIZATION, self.config.USE_XPRS) ini_file.remove_option(self.config.OPTIMIZATION, self.config.INBASIS) ini_file.remove_option(self.config.OPTIMIZATION, self.config.OUTBASIS) if self.is_accurate(): ini_file['general']['mode'] = 'expansion' ini_file['other preferences']['unit-commitment-mode'] = 'accurate' ini_file[self.config.OPTIMIZATION]['include-tc-minstablepower'] = 'true' ini_file[self.config.OPTIMIZATION]['include-tc-min-ud-time'] = 'true' ini_file[self.config.OPTIMIZATION]['include-dayahead'] = 'true' else: ini_file['general']['mode'] = 'Economy' ini_file['other preferences']['unit-commitment-mode'] = 'fast' ini_file[self.config.OPTIMIZATION]['include-tc-minstablepower'] = 'false' ini_file[self.config.OPTIMIZATION]['include-tc-min-ud-time'] = 'false' ini_file[self.config.OPTIMIZATION]['include-dayahead'] = 'false' with open(self.general_data(), 'w') as out_file: ini_file.write(out_file) def launch_antares(self): """ launch antares :return: name of the new simulation's directory """ # if not os.path.isdir(driver.antares_output()): # os.mkdir(driver.antares_output(), ) old_output = os.listdir(self.antares_output()) print([self.antares(), self.data_dir()]) with open(self.antares() + '.log', 'w') as output_file: returned_l = subprocess.call(self.antares() +" "+ self.data_dir(), shell=True, stdout=output_file, stderr=output_file) if returned_l != 0: print("WARNING: exited antares with status %d" % returned_l) new_output = os.listdir(self.antares_output()) print(old_output) print(new_output) assert len(old_output) + 1 == len(new_output) diff = list(set(new_output) - set(old_output)) return diff[0] def post_antares(self, antares_output_name): """ creates necessary files for simulation using the antares simulation output files, the existing configuration files, get_names and the lpnamer executable :param antares_output_name: name of the antares simulation output directory :return: path to the lp output directory """ output_path = os.path.normpath(os.path.join(self.antares_output(), antares_output_name)) self.get_names(antares_output_name) lp_path = self.lp_step(antares_output_name) self.set_options(output_path) return lp_path def get_names(self, antares_output_name): """ produces a .txt file describing the weekly problems: each line of the file contains : - mps file name - variables file name - constraints file name :param antares_output_name: name of the antares simulation output directory produces a file named with xpansionConfig.MPS_TXT """ output_path = os.path.normpath(os.path.join(self.antares_output(), antares_output_name)) mps_txt = read_and_write_mps(output_path) # print(mps_txt) with open(os.path.normpath(os.path.join(output_path, self.config.MPS_TXT)), 'w') as file_l: for line in mps_txt.items(): file_l.write(line[1][0] + ' ' + line[1][1] + ' ' + line[1][2] + '\n') glob_path= Path(output_path) area_files = [str(pp) for pp in glob_path.glob("area*.txt")] interco_files = [str(pp) for pp in glob_path.glob("interco*.txt")] assert len(area_files) == 1 assert len(interco_files) == 1 shutil.copy(area_files[0], os.path.normpath(os.path.join(output_path, 'area.txt'))) shutil.copy(interco_files[0], os.path.normpath(os.path.join(output_path, 'interco.txt'))) def lp_step(self, antares_output_name): """ copies area and interco files and launches the lp_namer :param output_path: path to the antares simulation output directory produces a file named with xpansionConfig.MPS_TXT """ output_path = os.path.normpath(os.path.join(self.antares_output(), antares_output_name)) lp_path = os.path.normpath(os.path.join(output_path, 'lp')) if os.path.isdir(lp_path): shutil.rmtree(lp_path) os.makedirs(lp_path) is_relaxed = 'relaxed' if self.is_relaxed() else 'integer' with open(self.exe_path(self.config.LP_NAMER) + '.log', 'w') as output_file: lp_cmd = self.exe_path(self.config.LP_NAMER) +" "+ output_path +" "+ is_relaxed +" "+ self.additional_constraints() returned_l = subprocess.call(lp_cmd, shell=True, stdout=output_file, stderr=output_file) if returned_l != 0: print("ERROR: exited lpnamer with status %d" % returned_l) sys.exit(1) return lp_path def launch_optimization(self, lp_path): """ launch the optimization of the antaresXpansion problem using the specified solver :param lp_path: path to the lp directory containing input files (c.f. generate_mps_files) :param solver: name of the solver to be used :type solver: value in [XpansionConfig.MERGE_MPS, XpansionConfig.BENDERS_MPI, XpansionConfig.BENDERS_SEQUENTIAL] """ old_cwd = os.getcwd() os.chdir(lp_path) print('Current directory is now : ', os.getcwd()) solver = None if self.config.method == "mpibenders": solver = self.config.BENDERS_MPI elif self.config.method == "mergeMPS": solver = self.config.MERGE_MPS mergemps_lp_log = "log_merged.lp" if os.path.isfile(mergemps_lp_log): os.remove(mergemps_lp_log) mergemps_mps_log = "log_merged.mps" if os.path.isfile(mergemps_mps_log): os.remove(mergemps_lp_log) elif self.config.method == "sequential": solver = self.config.BENDERS_SEQUENTIAL elif self.config.method == "both": print("metod both is not handled yet") sys.exit(1) else: print("Illegal optim method") sys.exit(1) #delete execution logs logfile_list = glob.glob('./' +solver + 'Log*') for file_path in logfile_list: try: os.remove(file_path) except OSError: print("Error while deleting file : ", file_path) if os.path.isfile(solver + '.log'): os.remove(solver + '.log') print('Launching {}, logs will be saved to {}.log'.format(solver, os.path.normpath(os.path.join( os.getcwd(), solver)))) with open(solver + '.log', 'w') as output_file: returned_l = subprocess.call(self.solver_cmd(solver), shell=True, stdout=output_file, stderr=output_file) if returned_l != 0: print("ERROR: exited solver with status %d" % returned_l) sys.exit(1) os.chdir(old_cwd) def set_options(self, output_path): """ generates a default option file for the solver """ # computing the weight of slaves options_values = self.config.options_default options_values["SLAVE_WEIGHT_VALUE"] = str(self.nb_years()) print('Number of years is {}, setting SLAVE_WEIGHT_VALUE to {} '. format(self.nb_years(), options_values["SLAVE_WEIGHT_VALUE"])) options_values["GAP"] = self.optimality_gap() options_values["MAX_ITERATIONS"] = self.max_iterations() # generate options file for the solver options_path = os.path.normpath(os.path.join(output_path, 'lp', self.config.OPTIONS_TXT)) with open(options_path, 'w') as options_file: options_file.writelines(["%30s%30s\n" % (kvp[0], kvp[1]) for kvp in options_values.items()]) def generate_mps_files(self): """ launches antares to produce mps files """ print("starting mps generation") # setting antares options print("-- pre antares") self.pre_antares() # launching antares print("-- launching antares") antares_output_name = self.launch_antares() # writting things print("-- post antares") lp_path = self.post_antares(antares_output_name) return lp_path

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0.085034

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null

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null

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1

0

57199578121fb89b3db3e976c4737dd3dcc14bf5

2,258

py

Python

lambdas/get_users.py

charvi-a/320-S20-Track1

ac97504fc1fdedb1c311773b015570eeea8a8663

[ "BSD-3-Clause" ]

9

2019-12-30T16:32:22.000Z

2020-03-03T20:14:47.000Z

lambdas/get_users.py

charvi-a/320-S20-Track1

ac97504fc1fdedb1c311773b015570eeea8a8663

[ "BSD-3-Clause" ]

283

2020-02-03T15:16:03.000Z

2020-05-05T03:18:59.000Z

lambdas/get_users.py

charvi-a/320-S20-Track1

ac97504fc1fdedb1c311773b015570eeea8a8663

[ "BSD-3-Clause" ]

3

2020-04-16T15:23:29.000Z

2020-05-12T00:38:41.000Z

from package.query_db import query from package.lambda_exception import LambdaException def handler(event, context): is_admin = event['is_admin'] is_supporter = event['is_supporter'] is_student = event['is_student'] if is_admin == "" and is_supporter == "" and is_student == "": get_users_sql = "SELECT id, first_name, last_name, email FROM users;" params = [] else: if is_admin != "": is_admin = event['is_admin'].lower() if is_admin == "true": is_admin = True else: is_admin = False else: is_admin = False if is_supporter != "": is_supporter = event['is_supporter'].lower() if is_supporter == "true": is_supporter = True else: is_supporter = False else: is_supporter = False if is_student != "": is_student = event['is_student'].lower() if is_student == "true": is_student = True else: is_student = False else: is_student = True is_admin_param = {'name' : 'is_admin', 'value' : {'booleanValue' : is_admin}} is_supporter_param = {'name' : 'is_supporter', 'value' : {'booleanValue' : is_supporter}} is_student_param = {'name' : 'is_student', 'value' : {'booleanValue' : is_student}} get_users_sql = "SELECT id, first_name, last_name, email FROM users WHERE is_admin = :is_admin AND is_supporter = :is_supporter AND is_student = :is_student;" params = [is_admin_param, is_supporter_param, is_student_param] try: users = query(get_users_sql, params)['records'] except Exception as e: raise LambdaException("500: Failed to get users, " + str(e)) response = { 'users' : [] } for u_id, f_name, l_name, email in users: current_users = response['users'] next_user = {'user_id' : u_id["longValue"], 'first_name' : f_name["stringValue"], 'last_name' : l_name["stringValue"], 'email' : email["stringValue"]} current_users.append(next_user) response['users'] = current_users return response

34.738462

171

0.574402

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2,258

4.662835

0.226054

0.09203

0.029581

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0.09696

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2,258

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0

0.076923

0

null

0

null

0

1

0

571ab14954af261729cb1d3fc0d5e206657e96fa

705

py

Python

leetCode/swap_nodes_in_pairs.py

yskang/AlgorithmPracticeWithPython

f7129bd1924a7961489198f0ee052d2cd1e9cf40

[ "MIT" ]

null

leetCode/swap_nodes_in_pairs.py

yskang/AlgorithmPracticeWithPython

f7129bd1924a7961489198f0ee052d2cd1e9cf40

[ "MIT" ]

1

2019-11-04T06:44:04.000Z

2019-11-04T06:46:55.000Z

leetCode/swap_nodes_in_pairs.py

yskang/AlgorithmPractice

31b76e38b4c2f1e3e29fb029587662a745437912

[ "MIT" ]

null

# Title: Swap Nodes in Pairs # Link: https://leetcode.com/problems/swap-nodes-in-pairs class ListNode: def __init__(self, val=0, next=None): self.val = val self.next = next class Problem: def swap_pairs(self, head: ListNode) -> ListNode: pre, pre.next = self, head while pre.next and pre.next.next: a = pre.next b = a.next pre.next, b.next, a.next = b, a, b.next pre = a return self.next def solution(): head = ListNode(1, ListNode(2, ListNode(3, ListNode(4)))) problem = Problem() return problem.swap_pairs(head) def main(): print(solution()) if __name__ == '__main__': main()

22.741935

61

0.58156

97

705

4.082474

0.371134

0.088384

0.055556

0.080808

0

0.00998

0.289362

705

31

62

22.741935

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0.047619

0

null

0

null

0

1

0

571ac253ee844d994243e9c2e1443c9c4aa20002

16,967

py

Python

detect_actions.py

CTewan/ACAM_Demo

b76cf4ce1289b8c311dbad1588f299ff67f7eaf3

[ "MIT" ]

null

detect_actions.py

CTewan/ACAM_Demo

b76cf4ce1289b8c311dbad1588f299ff67f7eaf3

[ "MIT" ]

null

detect_actions.py

CTewan/ACAM_Demo

b76cf4ce1289b8c311dbad1588f299ff67f7eaf3

[ "MIT" ]

null

import numpy as np import cv2 import imageio import tensorflow as tf import json import csv import os import sys sys.path.append("object_detection") sys.path.append("object_detection/deep_sort") sys.path.append("action_detection") import argparse import object_detection.object_detector as obj import action_detection.action_detector as act import time DISPLAY = False SHOW_CAMS = False def main(): parser = argparse.ArgumentParser() parser.add_argument('-v', '--video_path', type=str, required=False, default="") parser.add_argument('-d', '--display', type=str, required=False, default="True") args = parser.parse_args() display = (args.display == "True" or args.display == "true") #actor_to_display = 6 # for cams video_path = args.video_path basename = os.path.basename(video_path).split('.')[0] out_vid_path = "./output_videos/%s_output.mp4" % (basename if not SHOW_CAMS else basename+'_cams_actor_%.2d' % actor_to_display) clf_out_path = "./clf_output/{}_output.csv".format(basename if not SHOW_CAMS else basename+'_cams_actor_{}'.format(actor_to_display)) #out_vid_path = './output_videos/testing.mp4' # video_path = "./tests/chase1Person1View3Point0.mp4" # out_vid_path = 'output.mp4' main_folder = './' # NAS obj_detection_model = 'ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03' obj_detection_graph = os.path.join("object_detection", "weights", obj_detection_model, "frozen_inference_graph.pb") print("Loading object detection model at %s" % obj_detection_graph) obj_detector = obj.Object_Detector(obj_detection_graph) tracker = obj.Tracker() print("Reading video file %s" % video_path) reader = imageio.get_reader(video_path, 'ffmpeg') action_freq = 8 # fps_divider = 1 print('Running actions every %i frame' % action_freq) fps = reader.get_meta_data()['fps'] #// fps_divider print("FPS: {}".format(fps)) W, H = reader.get_meta_data()['size'] T = tracker.timesteps #if not display: writer = imageio.get_writer(out_vid_path, fps=fps) csv_file = open(clf_out_path, 'w', newline='') csv_writer = csv.writer(csv_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow(['Time', 'Person', 'Action', 'Probability']) print("Writing output to %s" % out_vid_path) # act_detector = act.Action_Detector('i3d_tail') # ckpt_name = 'model_ckpt_RGB_i3d_pooled_tail-4' act_detector = act.Action_Detector('soft_attn') #ckpt_name = 'model_ckpt_RGB_soft_attn-16' #ckpt_name = 'model_ckpt_soft_attn_ava-23' ckpt_name = 'model_ckpt_soft_attn_pooled_cosine_drop_ava-130' #input_frames, temporal_rois, temporal_roi_batch_indices, cropped_frames = act_detector.crop_tubes_in_tf([T,H,W,3]) memory_size = act_detector.timesteps - action_freq updated_frames, temporal_rois, temporal_roi_batch_indices, cropped_frames = act_detector.crop_tubes_in_tf_with_memory([T,H,W,3], memory_size) rois, roi_batch_indices, pred_probs = act_detector.define_inference_with_placeholders_noinput(cropped_frames) ckpt_path = os.path.join(main_folder, 'action_detection', 'weights', ckpt_name) act_detector.restore_model(ckpt_path) prob_dict = {} frame_cnt = 0 # Tewan min_teacher_features = 3 teacher_identified = 0 #missed_frame_cnt = 0 #max_age = 120 #frame_skips = 60 #next_frame = 0 teacher_ids = [] matched_id = None # Tewan for cur_img in reader: frame_cnt += 1 #if frame_cnt < next_frame: # continue # Detect objects and make predictions every 8 frames (0.3 seconds) #if frame_cnt % action_freq == 0: # Object Detection expanded_img = np.expand_dims(cur_img, axis=0) detection_list = obj_detector.detect_objects_in_np(expanded_img) detection_info = [info[0] for info in detection_list] # Updates active actors in tracker tracker.update_tracker(detection_info, cur_img) no_actors = len(tracker.active_actors) """ if no_actors == 0: missed_frame_cnt += 1 if missed_frame_cnt >= max_age: tracker.update_tracker(detection_info, cur_img) no_actors = len(tracker.active_actors) teacher_identified = False tracker.set_invalid_track() missed_frame_cnt = 0 print("Reset active actors. Current number: {}".format(no_actors)) """ if frame_cnt % action_freq == 0 and frame_cnt > 16: if no_actors == 0: print("No actor found.") continue video_time = round(frame_cnt / fps, 1) valid_actor_ids = [actor["actor_id"] for actor in tracker.active_actors] print("frame count: {}, video time: {}s".format(frame_cnt, video_time)) probs = [] cur_input_sequence = np.expand_dims(np.stack(tracker.frame_history[-action_freq:], axis=0), axis=0) rois_np, temporal_rois_np = tracker.generate_all_rois() if teacher_identified < min_teacher_features: prompt_img = visualize_detection_results(img_np=tracker.frame_history[-16], active_actors=tracker.active_actors, prob_dict=None) cv2.imshow('prompt_img', prompt_img[:,:,::-1]) cv2.waitKey(500) teacher_present = False teacher_id = _prompt_user_input() if not _check_teacher_in_frame(teacher_id=teacher_id): print("Teacher not in this frame. Continuing.") cv2.destroyWindow("prompt_img") pass else: if _check_valid_teacher_id(teacher_id=teacher_id, valid_actor_ids=valid_actor_ids): teacher_id = int(teacher_id) teacher_identified += 1 teacher_present = True else: while not teacher_present: print("Invalid ID.") teacher_id = _prompt_user_input() if not _check_teacher_in_frame(teacher_id=teacher_id): print("Teacher not in this frame. Continuing.") cv2.destroyWindow("prompt_img") break else: if _check_valid_teacher_id(teacher_id=teacher_id, valid_actor_ids=valid_actor_ids): teacher_id = int(teacher_id) teacher_identified += 1 teacher_present = True else: pass # Move on to next frame if teacher not in current frame if not teacher_present: continue cv2.destroyWindow("prompt_img") if teacher_id not in teacher_ids: teacher_ids.append(teacher_id) tracker.update_teacher_candidate_ids(teacher_candidate_id=teacher_id) else: tracker.set_valid_track() # Identify idx of teacher for ROI selection roi_idx = None found_id = False for idx, actor_info in enumerate(tracker.active_actors): actor_id = actor_info["actor_id"] for i in range(len(teacher_ids)-1, -1, -1): if actor_id == teacher_ids[i]: roi_idx = idx matched_id = actor_info["actor_id"] found_id = True break if found_id: break # Identify ROI and temporal ROI using ROI idx if roi_idx is not None: rois_np = rois_np[roi_idx] temporal_rois_np = temporal_rois_np[roi_idx] rois_np = np.expand_dims(rois_np, axis=0) temporal_rois_np = np.expand_dims(temporal_rois_np, axis=0) no_actors = 1 # If teacher not found (i.e. roi_idx is None) in current frame, move on to next frame else: continue #max_actors = 5 #if no_actors > max_actors: # no_actors = max_actors # rois_np = rois_np[:max_actors] # temporal_rois_np = temporal_rois_np[:max_actors] # Might have issue of not using attention map because only predict action for 1 actor (memory issue) feed_dict = {updated_frames:cur_input_sequence, # only update last #action_freq frames temporal_rois: temporal_rois_np, temporal_roi_batch_indices: np.zeros(no_actors), rois:rois_np, roi_batch_indices:np.arange(no_actors)} run_dict = {'pred_probs': pred_probs} if SHOW_CAMS: run_dict['cropped_frames'] = cropped_frames run_dict['final_i3d_feats'] = act_detector.act_graph.get_collection('final_i3d_feats')[0] run_dict['cls_weights'] = act_detector.act_graph.get_collection('variables')[-2] # this is the kernel out_dict = act_detector.session.run(run_dict, feed_dict=feed_dict) probs = out_dict['pred_probs'] # associate probs with actor ids print_top_k = 5 for bb in range(no_actors): #act_probs = probs[bb] #order = np.argsort(act_probs)[::-1] #cur_actor_id = tracker.active_actors[bb]['actor_id'] act_probs = probs[bb] order = np.argsort(act_probs)[::-1] cur_actor_id = tracker.active_actors[roi_idx]["actor_id"] #print(cur_actor_id == actor_id) #print("Person %i" % cur_actor_id) #print("act_probs: {}".format(act_probs)) #print("order: {}".format(order)) #print("tracker.active_actors[bb]: {}".format(tracker.active_actors[bb])) cur_results = [] for pp in range(print_top_k): #print('\t %s: %.3f' % (act.ACTION_STRINGS[order[pp]], act_probs[order[pp]])) cur_results.append((act.ACTION_STRINGS[order[pp]], act_probs[order[pp]])) csv_writer.writerow([video_time, cur_actor_id, act.ACTION_STRINGS[order[pp]], act_probs[order[pp]]]) prob_dict[cur_actor_id] = cur_results if frame_cnt > 16: out_img = visualize_detection_results(tracker.frame_history[-16], tracker.active_actors, prob_dict=prob_dict, teacher_id=matched_id) if SHOW_CAMS: if tracker.active_actors: actor_indices = [ii for ii in range(no_actors) if tracker.active_actors[ii]['actor_id'] == actor_to_display] if actor_indices: out_img = visualize_cams(out_img, cur_input_sequence, out_dict, actor_indices[0]) else: continue else: continue if display: cv2.imshow('results', out_img[:,:,::-1]) cv2.waitKey(10) writer.append_data(out_img) #if not display: writer.close() csv_file.close() def _prompt_user_input(): teacher_id = input("Enter the id of the teacher (type None if teacher is not present in this frame): ") return teacher_id def _check_teacher_in_frame(teacher_id): if teacher_id == "None" or teacher_id == "none": return False return True def _check_valid_teacher_id(teacher_id, valid_actor_ids): try: teacher_id = int(teacher_id) if teacher_id in valid_actor_ids: return True else: return False except: return False np.random.seed(10) COLORS = np.random.randint(0, 255, [1000, 3]) def visualize_detection_results(img_np, active_actors, prob_dict=None, teacher_id=None): score_th = 0.30 action_th = 0.20 # copy the original image first disp_img = np.copy(img_np) H, W, C = img_np.shape #for ii in range(len(active_actors)): for ii in range(len(active_actors)): cur_actor = active_actors[ii] actor_id = cur_actor['actor_id'] if teacher_id is not None: if actor_id != teacher_id: continue if prob_dict: cur_act_results = prob_dict[actor_id] if actor_id in prob_dict else [] try: if len(cur_actor["all_boxes"]) > 0: cur_box, cur_score, cur_class = cur_actor['all_boxes'][-16], cur_actor['all_scores'][0], 1 else: cur_box, cur_score, cur_class = cur_actor['all_boxes'][0], cur_actor['all_scores'][0], 1 except IndexError: continue if cur_score < score_th: continue top, left, bottom, right = cur_box left = int(W * left) right = int(W * right) top = int(H * top) bottom = int(H * bottom) conf = cur_score label = obj.OBJECT_STRINGS[cur_class]['name'] message = '%s_%i: %% %.2f' % (label, actor_id,conf) if prob_dict: action_message_list = ["%s:%.3f" % (actres[0][0:7], actres[1]) for actres in cur_act_results if actres[1]>action_th] color = COLORS[actor_id] color = (int(color[0]), int(color[1]), int(color[2])) cv2.rectangle(disp_img, (left,top), (right,bottom), color, 3) font_size = max(0.5,(right - left)/50.0/float(len(message))) cv2.rectangle(disp_img, (left, top-int(font_size*40)), (right,top), color, -1) cv2.putText(disp_img, message, (left, top-12), 0, font_size, (255-color[0], 255-color[1], 255-color[2]), 1) if prob_dict: #action message writing cv2.rectangle(disp_img, (left, top), (right,top+10*len(action_message_list)), color, -1) for aa, action_message in enumerate(action_message_list): offset = aa*10 cv2.putText(disp_img, action_message, (left, top+5+offset), 0, 0.5, (255-color[0], 255-color[1], 255-color[2]), 1) return disp_img def visualize_cams(image, input_frames, out_dict, actor_idx): #classes = ["walk", "bend", "carry"] #classes = ["sit", "ride"] classes = ["talk to", "watch (a", "listen to"] action_classes = [cc for cc in range(60) if any([cname in act.ACTION_STRINGS[cc] for cname in classes])] feature_activations = out_dict['final_i3d_feats'] cls_weights = out_dict['cls_weights'] input_frames = out_dict['cropped_frames'].astype(np.uint8) probs = out_dict["pred_probs"] class_maps = np.matmul(feature_activations, cls_weights) min_val = np.min(class_maps[:,:, :, :, :]) max_val = np.max(class_maps[:,:, :, :, :]) - min_val normalized_cmaps = np.uint8((class_maps-min_val)/max_val * 255.) t_feats = feature_activations.shape[1] t_input = input_frames.shape[1] index_diff = (t_input) // (t_feats+1) img_new_height = 400 img_new_width = int(image.shape[1] / float(image.shape[0]) * img_new_height) img_to_show = cv2.resize(image.copy(), (img_new_width,img_new_height))[:,:,::-1] #img_to_concat = np.zeros((400, 800, 3), np.uint8) img_to_concat = np.zeros((400, 400, 3), np.uint8) for cc in range(len(action_classes)): cur_cls_idx = action_classes[cc] act_str = act.ACTION_STRINGS[action_classes[cc]] message = "%s:%%%.2d" % (act_str[:20], 100*probs[actor_idx, cur_cls_idx]) for tt in range(t_feats): cur_cam = normalized_cmaps[actor_idx, tt,:,:, cur_cls_idx] cur_frame = input_frames[actor_idx, (tt+1) * index_diff, :,:,::-1] resized_cam = cv2.resize(cur_cam, (100,100)) colored_cam = cv2.applyColorMap(resized_cam, cv2.COLORMAP_JET) overlay = cv2.resize(cur_frame.copy(), (100,100)) overlay = cv2.addWeighted(overlay, 0.5, colored_cam, 0.5, 0) img_to_concat[cc*125:cc*125+100, tt*100:(tt+1)*100, :] = overlay cv2.putText(img_to_concat, message, (20, 13+100+125*cc), 0, 0.5, (255,255,255), 1) final_image = np.concatenate([img_to_show, img_to_concat], axis=1) return final_image[:,:,::-1] if __name__ == '__main__': main()

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16,967

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0.17036

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0.024427

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0.152025

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0.115063

0

0.025602

0.304768

16,967

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false

0.007273

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0.043636

0

null

0

null

0

1

0

571af6febfc1dc4cb09b37f0fb44cc848ccf1059

5,556

py

Python

tests/test_parametric_shapes/test_SweepMixedShape.py

RemDelaporteMathurin/paramak

10552f1b89820dd0f7a08e4a126834877e3106b4

[ "MIT" ]

null

tests/test_parametric_shapes/test_SweepMixedShape.py

RemDelaporteMathurin/paramak

10552f1b89820dd0f7a08e4a126834877e3106b4

[ "MIT" ]

null

tests/test_parametric_shapes/test_SweepMixedShape.py

RemDelaporteMathurin/paramak

10552f1b89820dd0f7a08e4a126834877e3106b4

[ "MIT" ]

null

import os import unittest from pathlib import Path import pytest from paramak import SweepMixedShape class test_object_properties(unittest.TestCase): def test_solid_construction(self): """checks that a SweepMixedShape solid can be created""" test_shape = SweepMixedShape( points=[ (-10, -10, "straight"), (-10, 10, "spline"), (0, 20, "spline"), (10, 10, "circle"), (0, 0, "circle"), (10, -10, "straight") ], path_points=[ (50, 0), (20, 50), (50, 100) ] ) test_shape.create_solid() assert test_shape.solid is not None def test_solid_construction(self): """checks that a SweepMixedShape solid can be created with workplane YZ""" test_shape = SweepMixedShape( points=[ (-10, -10, "straight"), (-10, 10, "spline"), (0, 20, "spline"), (10, 10, "circle"), (0, 0, "circle"), (10, -10, "straight") ], path_points=[ (50, 0), (20, 50), (50, 100) ], workplane='YZ', path_workplane="YX" ) assert test_shape.solid is not None def test_solid_construction_workplane_XZ(self): """checks that a SweepMixedShape solid can be created with workplane XZ""" test_shape = SweepMixedShape( points=[ (-10, -10, "straight"), (-10, 10, "spline"), (0, 20, "spline"), (10, 10, "circle"), (0, 0, "circle"), (10, -10, "straight") ], path_points=[ (50, 0), (20, 50), (50, 100) ], workplane='XZ', path_workplane="XY" ) assert test_shape.solid is not None def test_relative_shape_volume(self): """creates two SweepMixedShapes and checks that their relative volumes are correct""" test_shape_1 = SweepMixedShape( points=[ (-10, -10, "straight"), (-10, 10, "spline"), (0, 20, "spline"), (10, 10, "circle"), (0, 0, "circle"), (10, -10, "straight") ], path_points=[ (50, 0), (30, 50), (50, 100) ] ) test_shape_1.create_solid() test_shape_2 = SweepMixedShape( points=[ (-20, -20, "straight"), (-20, 20, "spline"), (0, 40, "spline"), (20, 20, "circle"), (0, 0, "circle"), (20, -20, "straight") ], path_points=[ (50, 0), (30, 50), (50, 100) ] ) test_shape_2.create_solid() assert test_shape_1.volume == pytest.approx( test_shape_2.volume * 0.25, rel=0.01) def test_iterable_azimuthal_placement(self): """checks that swept solids can be placed at multiple azimuth placement angles""" test_shape = SweepMixedShape( points=[ (-10, -10, "straight"), (-10, 10, "spline"), (0, 20, "spline"), (10, 10, "circle"), (0, 0, "circle"), (10, -10, "straight") ], path_points=[ (50, 0), (30, 50), (60, 100), (50, 150) ] ) test_shape.create_solid() test_volume = test_shape.volume test_shape.azimuth_placement_angle = [0, 90, 180, 270] assert test_shape.volume == pytest.approx(test_volume * 4, rel=0.01) def test_workplane_path_workplane_error_raises(self): """checks that errors are raised when disallowed workplane and path_workplane combinations are used""" def workplane_and_path_workplane_equal(): test_shape = SweepMixedShape( points=[ (-10, -10, "straight"), (-10, 10, "spline"), (0, 20, "spline"), (10, 10, "circle"), (0, 0, "circle"), (10, -10, "straight") ], path_points=[(50, 0), (30, 50), (60, 100), (50, 150)], workplane="XZ", path_workplane="XZ" ) def invalid_relative_workplane_and_path_workplane(): test_shape = SweepMixedShape( points=[ (-10, -10, "straight"), (-10, 10, "spline"), (0, 20, "spline"), (10, 10, "circle"), (0, 0, "circle"), (10, -10, "straight") ], path_points=[(50, 0), (30, 50), (60, 100), (50, 150)], workplane="XZ", path_workplane="YZ" ) self.assertRaises(ValueError, workplane_and_path_workplane_equal) self.assertRaises( ValueError, invalid_relative_workplane_and_path_workplane) if __name__ == "__main__": unittest.main()

29.089005

89

0.430166

508

5,556

4.519685

0.187008

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0.073171

0.04878

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0

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5,556

190

90

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0.645078

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0

0.091503

0

null

0

null

0

1

0

571dbed119712d82f6343f841d5c39a1d78ee427

996

py

Python

run_rnn.py

iqbaalmuhmd/CNNnumpyTest

eaecf5bc53a7b5c932a82d38cc6ca2a40430af4b

[ "MIT" ]

332

2017-06-13T10:40:05.000Z

2022-03-11T15:10:02.000Z

run_rnn.py

iqbaalmuhmd/CNNnumpyTest

eaecf5bc53a7b5c932a82d38cc6ca2a40430af4b

[ "MIT" ]

9

2017-06-16T02:36:06.000Z

2021-05-09T06:01:34.000Z

run_rnn.py

iqbaalmuhmd/CNNnumpyTest

eaecf5bc53a7b5c932a82d38cc6ca2a40430af4b

[ "MIT" ]

105

2017-06-15T06:40:44.000Z

2022-03-09T06:38:59.000Z

import numpy as np from deepnet.nnet import RNN from deepnet.solver import sgd_rnn def text_to_inputs(path): """ Converts the given text into X and y vectors X : contains the index of all the characters in the text vocab y : y[i] contains the index of next character for X[i] in the text vocab """ with open(path) as f: txt = f.read() X, y = [], [] char_to_idx = {char: i for i, char in enumerate(set(txt))} idx_to_char = {i: char for i, char in enumerate(set(txt))} X = np.array([char_to_idx[i] for i in txt]) y = [char_to_idx[i] for i in txt[1:]] y.append(char_to_idx['.']) y = np.array(y) vocab_size = len(char_to_idx) return X, y, vocab_size, char_to_idx, idx_to_char if __name__ == "__main__": X, y, vocab_size, char_to_idx, idx_to_char = text_to_inputs('data/Rnn.txt') rnn = RNN(vocab_size,vocab_size,char_to_idx,idx_to_char) rnn = sgd_rnn(rnn,X,y,10,10,0.1)

27.666667

79

0.625502

179

996

3.240223

0.307263

0.082759

0.124138

0.077586

0.298276

0.212069

0.1

0

0.009485

0.259036

996

35

80

28.457143

0.776423

0.180723

0

0.026515

0

1

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false

0

0.157895

0

0.263158

0

null

0

null

0

1

0

571ddbe314e19b402b88195037ee31e371ecdddf

5,421

py

Python

lcclassifier/experiments/attnstats.py

oscarpimentel/astro-lightcurves-classifier

f697b43e22bd8c92c1b9df514be8565c736dd7cc

[ "MIT" ]

1

2021-12-31T18:00:08.000Z

lcclassifier/experiments/attnstats.py

oscarpimentel/astro-lightcurves-classifier

f697b43e22bd8c92c1b9df514be8565c736dd7cc

[ "MIT" ]

null

lcclassifier/experiments/attnstats.py

oscarpimentel/astro-lightcurves-classifier

f697b43e22bd8c92c1b9df514be8565c736dd7cc

[ "MIT" ]

null

from __future__ import print_function from __future__ import division from . import _C import torch from fuzzytorch.utils import TDictHolder, tensor_to_numpy, minibatch_dict_collate import numpy as np from fuzzytools.progress_bars import ProgressBar, ProgressBarMulti import fuzzytools.files as files import fuzzytools.datascience.metrics as fcm from fuzzytools.matplotlib.utils import save_fig import matplotlib.pyplot as plt import fuzzytorch.models.seq_utils as seq_utils from scipy.optimize import curve_fit from lchandler import _C as _Clchandler from lchandler.plots.lc import plot_lightcurve from .utils import check_attn_scores EPS = _C.EPS ################################################################################################################################################### def local_slope_f(time, m, n): return time*m+n def get_local_slope(days, obs, j, dj, p0=[0,0], ): assert not dj%2==0 assert dj>=3 ji = max(0, j-dj//2) jf = min(j+dj//2+1, len(obs)) sub_days = days[ji:jf] # sequence steps sub_obs = obs[ji:jf] # sequence steps popt, pcov = curve_fit(local_slope_f, sub_days, sub_obs, p0=p0) local_slope_m, local_slope_n = popt return local_slope_m, local_slope_n, sub_days, sub_obs ################################################################################################################################################### def save_attnstats(train_handler, data_loader, save_rootdir, eps:float=EPS, dj=3, min_len=3, **kwargs): train_handler.load_model() # important, refresh to best model train_handler.model.eval() # important, model eval mode dataset = data_loader.dataset # get dataset is_parallel = 'Parallel' in train_handler.model.get_name() if not is_parallel: return attn_scores_collection = {b:[] for kb,b in enumerate(dataset.band_names)} with torch.no_grad(): tdicts = [] for ki,in_tdict in enumerate(data_loader): train_handler.model.autoencoder['encoder'].add_extra_return = True _tdict = train_handler.model(TDictHolder(in_tdict).to(train_handler.device)) train_handler.model.autoencoder['encoder'].add_extra_return = False tdicts += [_tdict] tdict = minibatch_dict_collate(tdicts) for kb,b in enumerate(dataset.band_names): p_onehot = tdict[f'input/onehot.{b}'][...,0] # (n,t) #p_rtime = tdict[f'input/rtime.{b}'][...,0] # (n,t) #p_dtime = tdict[f'input/dtime.{b}'][...,0] # (n,t) #p_x = tdict[f'input/x.{b}'] # (n,t,i) #p_rerror = tdict[f'target/rerror.{b}'] # (n,t,1) #p_rx = tdict[f'target/recx.{b}'] # (n,t,1) # print(tdict.keys()) uses_attn = any([f'attn_scores' in k for k in tdict.keys()]) if not uses_attn: return ### attn scores attn_scores = tdict[f'model/attn_scores/encz.{b}'] # (n,h,qt) assert check_attn_scores(attn_scores) attn_scores_mean = attn_scores.mean(dim=1)[...,None] # (n,h,qt)>(n,qt)>(n,qt,1) # mean attention score among the heads: not a distribution attn_scores_min_max = seq_utils.seq_min_max_norm(attn_scores_mean, p_onehot) # (n,qt,1) ### stats lcobj_names = dataset.get_lcobj_names() bar = ProgressBar(len(lcobj_names)) for k,lcobj_name in enumerate(lcobj_names): lcobj = dataset.lcset[lcobj_name] lcobjb = lcobj.get_b(b) # complete lc p_onehot_k = tensor_to_numpy(p_onehot[k]) # (n,t)>(t) b_len = p_onehot_k.sum() assert b_len<=len(lcobjb), f'{b_len}<={len(lcobjb)}' if not b_len>=min_len: continue attn_scores_k = tensor_to_numpy(attn_scores_mean[k,:b_len,0]) # (n,qt,1)>(t) attn_scores_min_max_k = tensor_to_numpy(attn_scores_min_max[k,:b_len,0]) # (n,qt,1)>(t) days = lcobjb.days[:b_len] # (t) obs = lcobjb.obs[:b_len] # (t) obse = lcobjb.obse[:b_len] # (t) snr = lcobjb.get_snr(max_len=b_len) max_obs = np.max(obs) peak_day = days[np.argmax(obs)] duration = days[-1]-days[0] bar(f'b={b}; lcobj_name={lcobj_name}; b_len={b_len}; snr={snr}; max_obs={max_obs}') lc_features = [] for j in range(0, b_len): j_features = { f'j':j, f'attn_scores_k.j':attn_scores_k[j], f'attn_scores_min_max_k.j':attn_scores_min_max_k[j], f'days.j':days[j], f'obs.j':obs[j], f'obse.j':obse[j], } local_slope_m, local_slope_n, sub_days, sub_obs = get_local_slope(days, obs, j, dj) # get local slope j_features.update({ f'local_slope_m.j~dj={dj}':local_slope_m, f'local_slope_n.j~dj={dj}':local_slope_n, f'peak_distance.j~dj={dj}~mode=local':days[j]-peak_day, f'peak_distance.j~dj={dj}~mode=mean':np.mean(sub_days)-peak_day, f'peak_distance.j~dj={dj}~mode=median':np.median(sub_days)-peak_day, }) lc_features += [j_features] attn_scores_collection[b] += [{ f'c':dataset.class_names[lcobj.y], f'b_len':b_len, f'peak_day':peak_day, f'duration':duration, f'snr':snr, f'max_obs':max_obs, f'lc_features':lc_features, }] bar.done() results = { 'model_name':train_handler.model.get_name(), 'survey':dataset.survey, 'band_names':dataset.band_names, 'class_names':dataset.class_names, 'max_day':dataset.max_day, 'attn_scores_collection':attn_scores_collection, } ### save file save_filedir = f'{save_rootdir}/{dataset.lcset_name}/id={train_handler.id}.d' files.save_pickle(save_filedir, results) # save file dataset.reset_max_day() # very important!! dataset.calcule_precomputed() # very important!! return

35.431373

147

0.65855

861

5,421

3.876887

0.212544

0.068904

0.030557

0.023966

0.180647

0.146195

0.11444

0.094068

0.038346

0.020971

0

0.00627

0.146836

5,421

153

148

35.431373

0.715459

0.110312

0

0.02459

0

0.121212

0.072193

0

0.032787

1

0.02459

false

0

0.131148

0.008197

0.196721

0.008197

0

null

0

null

0

1

0

571ea096124b732422144c10209f4cc5cb3c06c7

1,473

py

Python

get_item_by_key.py

flyco2016/my_python_module_project

6e1ac7f074f7b57403d7b7c6adadab17a26fc27d

[ "Apache-2.0" ]

null

get_item_by_key.py

flyco2016/my_python_module_project

6e1ac7f074f7b57403d7b7c6adadab17a26fc27d

[ "Apache-2.0" ]

1

2019-01-04T06:37:06.000Z

get_item_by_key.py

flyco2016/my_python_module_project

6e1ac7f074f7b57403d7b7c6adadab17a26fc27d

[ "Apache-2.0" ]

null

# 处理嵌套的根据键取值 def getItemByKey(obj, key, result=None): if isinstance(obj, dict): for k in obj: if key == k: if isinstance(result, list): if isinstance(obj[k], list): result.extend(obj[k]) else: result.append(obj[k]) elif result is None: result = obj[k] else: result = [result] result.append(obj[k]) else: if isinstance(obj[k], dict) or isinstance(obj[k], list): result = getItemByKey(obj[k], key, result) elif isinstance(obj, list): for i in obj: if isinstance(i, dict) or isinstance(i, list): result = getItemByKey(i, key, result) return result[0] if isinstance(result, list) and len(result) == 1 else result def getItemByKeyInMyMethod(dict_obj, key, default=None): import types for k ,v in dict_obj.items(): if k == key: return v else: if type(v) is dict: ret = getItemByKeyInMyMethod(v, key, default) if ret is not default: return ret return default if __name__ == "__main__": test_dic = {'a': 1, 'b': 2, 'c': {'a': 1, 'b': {'b': 4}}} r1 = getItemByKey(test_dic, 'b') r2 = getItemByKeyInMyMethod(test_dic, 'b') print(r1, r2, sep='\n')

33.477273

81

0.491514

172

1,473

4.133721

0.284884

0.045007

0.063291

0.061885

0.067511

0

0.011173

0.392396

1,473

44

82

33.477273

0.78324

0.006789

0

0.157895

0

0.012312

0

1

0.052632

false

0

0.026316

0

0.184211

0.026316

0

null

0

null

0

1

0

571f77622a48c2fb03cc44698429e534d7932593

7,166

py

Python

calories.py

davidsvaughn/har-pytorch

334733a1e870637c9077d16fc15e0b1954a6dfc5

[ "MIT" ]

5

2020-09-17T12:17:13.000Z

2022-02-28T08:07:49.000Z

calories.py

davidsvaughn/har-pytorch

334733a1e870637c9077d16fc15e0b1954a6dfc5

[ "MIT" ]

null

calories.py

davidsvaughn/har-pytorch

334733a1e870637c9077d16fc15e0b1954a6dfc5

[ "MIT" ]

null

import numpy as np import pandas as pd import json from datetime import datetime import psycopg2 import functools import requests ############################################################## ## https://www.exrx.net/Calculators/WalkRunMETs ## https://www.cdc.gov/growthcharts/clinical_charts.htm ## https://help.fitbit.com/articles/en_US/Help_article/1141 ############################################################## URL = 'https://f73lzrw31i.execute-api.us-west-2.amazonaws.com/default/demo_data_server' HEADER = {'x-api-key': 'XXXXXX'} class adict(dict): def __init__(self, *av, **kav): dict.__init__(self, *av, **kav) self.__dict__ = self def tofloat(x): try: return float(x.strip()) except: return None @functools.lru_cache(maxsize=250) def request_demo_data(pid): payload = {'pid': pid} r = requests.post(URL, headers=HEADER, data=json.dumps(payload)) return adict((k.strip("' "), tofloat(v)) for k,v in (item.split(':') for item in r.text[2:-2].split(','))) ############################################################################################# ############################################################################################# revibe = adict() revibe.DBNAME = 'revibe' revibe.HOST = 'prd.c5fw7irdcxik.us-west-2.rds.amazonaws.com' #revibe.PORT = '5432' revibe.USER = 'dave' revibe.PASS = 'tnoiSLoHjEBZE6JKsFgY' revibe.SSLMODE = 'require' CONN = None def get_conn_string(creds): conn_str = 'host='+ creds.HOST \ +' dbname='+ creds.DBNAME +' user=' + creds.USER \ +' password='+ creds.PASS \ + (' sslmode='+ creds.SSLMODE if 'SSLMODE' in creds else '') \ + (' port='+ creds.PORT if 'PORT' in creds else '') return conn_str def get_conn(creds): conn_str = get_conn_string(creds) return psycopg2.connect(conn_str) def run_sql(sql, verbose=False): global CONN if CONN is None: CONN = get_conn(revibe) if verbose: print(sql) with CONN: data = pd.read_sql(sql, CONN) if verbose: print(data.shape) return (data) def get_pid_data(pid): table = 'private.person_demographic_view' sql_command = "SELECT * FROM {} WHERE (person_id={});".format(table, pid) df = run_sql(sql_command) if df.size==0: raise ValueError('SQL returned no records:\n\t{}'.format(sql_command)) data = adict() bday = df.birthday.values[0] sex = df.sex_id.values[0] grade = df.grade.values[0] ht = df.height.values[0] wt = df.weight.values[0] wrist = df.wrist_id.values[0] data.pid = pid data.age = None if bday is not None: data.bday = str(bday) bday = pd.Timestamp(str(bday)).to_pydatetime() data.age = np.round((datetime.now()-bday).total_seconds() / (60*60*2*365), 2) ## in months data.sex = None if sex==0 or sex>2 else sex data.grade = None if grade==0 else grade data.ht = None if ht==0 else ht data.wt = None if wt==0 else wt data.wrist = None if wrist==0 else wrist return data ## revised Harris-Benedict BMR equations... def bmr_hb(dd, sex=None): try: sex = dd.sex if sex is None else sex if sex==1: return 6.078*dd.wt + 12.192*dd.ht - 0.473*dd.age + 88.4 if sex==2: return 4.196*dd.wt + 7.874*dd.ht - 0.36*dd.age + 447.6 return None except ex: return None ## basal metabolic rate (kCals per day) def BMR(dd): try: if dd.sex is None: bmr = (bmr_hb(dd,1) + bmr_hb(dd,2))/2 else: bmr = bmr_hb(dd) return int(round(bmr)) except ex: return None ## find index j into y that minimizes abs(x-y[j]) def xminy(x, y): return abs(x-y).argmin(axis=-1) class GrowthChart(object): ## columns: age ht_boy ht_girl wt_boy wt_girl def __init__(self, fn='growth.tsv'):#, path=None): # if path is None: path = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) # fn = os.path.join(path, fn) df = pd.read_csv(fn, sep='\t') self.G = df.values self.S = np.array([[0.415, 0.413], [0.675, 0.57]]) def fill_data(self, d): if d.age is None: if d.ht is None or d.wt is None: raise ValueError('Either birthday, or both height and weight, must be non-null') else: row = xminy(d.age, self.G[:,0]) cols = np.array([d.sex] if d.sex is not None else [1, 2]) if d.ht is None: d.ht = self.G[row, cols].mean() if d.wt is None: d.wt = self.G[row, cols+2].mean() d.ws = np.round(d.ht * self.S[0, cols-1].mean(), 2) ## walk stride d.rs = np.round(d.ht * self.S[1, cols-1].mean(), 2) ## run stride #d.bmr = BMR(d) ## basal metabolic rate (kCals per day) GC = None @functools.lru_cache(maxsize=250) def get_demo_data(pid): data = get_pid_data(pid) global GC if GC is None: GC = GrowthChart() GC.fill_data(data) return data def fixnum(x, dtype=float): if x is None: return None x = dtype(x) if x==0: return None return x def validate_demo_data(data): data.ht = fixnum(data.ht) data.wt = fixnum(data.wt) data.sex = fixnum(data.sex, int) if data.sex is not None and data.sex>2: data.sex = None data.age = None if data.bday is None: if data.ht is None or data.wt is None: raise ValueError('Either birthday, or both height and weight, must be non-null') else: bday = pd.Timestamp(str(data.bday)).to_pydatetime() data.age = np.round((datetime.now()-bday).total_seconds() / (60*60*2*365), 2) ## in months # data.bday = data.bday.strftime('%Y-%m-%d') @functools.lru_cache(maxsize=250) def make_demo_data(bday=None, ht=None, wt=None, sex=None): data = adict() data.bday = bday or None data.ht = ht or None data.wt = wt or None data.sex = sex or None validate_demo_data(data) ######### global GC if GC is None: GC = GrowthChart() GC.fill_data(data) return data ## s : speed in mph... sec by second vector of speeds.... ## w : weight in lbs ## mode : 2=='walk', 3=='run' ## returns : calories summed across all seconds def calsum(s, w, mode=2): su, wu = 26.8, 2.2 s = s*su w = w/wu if mode==3:## run mode vo = 0.2*s else: ## walk mode == 2 fwvo = 21.11 - 0.3593*s + 0.003*s*s - 3.5 wvo = 0.1*s d = 30 a = np.clip((s-(100-d))/(2*d), 0, 1) vo = wvo*(1.-a) + fwvo*a ############################# return np.sum(vo*w) / 12000.0 ################################### if __name__ == "__main__": pid = 135 ## 135,"1974-05-28",1,0,74,196,1 pid = 169 ## 169,"1980-12-01",1,12,72,170,2 pid = 18947 ## 18947,"2010-08-28",0,0,0,0,0 pid = 10885 ## # dd = request_demo_data(pid) # print(dd) # dd = get_demo_data(pid) # print(dd) ############# dd = make_demo_data(bday='2010-08-28', ht='54.035', wt='69.69', sex='3') # dd = make_demo_data(ht='70', wt='120', sex='2') print(dd)

30.887931

110

0.558889

1,099

7,166

3.561419

0.272975

0.022994

0.011242

0.018396

0.169647

0.164027

0.123659

0.106285

0

0.048942

0.241557

7,166

232

111

30.887931

0.671205

0.144711

0

0.202312

0

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0.087707

0.013209

0

1

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false

0.011561

0.040462

0.00578

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0.017341

0

null

0

null

0

1

0

5721e5bf810d647e593fd1d82e6a86cb2fa7e570

14,744

py

Python

alphad3m/alphad3m/metalearning/grammar_builder.py

VIDA-NYU/alphad3m

db40193a448300d87442c451f9da17fa5cb845fd

[ "Apache-2.0" ]

null

alphad3m/alphad3m/metalearning/grammar_builder.py

VIDA-NYU/alphad3m

db40193a448300d87442c451f9da17fa5cb845fd

[ "Apache-2.0" ]

null

alphad3m/alphad3m/metalearning/grammar_builder.py

VIDA-NYU/alphad3m

db40193a448300d87442c451f9da17fa5cb845fd

[ "Apache-2.0" ]

null

import logging import numpy as np from scipy import stats from collections import OrderedDict from alphad3m.metalearning.resource_builder import load_metalearningdb from alphad3m.metalearning.dataset_similarity import get_similar_datasets from alphad3m.primitive_loader import load_primitives_by_name, load_primitives_by_id logger = logging.getLogger(__name__) def load_related_pipelines(dataset_path, target_column, task_keywords): available_primitives = load_primitives_by_id() all_pipelines = load_metalearningdb() similar_datasets = get_similar_datasets('dataprofiles', dataset_path, target_column, task_keywords) task_pipelines = [] for similar_dataset in similar_datasets.keys(): if similar_dataset not in all_pipelines['pipeline_performances']: continue for pipeline_id, pipeline_performances in all_pipelines['pipeline_performances'][similar_dataset].items(): primitive_ids = all_pipelines['pipeline_structure'][pipeline_id] if is_available_primitive(primitive_ids, available_primitives): for index in range(len(pipeline_performances['score'])): primitives = [available_primitives[p] for p in primitive_ids] # Use the current names of primitives score = pipeline_performances['score'][index] metric = pipeline_performances['metric'][index] task_pipelines.append({'pipeline': primitives, 'score': score, 'metric': metric, 'dataset': similar_dataset, 'pipeline_repr': '_'.join(primitives)}) logger.info('Found %d related pipelines', len(task_pipelines)) return task_pipelines def create_metalearningdb_grammar(task_name, dataset_path, target_column, task_keywords): pipelines = load_related_pipelines(dataset_path, target_column, task_keywords) patterns, primitives = extract_patterns(pipelines) merged_patterns, empty_elements = merge_patterns(patterns) grammar = format_grammar(task_name, merged_patterns, empty_elements) return grammar, primitives def format_grammar(task_name, patterns, empty_elements): if len(patterns) == 0: logger.info('Empty patterns, no grammar have been generated') return None grammar = 'S -> %s\n' % task_name grammar += task_name + ' -> ' + ' | '.join([' '.join(p) for p in patterns]) for element in sorted(set([e for sublist in patterns for e in sublist])): # Sort to have a deterministic grammar production_rule = element + " -> 'primitive_terminal'" if element in empty_elements: production_rule += " | 'E'" grammar += '\n' + production_rule logger.info('Grammar obtained:\n%s', grammar) return grammar def extract_patterns(pipelines, max_nro_patterns=15, min_frequency=3, adtm_threshold=0.5, mean_score_threshold=0.5, ratio_datasets=0.2): available_primitives = load_primitives_by_name() pipelines = calculate_adtm(pipelines) patterns = {} for pipeline_data in pipelines: if pipeline_data['adtm'] > adtm_threshold: # Skip pipelines with average distance to the minimum higher than the threshold continue primitive_types = [available_primitives[p]['type'] for p in pipeline_data['pipeline']] pattern_id = ' '.join(primitive_types) if pattern_id not in patterns: patterns[pattern_id] = {'structure': primitive_types, 'primitives': set(), 'datasets': set(), 'pipelines': [], 'scores': [], 'adtms': [], 'frequency': 0} patterns[pattern_id]['primitives'].update(pipeline_data['pipeline']) patterns[pattern_id]['datasets'].add(pipeline_data['dataset']) patterns[pattern_id]['pipelines'].append(pipeline_data['pipeline']) patterns[pattern_id]['scores'].append(pipeline_data['score']) patterns[pattern_id]['adtms'].append(pipeline_data['adtm']) patterns[pattern_id]['frequency'] += 1 logger.info('Found %d different patterns, after creating the portfolio', len(patterns)) # TODO: Group these removing conditions into a single loop # Remove patterns with fewer elements than the minimum frequency patterns = {k: v for k, v in patterns.items() if v['frequency'] >= min_frequency} logger.info('Found %d different patterns, after removing uncommon patterns', len(patterns)) # Remove patterns with undesirable primitives (AlphaD3M doesn't have support to handle some of these primitives) blacklist_primitives = {'d3m.primitives.data_transformation.dataframe_to_ndarray.Common', 'd3m.primitives.data_transformation.list_to_dataframe.DistilListEncoder', 'd3m.primitives.data_transformation.ndarray_to_dataframe.Common', 'd3m.primitives.data_transformation.horizontal_concat.DSBOX', 'd3m.primitives.data_transformation.horizontal_concat.DataFrameCommon', 'd3m.primitives.data_transformation.multi_horizontal_concat.Common', 'd3m.primitives.data_transformation.conditioner.Conditioner', 'd3m.primitives.data_transformation.remove_semantic_types.Common', 'd3m.primitives.data_transformation.replace_semantic_types.Common', 'd3m.primitives.data_transformation.remove_columns.Common', 'd3m.primitives.operator.dataset_map.DataFrameCommon', 'd3m.primitives.data_transformation.i_vector_extractor.IVectorExtractor'} patterns = {k: v for k, v in patterns.items() if not blacklist_primitives & v['primitives']} logger.info('Found %d different patterns, after blacklisting primitives', len(patterns)) unique_datasets = set() for pattern_id in patterns: scores = patterns[pattern_id]['scores'] adtms = patterns[pattern_id]['adtms'] patterns[pattern_id]['mean_score'] = np.mean(scores) patterns[pattern_id]['mean_adtm'] = np.mean(adtms) unique_datasets.update(patterns[pattern_id]['datasets']) # Remove patterns with low performances patterns = {k: v for k, v in patterns.items() if v['mean_score'] >= mean_score_threshold} logger.info('Found %d different patterns, after removing low-performance patterns', len(patterns)) # Remove patterns with low variability patterns = {k: v for k, v in patterns.items() if len(v['datasets']) >= len(unique_datasets) * ratio_datasets} logger.info('Found %d different patterns, after removing low-variability patterns', len(patterns)) if len(patterns) > max_nro_patterns: logger.info('Found many patterns, selecting top %d (max_nro_patterns)' % max_nro_patterns) sorted_patterns = sorted(patterns.items(), key=lambda x: x[1]['mean_score'], reverse=True) patterns = {k: v for k, v in sorted_patterns[:max_nro_patterns]} primitive_hierarchy = {} all_pipelines = [] all_performances = [] all_primitives = [] local_probabilities = {} for pattern_id, pattern in patterns.items(): for primitive in pattern['primitives']: primitive_type = available_primitives[primitive]['type'] if primitive_type not in primitive_hierarchy: primitive_hierarchy[primitive_type] = set() primitive_hierarchy[primitive_type].add(primitive) performances = [1 - x for x in pattern['adtms']] # Use adtms as performances because their are scaled all_pipelines += pattern['pipelines'] all_primitives += pattern['primitives'] all_performances += performances correlations = calculate_correlations(pattern['primitives'], pattern['pipelines'], performances) local_probabilities[pattern_id] = {} for primitive, correlation in correlations.items(): primitive_type = available_primitives[primitive]['type'] if primitive_type not in local_probabilities[pattern_id]: local_probabilities[pattern_id][primitive_type] = {} local_probabilities[pattern_id][primitive_type][primitive] = correlation correlations = calculate_correlations(set(all_primitives), all_pipelines, all_performances) global_probabilities = {} for primitive, correlation in correlations.items(): primitive_type = available_primitives[primitive]['type'] if primitive_type not in global_probabilities: global_probabilities[primitive_type] = {} global_probabilities[primitive_type][primitive] = correlation # Make deterministic the order of the patterns and hierarchy patterns = sorted(patterns.values(), key=lambda x: x['mean_score'], reverse=True) primitive_hierarchy = OrderedDict({k: sorted(v) for k, v in sorted(primitive_hierarchy.items(), key=lambda x: x[0])}) logger.info('Patterns:\n%s', patterns_repr(patterns)) logger.info('Hierarchy:\n%s', '\n'.join(['%s:\n%s' % (k, ', '.join(v)) for k, v in primitive_hierarchy.items()])) patterns = [p['structure'] for p in patterns] primitive_probabilities = {'global': global_probabilities, 'local': local_probabilities, 'types': available_primitives} primitive_info = {'hierarchy': primitive_hierarchy, 'probabilities': primitive_probabilities} return patterns, primitive_info def calculate_correlations(primitives, pipelines, scores, normalize=True): correlations = {} for primitive in primitives: occurrences = [1 if primitive in pipeline else 0 for pipeline in pipelines] correlation_coefficient, p_value = stats.pointbiserialr(occurrences, scores) if np.isnan(correlation_coefficient): # Assign a positive correlation (1) to NaN values correlation_coefficient = 1 if normalize: # Normalize the Pearson values, from [-1, 1] to [0, 1] range correlation_coefficient = (correlation_coefficient - (-1)) / 2 # xi − min(x) / max(x) − min(x) correlations[primitive] = round(correlation_coefficient, 4) return correlations def calculate_adtm(pipelines): dataset_performaces = {} pipeline_performances = {} for pipeline_data in pipelines: # Even the same dataset can be run under different metrics. So, use the metric to create the id of the dataset id_dataset = pipeline_data['dataset'] + '_' + pipeline_data['metric'] if id_dataset not in dataset_performaces: dataset_performaces[id_dataset] = {'min': float('inf'), 'max': float('-inf')} performance = pipeline_data['score'] if performance > dataset_performaces[id_dataset]['max']: dataset_performaces[id_dataset]['max'] = performance if performance < dataset_performaces[id_dataset]['min']: dataset_performaces[id_dataset]['min'] = performance id_pipeline = pipeline_data['pipeline_repr'] if id_pipeline not in pipeline_performances: pipeline_performances[id_pipeline] = {} if id_dataset not in pipeline_performances[id_pipeline]: pipeline_performances[id_pipeline][id_dataset] = pipeline_data['score'] else: # A pipeline can have different performances for a given dataset, choose the best one if pipeline_data['score'] > pipeline_performances[id_pipeline][id_dataset]: pipeline_performances[id_pipeline][id_dataset] = pipeline_data['score'] for pipeline_data in pipelines: id_pipeline = pipeline_data['pipeline_repr'] id_dataset_pipeline = pipeline_data['dataset'] + '_' + pipeline_data['metric'] dtm = 0 for id_dataset in pipeline_performances[id_pipeline]: # Iterate over the datasets where the pipeline was used minimum = dataset_performaces[id_dataset]['min'] maximum = dataset_performaces[id_dataset]['max'] if id_dataset_pipeline == id_dataset: score = pipeline_data['score'] else: score = pipeline_performances[id_pipeline][id_dataset] if minimum != maximum: dtm += (maximum - score) / (maximum - minimum) adtm = dtm / len(pipeline_performances[id_pipeline]) pipeline_data['adtm'] = adtm return pipelines def merge_patterns(grammar_patterns): patterns = sorted(grammar_patterns, key=lambda x: len(x), reverse=True) empty_elements = set() skip_patterns = [] for pattern in patterns: for element in pattern: modified_pattern = [e for e in pattern if e != element] for current_pattern in patterns: if modified_pattern == current_pattern: empty_elements.add(element) skip_patterns.append(modified_pattern) for skip_pattern in skip_patterns: if skip_pattern in patterns: patterns.remove(skip_pattern) return patterns, empty_elements def is_available_primitive(pipeline_primitives, available_primitives, verbose=False): for primitive in pipeline_primitives: if primitive not in available_primitives: if verbose: logger.warning('Primitive %s is not longer available' % primitive) return False return True def patterns_repr(patterns): patterns_string = [] for pattern in patterns: pretty_string = '' pretty_string += 'structure: [%s]' % ', '.join([i for i in pattern['structure']]) pretty_string += ', frequency: %d' % pattern['frequency'] if 'mean_score' in pattern: pretty_string += ', mean_score: %.3f' % pattern['mean_score'] if 'mean_adtm' in pattern: pretty_string += ', mean_adtm: %.3f' % pattern['mean_adtm'] patterns_string.append(pretty_string) return '\n'.join(patterns_string) def test_dataset(dataset_id, task_name='TASK'): from os.path import join import json dataset_folder_path = join('/Users/rlopez/D3M/datasets/seed_datasets_current/', dataset_id) dataset_path = join(dataset_folder_path, 'TRAIN/dataset_TRAIN/tables/learningData.csv') problem_path = join(dataset_folder_path, 'TRAIN/problem_TRAIN/problemDoc.json') with open(problem_path) as fin: problem_doc = json.load(fin) task_keywords = problem_doc['about']['taskKeywords'] target_column = problem_doc['inputs']['data'][0]['targets'][0]['colName'] logger.info('Evaluating dataset %s with task keywords=%s' % (dataset_id, str(task_keywords))) create_metalearningdb_grammar(task_name, dataset_path, target_column, task_keywords) if __name__ == '__main__': test_dataset('185_baseball_MIN_METADATA')

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165

0.686788

1,690

14,744

5.756213

0.16213

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0.02097

0.035053

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0

null

0

null

0

1

0

5723328e5cd271a82c8d25b908bc2b420246795d

4,512

py

Python

deap_learning.py

fzjcdt/Genetic-CNN

6bd53f3f429434557b7fbf1122020259d910f618

[ "Apache-2.0" ]

2

2019-10-08T08:27:41.000Z

2021-12-02T07:37:27.000Z

deap_learning.py

fzjcdt/Genetic-CNN

6bd53f3f429434557b7fbf1122020259d910f618

[ "Apache-2.0" ]

null

deap_learning.py

fzjcdt/Genetic-CNN

6bd53f3f429434557b7fbf1122020259d910f618

[ "Apache-2.0" ]

null

from deap import base, creator, tools import random """ 每个individual是一个list，包含10个元素，需要演化到元素和最小 """ # ****************************Types******************************** # def create(name, base, **kargs): # Creates a new class named *name* inheriting from *base* # A negative weight element corresponds to the minimization of # the associated objective and positive weight to the maximization. creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) # an Individual class that is derived from a list with a fitness attribute set # to the just created fitness """ create("Foo", list, bar=dict, spam=1) This above line is exactly the same as defining in the :mod:`creator` module something like the following. :: class Foo(list): spam = 1 def __init__(self): self.bar = dict() """ creator.create("Individual", list, fitness=creator.FitnessMin) # ****************************Initialization******************************** IND_SIZE = 10 toolbox = base.Toolbox() # def register(self, alias, function, *args, **kargs): # Register a *function* in the toolbox under the name *alias*. # *args当function被调用时自动作为function相应参数 """ >>> def func(a, b, c=3): ... print(a, b, c) ... >>> tools = Toolbox() >>> tools.register("myFunc", func, 2, c=4) >>> tools.myFunc(3) 2 3 4 """ toolbox.register("attribute", random.random) # def initRepeat(container, func, n): # Call the function *container* with a generator function corresponding # to the calling *n* times the function *func*. """ >>> initRepeat(list, random.random, 2) # doctest: +ELLIPSIS, ... # doctest: +NORMALIZE_WHITESPACE [0.6394..., 0.0250...] """ # 将IND_SIZE个 random.random()加入到Individual里，即初始化Individual，每个Individual list里共IND_SIZE个初始值 toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attribute, n=IND_SIZE) # 将individual加入到population里，n由初始化时指定 toolbox.register("population", tools.initRepeat, list, toolbox.individual) # ****************************Operators******************************** def evaluate(individual): # 评估函数为individual里IND_SIZE个值的和 # 这里，很重要，返回的是(a, )，以为weight是(fitness, ) # 也可以返回(sum(individual), ) return sum(individual), # def cxTwoPoint(ind1, ind2): # Executes a two-point crossover on the input :term:`sequence` individuals. toolbox.register("mate", tools.cxTwoPoint) # gaussian mutation with mu and sigma # The *indpb* argument is the probability of each attribute to be mutated. # 元素增减一个小的值 toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.1) # def selTournament(individuals, k, tournsize, fit_attr="fitness"): # Select the best individual among *tournsize* randomly chosen # individuals, *k* times. The list returned contains # references to the input *individuals*. toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("evaluate", evaluate) def main(): pop = toolbox.population(n=50) CXPB, MUTPB, NGEN = 0.5, 0.2, 40 # map(func, *iterables) --> map object # Make an iterator that computes the function using arguments from # each of the iterables. # map 以参数序列中的每一个元素调用 function 函数，返回包含每次 function 函数返回值的新列表。 fitnesses = map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit # ind.fitness = fit for g in range(NGEN): # 每次拿三个，选其中最好的，一直选到len(pop)个 offspring = toolbox.select(pop, len(pop)) # 什么的offspring还指向是pop里的同一个对象，需要克隆一下 # 担心的是一个对象/individual被选到了两次或多次 offspring = list(map(toolbox.clone, offspring)) for child1, child2 in zip(offspring[::2], offspring[1::2]): if random.random() < CXPB: toolbox.mate(child1, child2) del child1.fitness.values del child2.fitness.values for mutant in offspring: if random.random() < MUTPB: toolbox.mutate(mutant) del mutant.fitness.values # 上面删除了fitness，下面找出删除的individual，只需要重新评估这些就行，不用重新评估所有 invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit pop[:] = offspring return pop for ind in main(): print(evaluate(ind))

33.176471

89

0.629876

516

4,512

5.48062

0.408915

0.037129

0.002122

0.019095

0.029703

0

0.014245

0.222074

4,512

135

90

33.422222

0.791453

0.407801

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false

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0.142857

0.02381

0

null

0

null

0

1

0

57262781980201cf7735ba35e8965dd0cb76ade8

1,674

py

Python

pacman/utils/replay_buffer.py

i-rme/openai-pacman

4a80ed023ed2bdf031990147acbbeea904b9fc8e

[ "MIT" ]

2

2020-01-26T23:06:57.000Z

2021-04-12T08:36:55.000Z

pacman/utils/replay_buffer.py

i-rme/openai-pacman

4a80ed023ed2bdf031990147acbbeea904b9fc8e

[ "MIT" ]

null

pacman/utils/replay_buffer.py

i-rme/openai-pacman

4a80ed023ed2bdf031990147acbbeea904b9fc8e

[ "MIT" ]

null

from collections import deque import random import numpy as np class ReplayBuffer: ''' construct a buffer object that stores the past moves and samples a set of subsamples ''' def __init__(self, buffer_size): self.buffer_size = buffer_size self.count = 0 self.buffer = deque() def add(self, s, a, r, d, s2): ''' add an experience to the buffer s: current state, a: action, r: reward, d: done, s2: next state ''' experience = (s, a, r, d, s2) if self.count < self.buffer_size: self.buffer.append(experience) self.count += 1 else: self.buffer.popleft() self.buffer.append(experience) def size(self): return self.count def clear(self): self.buffer.clear() self.count = 0 def sample(self, batch_size): ''' sample a total of elements equal to batch_size from buffer if buffer contains enough elements; otherwise, return all elements list1 = [1, 2, 3, 4, 5, 6] random.sample(list1, 3) -- OUTPUT: [3, 1, 2] ''' batch = [] if self.count < batch_size: batch = random.sample(self.buffer, self.count) else: batch = random.sample(self.buffer, batch_size) # map each experience in batch in batches of # [array([s1, ..., sN]), ..., array([s21, ..., s2N])] s_batch, a_batch, r_batch, d_batch, s2_batch = list(map(np.array, list(zip(*batch)))) return s_batch, a_batch, r_batch, d_batch, s2_batch

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93

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214

1,674

4.186916

0.364486

0.111607

0.046875

0.040179

0.196429

0.069196

0

0.021858

0.344086

1,674

61

94

27.442623

0.794171

0.299881

0

0.206897

0

1

0.172414

false

0

0.103448

0.034483

0.37931

0

null

0

null

0

1

0

5726ab8f943f02dfa0eee1936447786383a1ce72

9,126

py

Python

tests/entities/test_creature.py

Flame753/ARPG

f931d3437a83995b43bdddc68cb5ba89922dc259

[ "MIT" ]

null

tests/entities/test_creature.py

Flame753/ARPG

f931d3437a83995b43bdddc68cb5ba89922dc259

[ "MIT" ]

null

tests/entities/test_creature.py

Flame753/ARPG

f931d3437a83995b43bdddc68cb5ba89922dc259

[ "MIT" ]

null

# Standard library imports from pprint import pprint import unittest # Local application imports from context import entities from entities import creatures from entities import items from entities import currency from entities import slots class TestCreature(unittest.TestCase): def setUp(self): self.dagger = items.Dagger() self.copper_coin = currency.CopperCoin() self.bread = items.Bread() def equipment_slot_helper(self, creature_obj, answer): list_of_slots = [slots.Head, slots.Body, slots.Legs, slots.Boots, slots.OneHanded, slots.TwoHanded] for slot in list_of_slots: creature_obj.equippable_slots.slots.get(slot)._ensure_inventory() self.assertDictEqual(creature_obj.equippable_slots.slots.get(slot).inventory, answer) def test_class_initializer(self): creature_A = creatures.Creature() creature_B = creatures.Creature() self.assertFalse(creature_A is creature_B) self.assertFalse(creature_A.equippable_slots is creature_B.equippable_slots) self.assertEqual(creature_A.inventory, creature_B.inventory) self.assertFalse(creature_A.inventory is creature_B.inventory) self.assertEqual(creature_A.coin_pouch, creature_B.coin_pouch) self.assertFalse(creature_A.coin_pouch is creature_B.coin_pouch) def test_add_item(self): creature = creatures.Creature() creature.add_item(self.dagger) self.assertDictEqual(creature.inventory.inventory, {self.dagger: {'amount': 1}}) creature.add_item(self.copper_coin, 2) self.assertDictEqual(creature.coin_pouch.inventory, {self.copper_coin: {'amount': 2}}) creature.add_item(self.bread, 6) self.assertDictEqual(creature.inventory.inventory, {self.bread: {'amount': 6}, self.dagger: {'amount': 1}}) creature.add_item(self.dagger, 3) self.assertDictEqual(creature.inventory.inventory, {self.bread: {'amount': 6}, self.dagger: {'amount': 4}}) def test_remove_item(self): creature = creatures.Creature() # Testing when removing an item from a empty dict result = creature.remove_item(self.dagger) self.assertFalse(result) creature.add_item(self.dagger) creature.remove_item(self.dagger) self.assertDictEqual(creature.inventory.inventory, {}) creature.add_item(self.copper_coin, 8) creature.remove_item(self.copper_coin, 3) self.assertDictEqual(creature.coin_pouch.inventory, {self.copper_coin: {'amount': 5}}) def test_equip(self): creature = creatures.Creature() # Equipping dagger that is not in creature result = creature.equip(self.dagger) self.assertFalse(result) # Verifying that there is no inventory was added answer = {} self.equipment_slot_helper(creature, answer) result = creature.inventory.inventory self.assertDictEqual(result, answer) self.assertFalse(hasattr(creature.coin_pouch, 'inventory')) # Equipping non equipable item creature.add_item(self.copper_coin) result = creature.equip(self.copper_coin) self.assertFalse(result) # Verifying that there is no inventory was added answer = {self.copper_coin: {'amount': 1}} result = creature.coin_pouch.inventory self.assertDictEqual(result, answer) answer = {} self.equipment_slot_helper(creature, answer) result = creature.inventory.inventory self.assertDictEqual(result, answer) # Equipping a dagger creature.add_item(self.dagger) result = creature.equip(self.dagger) self.assertTrue(result) answer = {self.dagger: {'amount': 1}} result = creature.inventory.inventory self.assertDictEqual(result, answer) answer = {self.dagger: {'amount': 1}} result = creature.equippable_slots.slots.get(slots.OneHanded).inventory self.assertDictEqual(result, answer) # equipping a non equipable item creature.add_item(self.bread) result = creature.equip(self.bread) self.assertFalse(result) def test_unequip(self): creature = creatures.Creature() # Unequipping a item that doesn't exist result = creature.unequip(self.dagger) self.assertFalse(result) # Verifying that there is no inventory was added answer = {} self.equipment_slot_helper(creature, answer) self.assertFalse(hasattr(creature.inventory, 'inventory')) creature.add_item(self.copper_coin) result = creature.unequip(self.copper_coin) self.assertFalse(result) # Verifying that there is no inventory was added answer = {} self.equipment_slot_helper(creature, answer) self.assertFalse(hasattr(creature.inventory, 'inventory')) answer = {self.copper_coin: {'amount': 1}} result = creature.coin_pouch.inventory self.assertDictEqual(result, answer) # Preparing for next test case creature.remove_item(self.copper_coin) # Actually tesing the removal of a item creature.add_item(self.dagger) creature.equip(self.dagger) result = creature.unequip(self.dagger) self.assertTrue(result) answer = {} self.equipment_slot_helper(creature, answer) result = creature.coin_pouch.inventory self.assertDictEqual(result, answer) answer = {self.dagger: {'amount': 1}} result = creature.inventory.inventory self.assertDictEqual(result, answer) def test_calculate_item_worth(self): creature = creatures.Creature() copper_amount = 10 bread_amount = 5 dagger_amount = 5 creature.add_item(self.copper_coin, copper_amount) creature.add_item(self.bread, bread_amount) creature.add_item(self.dagger, dagger_amount) creature.equip(self.dagger) result = creature.calculate_item_worth(self.copper_coin) self.assertEqual(result, copper_amount*self.copper_coin.worth) result = creature.calculate_item_worth(self.dagger) self.assertEqual(result, dagger_amount*self.dagger.worth) result = creature.calculate_item_worth(self.bread) self.assertEqual(result, bread_amount*self.bread.worth) def test_calculate_total_worth(self): creature = creatures.Creature() copper_amount = 10 bread_amount = 5 dagger_amount = 5 creature.add_item(self.copper_coin, copper_amount) creature.add_item(self.bread, bread_amount) creature.add_item(self.dagger, dagger_amount) creature.equip(self.dagger) result = creature.calculate_total_worth() answer = (self.copper_coin.worth * copper_amount) + \ (self.dagger.worth * dagger_amount) + \ (self.bread.worth * bread_amount) self.assertEqual(result, answer) def test_type_error(self): creature = creatures.Creature() test_num = 2 test_string = 'Test' test_list = [7] test_dict = {"s":2} test_tuple = (2, "2") test_case = [test_num, test_string, test_list, test_dict, test_tuple, [], {}, ()] for case in test_case: func = creature.add_item self.assertRaises(TypeError, func, case) self.assertRaises(TypeError, func, (self.dagger, case)) func = creature.remove_item self.assertRaises(TypeError, func, case) self.assertRaises(TypeError, func, (self.dagger, case)) func = creature.equip self.assertRaises(TypeError, func, case) func = creature.unequip self.assertRaises(TypeError, func, case) func = creature.calculate_item_worth self.assertRaises(TypeError, func, case) func = creature.calculate_total_worth self.assertRaises(TypeError, func, case) def test_value_error(self): creature = creatures.Creature() test_case = -32 func = creature.add_item self.assertRaises(TypeError, func, (self.dagger, test_case)) func = creature.remove_item self.assertRaises(TypeError, func, (self.dagger, test_case)) def test_EquippedItemRemovealError(self): creature = creatures.Creature() # Tesing after removing item from inventory item should not exist in equipment slot creature.add_item(self.dagger) creature.equip(self.dagger) self.assertRaises(creatures.EquippedItemRemovealError, creature.remove_item, self.dagger) def suite(): suite = unittest.TestSuite() suite.addTest(TestCreature('test_addItem')) return suite if __name__ == '__main__': unittest.main() # runner = unittest.TextTestRunner() # runner.run(suite())

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0.655928

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0.039704

0.065315

0.638192

0.599691

0.538329

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0.415091

0.355105

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9,126

250

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36.504

0.845412

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0.122222

0.005556

0

null

0

null

0

1

0

572995aff10ad23755f80a0359fa3ca259ee111e

199

py

Python

testfiles/benchmarks/send_multiple.py

marcolamartina/PASTEL

8e1e0fd086a26b7c50f15fe87ffe5dbd007cf925

[ "MIT" ]

null

testfiles/benchmarks/send_multiple.py

marcolamartina/PASTEL

8e1e0fd086a26b7c50f15fe87ffe5dbd007cf925

[ "MIT" ]

null

testfiles/benchmarks/send_multiple.py

marcolamartina/PASTEL

8e1e0fd086a26b7c50f15fe87ffe5dbd007cf925

[ "MIT" ]

1

2020-07-08T11:23:22.000Z

import binascii from pwn import * def send(r,num): r.sendline(str(num)) port = 1234 server = '127.0.0.1' sleep(1) for i in range(10000): r = remote(server, port) send(r,i) r.close()

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36

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0.666667

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199

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null

0

null

0

1

0

573671e14e06512a6056d7ef96ce655d220e4a19

2,857

py

Python

Run_exphydro_distributed_type1_pso.py

sopanpatil/exp-hydro

7295dddc4df1028f669a223e1b631a4a91669515

[ "MIT" ]

11

2016-11-25T13:05:26.000Z

2022-03-25T03:24:16.000Z

Run_exphydro_distributed_type1_pso.py

sopanpatil/exp-hydro

7295dddc4df1028f669a223e1b631a4a91669515

[ "MIT" ]

null

Run_exphydro_distributed_type1_pso.py

sopanpatil/exp-hydro

7295dddc4df1028f669a223e1b631a4a91669515

[ "MIT" ]

6

2017-03-28T12:06:00.000Z

2021-09-16T17:50:34.000Z

#!/usr/bin/env python # Programmer(s): Sopan Patil. """ MAIN PROGRAM FILE Run this file to optimise the model parameters of the spatially distributed version of EXP-HYDRO model using Particle Swarm Optimisation (PSO) algorithm. Type 1 Model: - This type of distributed model is pixel based (i.e., all sub-components have the same drainage area). - All pixels receive the same meteorological inputs. - Channel routing is ignored and it is assumed that streamflow generated from each pixel reaches the catchment outlet on same day. """ import numpy import os import time import matplotlib.pyplot as plt from exphydro.distributed import ExphydroDistrParameters from exphydro.distributed.type1 import ExphydroDistrModel from hydroutils import Calibration, ObjectiveFunction start_time = time.time() ###################################################################### # SET WORKING DIRECTORY # Getting current directory, i.e., directory containing this file dir1 = os.path.dirname(os.path.abspath('__file__')) # Setting to current directory os.chdir(dir1) ###################################################################### # MAIN PROGRAM # Load meteorological and observed flow data P = numpy.genfromtxt('SampleData/P_test.txt') # Observed rainfall (mm/day) T = numpy.genfromtxt('SampleData/T_test.txt') # Observed air temperature (deg C) PET = numpy.genfromtxt('SampleData/PET_test.txt') # Potential evapotranspiration (mm/day) Qobs = numpy.genfromtxt('SampleData/Q_test.txt') # Observed streamflow (mm/day) # Specify the number of pixels in the catchment npixels = 5 # Specify the no. of parameter sets (particles) in a PSO swarm npart = 10 # Generate 'npart' initial EXP-HYDRO model parameters params = [ExphydroDistrParameters(npixels) for j in range(npart)] # Initialise the model by loading its climate inputs model = ExphydroDistrModel(P, PET, T, npixels) # Specify the start and end day numbers of the calibration period. # This is done separately for the observed and simulated data # because they might not be of the same length in some cases. calperiods_obs = [365, 2557] calperiods_sim = [365, 2557] # Calibrate the model to identify optimal parameter set paramsmax = Calibration.pso_maximise(model, params, Qobs, ObjectiveFunction.klinggupta, calperiods_obs, calperiods_sim) print ('Calibration run KGE value = ', paramsmax.objval) # Run the optimised model for validation period Qsim = model.simulate(paramsmax) kge = ObjectiveFunction.klinggupta(Qobs[calperiods_obs[1]:], Qsim[calperiods_sim[1]:]) print ('Independent run KGE value = ', kge) print("Total runtime: %s seconds" % (time.time() - start_time)) # Plot the observed and simulated hydrographs plt.plot(Qobs[calperiods_obs[0]:], 'b-') plt.plot(Qsim[calperiods_sim[0]:], 'r-') plt.show() ######################################################################

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0.103448

0

null

0

null

0

1

0

5738d01ad1ed866e8e47c9a1f5dadbf2cfce3611

11,104

py

Python

multi_input_multi_output/train.py

alt113/CS591-Multimodal-Spring2021

f28bade729818aa51fd131e86f1ba2271cca8947

[ "MIT" ]

null

multi_input_multi_output/train.py

alt113/CS591-Multimodal-Spring2021

f28bade729818aa51fd131e86f1ba2271cca8947

[ "MIT" ]

1

2021-05-03T18:59:43.000Z

2021-05-03T19:04:19.000Z

multi_input_multi_output/train.py

alt113/CS591-Multimodal-Spring2021

f28bade729818aa51fd131e86f1ba2271cca8947

[ "MIT" ]

null

import os from multi_input_multi_output.models import MultiNet from shared_weights.helpers import config, utils from shared_weights.helpers.siamese_network import create_encoder from data.data_tf import fat_dataset import tensorflow as tf from tensorflow import keras # ---------------------- def flatten_model(model_nested): layers_flat = [] for layer in model_nested.layers: try: layers_flat.extend(layer.layers) except AttributeError: layers_flat.append(layer) model_flat = keras.models.Sequential(layers_flat) return model_flat """ Data augmentation""" augmentation_input = keras.layers.Input(shape=config.IMG_SHAPE) data_augmentation = keras.layers.experimental.preprocessing.RandomTranslation( height_factor=(-0.2, 0.2), width_factor=(-0.2, 0.2), fill_mode="constant" )(augmentation_input) data_augmentation = keras.layers.experimental.preprocessing.RandomFlip(mode="horizontal")(data_augmentation) data_augmentation = keras.layers.experimental.preprocessing.RandomRotation(factor=0.15, fill_mode="constant")(data_augmentation) augmentation_output = keras.layers.experimental.preprocessing.RandomZoom(height_factor=(-0.3, 0.1), width_factor=(-0.3, 0.1), fill_mode="constant")(data_augmentation) data_augmentation = keras.Model(augmentation_input, augmentation_output) """ Unsupervised contrastive loss""" class RepresentationLearner(keras.Model): def __init__( self, encoder, projection_units, num_augmentations, temperature=1.0, dropout_rate=0.1, l2_normalize=False, **kwargs ): super(RepresentationLearner, self).__init__(**kwargs) self.encoder = encoder # Create projection head. self.projector = keras.Sequential( [ keras.layers.Dropout(dropout_rate), keras.layers.Dense(units=projection_units, use_bias=False), keras.layers.BatchNormalization(), keras.layers.ReLU(), ] ) self.num_augmentations = num_augmentations self.temperature = temperature self.l2_normalize = l2_normalize self.loss_tracker = keras.metrics.Mean(name="loss") @property def metrics(self): return [self.loss_tracker] def compute_contrastive_loss(self, feature_vectors, batch_size): num_augmentations = tf.shape(feature_vectors)[0] // batch_size if self.l2_normalize: feature_vectors = tf.math.l2_normalize(feature_vectors, -1) # The logits shape is [num_augmentations * batch_size, num_augmentations * batch_size]. logits = ( tf.linalg.matmul(feature_vectors, feature_vectors, transpose_b=True) / self.temperature ) # Apply log-max trick for numerical stability. logits_max = tf.math.reduce_max(logits, axis=1) logits = logits - logits_max # The shape of targets is [num_augmentations * batch_size, num_augmentations * batch_size]. # targets is a matrix consits of num_augmentations submatrices of shape [batch_size * batch_size]. # Each [batch_size * batch_size] submatrix is an identity matrix (diagonal entries are ones). targets = tf.tile(tf.eye(batch_size), [num_augmentations, num_augmentations]) # Compute cross entropy loss return keras.losses.categorical_crossentropy( y_true=targets, y_pred=logits, from_logits=True ) def call(self, inputs): # Create augmented versions of the images. augmented = [] for _ in range(self.num_augmentations): x = data_augmentation(inputs) augmented.append(x) augmented = keras.layers.Concatenate(axis=0)(augmented) # Generate embedding representations of the images. features = self.encoder(augmented) # Apply projection head. return self.projector(features) def train_step(self, data):#inputs): inputs = data[0] batch_size = tf.shape(inputs)[0] # Run the forward pass and compute the contrastive loss with tf.GradientTape() as tape: feature_vectors = self(inputs, training=True) loss = self.compute_contrastive_loss(feature_vectors, batch_size) # Compute gradients trainable_vars = self.trainable_variables gradients = tape.gradient(loss, trainable_vars) # Update weights self.optimizer.apply_gradients(zip(gradients, trainable_vars)) # Update loss tracker metric self.loss_tracker.update_state(loss) # Return a dict mapping metric names to current value return {m.name: m.result() for m in self.metrics} def test_step(self, data):#inputs): inputs = data[0] batch_size = tf.shape(inputs)[0] feature_vectors = self(inputs, training=False) loss = self.compute_contrastive_loss(feature_vectors, batch_size) self.loss_tracker.update_state(loss) return {"loss": self.loss_tracker.result()} """ Train the model""" network_input = keras.layers.Input(shape=config.IMG_SHAPE) # Load RGB vision encoder. r_encoder = create_encoder(base='resnet50', pretrained=True)(network_input) encoder_output = keras.layers.Dense(config.HIDDEN_UNITS)(r_encoder) r_encoder = keras.Model(network_input, encoder_output) # Create representation learner. r_representation_learner = RepresentationLearner( r_encoder, config.PROJECTION_UNITS, num_augmentations=2, temperature=0.1 ) r_representation_learner.build((None, 128, 128, 3)) # base_path = os.environ['PYTHONPATH'].split(os.pathsep)[1] # representation_learner.load_weights(base_path + '/multi_input_multi_output/simclr/weights/simclr_resnet50_rgb_scratch_weights.h5') r_representation_learner.load_weights(config.RGB_MODALITY_WEIGHT_PATH) functional_model = flatten_model(r_representation_learner.layers[0]) rgb_encoder = functional_model.layers[1] # Load Depth vision encoder. d_encoder = create_encoder(base='resnet50', pretrained=True)(network_input) encoder_output = keras.layers.Dense(config.HIDDEN_UNITS)(d_encoder) d_encoder = keras.Model(network_input, encoder_output) # Create representation learner. d_representation_learner = RepresentationLearner( d_encoder, config.PROJECTION_UNITS, num_augmentations=2, temperature=0.1 ) d_representation_learner.build((None, 128, 128, 3)) # base_path = os.environ['PYTHONPATH'].split(os.pathsep)[1] # representation_learner.load_weights(base_path + '/multi_input_multi_output/simclr/weights/simclr_resnet50_rgb_scratch_weights.h5') d_representation_learner.load_weights(config.DEPTH_MODALITY_WEIGHT_PATH) functional_model = flatten_model(d_representation_learner.layers[0]) depth_encoder = functional_model.layers[1] # ---------------------- # RGB rgb_input = keras.layers.Input(shape=config.IMG_SHAPE) # rgb_encoder = keras.applications.ResNet50V2(include_top=False, # weights=None, # input_shape=config.IMG_SHAPE, # pooling="avg") rgb = rgb_encoder(rgb_input) rgb = keras.layers.Dropout(config.DROPOUT_RATE)(rgb) rgb = keras.layers.Dense(config.HIDDEN_UNITS, activation="relu")(rgb) rgb = keras.layers.Dropout(config.DROPOUT_RATE)(rgb) rgb = keras.layers.Flatten()(rgb) rgb = keras.layers.Dense(config.NUM_OF_CLASSES, activation="softmax")(rgb) rgb_classifier = keras.models.Model(inputs=rgb_input, outputs=rgb, name='rgb_classifier') for layer in rgb_classifier.layers: layer._name += '_rgb' layer.trainable = True print('[INFO] built rgb classifier') print(rgb_classifier.summary()) # Depth depth_input = keras.layers.Input(shape=config.IMG_SHAPE) # depth_encoder = keras.applications.ResNet50V2(include_top=False, # weights=None, # input_shape=config.IMG_SHAPE, # pooling="avg") depth = depth_encoder(depth_input) depth = keras.layers.Dropout(config.DROPOUT_RATE)(depth) depth = keras.layers.Dense(config.HIDDEN_UNITS, activation="relu")(depth) depth = keras.layers.Dropout(config.DROPOUT_RATE)(depth) depth = keras.layers.Flatten()(depth) depth = keras.layers.Dense(config.NUM_OF_CLASSES, activation="softmax")(depth) depth_classifier = keras.models.Model(inputs=depth_input, outputs=depth, name='depth_classifier') for layer in depth_classifier.layers: layer._name += '_depth' layer.trainable = True print('[INFO] built depth classifier') print(depth_classifier.summary()) # Build and compile MultiNet multinet_class = MultiNet(rgb_classifier=rgb_classifier, rgb_output_branch=rgb, depth_classifier=depth_classifier, depth_output_branch=depth) multinet_class.compile() multinet_model = multinet_class.model print('[INFO] built MultiNet classifier') # train the network to perform multi-output classification train_ds = fat_dataset(split='train', data_type='all', batch_size=config.BATCH_SIZE, shuffle=True, pairs=False) val_ds = fat_dataset(split='validation', data_type='all', batch_size=config.BATCH_SIZE, shuffle=True, pairs=False) print("[INFO] training MultiNet...") counter = 0 history = None toCSV = [] while counter <= config.EPOCHS: counter += 1 print(f'* Epoch: {counter}') data_batch = 0 for imgs, labels in train_ds: data_batch += 1 history = multinet_model.train_on_batch(x=[imgs[:, 0], imgs[:, 1]], y={'dense_5_rgb': labels[:], 'dense_7_depth': labels[:]}, reset_metrics=False, return_dict=True) print(f'* Data Batch: {data_batch}') print(f'\t{history}') break if counter % 10 == 0: print("[VALUE] Testing model on batch") for val_data, val_labels in val_ds: val_results = multinet_model.test_on_batch(x=[val_data[:, 0], val_data[:, 1]], y={'dense_5_rgb': val_labels[:], 'dense_7_depth': val_labels[:]}) print(val_results) toCSV.append(val_results) print('Saving MultiNet validation results as CSV file') utils.save_model_history(H=toCSV, path_to_csv=config.FROZEN_SIAMESE_TRAINING_HISTORY_CSV_PATH) rgb_classifier.save_weights(config.MIMO_RGB_WEIGHTS) print("Saved RGB model weights to disk") # serialize weights to HDF5 depth_classifier.save_weights(config.MIMO_DEPTH_WEIGHTS) print("Saved Depth model weights to disk")

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11,104

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false

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0.073298

0

null

0

null

0

1

0

573a1fa313f96c01ab6df0ada017abeca301701e

856

py

Python

tools/rebuild_caches.py

newbdoc/lookyloo

53a8952fccaf9ae42fa582d3475283babd55d08a

[ "BSD-3-Clause" ]

148

2020-06-14T06:55:42.000Z

2022-03-19T05:37:02.000Z

tools/rebuild_caches.py

newbdoc/lookyloo

53a8952fccaf9ae42fa582d3475283babd55d08a

[ "BSD-3-Clause" ]

261

2020-06-16T22:29:27.000Z

2022-03-31T10:40:52.000Z

tools/rebuild_caches.py

newbdoc/lookyloo

53a8952fccaf9ae42fa582d3475283babd55d08a

[ "BSD-3-Clause" ]

27

2020-06-08T12:28:33.000Z

2022-02-15T18:50:50.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import argparse import logging from lookyloo.lookyloo import Indexing, Lookyloo logging.basicConfig(format='%(asctime)s %(name)s %(levelname)s:%(message)s', level=logging.INFO) def main(): parser = argparse.ArgumentParser(description='Rebuild the redis cache.') parser.add_argument('--rebuild_pickles', default=False, action='store_true', help='Delete and rebuild the pickles. Count 20s/pickle, it can take a very long time.') args = parser.parse_args() lookyloo = Lookyloo() if args.rebuild_pickles: lookyloo.rebuild_all() else: lookyloo.rebuild_cache() indexing = Indexing() indexing.clear_indexes() # This call will rebuild all the caches as needed. lookyloo.sorted_capture_cache() if __name__ == '__main__': main()

25.939394

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0

0.005797

0.193925

856

32

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0.817391

0.107477

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false

0

0.157895

0

0.210526

0

null

0

null

0

1

0

573b50d93fdcd613c5e4eb9cd5d3608413327c07

633

py

Python

src/game.py

LuisMarques99/Number-Guesser-Terminal

6abfac23268022f7ce3776a20d1d6f550955d6c8

[ "MIT" ]

null

src/game.py

LuisMarques99/Number-Guesser-Terminal

6abfac23268022f7ce3776a20d1d6f550955d6c8

[ "MIT" ]

null

src/game.py

LuisMarques99/Number-Guesser-Terminal

6abfac23268022f7ce3776a20d1d6f550955d6c8

[ "MIT" ]

null

from random import randrange def main(): MIN = 1 MAX = 100 NUMBER = randrange(MIN, MAX + 1) guesses = 9 print(f"Guess a number from {MIN} to {MAX}.\nYou have {guesses} chances. Start now!\n") while guesses > 0: guess = input(f"Guess ({guesses}): ") guesses -= 1 try: guess = int(guess) if guess == NUMBER: print("You won!") break if guesses == 0: print(f"\nYou ran out of guesses... Best luck next time.\nThe number was [{NUMBER}].") else: print("Smaller\n" if (guess > NUMBER) else "Bigger\n") except ValueError: print("Enter just the number.\n") if __name__ == "__main__": main()

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4.117021

0.542553

0.031008

0.067183

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0.224329

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false

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0.217391

0

null

0

null

0

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0

573b7032640a85abec559a72d8a9edcb24834621

378

py

Python

Data Structures and Algorithms/HackerRank Algo Solutions/EASY PROBLEMS/Arrays.py

akkik04/Python-DataStructures-and-Algorithms

8db63173218e5a9205dbb325935c71fec93b695c

[ "MIT" ]

1

2022-01-22T18:19:07.000Z

Data Structures and Algorithms/HackerRank Algo Solutions/EASY PROBLEMS/Arrays.py

akkik04/Python-DataStructures-and-Algorithms

8db63173218e5a9205dbb325935c71fec93b695c

[ "MIT" ]

null

Data Structures and Algorithms/HackerRank Algo Solutions/EASY PROBLEMS/Arrays.py

akkik04/Python-DataStructures-and-Algorithms

8db63173218e5a9205dbb325935c71fec93b695c

[ "MIT" ]

null

# ARRAYS-DS HACKERANK SOLUTION: # creating a function to reverse the array. def reverseArray(arr): # reversing the array. reversed = arr[::-1] # returning the reversed array. return reversed # receiving input. arr_count = int(input().strip()) arr = list(map(int, input().rstrip().split())) # printing the output. print(reverseArray(arr))

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null

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0

574587d505f7c19dabd0452d40b6544e75b9a682

10,136

py

Python

processing_scripts/database_update/pokedex_entry.py

CorentG/Pokecube-Issues-and-Wiki

690af5d8499561f65f761fd49fbf5fc2bc85c4c3

[ "MIT" ]

24

2019-02-02T20:37:53.000Z

2022-02-09T13:51:41.000Z

processing_scripts/database_update/pokedex_entry.py

CorentG/Pokecube-Issues-and-Wiki

690af5d8499561f65f761fd49fbf5fc2bc85c4c3

[ "MIT" ]

671

2018-08-20T08:46:35.000Z

2022-03-26T00:11:43.000Z

processing_scripts/database_update/pokedex_entry.py

CorentG/Pokecube-Issues-and-Wiki

690af5d8499561f65f761fd49fbf5fc2bc85c4c3

[ "MIT" ]

68

2018-09-25T21:03:40.000Z

2022-02-25T19:59:51.000Z

import csv_loader import moves_names def getSingle(name, data, file, value): return data.get_info(name,value, expected_file=file, use_names_map=True)[file][0] def getExpYield(name, data): return int(getSingle(name, data,"pokemon" ,"base_experience")) def getHeight(name, data): return int(getSingle(name, data,"pokemon" ,"height")) / 10.0 def getWeight(name, data): return int(getSingle(name, data,"pokemon" ,"weight")) / 10.0 def getGenderRatio(name, data): rates = {} val = int(getSingle(name, data,"pokemon_species" ,"gender_rate")) rates[-1] = 255 rates[0] = 0 rates[1] = 30 rates[2] = 62 rates[4] = 127 rates[6] = 191 rates[7] = 225 rates[8] = 254 return rates[val] def getCaptureRate(name, data): return int(getSingle(name, data,"pokemon_species" ,"capture_rate")) def getBaseFriendship(name, data): return int(getSingle(name, data,"pokemon_species" ,"base_happiness")) def getExpMode(name, data): stats = data.get_info(name,"growth_rate_id", expected_file="pokemon_species", use_names_map=True)["pokemon_species"] _id = stats[0] stats = data.get_entry(_id, expected_file="growth_rate_prose", use_names_map=True)["growth_rate_prose"] for row in stats: _id = row[1] if _id == '9': return row[2] return None def getLevelMoves(name, data): moves = {} names = [] try: moves_entries = data.get_entry(name, expected_file="pokemon_moves", use_names_map=True)["pokemon_moves"] version = 1 # First, locate the most recent version that has lvl up moves for entry in moves_entries: if entry[4] == "0": continue vers = int(entry[1]) if vers > version: version = vers version = str(version) # Now we can actually parse the moves for entry in moves_entries: # TODO figure out if a move is an evolution move, is that info here? if entry[4] == "0": continue if entry[1] != version: continue level = entry[4] move_id = entry[2] move = data.get_info(move_id,"identifier", expected_file="moves")["moves"][0] move_, conf = csv_loader.match_name(move, moves_names.moves) if(conf < 80): print("{} -> {} ({})".format(move, move_, conf)) else: move = move_ if level in moves.keys(): moves[level] = moves[level]+","+move else: moves[level] = move if not move in names: names.append(move) except: # print("No moves found for {}".format(name)) pass return moves, names def getAllMoves(name, data, exclude=[]): names = [] try: moves_entries = data.get_entry(name, expected_file="pokemon_moves", use_names_map=True)["pokemon_moves"] for entry in moves_entries: move_id = entry[2] move = data.get_info(move_id,"identifier", expected_file="moves")["moves"][0] move_, conf = csv_loader.match_name(move, moves_names.moves) if(conf < 80): print("{} -> {} ({})??".format(move, move_, conf)) else: move = move_ if move in exclude or move in names: continue names.append(move) except: # print("No moves found for {}".format(name)) pass return names def getTypes(name, data): types_nums = data.get_info(name,"type_id", expected_file="pokemon_types", use_names_map=True)["pokemon_types"] types = [] for num in types_nums: names = data.get_info(num,"identifier", expected_file="types")["types"] types.append(names[0]) return types def getStats(name, data): stats = [] # TODO maybe validate that these are in the correct order, the index is also stored # in the csv file, so that validation can be done if needed! stats = data.get_info(name,"base_stat", expected_file="pokemon_stats", use_names_map=True)["pokemon_stats"] return stats def getEVs(name, data): stats = [] # TODO maybe validate that these are in the correct order, the index is also stored # in the csv file, so that validation can be done if needed! stats = data.get_info(name,"effort", expected_file="pokemon_stats", use_names_map=True)["pokemon_stats"] return stats def getAbilities(name, data): hidden = [] abilities = ["",""] rows = data.get_entry(name, expected_file="pokemon_abilities", use_names_map=True)["pokemon_abilities"] for row in rows: ability_id = row[1] isHidden = row[2] slot = int(row[3]) - 1 ability_name = data.get_info(ability_id,"identifier", expected_file="abilities")["abilities"][0] if ability_name == '': continue if isHidden == "1": hidden.append(ability_name) elif slot < len(abilities): abilities[slot] = ability_name return abilities, hidden def sorter(e): return int(e) class Pokedex(object): def __init__(self, names, data, originals, do_moves, do_stats): self.pokemon = [] for name in names: defaults = None if name in originals: defaults = originals[name] try: entry = PokedexEntry(name, data, defaults, do_moves, do_stats) if do_moves and not "moves" in entry.map: continue self.pokemon.append(entry.map) except Exception as err: print("Error with {} {}, Using default if present? {}".format(name, err, defaults is not None)) if defaults is not None and do_stats: self.pokemon.append(defaults) class PokedexEntry(object): def __init__(self, name, data, defaults, do_moves, do_stats): _map = self.map = {} _map["name"] = name if do_stats: _map["number"] = int(getSingle(name, data, "pokemon", "species_id")) id = int(getSingle(name, data, "pokemon", "id")) is_default = id == _map["number"] if(is_default): _map["base"] = True _map["stats"] = {} statsOrder = ["hp", "atk", "def", "spatk", "spdef", "spd"] # Do the base stats stats = getStats(name, data) _map["stats"]["stats"] = {} values = _map["stats"]["stats"]["values"] = {} for i in range(len(statsOrder)): values[statsOrder[i]] = stats[i] if defaults is not None: _map["stats"]["sizes"] = defaults["stats"]["sizes"] else: print("Cannot copy sizes for {}".format(name)) # Do the evs stats = getEVs(name, data) _map["stats"]["evs"] = {} values = _map["stats"]["evs"]["values"] = {} for i in range(len(statsOrder)): if stats[i] == "0": continue values[statsOrder[i]] = stats[i] # Get the types types = getTypes(name,data) _map["stats"]["types"] = {} values = _map["stats"]["types"]["values"] = {} for i in range(len(types)): ident = "type{}".format(i+1) values[ident] = types[i] # Get Abilities abilities, hidden = getAbilities(name, data) _map["stats"]["abilities"] = {} values = _map["stats"]["abilities"]["values"] = {} if len(abilities) > 0: normals = abilities[0] if len(abilities) > 1: for i in range(1, len(abilities)): if abilities[i] != "": normals = normals +", "+abilities[i] values["normal"] = normals if len(hidden) > 0: hiddens = hidden[0] if len(hidden) > 1: for i in range(1, len(hidden)): if hidden[i] != "": hiddens = hiddens +", "+hidden[i] values["hidden"] = hiddens # Get the simple values _map["stats"]["mass"] = getWeight(name, data) _map["stats"]["baseExp"] = getExpYield(name, data) # This set is not defined for all targets, so try/except them try: _map["stats"]["captureRate"] = getCaptureRate(name, data) except: pass try: _map["stats"]["baseFriendship"] = getBaseFriendship(name, data) except: pass try: _map["stats"]["genderRatio"] = getGenderRatio(name, data) except: pass try: _map["stats"]["expMode"] = getExpMode(name, data) except: pass if do_moves: # Do the moves # First lvl up moves moves, names = getLevelMoves(name, data) moves_list = getAllMoves(name, data, exclude=names) if len(moves) != 0 or len(moves_list) != 0: _map["moves"] = {} elif defaults is not None: _map["moves"] = defaults["moves"] print("Not Updating moves for {}".format(name)) if len(moves) > 0: lvlMoves = _map["moves"]["lvlupMoves"] = {} levels = [x for x in moves.keys()] levels.sort(key=sorter) for level in levels: lvlMoves[level] = moves[level] # Then remainder moves = "" if len(moves_list)>0: moves = moves_list[0] for i in range(1, len(moves_list)): moves = moves +", "+moves_list[i] misc = _map["moves"]["misc"] = {} misc["moves"] = moves

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4.505608

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0

0.011726

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272

121

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false

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0.030837

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0.022026

0

null

0

null

0

1

0

5746c4fc2776ee414b40d5372100f22e8a3258f4

25,539

py

Python

tests/test_add.py

open-contracting/kingfisher-views

7887610a144493f2ccd0d9a22cf43157dc180479

[ "BSD-3-Clause" ]

2

2019-02-19T16:15:19.000Z

2020-07-25T04:05:45.000Z

tests/test_add.py

open-contracting/kingfisher-views

7887610a144493f2ccd0d9a22cf43157dc180479

[ "BSD-3-Clause" ]

142

2019-03-11T15:14:22.000Z

2020-11-11T19:26:09.000Z

tests/test_add.py

open-contracting/kingfisher-views

7887610a144493f2ccd0d9a22cf43157dc180479

[ "BSD-3-Clause" ]

5

2019-04-11T14:11:10.000Z

2020-07-30T22:45:59.000Z

import datetime import decimal from unittest.mock import patch import pytest from click.testing import CliRunner from psycopg2 import sql from manage import SUMMARIES, cli, construct_where_fragment from tests import assert_bad_argument, assert_log_records, assert_log_running, fixture, noop command = 'add' TABLES = { 'note', } SUMMARY_TABLES = set() SUMMARY_VIEWS = set() FIELD_LIST_TABLES = set() NO_FIELD_LIST_TABLES = set() NO_FIELD_LIST_VIEWS = set() for table_name, table in SUMMARIES.items(): FIELD_LIST_TABLES.add(f'{table_name}_field_list') if table.is_table: SUMMARY_TABLES.add(table_name) NO_FIELD_LIST_TABLES.add(f'{table_name}_no_field_list') else: SUMMARY_VIEWS.add(table_name) NO_FIELD_LIST_VIEWS.add(f'{table_name}_no_field_list') TABLES.add(f'{table_name}_no_data') def test_construct_where_fragment(db): assert construct_where_fragment(db.cursor, 'a', 'z') == " AND d.data->>'a' = 'z'" assert construct_where_fragment(db.cursor, 'a.b', 'z') == " AND d.data->'a'->>'b' = 'z'" assert construct_where_fragment(db.cursor, 'a.b.c', 'z') == " AND d.data->'a'->'b'->>'c' = 'z'" assert construct_where_fragment(db.cursor, 'a.b.c.d', 'z') == " AND d.data->'a'->'b'->'c'->>'d' = 'z'" assert construct_where_fragment(db.cursor, 'a.b.c', '') == " AND d.data->'a'->'b'->>'c' = ''" assert construct_where_fragment(db.cursor, '', 'z') == " AND d.data->>'' = 'z'" @pytest.mark.parametrize('collections, message', [ ('a', 'Collection IDs must be integers'), ('1,10,100', 'Collection IDs {10, 100} not found'), ]) def test_validate_collections(collections, message, caplog): runner = CliRunner() result = runner.invoke(cli, [command, collections]) assert result.exit_code == 2 assert_bad_argument(result, 'COLLECTIONS', message) assert_log_running(caplog, command) def test_validate_name(caplog): runner = CliRunner() result = runner.invoke(cli, [command, '1', '--name', 'camelCase']) assert result.exit_code == 2 assert_bad_argument(result, '--name', 'value must be lowercase') assert_log_running(caplog, command) @patch('manage.summary_tables', noop) @patch('manage.field_counts', noop) @patch('manage.field_lists', noop) @pytest.mark.parametrize('kwargs, name, collections', [ ({}, 'collection_1', (1,)), ({'collections': '1,2'}, 'collection_1_2', (1, 2)), ({'name': 'custom'}, 'custom', (1,)), ]) def test_command_name(kwargs, name, collections, db, caplog): schema = f'view_data_{name}' identifier = sql.Identifier(schema) with fixture(db, **kwargs) as result: assert db.schema_exists(schema) assert db.all('SELECT collection_id, schema FROM summaries.selected_collections WHERE schema=%(schema)s', {'schema': schema}) == [(collection, schema,) for collection in collections] assert db.all(sql.SQL('SELECT id, note FROM {schema}.note').format(schema=identifier)) == [ (1, 'Default'), ] assert result.exit_code == 0 assert result.output == '' assert_log_records(caplog, command, [ f'Arguments: collections={collections!r} note=Default name={kwargs.get("name")} tables_only=False ' 'filters=()', f'Added {name}', 'Running summary-tables routine', 'Running field-counts routine', 'Running field-lists routine', ]) @pytest.mark.parametrize('filters', [(), (('ocid', 'dolore'),)]) @pytest.mark.parametrize('tables_only, field_counts, field_lists, tables, views', [ (False, True, False, TABLES | SUMMARY_TABLES, SUMMARY_VIEWS), (True, True, False, TABLES | SUMMARY_TABLES | SUMMARY_VIEWS, set()), (False, False, True, TABLES | FIELD_LIST_TABLES | NO_FIELD_LIST_TABLES, SUMMARY_TABLES | SUMMARY_VIEWS | NO_FIELD_LIST_VIEWS), (True, False, True, TABLES | FIELD_LIST_TABLES | NO_FIELD_LIST_TABLES | SUMMARY_TABLES | SUMMARY_VIEWS | NO_FIELD_LIST_VIEWS, set()), ]) def test_command(db, tables_only, field_counts, field_lists, tables, views, filters, caplog): # Load collection 2 first, to check that existing collections aren't included when we load collection 1. with fixture(db, collections='2', tables_only=tables_only, field_counts=field_counts, field_lists=field_lists, filters=filters), fixture(db, tables_only=tables_only, field_counts=field_counts, field_lists=field_lists, filters=filters) as result: # Check existence of schema, tables and views. if field_counts: tables.add('field_counts') assert db.schema_exists('view_data_collection_1') assert db.schema_exists('view_data_collection_2') assert set(db.pluck("SELECT table_name FROM information_schema.tables WHERE table_schema = %(schema)s " "AND table_type = 'BASE TABLE'", {'schema': 'view_data_collection_1'})) == tables assert set(db.pluck("SELECT table_name FROM information_schema.tables WHERE table_schema = %(schema)s " "AND table_type = 'VIEW'", {'schema': 'view_data_collection_1'})) == views # Check contents of summary relations. rows = db.all(""" SELECT award_index, release_type, collection_id, ocid, release_id, award_id, title, status, description, value_amount, value_currency, date, contractperiod_startdate, contractperiod_enddate, contractperiod_maxextentdate, contractperiod_durationindays, total_suppliers, total_documents, document_documenttype_counts, total_items FROM view_data_collection_1.awards_summary ORDER BY id, award_index """) assert rows[0] == ( 0, # award_index 'release', # release_type 1, # collection_id 'dolore', # ocid 'ex laborumsit autein magna veniam', # release_id 'reprehenderit magna cillum eu nisi', # award_id 'laborum aute nisi eiusmod', # award_title 'pending', # award_status 'ullamco in voluptate', # award_description decimal.Decimal('-95099396'), # award_value_amount 'AMD', # award_value_currency datetime.datetime(3263, 12, 5, 21, 24, 19, 161000), # award_date datetime.datetime(4097, 9, 16, 5, 55, 19, 125000), # award_contractperiod_startdate datetime.datetime(4591, 4, 29, 6, 34, 28, 472000), # award_contractperiod_enddate datetime.datetime(3714, 8, 9, 7, 21, 37, 544000), # award_contractperiod_maxextentdate decimal.Decimal('72802012'), # award_contractperiod_durationindays 2, # total_suppliers 4, # total_documents { 'Excepteur nisi et': 1, 'proident exercitation in': 1, 'ut magna dolore velit aute': 1, 'veniam enim aliqua d': 1, }, # document_documenttype_counts 5, # total_items ) if filters: assert len(rows) == 4 else: assert len(rows) == 301 rows = db.all(""" SELECT party_index, release_type, collection_id, ocid, release_id, party_id, roles, identifier, unique_identifier_attempt, additionalidentifiers_ids, total_additionalidentifiers FROM view_data_collection_1.parties_summary ORDER BY id, party_index """) assert rows[0] == ( 0, # party_index 'release', # release_type 1, # collection_id 'dolore', # ocid 'ex laborumsit autein magna veniam', # release_id 'voluptate officia tempor dolor', # party_id [ 'ex ', 'in est exercitation nulla Excepteur', 'ipsum do', ], # roles 'ad proident dolor reprehenderit veniam-in quis exercitation reprehenderit', # identifier 'voluptate officia tempor dolor', # unique_identifier_attempt [ 'exercitation proident voluptate-sed culpa eamollit consectetur dolor l', 'magna-dolor ut indolorein in tempor magna mollit', 'ad occaecat amet anim-laboris ea Duisdeserunt quis sed pariatur mollit', 'elit mollit-officia proidentmagna', 'ex-minim Ut consectetur', ], # additionalidentifiers_ids 5, # total_additionalidentifiers ) if filters: assert len(rows) == 4 else: assert len(rows) == 296 if field_counts: # Check contents of field_counts table. rows = db.all('SELECT * FROM view_data_collection_1.field_counts') if filters: assert len(rows) == 1046 assert rows[0] == (1, 'release', 'awards', 1, 4, 1) else: assert len(rows) == 65235 assert rows[0] == (1, 'release', 'awards', 100, 301, 100) if field_lists: # Check the count of keys in the field_list field for the lowest primary keys in each summary relation. statement = """ SELECT count(*) FROM (SELECT jsonb_each(field_list) FROM ( SELECT field_list FROM view_data_collection_1.{table} ORDER BY {primary_keys} LIMIT 1) AS field_list ) AS each """ expected = { 'award_documents_summary': 11, 'award_items_summary': 26, 'award_suppliers_summary': 28, 'awards_summary': 469, 'buyer_summary': 28, 'contract_documents_summary': 11, 'contract_implementation_documents_summary': 11, 'contract_implementation_milestones_summary': 29, 'contract_implementation_transactions_summary': 83, 'contract_items_summary': 26, 'contract_milestones_summary': 27, 'contracts_summary': 469, 'parties_summary': 34, 'planning_documents_summary': 11, 'planning_milestones_summary': 29, 'planning_summary': 61, 'procuringentity_summary': 32, 'relatedprocesses_summary': 6, 'release_summary': 1046, 'tender_documents_summary': 15, 'tender_items_summary': 25, 'tender_milestones_summary': 23, 'tender_summary': 228, 'tenderers_summary': 31, } for table_name, table in SUMMARIES.items(): count = db.one(db.format(statement, table=table_name, primary_keys=table.primary_keys))[0] assert count == expected[table_name], f'{table_name}: {count} != {expected[table_name]}' def result_dict(statement): result = db.one(statement) return {column.name: result for column, result in zip(db.cursor.description, result)} statement = """ SELECT count(*) total, sum(coalesce((field_list ->> 'contracts')::int, 0)) contracts, sum(coalesce((field_list ->> 'awards')::int, 0)) awards, sum(coalesce((field_list ->> 'awards/id')::int, 0)) awards_id, sum(coalesce((field_list ->> 'awards/value/amount')::int, 0)) awards_amount FROM view_data_collection_1.contracts_summary """ if filters: assert result_dict(statement) == { 'awards': 1, 'awards_amount': 1, 'awards_id': 1, 'contracts': 0, 'total': 1, } else: assert result_dict(statement) == { 'awards': 213, 'awards_amount': 213, 'awards_id': 213, 'contracts': 0, 'total': 285, } statement = """ SELECT count(*) total, sum(coalesce((field_list ->> 'awards')::int, 0)) awards, sum(coalesce((field_list ->> 'contracts')::int, 0)) contracts, sum(coalesce((field_list ->> 'contracts/id')::int, 0)) contracts_id, sum(coalesce((field_list ->> 'contracts/value/amount')::int, 0)) contracts_amount FROM view_data_collection_1.awards_summary """ if filters: assert result_dict(statement) == { 'contracts': 1, 'contracts_amount': 1, 'contracts_id': 1, 'awards': 0, 'total': 4, } else: assert result_dict(statement) == { 'contracts': 213, 'contracts_amount': 213, 'contracts_id': 213, 'awards': 0, 'total': 301, } # All columns have comments. assert not db.all(""" SELECT isc.table_name, isc.column_name, isc.data_type FROM information_schema.columns isc WHERE isc.table_schema = %(schema)s AND LOWER(isc.table_name) NOT IN ('selected_collections', 'note') AND LOWER(isc.table_name) NOT LIKE '%%_no_data' AND LOWER(isc.table_name) NOT LIKE '%%_field_list' AND pg_catalog.col_description(format('%%s.%%s',isc.table_schema,isc.table_name)::regclass::oid, isc.ordinal_position) IS NULL """, {'schema': 'view_data_collection_1'}) expected = [] for collection_id in [2, 1]: expected.extend([ f'Arguments: collections=({collection_id},) note=Default name=None tables_only={tables_only!r} ' f'filters={filters!r}', f'Added collection_{collection_id}', 'Running summary-tables routine', ]) if field_counts: expected.append('Running field-counts routine') if field_lists: expected.append('Running field-lists routine') assert result.exit_code == 0 assert result.output == '' assert_log_records(caplog, command, expected) @pytest.mark.parametrize('filters', [ (('tender.procurementMethod', 'direct'),), (('tender.procurementMethod', 'direct'), ('tender.status', 'planned'),), ]) @pytest.mark.parametrize('tables_only, field_counts, field_lists, tables, views', [ (False, True, False, TABLES | SUMMARY_TABLES, SUMMARY_VIEWS), (True, True, False, TABLES | SUMMARY_TABLES | SUMMARY_VIEWS, set()), (False, False, True, TABLES | FIELD_LIST_TABLES | NO_FIELD_LIST_TABLES, SUMMARY_TABLES | SUMMARY_VIEWS | NO_FIELD_LIST_VIEWS), (True, False, True, TABLES | FIELD_LIST_TABLES | NO_FIELD_LIST_TABLES | SUMMARY_TABLES | SUMMARY_VIEWS | NO_FIELD_LIST_VIEWS, set()), ]) def test_command_filter(db, tables_only, field_counts, field_lists, tables, views, filters, caplog): # Load collection 2 first, to check that existing collections aren't included when we load collection 1. with fixture(db, collections='2', tables_only=tables_only, field_counts=field_counts, field_lists=field_lists, filters=filters), fixture(db, tables_only=tables_only, field_counts=field_counts, field_lists=field_lists, filters=filters) as result: # Check existence of schema, tables and views. if field_counts: tables.add('field_counts') assert db.schema_exists('view_data_collection_1') assert db.schema_exists('view_data_collection_2') assert set(db.pluck("SELECT table_name FROM information_schema.tables WHERE table_schema = %(schema)s " "AND table_type = 'BASE TABLE'", {'schema': 'view_data_collection_1'})) == tables assert set(db.pluck("SELECT table_name FROM information_schema.tables WHERE table_schema = %(schema)s " "AND table_type = 'VIEW'", {'schema': 'view_data_collection_1'})) == views # Check that the tender_summary table only has correctly filtered items rows = db.all(""" SELECT procurementmethod FROM view_data_collection_1.tender_summary """) for row in rows: assert row[0] == 'direct' if len(filters) > 1: assert len(rows) == 2 else: assert len(rows) == 19 # Check data_id's in the summary against the data table # This allows us to check that missing data doesn't have the filtered value rows = db.all(""" SELECT data_id FROM view_data_collection_1.release_summary """) if len(filters) > 1: assert len(rows) == 2 else: assert len(rows) == 19 data_ids = [row[0] for row in rows] rows = db.all(""" SELECT data.id, data.data->'tender'->'procurementMethod', data.data->'tender'->'status' FROM data JOIN release ON release.data_id=data.id WHERE release.collection_id=1 """) for row in rows: if row[1] == 'direct' and (len(filters) == 1 or row[2] == 'planned'): assert row[0] in data_ids else: assert row[0] not in data_ids # Check contents of summary relations. rows = db.all(""" SELECT award_index, release_type, collection_id, ocid, release_id, award_id, title, status, description, value_amount, value_currency, date, contractperiod_startdate, contractperiod_enddate, contractperiod_maxextentdate, contractperiod_durationindays, total_suppliers, total_documents, document_documenttype_counts, total_items FROM view_data_collection_1.awards_summary ORDER BY id, award_index """) assert rows[0] == ( 0, # award_index 'release', # release_type 1, # collection_id 'officia dolore non', # ocid 'laborum irure consectetur fugiat', # release_id 'dolorLorem fugiat ut', # award_id 'et', # award_title 'pending', # award_status 'adipisicing ame', # award_description decimal.Decimal('-7139109'), # award_value_amount 'AUD', # award_value_currency datetime.datetime(3672, 10, 26, 4, 38, 28, 786000), # award_date datetime.datetime(2192, 8, 27, 0, 9, 1, 626000), # award_contractperiod_startdate datetime.datetime(4204, 1, 22, 22, 4, 18, 268000), # award_contractperiod_enddate datetime.datetime(5117, 12, 26, 11, 33, 27, 496000), # award_contractperiod_maxextentdate decimal.Decimal('-30383739'), # award_contractperiod_durationindays 5, # total_suppliers 4, # total_documents { 'in sint enim labore': 1, 'mollit labore Lorem': 1, 'minim incididunt sed ipsum': 1, 'ad reprehenderit sit dolor enim': 1 }, # document_documenttype_counts 5, # total_items ) if len(filters) > 1: assert len(rows) == 7 else: assert len(rows) == 55 rows = db.all(""" SELECT party_index, release_type, collection_id, ocid, release_id, party_id, roles, identifier, unique_identifier_attempt, additionalidentifiers_ids, total_additionalidentifiers FROM view_data_collection_1.parties_summary ORDER BY id, party_index """) assert rows[0] == ( 0, # party_index 'release', # release_type 1, # collection_id 'officia dolore non', # ocid 'laborum irure consectetur fugiat', # release_id 'eu voluptateeiusmod ipsum ea', # party_id [ 'laborum', 'tempor', ], # roles 'cupidatat consequat in ullamco-in incididunt commodo elit', # identifier 'eu voluptateeiusmod ipsum ea', # unique_identifier_attempt [ 'non ei-commododolor laborum', ], # additionalidentifiers_ids 1, # total_additionalidentifiers ) if len(filters) > 1: assert len(rows) == 5 else: assert len(rows) == 56 if field_counts: # Check contents of field_counts table. rows = db.all('SELECT * FROM view_data_collection_1.field_counts') if len(filters) > 1: assert len(rows) == 1515 assert rows[0] == (1, 'release', 'awards', 2, 7, 2) else: assert len(rows) == 13077 assert rows[0] == (1, 'release', 'awards', 19, 55, 19) if field_lists: # Check the count of keys in the field_list field for the lowest primary keys in each summary relation. statement = """ SELECT count(*) FROM (SELECT jsonb_each(field_list) FROM ( SELECT field_list FROM view_data_collection_1.{table} ORDER BY {primary_keys} LIMIT 1) AS field_list ) AS each """ expected = { 'award_documents_summary': 11, 'award_items_summary': 29, 'award_suppliers_summary': 30, 'awards_summary': 492, 'buyer_summary': 31, 'contract_documents_summary': 11, 'contract_implementation_documents_summary': 11, 'contract_implementation_milestones_summary': 23, 'contract_implementation_transactions_summary': 83, 'contract_items_summary': 26, 'contract_milestones_summary': 26, 'contracts_summary': 492, 'parties_summary': 30, 'planning_documents_summary': 11, 'planning_milestones_summary': 27, 'planning_summary': 99, 'procuringentity_summary': 30, 'relatedprocesses_summary': 6, 'release_summary': 987, 'tender_documents_summary': 13, 'tender_items_summary': 28, 'tender_milestones_summary': 27, 'tender_summary': 265, 'tenderers_summary': 32, } for table_name, table in SUMMARIES.items(): count = db.one(db.format(statement, table=table_name, primary_keys=table.primary_keys))[0] assert count == expected[table_name], f'{table_name}: {count} != {expected[table_name]}' expected = [] for collection_id in [2, 1]: expected.extend([ f'Arguments: collections=({collection_id},) note=Default name=None tables_only={tables_only!r} ' f'filters={filters!r}', f'Added collection_{collection_id}', 'Running summary-tables routine', ]) if field_counts: expected.append('Running field-counts routine') if field_lists: expected.append('Running field-lists routine') assert result.exit_code == 0 assert result.output == '' assert_log_records(caplog, command, expected)

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