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python/yunet/trt_yunet.py
NobuoTsukamoto/tensorrt-examples
33
2171104
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ TensorRT YuNet . Copyright (c) 2021 <NAME> This software is released under the MIT License. See the LICENSE file in the project root for more information. """ # This source corresponds to TensorRT by referring to the following. # https://github.com/opencv/opencv_zoo/blob/e6e1754dcf0c058cad20166498e68f17c71fa3b1/models/face_detection_yunet/yunet.py from itertools import product import numpy as np import cv2 as cv import tensorrt as trt import common TRT_LOGGER = trt.Logger(trt.Logger.INFO) trt.init_libnvinfer_plugins(TRT_LOGGER, "") class TrtYuNet: def __init__( self, model_path, input_size=[160, 120], conf_threshold=0.6, nms_threshold=0.3, top_k=5000, keep_top_k=750, ): self._model_path = model_path self._engine = self._getEngine(self._model_path) self._context = self._engine.create_execution_context() self._input_size = input_size # [w, h] self._conf_threshold = conf_threshold self._nms_threshold = nms_threshold self._top_k = top_k self._keep_top_k = keep_top_k self._min_sizes = [[10, 16, 24], [32, 48], [64, 96], [128, 192, 256]] self._steps = [8, 16, 32, 64] self._variance = [0.1, 0.2] # Generate priors self._priorGen() @property def name(self): return self.__class__.__name__ def setInputSize(self, input_size): self._input_size = input_size # [w, h] # Regenerate priors self._priorGen() def _getEngine(self, engine_file_path): print("Reading engine from file {}".format(engine_file_path)) with open(engine_file_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime: return runtime.deserialize_cuda_engine(f.read()) def _preprocess(self, image): image = cv.resize(image, (self._input_size[0], self._input_size[1])) image = np.asarray(image, dtype="float32") image = image.transpose(2, 0, 1) return image def infer(self, image): # Preprocess input_blob = self._preprocess(image) # Forward inputs, outputs, bindings, stream = common.allocate_buffers(self._engine) inputs[0].host = np.ascontiguousarray(input_blob) outputs = common.do_inference_v2( self._context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream, ) # Postprocess results = self._postprocess(outputs) return results def _postprocess(self, output_blob): # Decode dets = self._decode(output_blob) # NMS keepIdx = cv.dnn.NMSBoxes( bboxes=dets[:, 0:4].tolist(), scores=dets[:, -1].tolist(), score_threshold=self._conf_threshold, nms_threshold=self._nms_threshold, top_k=self._top_k, ) # box_num x class_num if len(keepIdx) > 0: dets = dets[keepIdx] dets = np.squeeze(dets, axis=1) return dets[: self._keep_top_k] else: return np.empty(shape=(0, 15)) def _priorGen(self): w, h = self._input_size feature_map_2th = [int(int((h + 1) / 2) / 2), int(int((w + 1) / 2) / 2)] feature_map_3th = [int(feature_map_2th[0] / 2), int(feature_map_2th[1] / 2)] feature_map_4th = [int(feature_map_3th[0] / 2), int(feature_map_3th[1] / 2)] feature_map_5th = [int(feature_map_4th[0] / 2), int(feature_map_4th[1] / 2)] feature_map_6th = [int(feature_map_5th[0] / 2), int(feature_map_5th[1] / 2)] feature_maps = [ feature_map_3th, feature_map_4th, feature_map_5th, feature_map_6th, ] priors = [] for k, f in enumerate(feature_maps): min_sizes = self._min_sizes[k] for i, j in product(range(f[0]), range(f[1])): # i->h, j->w for min_size in min_sizes: s_kx = min_size / w s_ky = min_size / h cx = (j + 0.5) * self._steps[k] / w cy = (i + 0.5) * self._steps[k] / h priors.append([cx, cy, s_kx, s_ky]) self.priors = np.array(priors, dtype=np.float32) def _decode(self, outputBlob): loc = np.array(outputBlob[0]).reshape([-1, 14]) conf = np.array(outputBlob[1]).reshape([-1, 2]) iou = np.array(outputBlob[2]).reshape([-1, 1]) # get score cls_scores = conf[:, 1] iou_scores = iou[:, 0] # clamp _idx = np.where(iou_scores < 0.0) iou_scores[_idx] = 0.0 _idx = np.where(iou_scores > 1.0) iou_scores[_idx] = 1.0 scores = np.sqrt(cls_scores * iou_scores) scores = scores[:, np.newaxis] scale = np.array(self._input_size) # get bboxes bboxes = np.hstack( ( ( self.priors[:, 0:2] + loc[:, 0:2] * self._variance[0] * self.priors[:, 2:4] ) * scale, (self.priors[:, 2:4] * np.exp(loc[:, 2:4] * self._variance)) * scale, ) ) # (x_c, y_c, w, h) -> (x1, y1, w, h) bboxes[:, 0:2] -= bboxes[:, 2:4] / 2 # get landmarks landmarks = np.hstack( ( ( self.priors[:, 0:2] + loc[:, 4:6] * self._variance[0] * self.priors[:, 2:4] ) * scale, ( self.priors[:, 0:2] + loc[:, 6:8] * self._variance[0] * self.priors[:, 2:4] ) * scale, ( self.priors[:, 0:2] + loc[:, 8:10] * self._variance[0] * self.priors[:, 2:4] ) * scale, ( self.priors[:, 0:2] + loc[:, 10:12] * self._variance[0] * self.priors[:, 2:4] ) * scale, ( self.priors[:, 0:2] + loc[:, 12:14] * self._variance[0] * self.priors[:, 2:4] ) * scale, ) ) dets = np.hstack((bboxes, landmarks, scores)) return dets
6,476
workflows/nlp/visualization_views.py
xflows/textflows
18
2171884
''' NLP visualization views. @author: <NAME> <<EMAIL>> ''' from django.shortcuts import render import nlp def definition_sentences_viewer(request, input_dict, output_dict, widget): """ Parses the input XML and displays the definition sentences given as input. @author: <NAME>, 2012 """ sentences = nlp.parse_def_sentences(input_dict['candidates']) return render(request, 'visualizations/def_sentences.html',{'widget' : widget, 'sentences' : sentences}) def definition_sentences_viewer2(request, input_dict, output_dict, widget): """ Parses the input XML and displays the definition sentences given as input. @author: <NAME>, 2012 """ ids_sentence = input_dict["ids_sentence"] == "true" ids_article = input_dict["ids_article"] == "true" text_sentence = input_dict["text_sentence"] == "true" sentences = nlp.parse_def_sentences2(input_dict['candidates']) return render(request, 'visualizations/def_sentences2.html',{'widget' : widget, 'sentences' : sentences, 'ids_sentence': ids_sentence, 'ids_article': ids_article, 'text_sentence':text_sentence}) def term_candidate_viewer(request, input_dict, output_dict, widget): """ Parses the input and displays the term candidates. @author: <NAME>, 2012 """ terms = [] for line in input_dict['candidates'].split('\n'): try: score, cand, lemma = line.split('\t') except: continue terms.append({'score' : score, 'cand' : cand.replace('[', '').replace(']',''), 'lemma' : lemma.replace('<<', '').replace('>>','') }) terms = sorted(terms, key = lambda x: x['score'], reverse=True) return render(request, 'visualizations/terms.html', {'widget' : widget, 'terms' : terms})
1,836
volkscv/analyzer/statistics/processor/base.py
YuxinZou/volkscv
59
2170297
from ..base import Base from ..plotter import BasePlotter, Compose, SubPlotter class BaseProcessor(Base): """ Base class of processors. Args: data: Data to be processed. """ def __init__(self, data): self.data = data self.processor = [] def default_plot(self, *args, **kwargs): """ A default plot function. It will call the default_plot function of each processed processors in self.processor. """ for p in self.processor: p_ = getattr(self, p) if isinstance(p_, SubPlotter): p_.plot() elif isinstance(p_, BasePlotter): p_.figure(p_.text, *args, **kwargs) p_.title(p_.text) p_.plot() elif isinstance(p_, Compose): if p_.flag: p_.plot() else: p_.figure(p_.text, *args, **kwargs) p_.title(p_.text) p_.plot() elif isinstance(p_, BaseProcessor): p_.default_plot(*args, **kwargs) else: continue def plot(self): """ Call the plot method or each processor in self.processor.""" for p in self.processor: if hasattr(self, p): p_ = getattr(self, p) p_.plot()
1,377
model/DHS_resnext_back.py
lartpang/DHSNet-PyTorch
3
2172129
import torch import torch.nn.functional as F from torch import nn from model import ResNeXt101 class R3Net(nn.Module): def __init__(self, new_block): super(R3Net, self).__init__() self.upsample = lambda x: F.interpolate( x, scale_factor=2, mode='nearest' ) resnext = ResNeXt101() # 对应的五个阶段 self.layer0 = resnext.layer0 # 1/4 self.layer1 = resnext.layer1 # 1/4 self.layer2 = resnext.layer2 # 1/8 self.layer3 = resnext.layer3 # 1/16 self.layer4 = resnext.layer4 # 1/32 # 这里可以考虑去掉, 直接换成一个卷积与上采样的组合, 这样可以任意输入图片了. self.fc_line = nn.Linear(7 * 7 * 2048, 196) # 这里括号里的都是前期卷积的特征图的对应的通道数量 => in_channels self.rcl1 = new_block(1024) self.rcl2 = new_block(512) self.rcl3 = new_block(256) self.rcl4 = new_block(3) for m in self.modules(): if isinstance(m, nn.ReLU) or isinstance(m, nn.Dropout): m.inplace = True def forward(self, x_1): # 获取五个阶段的特征输出 x_4 = self.layer0(x_1) # x/4 x_4 = self.layer1(x_4) # x/4 x_8 = self.layer2(x_4) # x/8 x_16 = self.layer3(x_8) # x/16 = 14 x_32 = self.layer4(x_16) # x/32 bz = x_32.shape[0] x = x_32.view(bz, -1) # x = F.adaptive_avg_pool2d(x, (1, 1)).view(bz, -1) x = self.fc_line(x) # generate the SMRglobal x = x.view(bz, 1, 14, -1) x1 = torch.sigmoid(x) x = self.rcl1.forward(x_16, x) x2 = torch.sigmoid(x) x = self.upsample(x) x = self.rcl2.forward(x_8, x) x3 = torch.sigmoid(x) x = self.upsample(x) x = self.rcl3.forward(x_4, x) x4 = torch.sigmoid(x) x = self.upsample(x) x = self.upsample(x) x = self.rcl4.forward(x_1, x) x5 = torch.sigmoid(x) # 训练的时候要对7个预测都进行监督(深监督) return x1, x2, x3, x4, x5
2,040
net/fasterrcnn.py
TangZhenchaoTZC/Keras-mask-detection
3
2171995
"""fasterRCNN构建,这里只是返回模型""" from net import backbone as backbone from net import RPN as RPN from net import classify as classify from keras.layers import Input from keras.models import Model def get_model(flag,class_num): """直观反映流程,用于训练""" inputs = Input(shape=(None, None, 3)) roi_input = Input(shape=(None, 4)) #共享特征层 base_layers = backbone.ResNet50(inputs) #默认为9 anchor_num = len(flag.anchor_box_scales) * len(flag.anchor_box_ratios) #建立RPN rpn = RPN.RPN(base_layers,anchor_num) #[:2]切片,只选择列表内最初的两个索引0,1 #index=0,N行1列9*坐标个数的概率值,index=1,N行4列,N行1列9*坐标个数的的偏移信息 model_rpn = Model(inputs, rpn[:2]) #roi_input为建议框[None, 4],None与每次处理的建议框数量num_rois有关,config中定义为32 # classifier为[out_class,out_regr] # out_class为(Batch_size,32个建议框,21) # out_regr为(Batch_size,32个建议框,80) classifier = classify.end_classify(base_layers, roi_input, flag.num_rois, nb_classes=class_num, trainable=True) model_classifier = Model([inputs, roi_input], classifier) #model_all实际上是合并了RPN网络和分类网络,即为fasterrcnn网络 model_all = Model([inputs, roi_input], rpn[:2]+classifier) return model_rpn,model_classifier,model_all def get_predict_model(config,num_classes): """用于预测""" inputs = Input(shape=(None, None, 3)) roi_input = Input(shape=(None, 4)) feature_map_input = Input(shape=(None,None,1024)) base_layers = backbone.ResNet50(inputs) num_anchors = len(config.anchor_box_scales) * len(config.anchor_box_ratios) rpn = RPN.RPN(base_layers, num_anchors) model_rpn = Model(inputs, rpn) classifier = classify.end_classify(feature_map_input, roi_input, config.num_rois, nb_classes=num_classes, trainable=True) model_classifier_only = Model([feature_map_input, roi_input], classifier) return model_rpn,model_classifier_only
1,814
userbot/clients/__init__.py
CoeF/Ayiin-Userbot
2
2171585
from .client_list import client_id, clients_list from .logger import ayiin_userbot_on from .startup import ayiin_client, multiayiin
132
ReflectionPadding3D.py
gegewen/ccsnet_v1.0
2
2170915
from tensorflow.keras import backend as K from tensorflow.keras.layers import Layer import tensorflow as tf reg_weights = 0.001 class ReflectionPadding3D(Layer): def __init__(self, padding=(1, 1, 1), dim_ordering='default', **kwargs): super(ReflectionPadding3D, self).__init__(**kwargs) if dim_ordering == 'default': dim_ordering = K.image_data_format() self.padding = padding self.dim_ordering = dim_ordering def call(self, x, mask=None): top_pad = self.padding[0] bottom_pad = self.padding[0] left_pad = self.padding[1] right_pad = self.padding[1] front_pad = self.padding[2] back_pad = self.padding[2] paddings = [[0, 0], [left_pad, right_pad], [top_pad, bottom_pad], [front_pad, back_pad], [0, 0]] return tf.pad(x, paddings, mode='REFLECT', name=None) def compute_output_shape(self, input_shape): if self.dim_ordering == 'tf': rows = input_shape[1] + self.padding[0] + self.padding[0] if input_shape[1] is not None else None cols = input_shape[2] + self.padding[1] + self.padding[1] if input_shape[2] is not None else None dep = input_shape[3] + self.padding[2] + self.padding[2] if input_shape[3] is not None else None return (input_shape[0], rows, cols, dep, input_shape[4]) else: raise ValueError('Invalid dim_ordering:', self.dim_ordering) def get_config(self): config = super(ReflectionPadding3D, self).get_config() config.update({'padding': self.padding, 'dim_ordering': self.dim_ordering}) return config
1,751
flux_balance_analysis/collate_cluster_output.py
llambourne/isoenzymes_flux_balance
0
2171284
"""Script to read in all the costs and write to csv.""" import os import cPickle as pickle from collections import defaultdict import cobra from fba_utils import * def collate_cluster_output(modelPath): modelName = modelPath.split('/')[-1][:-4] modelDir = '../models/' + modelName clusterDir = os.path.join(modelDir, 'cluster_output') flcOutPath = os.path.join(modelDir, 'function_loss_costs.tsv') glcOutPath = os.path.join(modelDir, 'gene_loss_costs.tsv') noGrowthOutPath = os.path.join(modelDir, 'minimal_media_no_growth.tsv') dblGenePath = os.path.join(modelDir, 'double_gene_loss_growth.csv') dblFunctionPath = os.path.join(modelDir, 'double_function_loss_growth.csv') model = cobra.io.read_sbml_model(modelPath) modelGeneNames = set([g.id for g in model.genes]) with open('../data/processed/carbon_sources.txt', 'r') as f: carbonSourceNames = [l.strip() for l in f.readlines()] with open('../data/processed/nitrogen_sources.txt', 'r') as f: nitrogenSourceNames = [l.strip() for l in f.readlines()] mediaWithGrowth = [] mediaNoGrowth = [] glc = defaultdict(list) flc = defaultdict(list) dblGLC = {} dblFLC = {} for c in carbonSourceNames: for n in nitrogenSourceNames: fnGLC = ('gene_loss_cost_' + c + '_AND_' + n + '.pkl').replace(' ', '_') glcInPath = os.path.join(clusterDir, fnGLC) if not os.path.exists(glcInPath): model = minimal_media(model, c, n) if model.optimize().f > 0.01: raise UserWarning('growth on media but no file found\n' + c + ' AND ' + n) mediaNoGrowth.append((c, n)) continue with open(glcInPath, 'r') as f: mediaWithGrowth.append((c, n)) glcOneMedia = pickle.load(f) if set(glcOneMedia.keys()) != modelGeneNames: raise UserWarning('Unknown or missing genes in:'+glcInPath) for geneName, cost in glcOneMedia.items(): glc[geneName].append(cost) fnFLC = ('function_loss_cost_' + c + '_AND_' + n + '.pkl').replace(' ', '_') flcInPath = os.path.join(clusterDir, fnFLC) with open(flcInPath, 'r') as f: flcOneMedia = pickle.load(f) if set(flcOneMedia.keys()) != modelGeneNames: raise UserWarning('Unknown or missing genes in:'+flcInPath) for geneName, cost in flcOneMedia.items(): flc[geneName].append(cost) print len(mediaNoGrowth), '/', len(carbonSourceNames) * len(nitrogenSourceNames), print 'Environments with no wildtype growth' ########### Load double function knockouts ####################### for i in range(len(model.genes) - 1): filePath = os.path.join(clusterDir, 'growth_double_function_knockouts_'+str(i)+'.pkl') if not os.path.exists(filePath): raise UserWarning(filePath + ' does not exist') with open(filePath, 'r') as f: dblFunctionKO = pickle.load(f) if len(dblFunctionKO) != (len(model.genes) - i) - 1: raise UserWarning('Unexpected number of double knockouts' ' in file: ' + filePath + 'found ' + str(len(dblFunctionKO)) + ' expected: ' + str((len(model.genes) - i) - 1)) if any([k in dblFLC for k in dblFunctionKO]): raise UserWarning('Duplicate entries in results') dblFLC.update(dblFunctionKO) ########### Load double gene knockouts ####################### for i in range(len(model.genes) - 1): filePath = os.path.join(clusterDir, 'growth_double_gene_knockouts_'+str(i)+'.pkl') if not os.path.exists(filePath): raise UserWarning(filePath + ' does not exist') with open(filePath, 'r') as f: result = pickle.load(f) for i in range(result['data'].shape[0]): geneA = result['x'][i] for j in range(result['data'].shape[1]): geneB = result['y'][j] dblGLC[(geneA, geneB)] = result['data'][i, j] ################### write out data ############################### if not os.path.exists(noGrowthOutPath): with open(noGrowthOutPath, 'w') as f: for c, n in mediaNoGrowth: f.write(c + '\t' + n + '\n') else: print noGrowthOutPath, 'already exists' if not os.path.exists(flcOutPath): with open(flcOutPath, 'w') as f: f.write('ORF ID\t' + '\t'.join([' AND '.join(cn) for cn in mediaWithGrowth]) + '\n') for geneName, costs in flc.items(): f.write(geneName + '\t' + '\t'.join([str(c) for c in costs])+'\n') else: print flcOutPath, 'already exists' if not os.path.exists(glcOutPath): with open(glcOutPath, 'w') as f: f.write('ORF ID\t' + '\t'.join([' AND '.join(cn) for cn in mediaWithGrowth]) + '\n') for geneName, costs in glc.items(): f.write(geneName + '\t' + '\t'.join([str(c) for c in costs])+'\n') else: print glcOutPath, 'already exists' if not os.path.exists(dblGenePath): with open(dblGenePath, 'w') as f: for (geneA, geneB), growth in dblGLC.items(): f.write(geneA + ',' + geneB + ',' + str(growth) + '\n') else: print dblGenePath, 'already exists' if not os.path.exists(dblFunctionPath): with open(dblFunctionPath, 'w') as f: for (geneA, geneB), growth in dblFLC.items(): f.write(geneA + ',' + geneB + ',' + str(growth) + '\n') else: print dblFunctionPath, 'already exists' def main(): collate_cluster_output('../data/external/yeast_7.6/yeast_7.6.xml') if __name__ == '__main__': main()
6,224
PFPO/common/metrics.py
phillipcpark/PredictiveFPO
0
2171188
from mpmath import mp from collections import Counter from numpy import amax, argmax # def prec_recall(predicts, labels, classes, ignore_class, pred_thresh=None): correct = {} incorrect = {} true = {0:0, 1:0} for c in range(classes): correct[c] = 0 incorrect[c] = 0 for p_idx in range(len(predicts)): gt = int(labels[p_idx].detach().numpy()) if (gt == ignore_class): continue true[gt] += 1 pred_class = None if not(pred_thresh is None): max_prob = amax(predicts.detach().numpy()[p_idx]) if (max_prob >= pred_thresh): pred_class = argmax(predicts[p_idx].detach().numpy()) else: pred_class = 0 else: pred_class = argmax(predicts[p_idx].detach().numpy()) if (pred_class == gt): correct[pred_class] += 1 else: incorrect[pred_class] += 1 prec = {} rec = {} for c in range(classes): if (true[c] == 0): if (incorrect[c] == 0): prec[c] = 1.0 rec[c] = 1.0 else: prec[c] = 0.0 rec[c] = 1.0 else: if (correct[c] + incorrect[c] == 0): prec[c] = 0.0 rec[c] = 0.0 else: prec[c] = float(correct[c]) / (correct[c] + incorrect[c]) rec[c] = float(correct[c]) / true[c] return prec, rec # def relative_error(val_num, val_denom): mp.prec = 65 try: return mpmath.fabs((val_num - val_denom) / val_denom) except: return 0.0 # see if error sample is acceptable def accept_err(errs, thresh, accept_proportion): sz = len(errs) gt_thresh = 0 max_count = int(sz * (1.0 - accept_proportion)) accept_thresh = thresh for err in errs: if (err > accept_thresh): gt_thresh += 1 prop_gt_thresh = float(gt_thresh) / float(sz) if (prop_gt_thresh >= (1.0 - accept_proportion)): return False, prop_gt_thresh return True, prop_gt_thresh # def update_freq_delta(prec_freq_deltas, otc_tuned, otc_orig): # precision counts total = len(otc_tuned) orig_counts = {32:0, 64:0, 80:0} for prec in otc_orig: orig_counts[prec] += 1 prec_counts = {32:0, 64:0, 80:0} for prec in otc_tuned: prec_counts[prec] += 1 for k in prec_counts.keys(): prec_freq_deltas[k].append(float(prec_counts[k])/total - float(orig_counts[k])/total)
2,652
code/gui/config.py
matfurrier/QlikForecast
6
2171744
import qrspy host = 'localhost' port = 4242 certPath = 'C:/ProgramData/Qlik/Sense/Repository/Exported Certificates/.Local Certificates/' qrs = qrspy.ConnectQlik(server = host + ':' + str(port), certificate = (certPath + 'client.pem', certPath + 'client_key.pem')) print(qrs)
324
setup.py
Fitblip/DVR-Scan
0
2172143
#!/usr/bin/env python # # DVR-Scan: Video Motion Event Detection & Extraction Tool # -------------------------------------------------------------- # [ Site: https://github.com/Breakthrough/DVR-Scan/ ] # [ Documentation: http://dvr-scan.readthedocs.org/ ] # # Installation and build script for DVR-Scan. To install DVR-Scan in the # current environment, run: # # > python setup.py install # # This will allow you to use the `dvr-scan` command from the terminal or # command prompt. When upgrading to a new version, running the above command # will automatically overwrite any existing older version. # import sys from setuptools import setup if sys.version_info < (2, 6) or (3, 0) <= sys.version_info < (3, 3): print('DVR-Scan requires at least Python 2.6 or 3.3 to run.') sys.exit(1) def get_requires(): # type: () -> List[str] """ Get Requires: Returns a list of required packages. """ requires = ['numpy', 'tqdm'] if sys.version_info == (2, 6): requires += ['argparse'] return requires def get_extra_requires(): # type: () -> Dict[str, List[str]] """ Get Extra Requires: Returns a list of extra/optional packages. """ return { 'opencv:python_version <= "3.5"': ['opencv-python<=4.2.0.32'], 'opencv:python_version > "3.5"': ['opencv-python'], 'opencv-headless:python_version <= "3.5"': ['opencv-python-headless<=4.2.0.32'], 'opencv-headless:python_version > "3.5"': ['opencv-python-headless'], } setup( name='dvr-scan', version='1.1.0', description="Tool for finding and extracting motion events in video files (e.g. security camera footage).", long_description=open('package-info.rst').read(), author='<NAME>', author_email='<EMAIL>', url='https://github.com/Breakthrough/DVR-Scan', license="BSD 2-Clause", keywords="video computer-vision analysis", install_requires=get_requires(), extras_require=get_extra_requires(), packages=['dvr_scan'], package_data={'': ['../LICENSE*', '../package-info.rst']}, #include_package_data = True, # Only works with this line commented. #test_suite="unitest.py", entry_points={"console_scripts": ["dvr-scan=dvr_scan:main"]}, classifiers=[ 'Development Status :: 5 - Production/Stable', 'Environment :: Console', 'Environment :: Console :: Curses', 'Intended Audience :: Developers', 'Intended Audience :: End Users/Desktop', 'Intended Audience :: System Administrators', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: Multimedia :: Video', 'Topic :: Multimedia :: Video :: Conversion', 'Topic :: Multimedia :: Video :: Non-Linear Editor', 'Topic :: Utilities' ] )
3,322
visualisation/tests/test_visualisation.py
Zac-HD/awra_cms
20
2171927
from nose.tools import nottest def test_imports(): import awrams.visualisation # assert False @nottest def test_drive(): import awrams.visualisation.vis as vis import awrams.visualisation.results as res # from awrams.utils.catchments import CatchmentDB,CATCHMENT_SHAPEFILE # catchments = CatchmentDB() import os.path as o #import dirname,join import awrams.simulation as sim # res_dir = o.abspath(o.join('..','simulation','notebooks','_results')) res_dir = o.abspath(o.join(o.dirname(__file__),'..','..','simulation','notebooks','_results')) results = res.load_results(res_dir) results.path results.variables results[:,'1 dec 2010',:].spatial() v = results.variables.s0,results.variables.ss results[v,'dec 2010',vis.extents.from_boundary_coords(-39.5,143.5,-44,149)].spatial() vis.plt.savefig('map_of_tasmania.png', format='png', dpi=120) v = results.variables.qtot v.agg_method = 'sum' results[v,'dec 2010',vis.extents.from_boundary_coords(-39.5,143.5,-44,149)].spatial() results.variables.s0.data.shape results.variables.s0.agg_data.shape # v = results.variables.s0,results.variables.ss # results[v,'dec 2010',catchments.by_name.Lachlan_Gunning()].spatial(interpolation=None) #interpolation="bilinear") # # vis.show_extent(catchments.by_name.Lachlan_Gunning()) # vis.show_extent(catchments.by_name.Lachlan_Gunning(),vis.extents.from_boundary_coords(-40,142,-30,154)) # # catchments.list() v = results.variables.qtot,results.variables.ss results[v,'dec 2010',:].spatial(clim=(0,100),xlabel="longitude") q = results[v,'dec 2010',vis.extents.from_boundary_coords(-39.5,143.5,-44,149)] q.get_data_limits() q.spatial(clim=(0,200),xlabel="longitude") gridview = q.mpl view = gridview.children[0,1] view.ax.set_xlabel("ALSO LONGITUDE!") vis.plt.show() p = 'dec 2010 - jan 2011' e = vis.extents.from_cell_coords(-34,117) results[:,p,e].timeseries() # v = results.variables.qtot,results.variables.ss # p = 'dec 2010 - jan 2011' # e = catchments.by_name.Murrumbidgee_MittagangCrossing() # results[v,p,e].timeseries() # # results.variables.qtot.data.shape,results.variables.qtot.agg_data.shape
2,292
db_migrate.py
gimmy1/flask_taskR
0
2172206
from views import db from _config import DATABASE_PATH import sqlite3 from datetime import datetime with sqlite3.connect(DATABASE_PATH) as connection: # get a cursor object used to execute SQL commands c = connection.cursor() # temporarily change the name of tasks table c.execute("""ALTER TABLE users RENAME TO old_users""") # recreate a new tasks table with updated schema db.create_all() # retrieve data from old_tasks table c.execute("""SELECT name, email, password, FROM old_users ORDER BY id ASC""") # save all rows as a list of tuples; set posted_date to now and user_id to # 1 data = [(row[0], row[1], row[2], 'user') for row in c.fetchall()] # insert data to tasks table c.executemany( """INSERT INTO users (name, email, password, role) VALUES (?, ?, ?, ?)""", data) # delete old_tasks table c.execute("DROP TABLE old_users")
929
tests/contrib/django/app/middlewares.py
ocelotl/opentelemetry-auto-instr-python-1
2
2170998
# Copyright 2019, OpenTelemetry 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. from django.http import HttpResponse try: from django.utils.deprecation import MiddlewareMixin MiddlewareClass = MiddlewareMixin except ImportError: MiddlewareClass = object class CatchExceptionMiddleware(MiddlewareClass): def process_exception(self, request, exception): return HttpResponse(status=500) class HandleErrorMiddlewareSuccess(MiddlewareClass): """ Converts an HttpError (that may be returned from an exception handler) generated by a view or previous middleware and returns a 200 HttpResponse. """ def process_response(self, request, response): if response.status_code == 500: return HttpResponse(status=200) return response class HandleErrorMiddlewareClientError(MiddlewareClass): """ Converts an HttpError (that may be returned from an exception handler) generated by a view or previous middleware and returns a 404 HttpResponse. """ def process_response(self, request, response): if response.status_code == 500: return HttpResponse(status=404) return response
1,708
flax_tools/metrics/mean.py
cgarciae/flax-tools
2
2164953
from dataclasses import dataclass import enum import typing as tp import jax import jax.numpy as jnp import numpy as np from flax_tools import utils from flax_tools.metrics.metric import Metric, MapArgs from flax_tools.metrics.reduce import Reduce, Reduction @utils.dataclass class Mean(Reduce): @classmethod def new( cls, name: tp.Optional[str] = None, on: tp.Optional[utils.IndexLike] = None, ): return super().new( reduction=Reduction.weighted_mean, name=name, on=on, )
564
bin/exp_generator.py
limbo018/OpenMPL
42
2170267
import os algorithms = ["ILP", "SDP", "DL", "BACKTRACK"] shape = "POLYGON" file_path = "" out = "" coloring_distance = "100" layer1 = "15" layer2 = "16" color_num = "3" algo = "" thread_num = "1" record = "1" input_folder = "big/" output_folder = "benchout/" file_names = os.listdir(input_folder) fp = open('experiments.sh', 'w') for file_name in file_names: file_path = input_folder + file_name out = output_folder + "out_" + file_name for algo in algorithms: sh = "./OpenMPL " + "-shape " + shape + " -in " + file_path + " -out " + out + " -coloring_distance " + coloring_distance + " -uncolor_layer " + layer1 + " -uncolor_layer " + layer2 + " -color_num " + color_num + " -algo " + algo + " -thread_num " + thread_num + " -use_stitch -gen_stitch" + " -record " + record fp.write(sh) fp.write("\n") fp.write("\n") fp.close()
917
hint_cli/hint.py
agarthetiger/hint
1
2170665
import glob import os import logging import subprocess import click from rich.console import Console from rich.markdown import Markdown from hint_cli import repo, config_manager from hint_cli.format import format_for_stdout from markdown import parser logger = logging.getLogger(__name__) conf = None def print_markdown(hint_markdown): console = Console() md = Markdown(hint_markdown) console.print(md) def print_to_console(hint_text: str): for line in hint_text.split('\n'): # Skip blank lines if not line.strip(): continue formatted_line = format_for_stdout(line) click.echo(message=formatted_line) def get_section(hint_text: str, section: str): """ Return a section of text based on the section heading from the hint_text string. '\n' delimited string containing a markdown document. Args: hint_text (str): '\n' delimited string containing a markdown document. section (str): Section heading to search the hint_text for. Returns: str: Either the section of text from the hint_text with the section heading or an error message indicating the section could not be found. """ hint_text_list = hint_text.split("\n") section = section.lower() toc = parser.get_toc(hint_text_list) try: return "\n".join(hint_text_list[toc[section].start:toc[section].end]) except KeyError: return f"Section '{section}' not found in document." def get_display_text(hint_text, subsections): """ Get the requested sections of text from the hint_text document. Args: hint_text (str): Hint text to display subsections (tuple): Optional section(s) to return instead of the whole document. Returns: str: Text to display from the hint_text argument. """ if len(subsections) == 0: return hint_text else: display_text = "" for section in subsections: display_text += get_section(hint_text, section) if len(display_text) > 0: return display_text else: return hint_text def get_topic_from_repo(git_repo: str, topic: str): """ Get the topic text from the git repository. Args: git_repo: topic: Returns: """ local_path = repo.pull_repo(remote_repo=git_repo, local_path=config_manager.REPO_PATH) with open(f"{local_path}/{topic}.md", "r") as f: text = f.read() return text def create_or_edit_topic(topic: str): """ Creates or edits a markdown file with the name of the topic. Args: topic: Name of the topic to create or edit. """ subprocess.run(['vim', f"{config_manager.REPO_PATH}/{topic}.md"]) repo.push_all_changes(config_manager.REPO_PATH) def search_for_text_in_topics(search_term: str): """ Search for the requested text in all the hint topic files. Args: search_term (str): String to search in the topic files for. Returns: String message containing details of the files and the lines containing the matches. """ msg = "" for topic_file in glob.glob(f"{config_manager.REPO_PATH}/*.md"): with open(topic_file) as file: matches = [line for line in file.readlines() if line.find(search_term) != -1] if len(matches) > 0: msg += f"Matches found in {topic_file}\n" for match in matches: msg += f"{match}\n" return msg def search_for_topic(topic: str): """ Check if the topic (file) exists. Topics are files in a locally cloned git repository. The topic name should match a markdown file with a .md file extension. Future versions may fuzzy-match the filename, or search other locations such as a remote GitHub repository. The folder this methods uses to check for the file is defined by `config.REPO_PATH` Args: topic: Name of the topic to verify exists. Returns: bool: True if topic (file) exists, otherwise False. """ msg = "" if os.path.isfile(f"{config_manager.REPO_PATH}/{topic}.md"): msg = f'Hint found for `{topic}`.\n' else: msg = f'Hints for topic "{topic}" not found, run `hint --edit ' \ f'{topic}` to create it.\n' msg += search_for_text_in_topics(topic) print_to_console(msg) def display_topic(topic: str, subsections: tuple): """ Display the hint topic to the console. If there are no subsections specified, display the whole file, otherwise just display the subsections from the topic. Args: topic (str): Name of the topic to display subsections (tuple): Optional subsections to display from the specified topic. Returns: Nothing. Correct function is to print text to the console. """ # If no other flags passed, display the hint topic [and subsections] full_hint_text = get_topic_from_repo(git_repo=conf['hint']['repo'], topic=topic) display_text = get_display_text(full_hint_text, subsections) print_to_console(display_text) def _get_topics(ctx, args, incomplete: str): """ Callback function for Click command-line auto-completion. This allows tab-completion of existing hint topics. See https://click.palletsprojects.com/en/7.x/bashcomplete/?#what-it-completes Returns: (list): Filenames matching the incomplete arg value. """ return [filename.split('/')[-1][0:-3] for filename in glob.glob(f"{config_manager.REPO_PATH}/{incomplete}*.md")] @click.command() @click.option('-e', '--edit', is_flag=True) @click.option('-s', '--search', is_flag=True) @click.argument('topic', autocompletion=_get_topics) @click.argument('subsections', nargs=-1) @click.version_option() def cli(edit, search, topic, subsections): """ CLI entrypoint. Args: edit (bool): True if the topic should be edited. Will be created if it does not exist. search (bool): True if the topic string should be searched for. Search will first look for filename matches, and if not found the file contents will be searched instead. topic (string): Name of the topic to create, update or search for. subsections (tuple): Optional sub-sections to display. Only valid for the (default) display action. Returns: Non-zero exit code on failure, otherwise correct operation is to print output to the console. """ global conf conf = config_manager.get_config() if edit: create_or_edit_topic(topic=topic) elif search: search_for_topic(topic=topic) else: display_topic(topic=topic, subsections=subsections)
6,852
tests/python/tg/test_buffer_access_feature_lines.py
QinHan-Erin/AMOS
22
2171944
import tvm import tvm.te as te import tvm.tg as tg import numpy as np def pprint_dict(d): import json print(json.dumps(d, indent=2, sort_keys=False))
156
setup.py
joaopalmeiro/pygments-styles
0
2171959
from setuptools import setup, find_packages setup( name = 'pygments-styles', version = '1.0', description = "A set of custom Pygments styles", author = "<NAME>", author_email='<EMAIL>', license = 'MIT', install_requires = ['pygments'], packages = find_packages(), entry_points = ''' [pygments.styles] cv = styles.cv:CVStyle ''' )
385
algorithms/Python/strings/rabin-karp-algorithm.py
akrish4/DSA-
1
2172214
''' String pattern matching algorithm which performs efficiently for large text and patterns Algorithm: Rabin Karp works on the concept of hashing. If the substring of the given text is same as the pattern then the corresponding hash value should also be same. Exploiting this idea and designing a hash function which can be computed in O(m) time for both pattern and initial window of text. The subsequent window each will require only O(1) time. And we slide the window n-m times after the initial window. Therefore the overall complexity of calculating hash function for text is O(n-m+1) Once the hash value matches, the underlying string is again checked with pattern for matching Complexity: Best case: O(n-m+1) Worst case: O(nm) ''' def rabin_karp(T: str, P: str, q: int ,d: int = 256) -> None : ''' Parameters: T: string The string where the pattern needs to be searched P: string The pattern to be searched q: int An appropriately chosen prime number based on length of input strings The higher the prime number, the lower the collisions and spurious hits d: int, default value 256 Denotes the no of unique character that is used for encoding Example: >>> pos = rabin_karp("AAEXCRTDDEAAFT","AA",101) Pattern found at pos: 0 Pattern found at pos: 10 ''' n = len(T) # length of text m = len(P) # length of pattern p=0 # Hash value of pattern t=0 # Hash value of text #Computing h: (h=d^m-1 mod q) h=1 for i in range(1,m): h = (h*d)%q #Computing hash value of pattern and initial window (of size m) of text for j in range(m): p = (d*p + ord(P[j])) % q t = (d*t + ord(T[j])) % q found = False pos=[] # To store positions #Sliding window and matching for s in range(n-m+1): if p==t: # if hash value matches if P == T[s:s+m]: # check for string match pos.append(s) if not found: found = True if s<n-m: t = (d*(t-ord(T[s])*h) + ord(T[s+m])) % q # updating hash value of t for next window if t<0: t = t+q # To make sure t is positive integer if not found: # If pattern not found in text pos.append(-1) #Printing results if pos[0]==-1: print("Pattern not found") else: for i in pos: print(f"Pattern found at pos: {i}") if __name__ == "__main__": T = "AAEXCRTDDEAAFT" P = "AA" rabin_karp(T,P,101)
2,762
Django/todoDemo/models.py
Jackey-Sparrow/python-coil
0
2170645
from django.db import models class BlogPost(models.Model): title = models.CharField(max_length=150) body = models.TextField(), timeStamp = models.DateTimeField()
176
mods/NERO_Battle/module.py
gjacobrobertson/opennero-394n
0
2172243
import sys import NERO.module import NERO.constants as constants import NeroEnvironment import OpenNero import common # this value, between 0 (slowest) and 100 (fastest) # overrides the default from NERO Training BATTLE_DEFAULT_SPEEDUP = 80 class NeroModule(NERO.module.NeroModule): def __init__(self): NERO.module.NeroModule.__init__(self) if OpenNero.getAppConfig().rendertype != 'null': self.set_speedup(BATTLE_DEFAULT_SPEEDUP) print 'setting speedup for on-screen battle' def create_environment(self): return NeroEnvironment.NeroEnvironment() def load_team(self, location, team=constants.OBJECT_TYPE_TEAM_0): NERO.module.NeroModule.load_team(self, location, team) rtneat = OpenNero.get_ai('rtneat-%s' % team) if rtneat: rtneat.set_lifetime(sys.maxint) rtneat.disable_evolution() OpenNero.disable_ai() # don't run until button def start_rtneat(self, team=constants.OBJECT_TYPE_TEAM_0): NERO.module.NeroModule.start_rtneat(self, team) rtneat = OpenNero.get_ai('rtneat-%s' % team) if rtneat: rtneat.set_lifetime(sys.maxint) rtneat.disable_evolution() def delMod(): NERO.module.gMod = None def getMod(): if not NERO.module.gMod: NERO.module.gMod = NeroModule() return NERO.module.gMod script_server = None def getServer(): global script_server if script_server is None: script_server = common.menu_utils.GetScriptServer() common.startScript("NERO_Battle/menu.py") return script_server def parseInput(strn): if strn == "deploy" or len(strn) < 2: return mod = getMod() # first word is command rest is filename loc, val = strn.split(' ',1) vali = 1 if strn.isupper(): vali = int(val) if loc == "HP": mod.hpChange(vali) if loc == "SP": mod.set_speedup(vali) if loc == "load1": mod.load_team(val, constants.OBJECT_TYPE_TEAM_0) if loc == "load2": mod.load_team(val, constants.OBJECT_TYPE_TEAM_1) if loc == "rtneat": mod.deploy('rtneat', constants.OBJECT_TYPE_TEAM_0) mod.deploy('rtneat', constants.OBJECT_TYPE_TEAM_1) if loc == "qlearning": mod.deploy('qlearning', constants.OBJECT_TYPE_TEAM_0) mod.deploy('qlearning', constants.OBJECT_TYPE_TEAM_1) if loc == "pause": OpenNero.disable_ai() if loc == "resume": OpenNero.enable_ai() def ServerMain(): print "Starting mod NERO_Battle"
2,517
main.py
manoj2601/BitTorrent-Mechanism
0
2171924
#!/usr/bin/env python import socket import sys import hashlib import threading from threading import Lock import time import matplotlib matplotlib.use('Agg') import numpy as np import matplotlib.pyplot as plt import otherfunc #globally defined host = [] path = [] tcps = [] port = 80 chunkSize = 10240 #10KB chunks = [] i=0 totalBytes = int(sys.argv[3]) #given file size while(i < totalBytes): chunks.append((i, min(totalBytes-1, i+chunkSize-1))) i += chunkSize totalChunks = len(chunks) totalThreads = 0 executedTotal = [0]*totalThreads timeTaken = [0.0]*totalThreads downloadedParts = [""]*totalChunks isVisited = [False]*totalChunks def DrawPlot(): fig = plt.figure() plt.title(f"Relation in Time and total downloaded chunks by all threads") plt.xlabel("downloaded chunks") plt.ylabel("Time taken (in sec)") for i in range(0, totalThreads): X = [] for j in range(0, len(timeTaken[i])): X.append(len(X)+1) plt.plot(X, timeTaken[i], label=f"thread-{i}") plt.legend() plt.savefig(f"myPlot.png") plt.close() def getHost(threadID): h = host[0] p = path[0] tcps[0]-=1 if(tcps[0] ==0): host.pop(0) path.pop(0) tcps.pop(0) return h, p class myThread (threading.Thread): def __init__(self, threadID, name, hostID): threading.Thread.__init__(self) self.threadID = threadID self.name = name self.hostID = hostID def run(self): print("Starting " + self.name) downloadData(self.name, self.threadID, self.hostID) print("Exiting " + self.name) def downloadData(threadName, threadID, hostID): timetaken = [] chunksDownloaded = [] i = threadID cnt = 0 host1 = hostID[0] path1 = hostID[1] print(f"establishing a new tcp connection for {threadName}") clientSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) while True: try: print(f"hand-shaking the tcp connection {threadName}") clientSocket.connect((host1, port)) break except: print(f"Unable to hand-shake, retrying {threadName}") sTime = time.clock() while(i<totalChunks): currChunk = chunks[i] command = f"GET {path1} HTTP/1.1\r\nHost:{host1}\r\nConnection:keep-alive\r\nRange: bytes={currChunk[0]}-{currChunk[1]}\r\n\r\n" clientSocket.sendall(command.encode()) count = 0 start = False prev = ['a', 'a', 'a', 'a'] j=0 string = "" header = "" success = True while count < currChunk[1]-currChunk[0]+1: recev = True while True: try: data = clientSocket.recv(1) break except: recev = False break j=j+1 if not data or not recev: print(f"Data not received in {threadName}") success = False recev = True break if(start): string += data.decode() count = count+1 continue header += data.decode() otherfunc.updatLast4char(prev, data.decode()) start = otherfunc.isHeaderDone(prev) if(not success): #internet connection closed clientSocket.close() clientSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) while True: try: clientSocket.connect((host1, port)) break except: print(f"Unable to rehand-shake, retrying {threadName}") continue cnt+=1 executedTotal[threadID]+=1 isVisited[i] = True downloadedParts[i] = string print("downloaded chunk: ", i) i+=totalThreads chunksDownloaded.append(cnt) timetaken.append(time.clock()-sTime) # when max limit of requests for a tcp connection reached if(header.find('Connection: close') != -1): clientSocket.close() clientSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) while True: try: clientSocket.connect((host1, port)) break except: print(f"Unable to rehand-shake, retrying {threadName}") timeTaken[threadID] = timetaken clientSocket.close() def initialization(): global executedTotal global timeTaken global downloadedParts global isVisited executedTotal = [0]*totalThreads timeTaken = [None]*totalThreads for i in range(0, totalChunks): downloadedParts[i] = "" #initialization isVisited = [False]*totalChunks def DownloadFile(): initialization() allthreads = [] for i in range(0, totalThreads): h,p = getHost(i) thread = myThread(i, f"Thread-{i}", (h,p)) allthreads.append(thread) for i in range(0, totalThreads): allthreads[i].start() for t in allthreads: t.join() print("Saving file") filename = sys.argv[2] file1 = open(filename, "w") for i in range(0,totalChunks): file1.write(downloadedParts[i]) file1.close() print("total number of chunks executed by each Thread:") for i in range(0, totalThreads): print(f"thread-{i} ", executedTotal[i]) otherfunc.checkmd5sum(filename) print("File downloaded") def ReadInput(): input = open(sys.argv[1], "r") i=1 line = input.readline() print(line) global totalThreads totalThreads=0 while line != "": host1, path1, totalThreads1 = otherfunc.splitLine(line) global host global path global tcps host.append(host1) path.append(path1) tcps.append(totalThreads1) totalThreads += totalThreads1 line = input.readline() DownloadFile(); ReadInput() DrawPlot()
5,073
alfastaff_shedule/migrations/0004_auto_20201119_1407.py
spanickroon/Alfa-Staff
1
2171825
# Generated by Django 3.0.7 on 2020-11-19 11:07 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('alfastaff_shedule', '0003_scheduleforoneday_holiday'), ] operations = [ migrations.AddField( model_name='scheduleforoneday', name='day_off', field=models.BooleanField(blank=True, null=True, verbose_name='Выходной'), ), migrations.AlterField( model_name='scheduleforoneday', name='month', field=models.PositiveSmallIntegerField(blank=True, choices=[(1, 'Jun'), (2, 'Feb'), (3, 'Mar'), (4, 'Apr'), (5, 'May'), (6, 'Jun'), (7, 'Jul'), (8, 'Aug'), (9, 'Sep'), (10, 'Oct'), (11, 'Nov'), (12, 'Dec')], null=True, verbose_name='Месяц'), ), ]
815
bbcode/bbtags/smilies.py
unoti/django-bbcode
0
2171761
from bbcode import * from bbcode import settings import re class Smilies(SelfClosingTagNode): open_pattern = re.compile(':(?P<name>[a-zA-Z-]+):') def parse(self): name = self.match.groupdict()['name'] return '<img src="%s%s.gif" alt="%s" />' % ( settings.SMILEY_MEDIA_URL, name, name) class AlternativeSmilie(SelfClosingTagNode): def __init__(self, *args, **kwargs): if not hasattr(self, 'alias'): self.alias = self.__class__.__name__.lower() SelfClosingTagNode.__init__(self, *args, **kwargs) def parse(self): alias = self.match.group() return '<img src="%s%s.gif" alt="%s" />' % ( settings.SMILEY_MEDIA_URL, self.alias, alias) class LOL(AlternativeSmilie): # :D, :-D, :-d, :d open_pattern = re.compile(':-?(D|d)') class Smilie(AlternativeSmilie): # :), :-) open_pattern = re.compile(':-?\)') class Wink(AlternativeSmilie): # ;), ;-), ;-D, ;D, ;d, ;-d open_pattern = re.compile(';-?(\)|d|D)') class Razz(AlternativeSmilie): # :P, :-P, :p, :-p open_pattern = re.compile(':-?(P|p)') class Eek(AlternativeSmilie): # o_O.... open_pattern = re.compile('(o|O|0)_(o|O|0)') class Sad(AlternativeSmilie): # :-(, :( open_pattern = re.compile(':-?\(') class Crying(AlternativeSmilie): # ;_;, :'(, :'-( open_pattern = re.compile("(;_;|:'-?\()") class Yell(AlternativeSmilie): # ^.^ open_pattern = re.compile('^\.^') class Grin(AlternativeSmilie): # xD, XD, *g* open_pattern = re.compile('(xD|XD|\*g\*)') class Neutral(AlternativeSmilie): # :-|, :| open_pattern = re.compile('(:-?\|)') register(Smilies) register(LOL) register(Smilie) register(Wink) register(Razz) register(Eek) register(Sad) register(Crying) register(Yell) register(Grin) register(Neutral)
1,917
pixelsToStrokesView.py
rohun-tripati/pythonRepo
1
2170723
# This file is only python 3 compatible import glob import os import random import matplotlib matplotlib.use('Agg') import matplotlib.lines as mlines import matplotlib.patches as mpatches import matplotlib.pyplot as plt import numpy as np from os.path import join import global_constants def add_arrow_to_line2D( axes, line, arrow_locs=[0.3, 0.7], arrowstyle='-|>', arrowsize=3, transform=None): """ Add arrows to a matplotlib.lines.Line2D at selected locations. Parameters: ----------- axes: line: list of 1 Line2D obbject as returned by plot command arrow_locs: list of locations where to insert arrows, % of total length arrowstyle: style of the arrow arrowsize: size of the arrow transform: a matplotlib transform instance, default to data coordinates Returns: -------- arrows: list of arrows """ if (not (isinstance(line, list)) or not (isinstance(line[0], mlines.Line2D))): raise ValueError("expected a matplotlib.lines.Line2D object") x, y = line[0].get_xdata(), line[0].get_ydata() arrow_kw = dict(arrowstyle=arrowstyle, mutation_scale=10 * arrowsize) color = line[0].get_color() use_multicolor_lines = isinstance(color, np.ndarray) if use_multicolor_lines: raise NotImplementedError("multicolor lines not supported") else: arrow_kw['color'] = color linewidth = line[0].get_linewidth() if isinstance(linewidth, np.ndarray): raise NotImplementedError("multiwidth lines not supported") else: arrow_kw['linewidth'] = linewidth if transform is None: transform = axes.transData arrows = [] for loc in arrow_locs: s = np.cumsum(np.sqrt(np.diff(x) ** 2 + np.diff(y) ** 2)) n = np.searchsorted(s, s[-1] * loc) arrow_tail = (x[n], y[n]) arrow_head = (np.mean(x[n:n + 2]), np.mean(y[n:n + 2])) p = mpatches.FancyArrowPatch( arrow_tail, arrow_head, transform=transform, **arrow_kw) axes.add_patch(p) arrows.append(p) return arrows def arrow_view(strokepath, jump_points, arrow_size, output_path): strokefile = open(strokepath, "r") first = ["r", "g", "b"] second = ["^", "--", "s", "o"] x = [] y = [] fig, ax = plt.subplots(1, 1) for line in strokefile.readlines(): line = line.strip() if line == ".PEN_DOWN" or line == ".PEN_UP": # change colour rand = random.random() % 12 last = first[int(rand / 4)] + second[int(rand) % 4] jumppoints = jump_points if len(x) > 1: if len(x) > jumppoints: x = [x[index] for index in range(0, len(x), jumppoints)] y = [y[index] for index in range(0, len(y), jumppoints)] line = ax.plot(x, y, 'k-') add_arrow_to_line2D(ax, line, arrow_locs=np.linspace(0., 1., 200), arrowstyle='->', arrowsize=arrow_size) x = [] y = [] continue else: coor = line.split() x.append(int(coor[0])) y.append(int(coor[1])) export_to_image(strokepath, "_arrow_" + str(jump_points) + "_" + str(arrow_size), fig, output_path) def color_view(stroke_path, output_path): stroke_data = open(stroke_path, "r").readlines() first = ["r", "g", "b", "c", "k"] second = ["^", "--", "s", "o", "+", "*", "d"] for index, line_style in enumerate(second): fig = plt.figure() ax = fig.add_subplot(111) x = [] y = [] for line in stroke_data: line = line.strip() if line == ".PEN_DOWN" or line == ".PEN_UP": rand = int((random.random() * 100) % len(first)) last = first[rand] + line_style if len(x) > 1: ax.plot(x, y, last) x = [] y = [] continue else: coor = line.split() x.append(int(coor[0])) y.append(int(coor[1])) export_to_image(stroke_path, "_variation_" + str(index), fig, output_path) def normal_view(stroke_path, output_path): stroke_data = open(stroke_path, "r").readlines() x = [] y = [] min_distance, min_x, min_y, selection_folder = 10e10, -1, -1, "correct" for line in stroke_data: line = line.strip() if line == ".PEN_DOWN" or line == ".PEN_UP": if len(x) > 1: plt.plot(x, y, "b-") if x[0] * x[0] + y[0] * y[0] < min_distance: min_distance, min_x, min_y, selection_folder = x[0] * x[0] + y[0] * y[0], x[0], y[0], "correct" if len(x) > 2 and x[-1] * x[-1] + y[-1] * y[-1] < min_distance: min_distance, min_x, min_y, selection_folder = x[-1] * x[-1] + y[-1] * y[-1], x[-1], y[-1], "wrong" x = [] y = [] continue else: coor = line.split() x.append(int(float(coor[0]))) y.append(int(float(coor[1]))) export_to_image(stroke_path, "_variation_normal", plt, join(output_path, selection_folder)) plt.clf() def export_to_image(stroke_path, tag, fig, output_path): # Split by whatever is the system path delimiter directory, file_name = os.path.split(stroke_path) fig.savefig(join(output_path, file_name.rstrip(".tif.txt") + tag + ".png")) if __name__ == '__main__': output_path = "revision_data/stroke_to_images/" # image_path_list = [{"path" : global_constants.offline_word_ban_data_set, "filter" : "Image1."}] # image_path_list.append({"path" : global_constants.offline_word_eng_data_set, "filter" : "file8_24_3."}) # image_path_list.append({"path" : global_constants.offline_word_hin_data_set, "filter" : "file0_0_110."}) image_path_list = [{"path" : global_constants.online_char_hin_data, "filter": "file", "output": "char_hin"}, {"path" : global_constants.online_char_eng_data, "filter": "img", "output": "char_eng"}] number_to_output = 1000 for image_dictionary in image_path_list: image_files = glob.glob(join(image_dictionary["path"], "**", "*" + image_dictionary["filter"] + "*"), recursive=True) if len(image_files) != 0: os.makedirs(join(output_path, image_dictionary['output'], "correct"), exist_ok=True) os.makedirs(join(output_path, image_dictionary['output'], "wrong"), exist_ok=True) sorted_image_files = sorted(image_files) random.seed(0) random.shuffle(sorted_image_files) for index, input_image in enumerate(sorted_image_files): if index%100 ==0: print(image_dictionary["output"], index, number_to_output) normal_view(input_image, join(output_path, image_dictionary['output'])) # arrow_view(input_image, jump_points=5, arrow_size=2, output_path) # color_view(input_image, output_path) if index >= number_to_output: break
7,193
instantiate_laws.py
dominique-unruh/registers
2
2171863
#!/usr/bin/python3 import glob import os import re import sys from hashlib import sha1 from stat import S_IREAD, S_IRGRP, S_IROTH from typing import Union, Collection, Match, Dict, Optional, Tuple, Any, Set had_errors = False source_files: set[str] = set() generated_files: set[str] = set() def multisubst(mappings: Collection[(Union[re.Pattern, str])], content: str) -> str: replacements = [] patterns = [] i = 0 for pat, repl in mappings: if isinstance(pat, str): pat_str = re.escape(pat) else: pat_str = pat.pattern replacements.append(repl) patterns.append(f"(?P<GROUP_{i}>\\b(?:{pat_str})\\b)") i += 1 pattern = re.compile("|".join(patterns)) def repl_func(m: Match): # print(m) for name, text in m.groupdict().items(): if text is None: continue if text.startswith("GROUP_"): continue idx = int(name[6:]) # print(name, idx) return replacements[idx] assert False return pattern.sub(repl_func, content) def write_to_file(file, content): global generated_files generated_files.add(file) try: with open(file, 'rt') as f: old_content = f.read() if content == old_content: print("(Nothing changed, not writing.)") return os.remove(file) except FileNotFoundError: pass with open(file, 'wt') as f: f.write(content) os.chmod(file, S_IREAD | S_IRGRP | S_IROTH) def rewrite_laws(outputfile: str, template: str, substitutions: Dict[str, str]): global source_files print(f"Rewriting {template} -> {outputfile}") source_files.add(template) with open(template, 'rt') as f: content = f.read() new_content = multisubst(substitutions.items(), content) new_content = f"""(* * This is an autogenerated file. Do not edit. * The original is {template}. It was converted using instantiate_laws.py. *) """ + new_content write_to_file(outputfile, new_content) def read_instantiation_header(file: str) -> Optional[Tuple[str, Optional[str], Dict[str, str]]]: global source_files global had_errors source_files.add(file) with open(file, 'rt') as f: content = f.read() assert file.startswith("Axioms_") basename = file[len("Axioms_"):] assert basename.endswith(".thy") basename = basename[:-len(".thy")] m = re.compile(r"""\(\* AXIOM INSTANTIATION [^\n]*\n(.*?)\*\)""", re.DOTALL).search(content) if m is None: print(f"*** Could not find AXIOM INSTANTIATION header in {file}.") had_errors = True lines = [] else: lines = m.group(1).splitlines() substitutions = { 'theory Laws': f'theory Laws_{basename}', 'imports Laws': f'imports Laws_{basename}', 'theory Laws_Complement': f'theory Laws_Complement_{basename}', 'Axioms': f'Axioms_{basename}', 'Axioms_Complement': f'Axioms_Complement_{basename}' } # print(substitutions) for line in lines: if line.strip() == "": continue if re.match(r"^\s*#", line): continue m = re.match(r"^\s*(.+?)\s+\\<rightarrow>\s+(.+?)\s*$", line) if m is None: print(f"*** Invalid AXIOM INSTANTIATION line in {file}: {line}") had_errors = True continue key = m.group(1) val = m.group(2) if key in substitutions: print(f"*** Repeated AXIOM INSTANTIATION key in {file}: {line}") had_errors = True substitutions[key] = val # print(substitutions) laws_complement = f"Laws_Complement_{basename}.thy" if os.path.exists(f"Axioms_Complement_{basename}.thy") else None return (f"Laws_{basename}.thy", laws_complement, substitutions) def rewrite_all(): for f in glob.glob("Axioms_*.thy"): if f.startswith("Axioms_Complement"): continue lawfile, lawfile_complement, substitutions = read_instantiation_header(f) rewrite_laws(lawfile, "Laws.thy", substitutions) if lawfile_complement is not None: rewrite_laws(lawfile_complement, "Laws_Complement.thy", substitutions) def create_check_theory(): global source_files, generated_files print("Creating Check_Autogenerated_Files.thy") hash_checks = [] for kind, files in (("Source", source_files), ("Generated", generated_files)): for file in sorted(files): with open(file, 'rb') as f: hash = sha1(f.read()).hexdigest() hash_checks.append(f' check "{kind}" "{file}" "{hash}"') hash_checks_concat = ";\n".join(hash_checks) content = rf"""(* * This is an autogenerated file. Do not edit. * It was created using instantiate_laws.py. * It checks whether the other autogenerated files are up-to-date. *) theory Check_Autogenerated_Files (* These imports are not actually needed, but in jEdit, they will conveniently trigger a re-execution of the checking code below upon changes. *) imports Laws_Classical Laws_Quantum Laws_Complement_Quantum begin ML \<open> let fun check kind file expected = let val content = File.read (Path.append (Resources.master_directory \<^theory>) (Path.basic file)) val hash = SHA1.digest content |> SHA1.rep in if hash = expected then () else error (kind ^ " file " ^ file ^ " has changed.\nPlease run \"python3 instantiate_laws.py\" to recreated autogenerated files.\nExpected SHA1 hash " ^ expected ^ ", got " ^ hash) end in {hash_checks_concat} end \<close> end """ write_to_file("Check_Autogenerated_Files.thy", content) rewrite_all() create_check_theory() if had_errors: sys.exit(1)
5,785
oh/migrations/versions/11e366ca2e4f_support_default_ticket_description.py
akshitdewan/cs61a-apps
5
2172108
"""support-default-ticket-description Revision ID: 11e366ca2e4f Revises: <KEY> Create Date: 2021-02-13 05:20:07.890020 """ # revision identifiers, used by Alembic. revision = "11e366ca2e4f" down_revision = "<KEY>" from sqlalchemy import orm from alembic import op import sqlalchemy as sa import oh_queue.models from oh_queue.models import * def upgrade(): # Get alembic DB bind connection = op.get_bind() session = orm.Session(bind=connection) for course in session.query(ConfigEntry.course).distinct(): session.add( ConfigEntry( key="default_description", value="", public=True, course=course[0], ) ) session.commit() def downgrade(): # ### commands auto generated by Alembic - please adjust! ### pass # ### end Alembic commands ###
880
apps/users/task.py
Curiou/classify_email
0
2171489
# !/usr/bin/python # -*- coding: utf-8 -*- """ Author: <NAME> Contact: <EMAIL> or <EMAIL> File: task.py Time: 2020/8/23 18:07 File Intro: """ import time from celery import shared_task from celery import Celery, platforms from utils.send_msm import send_email platforms.C_FORCE_ROOT = True # 加上这一行 from django.core.mail import send_mail @shared_task def add(a, b): return a + b @shared_task def async_send_mail(*args, **kwargs): send_email(*args, **kwargs) return args
511
vertnet/parsers/base.py
rafelafrance/traiter_vertnet
0
2171054
"""Common logic for parsing trait notations.""" from typing import Callable, List from traiter.old.parser import Parser, RulesInput from vertnet.pylib.trait import Trait def fix_up_nop(trait, _): # pylint: disable=unused-argument """Fix problematic parses.""" return trait class Base(Parser): # pylint: disable=too-few-public-methods """Shared lexer logic.""" def __init__( self, rules: RulesInput, name: str = "parser", fix_up: Callable[[Trait, str], Trait] = None, ) -> None: """Build the trait parser.""" super().__init__(name=name, rules=rules) self.fix_up = fix_up if fix_up else fix_up_nop # pylint: disable=arguments-differ def parse(self, text: str, field: str = None) -> List[Trait]: """Find the traits in the text.""" traits = [] tokens = super().parse(text) for token in tokens: trait_list = token.action(token) # The action function can reject the token if not trait_list: continue # Some parses represent multiple traits, fix them all up if not isinstance(trait_list, list): trait_list = [trait_list] # Add the traits after any fix up. for trait in trait_list: trait = self.fix_up(trait, text) if trait: # The parse may fail during fix up if field: trait.field = field traits.append(trait) # from pprint import pp # pp(traits) return traits def convert(token): """Convert parsed tokens into a result.""" return Trait(value=token.group["value"].lower(), start=token.start, end=token.end)
1,782
tool/Global.py
GaoChrishao/Transformer_MT
2
2171366
# *_*coding:utf-8 *_* default_batch_size = 10 # 训练批次大小。 epochs = 20 # 训练迭代轮次。 # 是否使用GPU use_gpu = True gpu_index = 0 # 创建摸摸胸训练时,使用的GPU序号 map_gpu_index = 0 # 载入模型时,使用的GPU序号(如果设备没变,则与gpu_index一致) # encoder和decoder的层数 num_layers = 3 # 多头注意力中的头数 num_heads = 4 # 字嵌入和位置嵌入的维度 d_model = 256 embedding_dim = d_model # 全连接 d_ff = d_model * 4 # 特殊字符 char_space = ' ' char_start = '<start>' char_end = '<end>' char_unknown = '<?>' word_end = '<e>' # bpe标志 # 数据集 corpus_encoder_path = './data/de.txt' corpus_decoder_path = './data/en.txt' # bpe切分后的数据集 combined_vocab_path = './data/2k/de2en_2k_vocab.txt' # bpe字典 encoder_bpe_dic_path = './data/2k/de_2000.txt' decoder_bpe_dic_path = './data/2k/en_2000.txt' # 重新切分并预处理好的数据集 train_file_path = "./data/2k/train_2k.txt" valid_file_path = "./data/2k/valid_2k.txt" test_file_path = "./data/2k/test_2k.txt" # 训练集字典信息 data_path_vocab_desc = './data/2k/corps_2k_desc.txt' # 保存模型名称 modelName = "de2en_2k" # 模型存储路径 def modelPath(epoch): return './save/' + modelName + '_%04d' % (epoch + 1) + '.pt' # 打印训练进度 def printProgress(epoch, prog, batch_no, batch_all, batch_size, loss, accu, lr): print('\rEpoch:%04d prog:%.4f%% batch:%d/%d batch_size:%d mean_loss=%.6f mean_accu=%.2f%% lr=%.6f' % ( epoch + 1, prog, batch_no, batch_all, batch_size, loss, accu, lr), end="") # 输出参数到文件 def writeParametersToFile(n_layers, n_heads, d_model, d_ff, batch_size, encoder_len, decoder_len): progress = 'n_layers' + '%d' % (n_layers) + \ ' n_heads:%d' % (n_heads) + \ ' d_model:%d' % (d_model) + \ ' d_ff:%d' % (d_ff) + \ ' batch_size:%d' % (batch_size) + \ ' encoder_len:%d' % (encoder_len) + \ ' decoder_len:%d' % (decoder_len) + "\n" with open('./save/' + modelName + '.txt', 'a') as f: f.write('\n') f.write(progress) # 输出训练进度到文件 def writeProgreeToFile(epoch, batch_all, loss, train_accu, valid_accu, lr): progress = 'Epoch:' + '%04d' % (epoch + 1) + \ ' batch:%d' % (batch_all) + \ ' loss=' + '{:.6f}'.format(loss) + \ ' train_accu=' + '{:.6f}'.format(train_accu) + \ ' valid_accu=' + '{:.6f}'.format(valid_accu) + \ ' lr=' + '{:.6f}\n'.format(lr) with open('./save/' + modelName + '.txt', 'a') as f: f.write(progress)
2,393
leetcode/medium/Intersection_of_Two_Linked_Lists.py
shhuan/algorithms
0
2170627
# -*- coding: utf-8 -*- """ created by huash at 2015-04-12 09:37 Write a program to find the node at which the intersection of two singly linked lists begins. For example, the following two linked lists: A: a1 → a2 ↘ c1 → c2 → c3 ↗ B: b1 → b2 → b3 begin to intersect at node c1. Notes: If the two linked lists have no intersection at all, return null. The linked lists must retain their original structure after the function returns. You may assume there are no cycles anywhere in the entire linked structure. Your code should preferably run in O(n) time and use only O(1) memory. """ __author__ = 'huash' import sys import os # Definition for singly-linked list. class ListNode: def __init__(self, x): self.val = x self.next = None class Solution: # @param two ListNodes # @return the intersected ListNode def getIntersectionNode(self, headA, headB): if not headA or not headB: return None lenA = 1 ha = headA while ha.next: lenA += 1 ha = ha.next lenB = 1 hb = headB while hb.next: lenB += 1 hb = hb.next ha = headA hb = headB if lenA > lenB: for k in range(lenA-lenB): ha = ha.next elif lenA < lenB: for k in range(lenB-lenA): hb = hb.next while ha and hb and ha.val != hb.val: ha = ha.next hb = hb.next if ha: return ha return None s = Solution() l1 = ListNode('a1') l1.next = ListNode('a2') nn = l1.next nn.next = ListNode('c1') nn = nn.next nn.next = ListNode('c2') nn = nn.next nn.next = ListNode('c3') l2 = ListNode('b1') l2.next = ListNode('b2') nn = l2.next nn.next = ListNode('b3') nn = nn.next nn.next = ListNode('c1') nn = nn.next nn.next = ListNode('c2') nn = nn.next nn.next = ListNode('c3') print(s.getIntersectionNode(l1, l2))
2,040
puls/models/systems.py
za-creature/puls
1
2171151
# coding=utf-8 from __future__ import absolute_import, unicode_literals, division from puls.models.components import Component from puls.models.classes import Class, ClassField from puls.models.targets import Target from puls import app import mongoengine as mge import flask_wtf import datetime import wtforms as wtf class System(app.db.Document): # basic meta target = mge.ReferenceField(Target, required=True) budget = mge.FloatField(required=True) currency = mge.StringField(required=True) price = mge.FloatField(required=True) performance = mge.FloatField(required=True) components = mge.ListField(mge.ReferenceField(Component)) created = mge.DateTimeField(default=datetime.datetime.now)
729
Python/zzz_training_challenge/Python_Challenge/solutions/ch06_arrays/solutions/ex13_minesweeper.py
Kreijeck/learning
0
2171718
# Beispielprogramm für das Buch "Python Challenge" # # Copyright 2020 by <NAME> import random from ch06_arrays.intro.intro import print_array, get_dimension from ch06_arrays.intro.intro_directions_example import Direction def place_bombs_randomly(width, height, probability): bombs = [[False for x in range(width + 2)] for y in range(height + 2)] for y in range(1, len(bombs) - 1): for x in range(1, len(bombs[0]) - 1): bombs[y][x] = random.random() < probability return bombs def calc_bomb_count(bombs): max_y, max_x = get_dimension(bombs) bomb_count = [[0 for x in range(max_x)] for y in range(max_y)] for y in range(1, max_y - 1): for x in range(1, max_x - 1): if not bombs[y][x]: for current_dir in Direction: dx, dy = current_dir.to_dx_dy() if bombs[y + dy][x + dx]: bomb_count[y][x] += 1 else: bomb_count[y][x] = 9 return bomb_count def print_board(bombs, bomb_symbol, solution): for y in range(1, len(bombs) - 1): for x in range(1, len(bombs[0]) - 1): if bombs[y][x]: print(bomb_symbol, end=" ") elif solution is not None and len(solution) != 0: print(solution[y][x], end=" ") else: print(".", end=" ") print() print() def main(): bombs = place_bombs_randomly(10, 5, 0.5) print_board(bombs, "B", None) bomb_counts = calc_bomb_count(bombs) print_board(bombs, "B", bomb_counts) if __name__ == "__main__": main()
1,640
audacity_scripting/__init__.py
adthomas811/audacity-python-scripting
1
2170674
from datetime import datetime import logging from os import mkdir from os.path import abspath, dirname, isdir, isfile, join from time import sleep package_path = dirname(abspath(__file__)) log_dir_path = join(package_path, '_logs') LOGGER_NAME = __name__ if not isdir(log_dir_path): mkdir(log_dir_path) current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") log_file = join(log_dir_path, current_time + '.log') if isfile(log_file): sleep(1) current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") log_file = join(log_dir_path, current_time + '.log') # Create the Logger logger = logging.getLogger(LOGGER_NAME) logger.setLevel(logging.DEBUG) # Create the Handler for logging data to a file logger_handler = logging.FileHandler(log_file) logger_handler.setLevel(logging.DEBUG) # Create a Formatter for formatting the log messages logger_formatter = logging.Formatter('%(asctime)s - %(levelname)-8s - ' '%(message)s') # Add the Formatter to the Handler logger_handler.setFormatter(logger_formatter) # Add the Handler to the Logger logger.addHandler(logger_handler)
1,134
listing_app/urls.py
SenJames/Car-Listing
0
2171582
''' This is the views that links us to the main views. ''' from django.conf.urls import url from django.conf import settings from django.conf.urls.static import static from django.urls import path from . import views app_name = 'listing_app' urlpatterns= [ url(r"^home/", views.index, name='home'), url(r"^list/$", views.car_view, name='cars_list'), url(r"^grid/$", views.car_grid, name='cars_grid'), url(r"^list-page/$", views.car_page, name='cars_page'), url(r"^grid-page/$", views.car_page_grid, name='cars_page_grid'), url(r"^list-price/$", views.car_price_list, name='cars_price_list'), url(r"^grid-price/$", views.car_price_grid, name='cars_price_grid'), path('car_detail/<int:car_id>/', views.car_detailed, name='detail'), # path('car_detail/review/<int:car_id>/', views.review, name='detail') path("edit/<int:user_id>/", views.edit_dash, name="edit"), path("order/<int:user_id>", views.createOrder, name="create_order"), path("update_order/<str:pk>", views.updateOrder, name="update_order"), path("delete_order/<int:pk>", views.deleteOrder, name="delete_order"), path("order-page/<int:user_id>", views.orderPage, name="order_page"), path("register/", views.register, name="register"), path("dealer_register/", views.registerDealers, name="dealer_reg"), path("login/", views.login, name="login"), path("logout/", views.logout, name="logout"), path("contact/", views.contact_us, name="contact-us"), path("about-us/", views.about_us, name="about-us"), path("compare/", views.compareCar, name="compare"), # path("blog/", views.blog, name="blog"), path("blog-detail/", views.blog_detail, name="blog-detail"), ]+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
1,781
grafana/common/dashboards/aggregated/qtype_vs_tld.py
MikeAT/visualizer
6
2171987
# Copyright 2021 Internet Corporation for Assigned Names and Numbers. # # This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this # file, you can obtain one at https://mozilla.org/MPL/2.0/. # # Developed by Sinodun IT (sinodun.com) # # Aggregation QTYPE vs TLD plots import textwrap import grafanalib.core as GCore import grafanacommon as GCommon def dash(myuid, agginfo, nodesel, **kwargs): return GCommon.Dashboard( title = "QTYPE vs TLD", tags = [ agginfo['graph_tag'] ], uid = myuid, rows = [ GCore.Row( height = GCore.Pixels(GCore.DEFAULT_ROW_HEIGHT.num * 2), panels = [ GCommon.BarChart( title = 'QTYPE for most popular Undelegated TLDs queried', orientation = GCommon.BAR_CHART_ORIENTATION_HORIZONTAL, layout = GCommon.BarChartLayout( barmode = GCommon.BAR_CHART_LAYOUT_MODE_STACK, showlegend = True, xaxis = GCommon.BarChartAxis( title = 'Queries per second', ), yaxis = GCommon.BarChartAxis( autotick = False, axtype = GCommon.BAR_CHART_AXIS_TYPE_CATEGORY, tickmargin = 90, ), ), autotrace = True, targets = [ GCommon.ClickHouseTableTarget( database = agginfo['database'], table = 'QtypeUndelegatedTld' + agginfo['table_suffix'], round = agginfo['round'], query = textwrap.dedent("""\ SELECT notEmpty(QType) ? QType : concat('TYPE', toString(QueryType)) AS DisplayQType, sum(Cnt) / ($to - $from) AS TldCnt, empty(Tld) ? '"."' : (isValidUTF8(Tld) ? Tld : base64Encode(Tld)) AS DisplayTld FROM ( SELECT Tld, QueryType, sum(Cnt) AS Cnt, any(TotalCnt) AS TotalCnt FROM ( SELECT Tld, sum(toUInt64(Count)) AS TotalCnt FROM $table WHERE $timeFilter AND NodeID IN {nodesel} GROUP BY Tld ORDER BY TotalCnt DESC, Tld ASC LIMIT 40 ) AS TldCount ALL LEFT JOIN ( SELECT Tld, QueryType, sum(toUInt64(Count)) AS Cnt FROM $table WHERE $timeFilter AND NodeID IN {nodesel} GROUP BY Tld, QueryType UNION ALL ( SELECT Tld, QueryType, CAST(0 AS UInt64) AS Cnt FROM ( SELECT 0 AS Zero, Tld FROM $table WHERE $timeFilter AND NodeID IN {nodesel} GROUP BY Tld ) AS ZeroTld ALL LEFT JOIN ( SELECT 0 AS Zero, QueryType FROM $table WHERE $timeFilter AND NodeID IN {nodesel} GROUP BY QueryType ) AS ZeroTYpe USING Zero ) ) AS TldQTypeCounts USING Tld GROUP BY Tld, QueryType ) AS TldQTypeCountsTotal ALL INNER JOIN ( SELECT value_name AS QType, toUInt16(value) AS QueryType FROM {nodeinfo_database}.iana_text WHERE registry_name = 'QTYPE' ) AS QTypeName USING QueryType GROUP BY Tld, QueryType, QType ORDER BY sum(TotalCnt) ASC, DisplayQType, Tld DESC""".format( nodesel=nodesel, nodeinfo_database=agginfo['nodeinfo_database'])), refId = 'A' ) ], ), ], ), ] )
7,320
lib/Pentest.py
PinkRoccade-Local-Government-OSS/PinkWave
1
2170787
#!/usr/bin/python """ Create a Pentest object to start defined exploit(s) and automaticly log found exploits and tests per host. """ import time,colors,sys from os.path import dirname from Report import Report from Macro import Macro from PyExploit import PyExploit from ShellParse import ShellParse # Import PinkWave extensions appDir = dirname(dirname(__file__ )) sys.path.append(appDir) from extensions.Util import Util,vdkException class Pentest: def __init__(self): self.browser = None # Copy from ShellParse self.target = None self.requestNames = [] self.request = None self.exploits = None self.macros = [] self.creds = [] self.ports = [] self.ssl = None self.reportId = 0 self.wordlist = None def parameters(self): parameters = [] if self.request != "get": parameters.append("--request=%s" % self.request) if self.requestNames is not None: parameters.append("--requestNames=:%s" % ";".join(self.requestNames)) if len(self.macros) != 0: parameters.append("--macros=%s" % ";".join(self.macros)) if self.creds is not None: parameters.append("--creds=%s" % ";".join(self.creds)) if self.ports is not None: parameters.append("--ports=%s" % ";".join(self.ports)) if self.ssl != "443": parameters.append("--ssl=%s" % self.ssl) if self.wordlist is not None: parameters.append("--wordlist=%s" % self.wordlist) return "-".join(parameters) """ Create a Pentest object with variables """ def create(self, browser, shellParse): self.browser = browser for key,value in shellParse.propsValue().iteritems(): setattr(self,key,value) if self.request is None: self.request = "get" if self.target is not None and "http://" not in self.target and "https://" not in self.target: self.target = "http://" + self.target if self.ssl is not None: self.ssl = str(self.ssl) return self """ Execute Pentest, save found exploits to csv """ def start(self): # Allow execution of single macro if len(self.exploits) == 0: for m in self.macros: ma = Macro().start(m,self.browser) if self.target is None: return Util.createDir(Util.getReportDir(self.target)) pyExploit = PyExploit(self.exploits) for m in self.macros: ma = Macro().start(m,self.browser) timeStart = time.time() try: pyExploit.start(self) timeEnd = time.time() print "[^] No Exploit detected..." r = Report(pyExploit, "OK",(timeEnd-timeStart)) r.export() except vdkException as ex: timeEnd = time.time() if "_potential" in str(type(ex)): print "\033[1;91m[?] Potential Exploit detected! (%s)\033[1;m" % pyExploit.exploitPath else: print "\033[1;91m[!] Exploit detected! (%s)\033[1;m" % pyExploit.exploitPath r = Report(pyExploit, ex.message,(timeEnd-timeStart)) r.export() raise
3,318
client.py
ray-project/redis-replication
0
2171819
import redis import random import time client1 = redis.StrictRedis(port=6379) client2 = redis.StrictRedis(port=6380) client1.execute_command("flushall") client1.execute_command("replication.ready") written1 = 0 written2 = 0 t = time.time() for i in range(100000): a = random.randint(0, 100000) b = random.randint(0, 100000) try: client1.execute_command("replication.write", str(a), str(b)) written1 += 1 except: pass try: client2.execute_command("replication.write", str(a), str(b)) written2 += 1 except: pass if i % 10 == 0: if time.time() > t + 1.0: t = time.time() print("written1: {}, written2: {}".format(written1, written2))
745
locale/pot/api/core/_autosummary/pyvista-Light-positional-1.py
tkoyama010/pyvista-doc-translations
4
2171853
# Create a spotlight shining on the origin. # import pyvista as pv light = pv.Light(position=(1, 1, 1)) light.positional = True light.cone_angle = 30
150
clip/model_zoo.py
ttlmh/Bridge-Prompt
2
2171914
import os def get_model_path(ckpt): if os.path.isfile(ckpt): return ckpt else: print('not found pretrained model in {}'.format(ckpt)) raise FileNotFoundError
190
src/proposals/management/commands/loadproposals.py
kaka-lin/pycon.tw
47
2171930
import json from django.apps import apps from django.contrib.auth import get_user_model from django.core.management.base import BaseCommand User = get_user_model() class Command(BaseCommand): help = 'Load talk proposals from data dumped by `manage.py dumpdata`.' def add_arguments(self, parser): parser.add_argument('filename', help='Name of file to load data from') def handle(self, *args, filename, **options): with open(filename) as f: data = json.load(f) for dataset in data: model = apps.get_model(*dataset['model'].split('.')) fields = dataset.pop('fields', {}) submitter = fields.pop('submitter', None) if submitter is not None: try: submitter = User.objects.get(pk=submitter) except User.DoesNotExist: submitter = User.objects.first() fields['submitter'] = submitter model.objects.update_or_create(fields, pk=dataset['pk'])
1,032
src/water_management/exceptionclass.py
viswan29/watermanagement
0
2171101
""" Exception class to raise exception """ class NotAllowed(Exception): def __init__(self, m): self.message = m def __str__(self): return self.message
181
test_civic_jabber_ingest/models/test_article.py
civic-jabber/data-ingest
0
2170470
import datetime from civic_jabber_ingest.models.article import Article MOCK_ARTICLE = { "id": "1", "extraction_date": datetime.datetime(2020, 6, 8), "source_id": "4", "source_name": "Parrot News", "source_brand": "Parrot News", "source_description": "A news source for parrots", "title": "You'll never believe the size of these parrots!", "body": "Parrts are big and parrts are tall!", "summary": "Parrts are big and parrts are tall!", "keywords": ["big", "parrot"], "images": ["big_bird.jpg", "little_bird.jpg"], "url": "https://www.birds.com/news/big-birds", } def test_article_loads_from_dict(): article = Article.from_dict(MOCK_ARTICLE) assert article.to_dict() == MOCK_ARTICLE
743
cico_common.py
imapex/CICO
0
2172202
import os meraki_api_token = os.getenv("MERAKI_API_TOKEN") meraki_org = os.getenv("MERAKI_ORG") spark_api_token = os.getenv("SPARK_API_TOKEN") s3_bucket = os.getenv("S3_BUCKET") s3_key = os.getenv("S3_ACCESS_KEY_ID") s3_secret = os.getenv("S3_SECRET_ACCESS_KEY") def meraki_support(): if meraki_api_token and meraki_org: return True else: return False def spark_call_support(): if spark_api_token: return True else: return False def umbrella_support(): if s3_bucket and s3_key and s3_secret: return True else: return False
601
lecture.py
RaoulChartreuse/pycin-
1
2171896
import numpy as np import matplotlib.pyplot as plt import argparse # l'argument parser ap = argparse.ArgumentParser() ap.add_argument("-i", "--input", required = True, help = "Chemin vers le fichier video") args = ap.parse_args() filename= args.input #r=np.load(filename) #Ecriture de la version c++ r=np.fromfile(filename,sep=" ") x=np.arange(r.size) print r,x fig, ax = plt.subplots(figsize=(8, 4)) ax.hist(r[~np.isnan(r)],200,histtype='step') ax.set_yscale("log", nonposy='clip') plt.show()
546
siptrackd_twisted/attribute.py
sii/siptrackd
0
2171840
from twisted.web import xmlrpc from twisted.internet import defer import xmlrpclib from siptrackdlib import attribute import siptrackdlib.errors from siptrackd_twisted import helpers from siptrackd_twisted import gatherer from siptrackd_twisted import baserpc import siptrackd_twisted.errors class AttributeRPC(baserpc.BaseRPC): node_type = 'attribute' @helpers.ValidateSession() @defer.inlineCallbacks def xmlrpc_add(self, session, parent_oid, name, atype, value): """Create a new attribute.""" parent = self.object_store.getOID(parent_oid, user = session.user) # Binary data is converted into xmlrpclib.Binary objects. If this is # a binary attribute, make sure we received an xmlrpclib.Binary object # and extract the data. if atype == 'binary': try: value = str(value) except: raise siptrackdlib.errors.SiptrackError('attribute value doesn\'t match type') obj = parent.add(session.user, 'attribute', name, atype, value) yield self.object_store.commit(obj) defer.returnValue(obj.oid) @helpers.ValidateSession() @defer.inlineCallbacks def xmlrpc_set_value(self, session, oid, value): """Set an existing attributes value.""" attribute = self.getOID(session, oid) # Binary data is converted into xmlrpclib.Binary objects. If this is # a binary attribute, make sure we received an xmlrpclib.Binary object # and extract the data. if attribute.atype == 'binary': try: value = str(value) except: raise siptrackdlib.errors.SiptrackError('attribute value doesn\'t match type') attribute.value = value yield self.object_store.commit(attribute) defer.returnValue(True) class VersionedAttributeRPC(baserpc.BaseRPC): node_type = 'versioned attribute' @helpers.ValidateSession() @defer.inlineCallbacks def xmlrpc_add(self, session, parent_oid, name, atype, max_versions, value = None): """Create a new versioned attribute.""" parent = self.object_store.getOID(parent_oid, user = session.user) # Binary data is converted into xmlrpclib.Binary objects. If this is # a binary attribute, make sure we received an xmlrpclib.Binary object # and extract the data. if atype == 'binary': try: value = value.data except: raise siptrackdlib.errors.SiptrackError('attribute value doesn\'t match type') obj = parent.add(session.user, 'versioned attribute', name, atype, value, max_versions) yield self.object_store.commit(obj) defer.returnValue(obj.oid) @helpers.ValidateSession() @defer.inlineCallbacks def xmlrpc_set_value(self, session, oid, value): """Set an existing attributes value.""" attribute = self.getOID(session, oid) # Binary data is converted into xmlrpclib.Binary objects. If this is # a binary attribute, make sure we received an xmlrpclib.Binary object # and extract the data. if attribute.atype == 'binary': try: value = value.data except: raise siptrackdlib.errors.SiptrackError('attribute value doesn\'t match type') attribute.value = value yield self.object_store.commit(attribute) defer.returnValue(True) @helpers.ValidateSession() @defer.inlineCallbacks def xmlrpc_set_max_versions(self, session, oid, max_versions): """Set an existing attributes value.""" attribute = self.getOID(session, oid) attribute.max_versions = max_versions yield self.object_store.commit(attribute) defer.returnValue(True) class EncryptedAttributeRPC(baserpc.BaseRPC): node_type = 'encrypted attribute' @helpers.ValidateSession() @defer.inlineCallbacks def xmlrpc_add(self, session, parent_oid, name, atype, value): """Create a new attribute.""" parent = self.object_store.getOID(parent_oid, user = session.user) obj = parent.add(session.user, 'encrypted attribute', name, atype, value) yield self.object_store.commit(obj) defer.returnValue(obj.oid) @helpers.ValidateSession() @defer.inlineCallbacks def xmlrpc_set_value(self, session, oid, value): """Set an existing attributes value.""" attribute = self.getOID(session, oid) attribute.value = value yield self.object_store.commit(attribute) defer.returnValue(True) def encrypted_attribute_data_extractor(node, user): # errors.SiptrackError is raised by attribute.getPassword often when # searching objects or listing objects with enca attributes connected # to keys that the current user does not have access to. So default to # showing a blank attribute. # # TODO: Some indication that the user lacks password key access to show # the encrypted attribute. Or solve it in siptrackweb by simply showing # the password key each attribute is connected to along with the blank # attribute value. try: value = node.getAttribute(user) except Exception as e: value = '' pass return [node.name, node.atype, value] def attribute_data_extractor(node, user): value = node.value # Binary data needs to be wrapped in xmlrpclib.Binary. # if node.atype == 'binary': # value = xmlrpclib.Binary(value) return [node.name, node.atype, value] def versioned_attribute_data_extractor(node, user): values = node.values # Binary data needs to be wrapped in xmlrpclib.Binary. if node.atype == 'binary': values = [xmlrpclib.Binary(value) for value in node.values] return [node.name, node.atype, values, node.max_versions] gatherer.node_data_registry.register(attribute.Attribute, attribute_data_extractor) gatherer.node_data_registry.register(attribute.VersionedAttribute, versioned_attribute_data_extractor) gatherer.node_data_registry.register( attribute.EncryptedAttribute, encrypted_attribute_data_extractor )
6,211
src/boptx/algorithms/__init__.py
sebhoerl/boptx
1
2171796
from .uniform import UniformAlgorithm from .fdsa import FDSAAlgorithm from .spsa import SPSAAlgorithm from .es import EvolutionarySearchAlgorithm from .de import DifferentialEvolutionAlgorithm from .nes import XNESAlgorithm from .nes import ElitistXNESAlgorithm from .nelder_mead import NelderMeadAlgorithm from .cmaes import CMAESAlgorithm from .sensitivity import SensitivityAlgorithm from .opdyts import OpdytsAlgorithm from .cmaese import CMAES1P1Algorithm
461
l1t_cli/commands/dqm/gui/start/__init__.py
kreczko/l1t-cli
0
2170736
""" dqm gui start: starts the offline DQM GUI on port 8060 Usage: dqm gui start """ import logging import os import hepshell from hepshell.interpreter import time_function LOG = logging.getLogger(__name__) from l1t_cli.commands.dqm.gui.setup import DQM_GUI_PATH class Command(hepshell.Command): def __init__(self, path=__file__, doc=__doc__): super(Command, self).__init__(path, doc) @time_function('dqm gui start', LOG) def run(self, args, variables): self.__prepare(args, variables) commands = [ 'cd {DQM_GUI_PATH}', 'source current/apps/dqmgui/128/etc/profile.d/env.sh', '$PWD/current/config/dqmgui/manage -f dev start "I did read documentation"' ] all_in_one = ' && '.join(commands) all_in_one = all_in_one.format(DQM_GUI_PATH = DQM_GUI_PATH) from hepshell.interpreter import call code, _, stderr = call(all_in_one, logger=LOG, shell = True) if not code == 0: msg = 'Could not start DQM GUI: {0}'.format(stderr) LOG.error(msg) return False self.__text = 'DQM GUI now available at http://localhost:8060/dqm/dev' return True def __can_run(self): # if DQMOffline exists return True
1,345
network_class.py
MagicGrapes/SmithChartPy
0
2171699
""" Author: <NAME> October 29, 2016 Description: Classes and functions to perform basic network abstraction and plotting """ from numpy import pi as pi class network(object): """Class for one dimension network (i.e. a matching network).""" element_array=[] def __init__(self): self.ZP2=lambda freq: 50.0 #Network termination on output self.ZP1=lambda freq: 50.0 #Network termination on output def compute_node_impedances(self,freq): """Calculate impedances at each node walking back from the output impedance, Zp2""" Zarr=[self.ZP2(freq)] for elem in self.element_array: if elem.orientation==0: #series Zarr.append(Zarr[-1]+elem.Z(freq)) elif elem.orientation==1: #shunt Zarr.append(1.0/(1.0/Zarr[-1]+elem.Y(freq))) #Zarr.reverse() return Zarr def print_net(self): for elem in self.element_array: print(elem.name, elem.val) def move_element(self,n_a,n_b): """ Moves element to new index shifting other elements accordingly. Simplies drag-drop action of components """ self.element_array.insert(n_b,self.element_array.pop(n_a)) class element(object): """Class for a single impedance/admittance element (i.e. capacitor, indcutor, etc.).""" def __init__(self,*args,**kargs): self.name='' self.icon='' self.orientation=0 self.default='' if 'shunt'in kargs: self.orientation=kargs['shunt'] # 0: Series, 1:Shunt self.val={} self.Zfunc=lambda self,x: 1e-14 #function to define series impedance self.Yfunc=lambda self,x: 1e14 #function to define admittance # def __setval__(self,val): # self.val=val def Z(self,freq): return self.Zfunc(self,freq) def Y(self,freq): return self.Yfunc(self,freq) def set_val(self,val,**kargs): self.val[self.default]=val #Populate self.val with kargs dictionary at later date class cap(element): """Modification of element class to model an ideal capacitor""" def __init__(self,*args,**kargs): element.__init__(self,*args,**kargs) self.name='cap' self.default='C' if 'min' in kargs: self.val['min']=kargs['min'] else: self.val['min']=1e-12 if 'max' in kargs: self.val['max']=kargs['max'] else: self.val['max']=12.1e-12 if 'step' in kargs: self.val['step']=kargs['step'] else: self.val['step']=0.1e-12 self.val['unit']='pF' #Unit not used to scale value variable if len(args)!=1: print("ERROR: cap(element) requires 1 argument") else: self.val[self.default]=args[0] #self.Zfunc=lambda self,freq: 1j/(2*pi*freq*self.val['C']) #function to define series impedance self.Yfunc=lambda self,freq: (1j*2*pi*freq*self.val['C']) #function to define admittance self.Zfunc=lambda self,freq: 1.0/self.Yfunc(self,freq) class ind(element): """Modification of element class to model an ideal capacitor""" def __init__(self,*args,**kargs): element.__init__(self,*args,**kargs) self.name='ind' self.default='L' if 'min' in kargs: self.val['min']=kargs['min'] else: self.val['min']=1e-9 if 'max' in kargs: self.val['max']=kargs['max'] else: self.val['max']=12.1e-9 if 'step' in kargs: self.val['step']=kargs['step'] else: self.val['step']=0.1e-9 self.val['unit']='nH' #Unit not used to scale value variable if len(args)!=1: print("ERROR: ind(element) requires 1 argument") else: self.val['L']=args[0] self.Zfunc=lambda self,freq: 1j*2*pi*freq*self.val['L'] #function to define series impedance self.Yfunc=lambda self,freq: 1.0/self.Zfunc(self,freq) class indQ(element): """Modification of element class to model an capacitor with a fixed Q""" def __init__(self,*args,**kargs): element.__init__(self) self.name='indQ' if len(args)!=2: print("ERROR: indQ(element) requires 2 arguments") else: self.val['L']=args[0] self.val['Q']=args[1] #function to define series impedance self.Zfunc=lambda self,freq: 2*pi*freq*self.val['L']/self.val['Q']+1j*2*pi*freq*self.val['L'] #function to define admittance self.Yfunc=lambda self,freq: 1.0j/self.Zfunc(self,x) class capQ(element): """Modification of element class to model an capacitor with a fixed L""" def __init__(self,*args,**kargs): element.__init__(self) self.name='capQ' if len(args)!=2: print("ERROR: capQ(element) requires 2 arguments") else: self.val['C']=args[0] self.val['Q']=args[1] #function to define series impedance self.Zfunc=lambda self,freq: (1.0/(2*pi*freq*self.val['C']))/self.val['Q']+1.0j/(2*pi*freq*self.val['C']) #function to define admittance self.Yfunc=lambda self,freq: 1.0/self.Zfunc(self,x) if __name__=='__main__': net=network() #TEST CASE -- matching 50.0 Ohms to ~5.0 Ohms at 2 GHz L1=ind(0.75e-9) C1=cap(6.3e-12,shunt=1) L2=ind(2.0e-9) C2=cap(1.6e-12,shunt=1) net.element_array.append(C2) net.element_array.append(L2) net.element_array.append(C1) net.element_array.append(L1) print(net.compute_node_impedances(2.0e9))
5,807
IRIS_data_download/IRIS_download_support/obspy/io/reftek/__init__.py
earthinversion/Fnet_IRIS_data_automated_download
2
2171919
# -*- coding: utf-8 -*- """ obspy.io.reftek - REFTEK130 read support for ObsPy ================================================== This module provides read support for the RefTek 130 data format. Currently the low level read routines are designed to operate on waveform files written by RefTek 130 digitizers which are composed of event header/trailer and data packages. These packages do not store information on network or location code during acquisition. Furthermore, it is unclear how consistently the level of detail on the recorded channel codes is set in the headers (real world test data at hand recorded with a Reftek 130 do contain the information on the first two channel code characters, the band and instrument code but lack information on the component codes, i.e. ZNE, which with high likelihood were set in the acquisition parameters). Therefore, additional information on network and location codes should be supplied and ideally component codes should be supplied as well when reading files with :func:`~obspy.core.stream.read` (or should be filled in manually after reading). See the low-level routine :func:`obspy.io.reftek.core._read_reftek130` for additional arguments that can be supplied to :func:`~obspy.core.stream.read`. Currently, only event header/trailer (EH/ET) and data packets (DT) are implemented and any other packets will be ignored (a warning is shown if any other packets are encountered during reading). So far, only data encoding "C0" (STEIM 1 compressed data) is implemented due to the lack of test data in other encodings. Reading ------- Reading Reftek130 data is handled by using ObsPy's standard :func:`~obspy.core.stream.read` function. The format is detected automatically and optionally can be explicitly set if known beforehand to skip format detection. >>> from obspy import read >>> st = read("/path/to/225051000_00008656") # doctest: +SKIP >>> st # doctest: +SKIP <obspy.core.stream.Stream object at 0x...> >>> print(st) # doctest: +SKIP 8 Trace(s) in Stream: .KW1..EH0 | 2015-10-09T22:50:51.000000Z - ... | 200.0 Hz, 3165 samples .KW1..EH0 | 2015-10-09T22:51:06.215000Z - ... | 200.0 Hz, 892 samples .KW1..EH0 | 2015-10-09T22:51:11.675000Z - ... | 200.0 Hz, 2743 samples .KW1..EH1 | 2015-10-09T22:50:51.000000Z - ... | 200.0 Hz, 3107 samples .KW1..EH1 | 2015-10-09T22:51:05.925000Z - ... | 200.0 Hz, 768 samples .KW1..EH1 | 2015-10-09T22:51:10.765000Z - ... | 200.0 Hz, 2925 samples .KW1..EH2 | 2015-10-09T22:50:51.000000Z - ... | 200.0 Hz, 3405 samples .KW1..EH2 | 2015-10-09T22:51:08.415000Z - ... | 200.0 Hz, 3395 samples Network, location and component codes can be specified during reading: >>> st = read("/path/to/225051000_00008656", network="BW", location="", ... component_codes="ZNE") >>> st # doctest: +ELLIPSIS <obspy.core.stream.Stream object at 0x...> >>> print(st) # doctest: +ELLIPSIS 8 Trace(s) in Stream: BW.KW1..EHE | 2015-10-09T22:50:51.000000Z - ... | 200.0 Hz, 3405 samples BW.KW1..EHE | 2015-10-09T22:51:08.415000Z - ... | 200.0 Hz, 3395 samples BW.KW1..EHN | 2015-10-09T22:50:51.000000Z - ... | 200.0 Hz, 3107 samples BW.KW1..EHN | 2015-10-09T22:51:05.925000Z - ... | 200.0 Hz, 768 samples BW.KW1..EHN | 2015-10-09T22:51:10.765000Z - ... | 200.0 Hz, 2925 samples BW.KW1..EHZ | 2015-10-09T22:50:51.000000Z - ... | 200.0 Hz, 3165 samples BW.KW1..EHZ | 2015-10-09T22:51:06.215000Z - ... | 200.0 Hz, 892 samples BW.KW1..EHZ | 2015-10-09T22:51:11.675000Z - ... | 200.0 Hz, 2743 samples Reftek 130 specific metadata (from event header packet) is stored in ``stats.reftek130``. >>> print(st[0].stats) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE network: BW station: KW1 location: channel: EHE starttime: 2015-10-09T22:50:51.000000Z endtime: 2015-10-09T22:51:08.020000Z sampling_rate: 200.0 delta: 0.005 npts: 3405 calib: 1.0 _format: REFTEK130 reftek130: ... Details on the individual packets can be retrieved with the low level :class:`~obspy.io.reftek.core.Reftek130` object: >>> from obspy.core.util.base import get_example_file >>> from obspy.io.reftek.core import Reftek130 >>> rt = Reftek130.from_file(get_example_file("225051000_00008656")) >>> print(rt) # doctest: +ELLIPSIS Reftek130 (29 packets, file: ...225051000_00008656) Packet Sequence Byte Count Data Fmt Sampling Rate Time | Packet Type | Event # | Station | Channel # | | | Unit ID | | Data Stream # | | # of samples | | | | Exper.# | | | | | | | | 0000 EH AE4C 0 416 427 0 C0 KW1 200 2015-10-09T22:50:51.000000Z 0001 DT AE4C 0 1024 427 0 C0 0 549 2015-10-09T22:50:51.000000Z 0002 DT AE4C 0 1024 427 0 C0 1 447 2015-10-09T22:50:51.000000Z 0003 DT AE4C 0 1024 427 0 C0 2 805 2015-10-09T22:50:51.000000Z 0004 DT AE4C 0 1024 427 0 C0 0 876 2015-10-09T22:50:53.745000Z 0005 DT AE4C 0 1024 427 0 C0 1 482 2015-10-09T22:50:53.235000Z 0006 DT AE4C 0 1024 427 0 C0 1 618 2015-10-09T22:50:55.645000Z 0007 DT AE4C 0 1024 427 0 C0 2 872 2015-10-09T22:50:55.025000Z 0008 DT AE4C 0 1024 427 0 C0 0 892 2015-10-09T22:50:58.125000Z 0009 DT AE4C 0 1024 427 0 C0 1 770 2015-10-09T22:50:58.735000Z 0010 DT AE4C 0 1024 427 0 C0 2 884 2015-10-09T22:50:59.385000Z 0011 DT AE4C 0 1024 427 0 C0 0 848 2015-10-09T22:51:02.585000Z 0012 DT AE4C 0 1024 427 0 C0 1 790 2015-10-09T22:51:02.585000Z 0013 DT AE4C 0 1024 427 0 C0 2 844 2015-10-09T22:51:03.805000Z 0014 DT AE4C 0 1024 427 0 C0 0 892 2015-10-09T22:51:06.215000Z 0015 DT AE4C 0 1024 427 0 C0 1 768 2015-10-09T22:51:05.925000Z 0016 DT AE4C 0 1024 427 0 C0 2 884 2015-10-09T22:51:08.415000Z 0017 DT AE4C 0 1024 427 0 C0 1 778 2015-10-09T22:51:10.765000Z 0018 DT AE4C 0 1024 427 0 C0 0 892 2015-10-09T22:51:11.675000Z 0019 DT AE4C 0 1024 427 0 C0 2 892 2015-10-09T22:51:12.835000Z 0020 DT AE4C 0 1024 427 0 C0 1 736 2015-10-09T22:51:14.655000Z 0021 DT AE4C 0 1024 427 0 C0 0 892 2015-10-09T22:51:16.135000Z 0022 DT AE4C 0 1024 427 0 C0 2 860 2015-10-09T22:51:17.295000Z 0023 DT AE4C 0 1024 427 0 C0 1 738 2015-10-09T22:51:18.335000Z 0024 DT AE4C 0 1024 427 0 C0 0 892 2015-10-09T22:51:20.595000Z 0025 DT AE4C 0 1024 427 0 C0 1 673 2015-10-09T22:51:22.025000Z 0026 DT AE4C 0 1024 427 0 C0 2 759 2015-10-09T22:51:21.595000Z 0027 DT AE4C 0 1024 427 0 C0 0 67 2015-10-09T22:51:25.055000Z 0028 ET AE4C 0 416 427 0 C0 KW1 200 2015-10-09T22:50:51.000000Z (detailed packet information with: 'print(Reftek130.__str__(compact=False))') >>> print(rt.__str__(compact=False)) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS Reftek130 (29 packets, file: ...225051000_00008656) EH Packet packet_sequence: 0 experiment_number: 0 unit_id: AE4C byte_count: 416 time: 2015-10-09T22:50:51.000000Z event_number: 427 data_stream_number: 0 data_format: C0 flags: 0 -------------------- _reserved_2: _reserved_3: ... detrigger_time: None digital_filter_list: first_sample_time: 2015-10-09T22:50:51.000000Z last_sample_time: None position: ... sampling_rate: 200 station_channel_number: (None, None, None, None, None, None, None, ...) station_comment: STATION COMMENT station_name: KW1 station_name_extension: stream_name: EH time_quality: ? time_source: 1 total_installed_channels: 3 trigger_time: 2015-10-09T22:50:51.000000Z trigger_time_message: Trigger Time = 2015282225051000 <BLANKLINE> trigger_type: CON DT Packet packet_sequence: 1 experiment_number: 0 unit_id: AE4C byte_count: 1024 time: 2015-10-09T22:50:51.000000Z event_number: 427 data_stream_number: 0 channel_number: 0 number_of_samples: 549 data_format: C0 flags: 0 DT Packet packet_sequence: 2 experiment_number: 0 unit_id: AE4C byte_count: 1024 time: 2015-10-09T22:50:51.000000Z event_number: 427 data_stream_number: 0 channel_number: 1 number_of_samples: 447 data_format: C0 flags: 0 ... :copyright: The ObsPy Development Team (<EMAIL>) :license: GNU Lesser General Public License, Version 3 (https://www.gnu.org/copyleft/lesser.html) """ from __future__ import (absolute_import, division, print_function, unicode_literals) from future.builtins import * # NOQA if __name__ == '__main__': import doctest doctest.testmod(exclude_empty=True)
8,868
app_gerenciador_de_livros/migrations/0004_alter_livros_estrelas.py
raphaelaferraz/book_manager
1
2170058
# Generated by Django 4.0.1 on 2022-01-07 23:45 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('app_gerenciador_de_livros', '0003_alter_livros_estrelas'), ] operations = [ migrations.AlterField( model_name='livros', name='estrelas', field=models.IntegerField(), ), ]
399
utils/hazmat/structs/weakref.py
thatbirdguythatuknownot/pyutils
3
2171488
import weakref import ctypes from .base import Struct from .common import PyObject, PyObject_p, Py_hash_t, update_types #https://github.com/python/cpython/blob/master/Include/weakrefobject.h class PyWeakReference(Struct): @property def value(self): return self.wr_object PyWeakReference._fields_ = [ #pylint: disable=protected-access ("ob_base", PyObject), ("wr_object", PyObject_p), ("wr_callback", PyObject_p), ("hash", Py_hash_t), ("wr_prev", ctypes.POINTER(PyWeakReference)), ("wr_next", ctypes.POINTER(PyWeakReference)) ] update_types({weakref.ref: PyWeakReference})
582
Python/DNA_manipulation/Mutation.py
LilyYC/legendary-train
0
2170211
def correct_mutations(strands, clean, names, sequences): """ (list of str, str, list of str, list of str) -> NoneType Precondition: strands and clean only contain characters in 'A', 'T', 'C' or 'G'. clean contains exactly one 1-cutter from names and sequences. sequences is the corresponding list of recognition sequences of names. len(names) == len(sequences). Modify strands by replacing all bases starting at the 1-cutter in strands with all bases starting at the 1-cutter in clean. >>> correct_mutations(['CCCAGCTGGG', 'CGTTTTTAAAAA'], 'AGAGCTTTT', ['AluI', 'BamHI'], ['AGCT', 'GGATCC']) >>> strands ['CCCAGCTTTT', 'CGTTTTTAAAAA'] >>> correct_mutations(['AAGGCCCCCGGG', 'TTGGCCGGCC'], 'TAGGCCAA', ['HaeIII', 'SmaI'], ['GGCC', 'CCCGGG']) >>> strands ['AAGGCCAA', 'TTGGCCGGCC'] """ result = [] for item in sequences: for ch in strands: if item in clean and ch.count(item) == 1: a = ch[0:ch.find(item)] + clean[clean.find(item):] result.append(a) else: result.append(ch) if _name_=='_main_': import docttest docttest.testmod()
1,233
crypt/DH.py
galicea/eduprog
0
2171086
#!/usr/bin/env python """ Diffie-Hellman Key Demo in Python author: <NAME>, Galicea License: GNU General Public License <http://www.gnu.org/licenses/>. pip install primesieve pip install numpy """ import numpy as np import primesieve from random import randint def generate_primes(n1,n2): return primesieve.primes(n1, n2) def int_from_bytes(n): try: # python 3 int.from_bytes(n, byteorder="big") except: import struct format = 'Q' * (len(n) / 8) return struct.unpack(format, n)[0] class DiffieHellman(object): """ Demo implementation of the Diffie-Hellman protocol. """ def __init__(self, generator=2, group=None, keyBytes=72): """ Generate group and keys. """ if group: self.group=group else: self.group = self.getPrime() self.generator = generator self.privateKey = self.genPrivateKey(keyBytes) self.publicKey = self.genPublicKey() def genPrivateKey(self, size): """ Private Key = random with the specified number of bits (size*8) http://stefanocappellini.com/generate-pseudorandom-bytes-with-python/ """ return np.random.bytes(size) def genPublicKey(self): """ key = generator ** privateKey % group. """ return pow(self.generator, int_from_bytes(self.privateKey), self.group) def sharedSecret(self, otherKey): """ sharedSecret = otherKey ** privateKey % group """ return pow(otherKey, int_from_bytes(self.privateKey), self.group) def getPrime(self, min=1000): """ Eratosthenes sieve http://code.activestate.com/recipes/117119/ https://github.com/hickford/primesieve-python """ prime_list = generate_primes(min, min+100) while (not prime_list): prime_list = generate_primes(min, min+100) n=randint(0, len(prime_list)-1) return prime_list[n] if __name__=="__main__": first = DiffieHellman() # generator and group as public print "generator=%s, group=%s" % (first.generator, first.group) two = DiffieHellman(generator=first.generator, group=first.group) print 'shared secret [1] = %s' % first.sharedSecret(two.publicKey) print 'shared secret [2] = %s' % two.sharedSecret(first.publicKey)
2,098
src/content.py
tensor-nsk-lesson/Social_network
0
2170505
from connect import connect def get_content_by_type(status): conn = connect() cur = conn.cursor() cur.execute('select * from "GlobalContent" where "Status" = '+status.__str__()) rows = cur.fetchall() result = [] for row in rows: data = { "IdFile": row[0], "File": row[1], "Status": row[2] } result.append(data) conn.close() return result def add_content_for_user(id_user, id_file): conn = connect() cur = conn.cursor() cur.execute('select * from "GlobalContent" where "IdFile" = '+id_file.__str__()) result = cur.fetchone() cur.execute('insert into "LocalContent" ("IdContent", "IdFile") values('+id_user.__str__()+','+result[0].__str__()+')') conn.commit() conn.close() return
838
app/src/data_collector.py
mss28/vulnerability_data_analysis
0
2172158
#!/usr/bin/env python # -*- coding: utf-8 -*- import json import re import time import redis import requests import traceback localRedis = redis.StrictRedis(host='redis', port=6379, decode_responses=True) download_url_template = "https://raw.githubusercontent.com/{:s}/{:s}/package.json" npm_search_url_template = 'https://api.github.com/search/code?q={:s} in:file path:/+extension:json+filename:package.json&sort=stars&order=desc&page={:d}' auth_user = '' # put git user name auth_pass = '' # put git password # Redis keys ALL_LIBS = "all_libs" # store name all libraries LIB_STAT_KEY = '{:s}.is_loaded' # store loading status of a library SLEEP_DURATION = 60 def page_visit_torify(url): session = requests.session() session.proxies = {'http': 'socks5h://localhost:9050', 'https': 'socks5h://localhost:9050'} return session.get(url) def get_download_ur(html_url): result = re.search(r'^https://github.com/(.*)/blob/(.*)/package\.json$', html_url) download_url = None if result: repo_id, commit_hash = result.groups() download_url = download_url_template.format(repo_id, commit_hash) return download_url def download_file(download_url): # make sure to service tor start # req = requests.get(download_url) req = page_visit_torify(download_url) return json.loads(req.text.encode('utf-8').decode("utf8")) def get_from_dependencies(key, packages, target_lib_name): key_lib = packages.get(key) if key_lib is None: return None else: return key_lib.get(target_lib_name, None) def find_library_version(target_lib_name, package_json): result = get_from_dependencies('dependencies', package_json, target_lib_name) if result is None: result = get_from_dependencies('devDependencies', package_json, target_lib_name) if result is None: result = get_from_dependencies('bundledDependencies', package_json, target_lib_name) if result is None: result = get_from_dependencies('optionalDependencies', package_json, target_lib_name) if result is None: result = get_from_dependencies('peerDependencies', package_json, target_lib_name) return result def get_req_status(req): headers = req.headers # print("\t***Headers***") # print(headers) # print("***---***") status_code = headers['Status'] status, last = False, 99999999 if status_code == "200 OK": status = True if 'Link' in headers: last_reg = re.search(r'<https://api.github.com/search/code.*page=(.*)>; rel="last"', headers['Link']) if last_reg is not None: last = int(last_reg.groups()[0]) elif status_code == "422 Unprocessable Entity": raise Exception(status_code) return status, last def search(target_lib_name, output_file): print('\tSearching for the library: {:s}'.format(target_lib_name)) current_page = 1 max_page = 1 while current_page <= max_page: url = npm_search_url_template.format(target_lib_name, current_page) req, status, last_page = None, False, 0 while not status: req = requests.get(url, auth=(auth_user, auth_pass)) status, last_page = get_req_status(req) if not status: print( "\t......................Putting to sleep for {:d} seconds......................".format( SLEEP_DURATION) ) time.sleep(SLEEP_DURATION) max_page = last_page r = json.loads(req.text.encode('utf-8').decode("utf8")) for item in r['items']: repo = item['repository'] html_url = item['html_url'] download_url = get_download_ur(html_url) if download_url is not None: package_json = download_file(download_url) lib_version = find_library_version(target_lib_name, package_json) result = { 'target_lib_name': target_lib_name, "repo_id": repo['id'], "repo_name": "https://github.com/{:s}".format(repo['full_name']), "lib_version": lib_version } if output_file is not None: output_file.write('{:s}, {:s}, "{:s}", "{:s}"\n'.format( target_lib_name, str(result['repo_id']), str(result['repo_name']), str(result['lib_version']))) localRedis.set('repo.result.' + str(result['repo_id']), json.dumps(result)) print('\t' + str(result)) repository_id = repo['id'] localRedis.sadd('repo_ids', repository_id) localRedis.set('repo.' + str(repository_id), json.dumps(item)) current_page += 1 def read_json(file_path): with open(file_path, 'r') as file: data = file.read() return json.loads(data) def store_list(data): for d in data: localRedis.sadd(ALL_LIBS, d['lib_name']) def run(input_file_path, start, end): data = read_json(input_file_path) store_list(data) all_data = list(localRedis.smembers(ALL_LIBS)) all_data.sort() sliced_data, counter = all_data, 1 if start is not None and end is not None: sliced_data = all_data[start - 1: end] counter = start output_file = open("../data/vulnerabilities.csv", "a") for lib_name in sliced_data: print('Loading the library: {:s}'.format(lib_name)) key = LIB_STAT_KEY.format(lib_name) is_loaded = localRedis.get(key) if is_loaded is None: is_loaded = 0 else: is_loaded = int(is_loaded) # 0 not loaded # 1 on going # 2 failed # 3 completed if is_loaded == 3: print('\t{:d}. {:s} already loaded'.format(counter, lib_name)) else: try: localRedis.set(key, 1) search(lib_name, output_file) except Exception: print("\t", end='') traceback.print_exc() print('\t{:d}. {:s} failed'.format(counter, lib_name)) localRedis.set(key, 2) else: print('\t{:d}. {:s} completed'.format(counter, lib_name)) localRedis.set(key, 3) counter += 1 def test(lib_name): search(lib_name, None) if __name__ == "__main__": # test('electron') should_run = True # TODO: Take git credentials from console input if auth_user == '': should_run = False print('Provide git user name') if auth_pass == '': should_run = False print('Provide git password') # split = input("Do you want to load all? (y) if yes, (n) if no: ") start_num, end_num = None, None #if split == "y": # start_num = int(input("Start number:")) # end_num = int(input("End number:")) if should_run: run('../data/npm_advisories.json', start_num, end_num) else: print('Not ready to run, check messages')
7,098
reproman/distributions/singularity.py
kyleam/niceman
13
2171711
# ex: set sts=4 ts=4 sw=4 noet: # ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the reproman package for the # copyright and license terms. # # ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Support for Singularity distribution(s).""" import attr import json import logging import os import tempfile import uuid lgr = logging.getLogger('reproman.distributions.singularity') from .base import Package from .base import Distribution from .base import DistributionTracer from .base import TypedList from .base import _register_with_representer from ..dochelpers import borrowdoc, exc_str from ..utils import attrib, md5sum, chpwd @attr.s(slots=True, frozen=True) class SingularityImage(Package): """Singularity image information""" md5 = attrib(default=attr.NOTHING) # Optional bootstrap = attrib() maintainer = attrib() deffile = attrib() schema_version = attrib() build_date = attrib() build_size = attrib() singularity_version = attrib() base_image = attrib() mirror_url = attrib() url = attrib() path = attrib() _register_with_representer(SingularityImage) @attr.s class SingularityDistribution(Distribution): """ Class to provide commands to Singularity. """ images = TypedList(SingularityImage) def initiate(self, session): """ Perform any initialization commands needed in the environment. Parameters ---------- session : object The Session to work in """ # Raise reproman.support.exceptions.CommandError exception if # Singularity is not to be found. session.execute_command(['singularity', 'selftest']) def install_packages(self, session): """ Install the Singularity images associated to this distribution by the provenance into the environment. Parameters ---------- session : object Session to work in """ # TODO: Currently we have no way to locate the image given the metadata _register_with_representer(SingularityDistribution) class SingularityTracer(DistributionTracer): """Singularity image tracer If a given file is not identified as a singularity image, the files are quietly passed on to the next tracer. """ HANDLES_DIRS = False @borrowdoc(DistributionTracer) def identify_distributions(self, files): if not files: return images = [] remaining_files = set() url = None path = None for file_path in files: try: if file_path.startswith('shub:/'): # Correct file path for path normalization in retrace.py if not file_path.startswith('shub://'): file_path = file_path.replace('shub:/', 'shub://') temp_path = "{}.simg".format(uuid.uuid4()) with chpwd(tempfile.gettempdir()): msg = "Downloading Singularity image {} for tracing" lgr.info(msg.format(file_path)) self._session.execute_command(['singularity', 'pull', '--name', temp_path, file_path]) image = json.loads(self._session.execute_command( ['singularity', 'inspect', temp_path])[0]) url = file_path md5 = md5sum(temp_path) os.remove(temp_path) else: path = os.path.abspath(file_path) image = json.loads(self._session.execute_command( ['singularity', 'inspect', file_path])[0]) md5 = md5sum(file_path) images.append(SingularityImage( md5=md5, bootstrap=image.get( 'org.label-schema.usage.singularity.deffile.bootstrap'), maintainer=image.get('MAINTAINER'), deffile=image.get( 'org.label-schema.usage.singularity.deffile'), schema_version=image.get('org.label-schema.schema-version'), build_date=image.get('org.label-schema.build-date'), build_size=image.get('org.label-schema.build-size'), singularity_version=image.get( 'org.label-schema.usage.singularity.version'), base_image=image.get( 'org.label-schema.usage.singularity.deffile.from'), mirror_url=image.get( 'org.label-schema.usage.singularity.deffile.mirrorurl'), url=url, path=path )) except Exception as exc: lgr.debug("Probably %s is not a Singularity image: %s", file_path, exc_str(exc)) remaining_files.add(file_path) if not images: return dist = SingularityDistribution( name="singularity", images=images ) yield dist, remaining_files @borrowdoc(DistributionTracer) def _get_packagefields_for_files(self, files): return @borrowdoc(DistributionTracer) def _create_package(self, **package_fields): return
5,522
API/views.py
MrAbdelaziz/GestionStoc_django
3
2169444
from django.contrib.auth import authenticate from django.shortcuts import render from rest_framework import viewsets, generics, status from rest_framework import permissions from rest_framework.decorators import api_view from rest_framework.response import Response from rest_framework.views import APIView from .serializers import * from .models import * class ClientViewSet(viewsets.ModelViewSet): queryset = Client.objects.all().order_by('nom') serializer_class = ClientSerializer class FournisseurViewSet(viewsets.ModelViewSet): queryset = Fournisseur.objects.all().order_by('libelle') serializer_class = FournisseurSerializer class ProduitViewSet(viewsets.ModelViewSet): queryset = Produit.objects.all().order_by('reference') serializer_class = ProduitSerializer filter_fields = { 'quantite': ['gte', 'lte'] } # permission_classes = [permissions.IsAuthenticatedOrReadOnly] ss # def destroy(self, request, *args, **kwargs): # instance = self.get_object() # self.perform_destroy(instance) # return Response(status=status.HTTP_204_NO_CONTENT) # # def perform_destroy(self, instance): # instance.delete() class AchatViewSet(viewsets.ModelViewSet): queryset = Achat.objects.all().order_by('date_Achat') serializer_class = AchatSerializer class UserViewSet(viewsets.ModelViewSet): queryset = User.objects.all().order_by('username') serializer_class = UserSerializer class CountViewSet(APIView): def get(self, request, format=None): Produit_count = Produit.objects.all().count() Client_count = Client.objects.all().count() Fournisseur_count = Fournisseur.objects.all().count() Achat_count = Achat.objects.all().count() content = { 'produits_count': Produit_count, 'Client_count':Client_count, 'Fournisseur_count':Fournisseur_count, 'Achat_count':Achat_count } return Response(content) class RiskViewSet(APIView): def get(self, request, format=None): countprod = request.GET.get('prodid', False) prods = Produit.objects.filter(quantite__gt=0) return Response(prods) class LoginnViewSet(APIView): def get(self, request, format=None): username = request.GET.get('username', False) password = request.GET.get('password', False) user = authenticate(username=username, password=password) if user is not None and user.is_active: return Response(user) return Response(user)
2,595
api/urls_index.py
Samge0/UmengEventManage
0
2171974
#!/usr/bin/env python3 # -*- coding: utf-8 -*- # @Time : 2021/10/26 上午10:27 # @Author : Samge from django.conf.urls import url from django.views.generic import RedirectView from .views.v_key import * # http路由 urlpatterns = [ url(r'^$', index, name='index'), url(r'^favicon.ico$', RedirectView.as_view(url=r'static/favicon.ico')), ]
346
pcapkit/reassembly/ipv4.py
chellvs/PyPCAPKit
131
2169938
# -*- coding: utf-8 -*- """IPv4 fragments reassembly :mod:`pcapkit.reassembly.ipv4` contains :class:`~pcapkit.reassembly.ipv4.IPv4_Reassembly` only, which reconstructs fragmented IPv4 packets back to origin. Glossary -------- ipv4.packet Data structure for **IPv4 datagram reassembly** (:meth:`~pcapkit.reassembly.reassembly.Reassembly.reassembly`) is as following: .. code-block:: python packet_dict = dict( bufid = tuple( ipv4.src, # source IP address ipv4.dst, # destination IP address ipv4.id, # identification ipv4.proto, # payload protocol type ), num = frame.number, # original packet range number fo = ipv4.frag_offset, # fragment offset ihl = ipv4.hdr_len, # internet header length mf = ipv4.flags.mf, # more fragment flag tl = ipv4.len, # total length, header includes header = ipv4.header, # raw bytearray type header payload = ipv4.payload, # raw bytearray type payload ) ipv4.datagram Data structure for **reassembled IPv4 datagram** (element from :attr:`~pcapkit.reassembly.reassembly.Reassembly.datagram` *tuple*) is as following: .. code-block:: python (tuple) datagram |--> (dict) data | |--> 'NotImplemented' : (bool) True --> implemented | |--> 'index' : (tuple) packet numbers | | |--> (int) original packet range number | |--> 'packet' : (Optional[bytes]) reassembled IPv4 packet |--> (dict) data | |--> 'NotImplemented' : (bool) False --> not implemented | |--> 'index' : (tuple) packet numbers | | |--> (int) original packet range number | |--> 'header' : (Optional[bytes]) IPv4 header | |--> 'payload' : (Optional[tuple]) partially reassembled IPv4 payload | |--> (Optional[bytes]) IPv4 payload fragment |--> (dict) data ... ipv4.buffer Data structure for internal buffering when performing reassembly algorithms (:attr:`~pcapkit.reassembly.reassembly.Reassembly._buffer`) is as following: .. code-block:: python (dict) buffer --> memory buffer for reassembly |--> (tuple) BUFID : (dict) | |--> ipv4.src | | |--> ipc6.dst | | |--> ipv4.label | | |--> ipv4_frag.next | | |--> 'TDL' : (int) total data length | |--> RCVBT : (bytearray) fragment received bit table | | |--> (bytes) b'\\x00' -> not received | | |--> (bytes) b'\\x01' -> received | | |--> (bytes) ... | |--> 'index' : (list) list of reassembled packets | | |--> (int) packet range number | |--> 'header' : (bytearray) header buffer | |--> 'datagram' : (bytearray) data buffer, holes set to b'\\x00' |--> (tuple) BUFID ... """ from pcapkit.reassembly.ip import IP_Reassembly __all__ = ['IPv4_Reassembly'] class IPv4_Reassembly(IP_Reassembly): """Reassembly for IPv4 payload. Example: >>> from pcapkit.reassembly import IPv4_Reassembly # Initialise instance: >>> ipv4_reassembly = IPv4_Reassembly() # Call reassembly: >>> ipv4_reassembly(packet_dict) # Fetch result: >>> result = ipv4_reassembly.datagram """ ########################################################################## # Properties. ########################################################################## @property def name(self): """Protocol of current packet. :rtype: Literal['Internet Protocol version 4'] """ return 'Internet Protocol version 4' @property def protocol(self): """Protocol of current reassembly object. :rtype: Literal['IPv4'] """ return 'IPv4'
4,361
tests/test_client.py
lspvic/yawxt
4
2170633
# -*- coding: utf-8 -*- '''Tests for WxClient API''' from __future__ import unicode_literals import requests import pytest from yawxt import ( MaxQuotaError, User, APIError, Location, ChangeIndustryError) @pytest.mark.xfail(raises=MaxQuotaError) def test_get_users(client): openid = next(client.get_openid_iter()) assert len(openid) > 0 @pytest.mark.xfail(raises=MaxQuotaError) def test_users_count(client): assert client.get_user_count() > 0 @pytest.mark.xfail(raises=MaxQuotaError) def test_get_user_info(client, openid): user = client.get_user(openid) assert isinstance(user, User) assert user.nickname is not None assert user.openid is not None def test_user_model(client, monkeypatch): info = { "subscribe": 1, "openid": "o9KLls80ReakhjsbmHUZxjbz9K8c", "nickname": "五音盒", "sex": 1, "language": "zh_CN", "city": "杭州", "province": "浙江", "country": "中国", "headimgurl": ( "http://wx.qlogo.cn/mmopen/ajSDdqHZLLCXFhHOkecFpWDCW" "l5icpYpzzwc39E4nmyfSicjfg40EWSicf0R7VEDakCySlTybGJtWH4G" "53P01itBqA/0"), "subscribe_time": 1440489434, "remark": "", "groupid": 0, "tagid_list": []} def mock_api_return(resp): return info monkeypatch.setattr(requests.Response, "json", mock_api_return) user = client.get_user(info["openid"]) assert user.openid == info["openid"] assert user.nickname == info["nickname"] assert user.city == info["city"] assert user.province == info["province"] assert user.tagid_list == "" assert user.tagids == info["tagid_list"] assert user.subscribe == info["subscribe"] assert user.sex == info["sex"] assert user.language == info["language"] assert user.country == info["country"] assert user.headimgurl == info["headimgurl"] assert user.subscribe_time == info["subscribe_time"] assert user.remark == info["remark"] assert user.groupid == info["groupid"] @pytest.mark.xfail(raises=APIError) def test_semantic_parse(client, openid): info = client.get_user(openid) query = "查一下明天从北京到上海的南航机票" location = Location(23.1374665, 113.352425, openid=info.openid) result = client.semantic_parse(query, city=info.city, location=location) assert isinstance(result, dict) if "type" not in result: raise APIError(20701, str(result)) @pytest.mark.xfail(raises=MaxQuotaError) def test_preview_message(client, openid): text = "能看到我发消息吗?" msg_id = client.preview_message(openid, text) assert msg_id is None def test_js_config(client): url = "http://example.com" config = client.js_sign(url, debug=False) assert config["debug"] == "false" assert len(config["signature"]) == 40 @pytest.mark.xfail(raises=ChangeIndustryError) def test_template_set_industry(client): client.set_industry(1, 24) @pytest.mark.xfail(raises=MaxQuotaError) def test_template_get_industry(client): result = client.get_industry() assert 'primary_industry' in result assert 'first_class' in result['primary_industry'] assert 'secondary_industry' in result assert 'second_class' in result['secondary_industry'] @pytest.mark.xfail(raises=MaxQuotaError) def test_template_add_template(client): template_id = client.add_sys_template('TM00015') assert bool(template_id) is True @pytest.mark.xfail(raises=MaxQuotaError) def test_template_all_list(client): result = client.get_template_list() assert isinstance(result, list) assert len(result) >= 1 @pytest.mark.xfail(raises=MaxQuotaError) def test_template_send_message(client, openid): template_id = client.get_template_list()[0]['template_id'] to_openid = openid data = { "first": { "value": "恭喜你购买成功!", "color": "#173177"}, "orderMoneySum": { "value": "28.85", "color": "#173177"}, "orderProductName": { "value": "巧克力", "color": "#173177"}, "remark": { "value": "欢迎再次购买!", "color": "#173177"} } client.send_template_message( to_openid, template_id, data=data, url="http://qq.com/") @pytest.mark.xfail(raises=MaxQuotaError) def test_template_del(client): template_id = client.get_template_list()[0]['template_id'] client.del_template(template_id) assert template_id not in map( lambda t: t["template_id"], client.get_template_list())
4,733
ansible-tests/validations/files/rogue_dhcp.py
rthallisey/clapper
13
2172028
#!/tmp/validations-venv/bin/python # Disable scapy's warning to stderr: import logging import sys logging.getLogger("scapy.runtime").setLevel(logging.ERROR) from scapy.all import * def find_dhcp_servers(timeout_sec): conf.checkIPaddr = False fam, hw = get_if_raw_hwaddr(conf.iface) dhcp_discover = (Ether(dst="ff:ff:ff:ff:ff:ff") / IP(src="0.0.0.0", dst="255.255.255.255") / UDP(sport=68, dport=67) / BOOTP(chaddr=hw) / DHCP(options=[("message-type", "discover"), "end"])) ans, unans = srp(dhcp_discover, multi=True, timeout=timeout_sec, verbose=False) return [(unicode(packet[1][IP].src), packet[1][Ether].src) for packet in ans] def main(): dhcp_servers = find_dhcp_servers(30) if dhcp_servers: sys.stderr.write('Found %d DHCP servers:' % len(dhcp_servers)) sys.stderr.write("\n".join(("* %s (%s)" % (ip, mac) for (ip, mac) in dhcp_servers))) sys.exit(1) else: print "No DHCP servers found." if __name__ == '__main__': main()
1,102
tests/test_utils.py
whatsnowplaying/audio-metadata
45
2172030
from pathlib import Path from ward import ( each, raises, test, ) from audio_metadata.utils import ( apply_unsynchronization, decode_bytestring, decode_synchsafe_int, determine_encoding, encode_synchsafe_int, get_image_size, humanize_bitrate, humanize_duration, humanize_sample_rate, remove_unsynchronization, split_encoded ) images = (Path(__file__).parent / 'image').glob('*.*') @test( "apply_unsynchronization", tags=['unit', 'utils', 'apply_unsynchronization'], ) def _( b=each( b'TEST', b'\xFF', b'\x00', b'\xFF\xFE', b'\xFF\x00', b'\xFF\x00\xFF', b'\xFF\x00\x00', b'\xFF\x00\xFF\xFE', ), expected=each( b'TEST', b'\xFF', b'\x00', b'\xFF\x00\xFE', b'\xFF\x00\x00', b'\xFF\x00\x00\xFF', b'\xFF\x00\x00\x00', b'\xFF\x00\x00\xFF\x00\xFE', ), ): assert apply_unsynchronization(b) == expected @test( "remove_unsynchronization", tags=['unit', 'utils', 'remove_unsynchronization'], ) def _( b=each( b'TEST', b'\xFF', b'\x00', b'\xFF\x00\xFE', b'\xFF\x00\x00', b'\xFF\x00\x00\xFF', b'\xFF\x00\x00\x00', b'\xFF\x00\x00\xFF\x00\xFE', ), expected=each( b'TEST', b'\xFF', b'\x00', b'\xFF\xFE', b'\xFF\x00', b'\xFF\x00\xFF', b'\xFF\x00\x00', b'\xFF\x00\xFF\xFE', ), ): assert remove_unsynchronization(b) == expected @test( "decode_synchsafe_int", tags=['unit', 'utils', 'decode_synchsafe_int'], ) def _( args=each( (b'\x00\x00\x01\x7f', 7), (b'\x00\x00\x02\x7f', 6), (b'\x00\x00\x01\x7f', 6), ), expected=each( 255, 255, 191, ) ): assert decode_synchsafe_int(*args) == expected @test( "Decoding too large synchsafe int raises ValueError", tags=['unit', 'utils', 'decode_synchsafe_int'], ) def _( args=each( (b'\x80\x00\x00\x00', 7), (b'@\x00\x00\x00', 6), ) ): with raises(ValueError): decode_synchsafe_int(*args) @test( "encode_synchsafe_int", tags=['unit', 'utils', 'encode_synchsafe_int'], ) def _( args=each( (255, 7), (255, 6), ), expected=each( b'\x00\x00\x01\x7f', b'\x00\x00\x02\x7f', ), ): assert encode_synchsafe_int(*args) == expected @test( "Encoding too large synchsafe int raises ValueError", tags=['unit', 'utils', 'encode_synchsafe_int'], ) def _( args=each( (268435456, 7), (16777216, 6), ) ): with raises(ValueError): encode_synchsafe_int(*args) @test( "decode_bytestring", tags=['unit', 'utils', 'decode_bytestring'], ) def _( b=each( b'test\x00', b'\xff\xfet\x00e\x00s\x00t\x00', b'\xff\xfet\x00e\x00s\x00t\x00\x00', b'\xfe\xff\x00t\x00e\x00s\x00t', b'\xfe\xff\x00t\x00e\x00s\x00t\x00', b'test\x00', b'test\x00', b'' ), encoding=each( 'iso-8859-1', 'utf-16-le', 'utf-16-le', 'utf-16-be', 'utf-16-be', 'utf-8', None, None, ), expected=each( 'test', 'test', 'test', 'test', 'test', 'test', 'test', '', ) ): if encoding is None: assert decode_bytestring(b) == expected else: assert decode_bytestring(b, encoding=encoding) == expected @test( "determine_encoding", tags=['unit', 'utils', 'determine_encoding'], ) def _( b=each( b'\x00', b'\x01\xff\xfe', b'\x01\xfe\xff', b'\x02', b'\x03', b'\x04', b'', ), encoding=each( 'iso-8859-1', 'utf-16-le', 'utf-16-be', 'utf-16-be', 'utf-8', 'iso-8859-1', 'iso-8859-1', ), ): assert determine_encoding(b) == encoding @test( "get_image_size", tags=['unit', 'utils', 'get_image_size'] ) def test_get_image_size(): for image in images: with image.open('rb') as f: assert get_image_size(f) == (16, 16) with raises(ValueError): get_image_size(b'') @test( "humanize_bitrate", tags=['unit', 'utils', 'humanize_bitrate'], ) def _( bitrate=each( None, 0, 1, 100, 1000, ), humanized=each( None, '0 bps', '1 bps', '100 bps', '1 Kbps', ), ): assert humanize_bitrate(bitrate) == humanized @test( "humanize_duration", tags=['unit', 'utils', 'humanize_duration'], ) def _( duration=each( None, 0, 1, 60, 3600, ), humanized=each( None, '00:00', '00:01', '01:00', '01:00:00', ), ): assert humanize_duration(duration) == humanized @test( "humanize_sample_rate", tags=['unit', 'utils', 'humanize_sample_rate'], ) def _( sample_rate=each( None, 0, 1, 1000, 44100, ), humanized=each( None, '0.0 Hz', '1.0 Hz', '1.0 KHz', '44.1 KHz', ), ): assert humanize_sample_rate(sample_rate) == humanized @test( "split_encoded ({b}, {encoding})", tags=['unit', 'utils', 'split_encoded'], ) def _( b=each( b'test\x00', b'\xff\xfe\x00\x00\xff\xfet\x00e\x00s\x00t\x00\x00\x00', b'\xff\xfe\x00\x00\xff\xfet\x00e\x00s\x00t\x00\x00', b'\xff\xfet\x00e\x00s\x00t\x00\x00\x00', b'\xfe\xff\x00t\x00e\x00s\x00t\x00\x00', b'\xfe\xff\x00t\x00e\x00s\x00t\x00', b'test\x00', b'test', ), encoding=each( 'iso-8859-1', 'utf-16-le', 'utf-16-le', 'utf-16-le', 'utf-16-be', 'utf-16-be', 'utf-8', 'iso-9959-1', ), expected=each( [b'test'], [b'\xff\xfe', b'\xff\xfet\x00e\x00s\x00t\x00'], [b'\xff\xfe', b'\xff\xfet\x00e\x00s\x00t\x00'], [b'\xff\xfet\x00e\x00s\x00t\x00'], [b'\xfe\xff\x00t\x00e\x00s\x00t'], [b'\xfe\xff\x00t\x00e\x00s\x00t'], [b'test'], [b'test'], ), ): assert split_encoded(b, encoding) == expected
5,232
ipredictor/tests/test_anni.py
zenio/ipredictor
1
2171885
#: -*- coding: utf-8 -*- """ Interval-valued data prediction Artificial Neural Network model tests """ import unittest import pandas as pd import numpy as np from ipredictor.models import ANNI class ANNITestCase(unittest.TestCase): def setUp(self): self.lookback = 2 self.data_length = self.lookback * 4 self.values = [np.array([[i+1], [i]]) for i in range(1, self.data_length + 1)] self.dataframe = pd.DataFrame.from_items([('values', self.values)]) self.model = ANNI(self.dataframe, lookback=self.lookback) def test_if_neurons_amount_properly_configured(self): self.assertEqual(self.model.input_neurons, self.lookback * 2) self.assertEqual(self.model.hidden_neurons, self.lookback * 4) self.assertEqual(self.model.output_neurons, 2) def test_if_initial_data_is_flattened(self): self.assertEqual(self.model.X.shape[0], self.data_length * 2) #: monkey patching for data rescale self.model.Xf = self.model.X self.model._rescale_values() rescaled_flat = self.model.Xf self.assertEqual(rescaled_flat[0], self.values[0][0]) self.assertEqual(rescaled_flat[1], self.values[0][1]) def test_if_training_dataset_is_properly_configured(self): trainX = self.model.trainingX trainY = self.model.trainingY self.assertEqual(len(trainX[0]), self.lookback * 2) self.assertEqual(len(trainY), len(self.values) - self.lookback) def test_if_can_predict_proper_values(self): STEPS = 5 prediction = self.model.predict(steps=STEPS) self.assertEqual(len(prediction), STEPS) self.assertEqual(prediction['values'][0].shape, (2,1))
1,580
src/app.py
BureauTech/BTAlert-AI
1
2171960
from dotenv import load_dotenv from flask import Flask, Response from metrics.collector import Collector # from slack.messenger import Messenger # from slack.alerts.info_alert import InfoAlert # from slack.alerts.warning_alert import WarningAlert load_dotenv() # messenger = Messenger() # print( # messenger.send_alert(WarningAlert()) # ) app = Flask(__name__) collector = Collector() @app.route('/metrics') def get_metrics(): return Response(collector.get_metrics(), mimetype='text/plain') if __name__ == '__main__': app.run('localhost', 5050)
565
ML/Projects/Exploring_MNIST/networks/googLeNet.py
xuyannus/Machine-Learning-Collection
3,094
2172098
import torch import torch.nn as nn import torch.nn.functional as F class Inception(nn.Module): def __init__( self, in_channels, out1x1, out3x3reduced, out3x3, out5x5reduced, out5x5, outpool ): super().__init__() self.branch_1 = BasicConv2d(in_channels, out1x1, kernel_size=1, stride=1) self.branch_2 = nn.Sequential( BasicConv2d(in_channels, out3x3reduced, kernel_size=1), BasicConv2d(out3x3reduced, out3x3, kernel_size=3, padding=1), ) # Is in the original googLeNet paper 5x5 conv but in Inception_v2 it has shown to be # more efficient if you instead do two 3x3 convs which is what I am doing here! self.branch_3 = nn.Sequential( BasicConv2d(in_channels, out5x5reduced, kernel_size=1), BasicConv2d(out5x5reduced, out5x5, kernel_size=3, padding=1), BasicConv2d(out5x5, out5x5, kernel_size=3, padding=1), ) self.branch_4 = nn.Sequential( nn.MaxPool2d(kernel_size=3, stride=1, padding=1), BasicConv2d(in_channels, outpool, kernel_size=1), ) def forward(self, x): y1 = self.branch_1(x) y2 = self.branch_2(x) y3 = self.branch_3(x) y4 = self.branch_4(x) return torch.cat([y1, y2, y3, y4], 1) class GoogLeNet(nn.Module): def __init__(self, img_channel): super().__init__() self.first_layers = nn.Sequential( BasicConv2d(img_channel, 192, kernel_size=3, padding=1) ) self._3a = Inception(192, 64, 96, 128, 16, 32, 32) self._3b = Inception(256, 128, 128, 192, 32, 96, 64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self._4a = Inception(480, 192, 96, 208, 16, 48, 64) self._4b = Inception(512, 160, 112, 224, 24, 64, 64) self._4c = Inception(512, 128, 128, 256, 24, 64, 64) self._4d = Inception(512, 112, 144, 288, 32, 64, 64) self._4e = Inception(528, 256, 160, 320, 32, 128, 128) self._5a = Inception(832, 256, 160, 320, 32, 128, 128) self._5b = Inception(832, 384, 192, 384, 48, 128, 128) self.avgpool = nn.AvgPool2d(kernel_size=8, stride=1) self.linear = nn.Linear(1024, 10) def forward(self, x): out = self.first_layers(x) out = self._3a(out) out = self._3b(out) out = self.maxpool(out) out = self._4a(out) out = self._4b(out) out = self._4c(out) out = self._4d(out) out = self._4e(out) out = self.maxpool(out) out = self._5a(out) out = self._5b(out) out = self.avgpool(out) out = out.view(out.size(0), -1) out = self.linear(out) return out class BasicConv2d(nn.Module): def __init__(self, in_channels, out_channels, **kwargs): super().__init__() self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs) self.bn = nn.BatchNorm2d(out_channels, eps=0.001) def forward(self, x): x = self.conv(x) x = self.bn(x) return F.relu(x, inplace=True) def test(): net = GoogLeNet(1) x = torch.randn(3, 1, 32, 32) y = net(x) print(y.size()) # test()
3,262
implement/xgboostmodel.py
Arun-Singh-Chauhan-09/Supply-demand-forecasting
59
2171158
import sys import os sys.path.insert(0, os.path.abspath('..')) from preprocess.preparedata import PrepareData import numpy as np from utility.runtype import RunType from utility.datafilepath import g_singletonDataFilePath from preprocess.splittrainvalidation import HoldoutSplitMethod import xgboost as xgb from evaluation.sklearnmape import mean_absolute_percentage_error_xgboost from evaluation.sklearnmape import mean_absolute_percentage_error from utility.modelframework import ModelFramework from utility.xgbbasemodel import XGBoostGridSearch from evaluation.sklearnmape import mean_absolute_percentage_error_xgboost_cv from utility.xgbbasemodel import XGBoostBase import logging import sys class DidiXGBoostModel(XGBoostBase, PrepareData, XGBoostGridSearch): def __init__(self): PrepareData.__init__(self) XGBoostGridSearch.__init__(self) XGBoostBase.__init__(self) self.best_score_colname_in_cv = 'test-mape-mean' self.do_cross_val = False self.train_validation_foldid = -2 if self.do_cross_val is None: root = logging.getLogger() root.setLevel(logging.DEBUG) root.addHandler(logging.StreamHandler(sys.stdout)) root.addHandler(logging.FileHandler('logs/finetune_parameters.log', mode='w')) return def set_xgb_parameters(self): early_stopping_rounds = 3 self.xgb_params = {'silent':1, 'colsample_bytree': 0.8, 'silent': 1, 'lambda ': 1, 'min_child_weight': 1, 'subsample': 0.8, 'eta': 0.01, 'objective': 'reg:linear', 'max_depth': 7} # self.xgb_params = {'silent':1 } self.xgb_learning_params = { 'num_boost_round': 200, 'callbacks':[xgb.callback.print_evaluation(show_stdv=True),xgb.callback.early_stop(early_stopping_rounds)], 'feval':mean_absolute_percentage_error_xgboost_cv} if self.do_cross_val == False: self.xgb_learning_params['feval'] = mean_absolute_percentage_error_xgboost return def get_paramgrid_1(self): """ This method must be overriden by derived class when its objective is not reg:linear """ param_grid = {'max_depth':[6], 'eta':[0.1], 'min_child_weight':[1],'silent':[1], 'objective':['reg:linear'],'colsample_bytree':[0.8],'subsample':[0.8], 'lambda ':[1]} return param_grid def get_paramgrid_2(self, param_grid): """ This method must be overriden by derived class if it intends to fine tune parameters """ self.ramdonized_search_enable = False self.randomized_search_n_iter = 150 self.grid_search_display_result = True param_grid['eta'] = [0.01] #train-mape:-0.448062+0.00334926 test-mape:-0.448402+0.00601761 # param_grid['max_depth'] = [7] #train-mape:-0.363007+0.00454276 test-mape:-0.452832+0.00321641 # param_grid['colsample_bytree'] = [0.8] param_grid['max_depth'] = range(5,8) #train-mape:-0.363007+0.00454276 test-mape:-0.452832+0.00321641 param_grid['colsample_bytree'] = [0.6,0.8,1.0] # param_grid['lambda'] = range(1,15) # param_grid['max_depth'] = [3,4] # param_grid['eta'] = [0.01,0.1] # 0.459426+0.00518875 # param_grid['subsample'] = [0.5] #0.458935+0.00522205 # param_grid['eta'] = [0.005] #0.457677+0.00526401 return param_grid def get_learning_params(self): """e This method must be overriden by derived class if it intends to fine tune parameters """ num_boost_round = 100 early_stopping_rounds = 5 kwargs = {'num_boost_round':num_boost_round, 'feval':mean_absolute_percentage_error_xgboost_cv, 'callbacks':[xgb.callback.print_evaluation(show_stdv=True),xgb.callback.early_stop(early_stopping_rounds)]} return kwargs if __name__ == "__main__": obj= DidiXGBoostModel() obj.run()
4,070
dump/python/multiprocessing-basics/multiprocessing_simply.py
zgo23/learn-programming
0
2172006
import multiprocessing def worker(): """worker function""" print("worker") return if __name__ == "__main__": jobs = [] for i in range(5): p = multiprocessing.Process(target=worker) jobs.append(p) p.start()
253
api/pub_sub/pub_sub.py
cds-snc/scan-websites
5
2169340
from enum import Enum import json import os from boto3wrapper.wrapper import get_session from collections import defaultdict from logger import log # Import so that the application is aware of these Models # Required so that models are initialized before they're referenced from models.A11yReport import A11yReport # noqa: F401 from models.A11yViolation import A11yViolation # noqa: F401 from models.SecurityReport import SecurityReport # noqa: F401 from models.SecurityViolation import SecurityViolation # noqa: F401 from models.Organisation import Organisation # noqa: F401 from models.Scan import Scan # noqa: F401 from models.Template import Template # noqa: F401 from models.TemplateScan import TemplateScan # noqa: F401 from models.TemplateScanTrigger import TemplateScanTrigger # noqa: F401 from models.User import User # noqa: F401 class AvailableScans(Enum): OWASP_ZAP = "OWASP Zap" NUCLEI = "Nuclei" AXE_CORE = "axe-core" validator_list = {} common_validations = [ "id", "url", "type", "queue", "product", "revision", "template_id", ] # Append to common_validations if additional validations are required for only one template validator_list[AvailableScans.OWASP_ZAP.value] = common_validations validator_list[AvailableScans.NUCLEI.value] = common_validations validator_list[AvailableScans.AXE_CORE.value] = common_validations def validate_mandatory(payload, scan_type): if scan_type not in validator_list: raise ValueError("Mandatory validator not defined") for mandatory_key in validator_list[scan_type]: if mandatory_key not in payload: raise ValueError(f"{mandatory_key} not defined") def dispatch(payloads): state_machine_queue = defaultdict(list) for payload in payloads: if "type" not in payload: raise ValueError("type is not defined") validate_mandatory(payload, payload["type"]) if payload["event"] == "sns": send(payload["queue"], payload) elif payload["event"] == "stepfunctions": state_machine_queue[payload["queue"]].append(payload) if state_machine_queue: for queue in state_machine_queue: execute(queue, state_machine_queue[queue]) def send(topic_arn, payload): if topic_arn: if os.environ.get("AWS_LOCALSTACK", False): client = get_session().client("sns", endpoint_url="http://localstack:4566") else: client = get_session().client("sns") client.publish( TargetArn=topic_arn, Message=json.dumps({"default": json.dumps(payload)}), MessageStructure="json", ) else: log.error("Topic ARN is not defined") def execute(state_machine, payloads): if state_machine: client = get_session().client("stepfunctions") response = client.list_state_machines() stateMachine = [ stateMachine for stateMachine in response["stateMachines"] if stateMachine.get("name") == state_machine ] if stateMachine: response = client.start_execution( stateMachineArn=stateMachine[0]["stateMachineArn"], input=json.dumps({"payload": payloads}), ) else: log.error(f"State machine: {state_machine} is not defined") else: log.error("State machine name is not defined")
3,443
app/app.py
tanaka0x/sifac_character_detect_app
0
2170324
import falcon from falcon_multipart.middleware import MultipartMiddleware from falcon_cors import CORS from .images import Images from .ssd import inference cors = CORS(allow_all_origins=True, allow_all_methods=True, allow_all_headers=True) api = application = falcon.API(middleware=[cors.middleware, MultipartMiddleware()]) api.add_route('/images', Images(infer_fn=inference))
378
demo/toga_demo/__main__.py
luizoti/toga
1,261
2170840
#!/usr/bin/env python from toga_demo.app import main def run(): main().main_loop() if __name__ == '__main__': run()
128
bot/__init__.py
eivl/code-jam-1
11
2171106
# coding=utf-8 import ast import logging import sys from logging import Logger, StreamHandler import discord.ext.commands.view logging.TRACE = 5 logging.addLevelName(logging.TRACE, "TRACE") def monkeypatch_trace(self, msg, *args, **kwargs): """ Log 'msg % args' with severity 'TRACE'. To pass exception information, use the keyword argument exc_info with a true value, e.g. logger.trace("Houston, we have an %s", "interesting problem", exc_info=1) """ if self.isEnabledFor(logging.TRACE): self._log(logging.TRACE, msg, args, **kwargs) Logger.trace = monkeypatch_trace # Set up logging logging_handlers = [StreamHandler(stream=sys.stderr)] logging.basicConfig( format="%(asctime)s Bot: | %(name)30s | %(levelname)8s | %(message)s", datefmt="%b %d %H:%M:%S", level=logging.TRACE, handlers=logging_handlers ) log = logging.getLogger(__name__) # Silence discord and websockets logging.getLogger("discord.client").setLevel(logging.ERROR) logging.getLogger("discord.gateway").setLevel(logging.ERROR) logging.getLogger("discord.state").setLevel(logging.ERROR) logging.getLogger("discord.http").setLevel(logging.ERROR) logging.getLogger("websockets.protocol").setLevel(logging.ERROR) def _skip_string(self, string: str) -> bool: """ Our version of the skip_string method from discord.ext.commands.view; used to find the prefix in a message, but allowing prefix to ignore case sensitivity """ strlen = len(string) if self.buffer.lower()[self.index:self.index + strlen] == string: self.previous = self.index self.index += strlen return True return False def _get_word(self) -> str: """ Invokes the get_word method from discord.ext.commands.view used to find the bot command part of a message, but allows the command to ignore case sensitivity, and allows commands to have Python syntax. Example of valid Python syntax calls: ------------------------------ bot.tags.set("test", 'a dark, dark night') bot.help(tags.delete) bot.hELP(tags.delete) """ pos = 0 while not self.eof: try: current = self.buffer[self.index + pos] if current.isspace() or current == "(": break pos += 1 except IndexError: break self.previous = self.index result = self.buffer[self.index:self.index + pos] self.index += pos next = None # Check what's after the '(' if len(self.buffer) != self.index: next = self.buffer[self.index + 1] # Is it possible to parse this without syntax error? syntax_valid = True try: ast.literal_eval(self.buffer[self.index:]) except SyntaxError: log.warning("The command cannot be parsed by ast.literal_eval because it raises a SyntaxError.") # TODO: It would be nice if this actually made the bot return a SyntaxError. ClickUp #1b12z # noqa: T000 syntax_valid = False # Conditions for a valid, parsable command. python_parse_conditions = ( current == "(" and next and next != ")" and syntax_valid ) if python_parse_conditions: log.debug(f"A python-style command was used. Attempting to parse. Buffer is {self.buffer}. " "A step-by-step can be found in the trace log.") # Parse the args log.trace("Parsing command with ast.literal_eval.") args = self.buffer[self.index:] args = ast.literal_eval(args) # Force args into container if isinstance(args, str): args = (args,) # Type validate and format new_args = [] for arg in args: # Other types get converted to strings if not isinstance(arg, str): log.trace(f"{arg} is not a str, casting to str.") arg = str(arg) # Adding double quotes to every argument log.trace(f"Wrapping all args in double quotes.") new_args.append(f'"{arg}"') # Add the result to the buffer new_args = " ".join(new_args) self.buffer = f"{self.buffer[:self.index]} {new_args}" log.trace(f"Modified the buffer. New buffer is now {self.buffer}") # Recalibrate the end since we've removed commas self.end = len(self.buffer) elif current == "(" and next == ")": # Move the cursor to capture the ()'s log.debug("User called command without providing arguments.") pos += 2 result = self.buffer[self.previous:self.index + (pos+2)] self.index += 2 if isinstance(result, str): return result.lower() # Case insensitivity, baby return result # Monkey patch the methods discord.ext.commands.view.StringView.skip_string = _skip_string discord.ext.commands.view.StringView.get_word = _get_word
4,914
calculations.py
sirmammingtonham/efficientpicomputation
0
2172052
m1 = 1 digits = int(input("How many digits of pi would you like to compute?")) m2 = 10**digits # Mass of object at rest: m1 # Mass of moving object: m2 initVector = [0,1] # [Velocity of m1, Velocity of m2] currentVector = initVector def mulA(vec): msum = (m1 + m2) a11 = (m1 - m2)/float(msum) a12 = 2*m2/float(msum) a21 = 2*m1/float(msum) a22 = (m2 - m1)/float(msum) v1 = a11*vec[0] + a12*vec[1] v2 = a21*vec[0] + a22*vec[1] return [v1, v2] # When the lighter mass hits the wall, # reverse its velocity but maintain velocity of larger mass def mulW(vec): v1 = (-1)*vec[0] v2 = vec[1] return [v1, v2] # Checks if the speed of the inner/smaller mass is # faster than the outer/larger one def checkspeeds(v1, v2): if (v1 <= 0 and v2 <=0): if(abs(v1) >= abs(v2)): return False return True collisioncount = 0 mtype = 'A' while(checkspeeds(currentVector[0], currentVector[1])): if (mtype == 'A'): newVec = mulA(currentVector) currentVector = newVec collisioncount += 1 #increment counter on collision mtype = 'W' elif (mtype == 'W'): newVec = mulW(currentVector) currentVector = newVec collisioncount += 1 #increment counter on collision mtype = 'A' print(f"Mass at moving initally: " ) print(f"Total number of collisions of the masses: {collisioncount}")
1,411
Build/python/mtm/ioc/IocAssertions.py
xjjon/Zenject
3,044
2171972
from mtm.util.Assert import * import collections def IsInstanceOf(*classes): def test(obj): assertThat(obj is None or isinstance(obj, classes), \ "Expected one of types '{0}' but found '{1}'".format(', '.join(map(lambda t: t.__name__, classes)), type(obj).__name__)) return test def HasAttributes(*attributes): def test(obj): for each in attributes: if not hasattr(obj, each): return False return True return test def HasMethods(*methods): def test(obj): for methodName in methods: assertThat(hasattr(obj, methodName), \ "Unable to find method '{0}' on object with type '{1}'".format(methodName, type(obj).__name__)) assertThat(isinstance(getattr(obj, methodName), collections.Callable)) return True return test
817
decisionProject/vote/views.py
wldusdhso/likelion_ideaton2
0
2171808
from django.shortcuts import render, get_object_or_404, redirect from .models import Question, Choice from django.utils import timezone from django.contrib.auth.models import User # Create your views here. def vote_list(request): question_list = Question.objects.order_by('pub_date') return render(request, 'vote_list.html', {'question_list': question_list}) def detail(request, question_id): question = get_object_or_404(Question, pk=question_id) return render(request, 'detail.html', {'question': question}) def vote(request, question_id): question = get_object_or_404(Question, pk=question_id) return render(request, 'vote.html', {'question': question}) def create(request): if request.method =='POST': new_question = Question() new_question.title = request.POST['title'] new_question.pub_date = timezone.now() new_question.writer = request.user new_question.save() for i in range(int(request.POST['count'])): new_choice = Choice() text = 'text'+str(i) new_choice.question = new_question new_choice.text = request.POST[text] new_choice.save() return redirect('vote:vote_list') else : return render(request, 'create.html') def add_vote(request, question_id): question = get_object_or_404(Question, pk=question_id) question.total_votes += 1 selected_choice = question.choice_set.get(pk=request.POST['choice']) selected_choice.votes += 1 selected_choice.save() question.save() return redirect('vote:detail', question_id) def update(request, question_id): if request.method =='POST': upd_question = get_object_or_404(Question, pk=question_id) upd_question.title = request.POST['title'] upd_question.pub_date = timezone.now() upd_question.writer = request.POST['writer'] choice_set = upd_question.choice_set.all() for elem in choice_set: elem.delete() upd_question.save() # print(request.POST['count']) for i in range(int(request.POST['count'])): new_choice = Choice() text = 'text'+str(i) new_choice.question = upd_question new_choice.text = request.POST[text] new_choice.save() return redirect('vote:vote_list') else : question = get_object_or_404(Question, pk=question_id) return render(request, 'update.html', {'question': question}) def delete(request, question_id): del_question = get_object_or_404(Question, pk=question_id) del_question.delete() return redirect('vote:vote_list') def delete_choice(request, question_id, choice_id): question = get_object_or_404(Question, pk=question_id) deleted_choice = question.choice_set.get(pk=choice_id) delete_choice.delete() question.save() return redirect('/')
2,920
src/models.py
kazqvaizer/checklistbot
5
2168627
from datetime import datetime, timedelta from typing import Optional import peewee as pw from envparse import env from telegram.update import Update env.read_envfile() db = pw.SqliteDatabase(env("DATABASE_URL")) def _utcnow(): return datetime.utcnow() def _get_recent_threshold(): return _utcnow() - timedelta(hours=2) def _format_item(index: int, item: "TodoItem",) -> str: line = f"{index}. {item.text}" return f"<s>{line}</s>" if item.is_checked else line class BaseModel(pw.Model): created = pw.DateTimeField(default=_utcnow) modified = pw.DateTimeField(null=True) class Meta: database = db def save(self, *args, **kwargs): if self.id is not None: self.modified = _utcnow() return super().save(*args, **kwargs) class Chat(BaseModel): chat_id = pw.BigIntegerField(unique=True) chat_type = pw.CharField(null=True) username = pw.CharField(null=True) first_name = pw.CharField(null=True) last_name = pw.CharField(null=True) language_code = pw.CharField(default="en") enabled = pw.BooleanField(default=True) @property def items(self) -> pw.Select: return self.todo_items.select().order_by(TodoItem.id.asc()) @property def has_not_checked_items(self) -> bool: return self.items.where(TodoItem.is_checked == False).exists() @property def has_no_items_at_all(self) -> bool: return not self.items.exists() @property def has_recently_modified_items(self) -> bool: return self.items.where(TodoItem.modified > _get_recent_threshold()).exists() @property def has_recently_created_items(self) -> bool: return self.items.where(TodoItem.created > _get_recent_threshold()).exists() @property def has_no_recent_activity(self) -> bool: return not (self.has_recently_modified_items or self.has_recently_created_items) def get_item_by_index(self, index: int) -> Optional["TodoItem"]: return self.items.offset(index - 1).first() if index > 0 else None def get_formatted_items(self) -> str: return "\n".join([_format_item(*args) for args in enumerate(self.items, 1)]) def delete_items(self): TodoItem.delete().where(TodoItem.chat == self).execute() @classmethod def get_or_create_from_update(cls, update: Update) -> "Chat": chat_id = update.effective_chat.id defaults = dict( chat_type=update.effective_chat.type, username=update.effective_chat.username, first_name=update.effective_chat.first_name, last_name=update.effective_chat.last_name, language_code=update.effective_user.language_code or "en", ) return cls.get_or_create(chat_id=chat_id, defaults=defaults)[0] class Message(BaseModel): message_id = pw.IntegerField(null=True) chat = pw.ForeignKeyField(Chat, backref="messages") date = pw.DateTimeField(null=True) text = pw.CharField(null=True) @classmethod def create_from_update(cls, update: Update) -> "Message": return cls.create( chat=Chat.get_or_create_from_update(update), message_id=update.effective_message.message_id, date=update.effective_message.date, text=update.effective_message.text, ) class TodoItem(BaseModel): chat = pw.ForeignKeyField(Chat, backref="todo_items") is_checked = pw.BooleanField(default=False) text = pw.CharField() app_models = BaseModel.__subclasses__()
3,538
tuchong/tuchong/items.py
XuShengYuu/Tuchong
2
2171998
# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # https://doc.scrapy.org/en/latest/topics/items.html # import scrapy from scrapy import Item, Field class TuchongItem(Item): # define the fields for your item here like: # name = scrapy.Field() collection = 'images' author_id = Field() comments = Field() delete = Field() favorites = Field() image_count = Field() images = Field() post_id = Field() published_at = Field() site = Field() tags = Field() title = Field() title_image = Field() type = Field() update = Field() url = Field()
659
WebMirror/management/rss_parser_funcs/feed_parse_extractRisingDragons.py
fake-name/ReadableWebProxy
193
2169861
def extractRisingDragons(item): """ # Rising Dragons Translation """ vol, chp, frag, postfix = extractVolChapterFragmentPostfix(item['title']) if not (chp or vol) or 'preview' in item['title'].lower(): return None if 'God and Devil World' in item['tags'] and 'Release' in item['tags']: return buildReleaseMessageWithType(item, '<NAME>', vol, chp, frag=frag, postfix=postfix) return False
400
preprocessing/use_cases/dialogues.py
MinCiencia/ECQQ
4
2172238
import use_cases.utils.textools as tt from use_cases.utils.comunas import get_comunas_id import pandas as pd import numpy as np import re, os def change_valid_to_bool(x): if x == '1': x = True else: x = False return x def create_table_dialogues(frame, filter): new_frame = frame.copy() filter = filter.rename(columns={'ID_diag': 'ID'}) new_frame['Grupo'] = tt.check_nan(new_frame['Grupo']) new_frame = pd.merge(new_frame, filter, how="inner", on=["ID"]) new_frame = new_frame[['ID Archivo', 'Fecha', 'Hora Inicio', 'Hora Termino', 'Lugar', 'Dirección', 'Comuna', 'Participantes', 'Grupo', 'Valido']] new_frame = tt.to_unicode(new_frame) new_frame = tt.eliminate_nrs(new_frame) new_frame = new_frame.rename(columns={'file_id':'diag_id'}) new_frame.columns =['id', 'date', 'init_time', 'end_time', 'location', 'address', 'comuna_id', 'n_members', 'group_name', 'valid'] new_frame = new_frame.apply(lambda x: get_comunas_id(x, 'comuna_id'), 1) new_frame['valid'] = new_frame['valid'].apply(lambda x: change_valid_to_bool(x), 1) return new_frame
1,239
035.py
xianlinfeng/project_euler_python3
0
2172236
import eulerlib def is_circular_prime(n): s = str(n) return all(eulerlib.is_prime(int(s[i:] + s[:i])) for i in range(len(s))) def compute(): ans = sum(1 for i in range(1, 1000000) if is_circular_prime(i)) return ans if __name__ == "__main__": print(compute())
286
notebooks/cnn_tester.py
MichoelSnow/data_science
0
2171426
from fastai.groups.default_cnn import * PATH = '/data/msnow/nih_cxr/' arch = resnet34 sz = 64 bs = 64 # data = get_data(sz,bs) # learn = ConvLearner.pretrained(arch, data) # def get_data(sz, bs): # tfms = tfms_from_model(arch, sz, aug_tfms=transforms_basic, max_zoom=1.05) # return ImageClassifierData.from_csv(PATH, 'trn', f'{PATH}data_trn.csv', tfms=tfms, # val_idxs=val_idx, test_name='tst', bs=bs, cat_separator='|')
452
backend/apps/workers/migrations/0002_rename_charge_worker_position.py
jorgejimenez98/backend-evaluacion-desempenno
0
2172049
# Generated by Django 3.2.2 on 2021-06-02 05:35 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('workers', '0001_initial'), ] operations = [ migrations.RenameField( model_name='worker', old_name='charge', new_name='position', ), ]
354
dataset.py
bigpo/CIFAR-ZOO
0
2171792
import os.path as osp from PIL import Image import numpy as np from torch.utils.data import Dataset class CustomDataset(Dataset): def __init__(self, ann_file, root, transform=None, train=True): # prefix of images path self.img_prefix = root # load annotations (and proposals) self.img_infos = self.load_annotations(ann_file) self.is_train = train self.transform = transform def __len__(self): return len(self.img_infos) def load_annotations(self, ann_file): with open(ann_file) as fp: img_infos = [line.rstrip('\n') for line in fp] return [{'filename': line.split(' ')[0], 'label': line.split(' ')[1]} for line in img_infos] def get_ann_info(self, idx): return self.img_infos[idx]['label'] def _rand_another(self): pool = range(len(self.img_infos)) return np.random.choice(pool) def __getitem__(self, idx): if not self.is_train: return self.prepare_test_img(idx) while True: data = self.prepare_train_img(idx) if data is None: idx = self._rand_another() continue return data def prepare_train_img(self, idx): img_info = self.img_infos[idx] img = Image.open(osp.join(self.img_prefix, img_info['filename'])) img = self.transform(img) label = self.get_ann_info(idx) return img, int(label) def prepare_test_img(self, idx): """Prepare an image for testing (multi-scale and flipping)""" img_info = self.img_infos[idx] img = Image.open(osp.join(self.img_prefix, img_info['filename'])) img = self.transform(img) label = self.get_ann_info(idx) return img, int(label)
1,878
pulotu/datatables.py
blurks/pulotu
0
2172216
from sqlalchemy.orm import joinedload from clld.db.util import get_distinct_values from clld.db.models import common from clld.web import datatables from clld.web.datatables.base import LinkCol, Col, LinkToMapCol from clld.web.datatables.parameter import Parameters from clld.web.datatables.value import Values, ValueNameCol, RefsCol, ValueSetCol from clldutils.misc import dict_merged, slug from pulotu import models class Cultures(datatables.Languages): def get_options(self): opts = datatables.Languages.get_options(self) opts['iDisplayLength'] = 150 return opts def col_defs(self): return [ LinkCol(self, 'name'), Col(self, 'ethonyms', model_col=models.Variety.ethonyms), Col(self, 'latitude', sDescription='<small>The geographic latitude</small>'), Col(self, 'longitude', sDescription='<small>The geographic longitude</small>'), LinkToMapCol(self, 'm'), ] class CategoryCol(Col): def order(self): return models.Feature.category_ord class SectionCol(Col): def order(self): return models.Feature.section_ord class SubsectionCol(Col): def order(self): return models.Feature.subsection_ord class Questions(Parameters): def __init__(self, req, model, **kw): self.focus = kw.pop('focus', req.params.get('focus', None)) if self.focus: kw['eid'] = 'dt-parameters-' + slug(self.focus) super(Questions, self).__init__(req, model, **kw) def xhr_query(self): return dict_merged(super(Questions, self).xhr_query(), focus=self.focus) def base_query(self, query): if self.focus: query = query.filter(models.Feature.category == self.focus) return query def get_options(self): opts = super(Questions, self).get_options() opts['aaSorting'] = [[0, 'asc'], [1, 'asc'], [2, 'asc']] return opts def col_defs(self): return [ #CategoryCol(self, 'category', model_col=models.Feature.category, choices=get_distinct_values(models.Feature.category)), SectionCol(self, 'section', model_col=models.Feature.section, choices=get_distinct_values(models.Feature.section)), SubsectionCol(self, 'subsection', model_col=models.Feature.subsection), LinkCol(self, 'name'), ] class ResponseCol(ValueNameCol): def format(self, item): return self.get_attrs(item)['label'] class Responses(Values): def col_defs(self): name_col = ResponseCol(self, 'value') if self.parameter and self.parameter.domain: name_col.choices = [de.name for de in self.parameter.domain] res = [] if self.parameter: return res + [ LinkCol(self, 'language', model_col=common.Language.name, get_object=lambda i: i.valueset.language), name_col, RefsCol(self, 'source'), LinkToMapCol(self, 'm', get_object=lambda i: i.valueset.language), ] if self.language: return res + [ name_col, LinkCol(self, 'parameter', sTitle=self.req.translate('Parameter'), model_col=common.Parameter.name, get_object=lambda i: i.valueset.parameter), RefsCol(self, 'source'), ] return res + [ name_col, ValueSetCol(self, 'valueset', bSearchable=False, bSortable=False), ] def includeme(config): config.register_datatable('languages', Cultures) config.register_datatable('parameters', Questions) config.register_datatable('values', Responses)
3,913
declutterDesktop.py
goonerlagoon/Declutter-Desktop
0
2172231
''' small script for organizing my desktop by clearing everything except shortcut links and placing them in their appropriate location (i.e. "home_base"). Author: <NAME>. Date: 12-11-2021 ''' from pathlib import Path def main(): desktop_folder = Path.home() / 'Desktop' home_base = Path.home() / 'Documents' for file_obj in desktop_folder.glob('*/'): if file_obj.is_dir(): try: file_obj.replace(home_base / file_obj.parts[-1]) # parts[-1] grabs the file name plus ext except IOError as err: print(f"couldn't move {file_obj}. here's the error msg: {err}") continue except Exception as err: print(f"couldnt move {file_obj}. err msg: {err}") continue elif file_obj.suffix == '.lnk': continue else: # extract the extension of each file and make it the dir name # if it doesnt already exist sfx = file_obj.suffix[1:].upper() + 's' try: extension_dir = home_base / sfx extension_dir.mkdir(exist_ok=True) file_obj.replace(extension_dir / file_obj.parts[-1]) except Exception as err: print(f"couldnt move {file_obj}. err msg: {err}") continue print("*" * 40) print("PROCESS COMPLETE!") if __name__ == '__main__': main()
1,481
src/codersstrikeback.py
newlyedward/datascinece
2
2170503
import math class Point: def __init__(self, x, y): self.x = x self.y = y def get_distance(self, point): return math.sqrt(self.get_distance2(point)) def get_distance2(self, point): return (self.x - point.x) ** 2 + (self.y - point.y) ** 2 @property def angle(self): return self.get_relative_angle(Point(0, 0)) def get_relative_angle(self, point): """ 获取输入点相对于当前点的x轴轴角度 x轴:16000 units wide y轴:9000 units high X=0, Y=0 is the top left pixel An angle of 0 corresponds to facing east, 90 is south, 180 west and 270 north :param point: 指向的目标点,当前点位起点 :return: """ if self == point: return 0 d = self.get_distance(point) dx = (point.x - self.x) / d angle = math.acos(dx) * 180 / math.pi if self.y < point.y: angle = 360 - angle return angle def __eq__(self, other): return self.x == other.x and self.y == other.y def __repr__(self): return "({}, {})".format(self.x, self.y) class Unit(Point): def __init__(self, x, y, radius): super().__init__(x, y) self.radius = radius def collision(self, unit): pass def bounce(self, unit): pass class Pod(Unit): def __init__(self, x, y, checkpoint, radius=400): super().__init__(x, y, radius) self.checkpoint = checkpoint self.radius = radius # 假设开始就朝向目标 self.orient = self.get_relative_angle(checkpoint) self.checkpoint_angle = 0 # 速度分解 self.vx = 0 self.vy = 0 self.timeout = 100 # def set_orient(self, checkpoint_angle): # # 如果有checkpoint_angle 输入 # self.checkpoint_angle = checkpoint_angle # self.orient = (self.get_relative_angle(self.checkpoint) + 180 - checkpoint_angle) % 360 def set_checkpoint(self, checkpoint): # 改变目标后,pod的方向orient不会变,需要修改checkpoint_angle self.checkpoint = checkpoint self.checkpoint_angle = self.cal_checkpoint_angle() def cal_checkpoint_angle(self): angle = self.get_relative_angle(self.checkpoint) # 统一顺时针(往右)转动为正值,往左转是负值,返回值为 [-180, 180] right = angle - self.orient if self.orient <= angle else 360 - self.orient + angle left = self.orient - angle if self.orient >= angle else self.orient + 360 - angle if right < left: return right else: return -left def rotate(self): """ 最大转向角度为+-18度,实际达不到,可能跟thrust有关系 :return: """ if self.checkpoint_angle > 18: delta_angle = 18 elif self.checkpoint_angle < -18: delta_angle = -18 else: delta_angle = self.checkpoint_angle self.orient += delta_angle if self.orient >= 360: self.orient -= 360 elif self.orient < 0: self.orient += 360 def cal_velocity(self, thrust): """ 速度有一个上限 :param thrust: :return: """ radian = self.orient * math.pi / 180 self.vx += math.cos(radian) * thrust self.vy += math.sin(radian) * thrust def move(self, turn=1): """ 如果有碰撞,turn设置为0.5, 一半为一个turn的移动 :param turn: :return: """ self.x += self.vx * turn self.y += self.vy * turn def end(self): """ Once the pod has moved we need to apply friction and round (or truncate) the values. :return: """ self.x = round(self.x) self.y = round(self.y) # 摩擦系数 0.85 friction = 0.85 self.vx = int(self.vx * friction) self.vy = int(self.vy * friction) # Don't forget that the timeout goes down by 1 each turn. # It is reset to 100 when you pass a checkpoint self.timeout -= 1 def simulate_a_turn(self, thrust): self.rotate() self.cal_velocity(thrust) self.move() self.end() if __name__ == "__main__": # from src.codersstrikeback import Unit mypos = Point(10705, 5691) target = Point(3571, 5202)
4,217
url_shortener/tests.py
wandering-tales/django-miny-tiny-url
1
2170768
import pytest from django.urls import reverse from rest_framework import status from rest_framework.test import APITestCase from url_shortener.baseconv import base10to62 from url_shortener.models import ShortURL from url_shortener.serializers import ShortURLSerializer pytestmark = pytest.mark.django_db class ShortURLTests(APITestCase): def test_create_short_url(self): """ Ensure we can create a new short URL object and that the response data matches the current object instance. """ # Create a short URL url = reverse('shorturl-list') data = {'url': 'https://en.wikipedia.org/wiki/Test-driven_development'} response = self.client.post(url, data, format='json') # Verify response status self.assertEqual(response.status_code, status.HTTP_201_CREATED) # Check if the number of short URL instances is one self.assertEqual(ShortURL.objects.count(), 1) # Retrieve the first (and only) short URL instance instance = ShortURL.objects.get() # Check if the 'url' attribute in the response data # matches the 'url' field of the model instance self.assertEqual(response.data['url'], instance.url) # Check if the 'short_url' attribute in the response data # matches the one computed by base 10 to base 62 converter self.assertEqual(response.data['short_url'], base10to62.from_decimal(instance.id)) # Check if the 'usage_count' attribute in the response data is equal to 0 self.assertEqual(response.data['usage_count'], 0) def test_forward_short_url(self): """ Ensure a short URL call is permanently redirected to the original URL. """ # Create a short URL url = reverse('shorturl-list') data = {'url': 'http://lkml.iu.edu/hypermail/linux/kernel/1510.3/02866.html'} response = self.client.post(url, data, format='json') short_url = response.data['short_url'] # Call short URL url = reverse('shorturl-detail', args=[short_url]) response = self.client.get(url) # Verify the response status code is a 301 self.assertEqual(response.status_code, status.HTTP_301_MOVED_PERMANENTLY) # Verify the location header is set to the target URL self.assertEqual(response.get('location'), data['url']) def test_info_short_url(self): """ Ensure the short URL info API response is the serialization of a short URL instance. """ # Create a short URL url = reverse('shorturl-list') data = {'url': 'https://uwsgi-docs.readthedocs.io/en/latest/articles/TheArtOfGracefulReloading.html'} response = self.client.post(url, data, format='json') short_url = response.data['short_url'] # Get short URL info url = reverse('shorturl-info', args=[short_url]) response = self.client.get(url) # Retrieve the first (and only) short URL instance instance = ShortURL.objects.get() # Serialize the short URL instance serializer = ShortURLSerializer(instance) # Ensure the short URL info API response is equal to the serialized data self.assertEqual(response.data, serializer.data) def test_usage_count(self): """ Ensure the usage count is increased at every call to a short URL. """ # Create a short URL url = reverse('shorturl-list') data = {'url': 'https://docs.djangoproject.com/en/2.0/ref/urlresolvers/#reverse'} response = self.client.post(url, data, format='json') short_url = response.data['short_url'] # Call short URL three times url = reverse('shorturl-detail', args=[short_url]) self.client.get(url) self.client.get(url) self.client.get(url) # Get short URL info url = reverse('shorturl-info', args=[short_url]) response = self.client.get(url) # Verify the current usage count is equal to 3 self.assertEqual(response.data['usage_count'], 3) def test_duplicated_urls(self): """ Ensure it's impossible to create two short URLs for the same original URL. """ # Create a short URL url = reverse('shorturl-list') data = {'url': 'https://www.djangosnippets.org/snippets/1431/'} response = self.client.post(url, data, format='json') # Verify response status self.assertEqual(response.status_code, status.HTTP_201_CREATED) # Create a short URL for the same URL response = self.client.post(url, data, format='json') # Verify response status self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
4,781
tests/modules/newsgroups/test_newsgroups.py
rlin0/donut
0
2172212
import flask from donut.modules.newsgroups import helpers from donut.testing.fixtures import client from datetime import datetime def test_get_newsgroups(client): assert helpers.get_newsgroups() == [{ 'group_name': 'Donut Devteam', 'group_id': 1 }, { 'group_name': 'IHC', 'group_id': 3 }] def test_get_my_newsgroups(client): assert helpers.get_my_newsgroups(5) == [{ 'group_name': 'IHC', 'group_id': 3 }] def test_get_can_send_groups(client): assert helpers.get_my_newsgroups(5, True) == [{ 'group_name': 'IHC', 'group_id': 3 }, { 'group_name': 'Donut Devteam', 'group_id': 1 }] assert helpers.get_my_newsgroups(100, True) == [{ 'group_name': 'Donut Devteam', 'group_id': 1 }] def test_get_my_positions(client): assert helpers.get_posting_positions(3, 5) == [{ 'pos_name': 'IHC Member', 'pos_id': 5 }] assert helpers.get_posting_positions(3, 1) == () def test_get_user_actions(client): assert helpers.get_user_actions(5, 3) == { 'send': 1, 'control': 0, 'receive': 1 } def test_email_newsgroup(client): data = { 'group': 1, 'subject': 'Subj', 'plain': 'msg to donut', 'poster': 'Head' } helpers.insert_email(1, data) res = helpers.get_past_messages(1, 1)[0] expected = { 'subject': 'Subj', 'message': 'msg to donut', 'user_id': 1, 'post_as': 'Head' } for key in expected: assert res[key] == expected[key] expected['group_name'] = 'Donut Devteam' expected['group_id'] = 1 res = helpers.get_post(1) for key in expected: assert res[key] == expected[key] def test_subscriptions(client): helpers.apply_subscription(5, 1) assert helpers.get_applications(1) == [{ 'user_id': 5, 'group_id': 1, 'name': '<NAME>', 'email': '<EMAIL>' }] helpers.remove_application(5, 1) assert helpers.get_applications(1) == () def test_get_owners(client): assert helpers.get_owners(2) == { 'President': [{ 'user_id': 2, 'full_name': '<NAME>' }, { 'user_id': 4, 'full_name': '<NAME>' }] }
2,362
tlx/util/cli_apps/get_aws_creds.py
eL0ck/tlx
0
2168195
#!/usr/bin/env python from __future__ import print_function import click import os import sys from tlx.util import Session @click.command(context_settings=dict(max_content_width=120)) @click.option('--profile', '-p', default='default', help="A profile defined in `~/.aws/credentials`. If it requires an MFA token a prompt will be given") @click.option('--quiet', default=False, is_flag=True, help='If using the outputs directly, see --help.') def main(profile, quiet): """ GET AWS CREDS (GAC): Gets temporary AWS session from a profile (user or role). Allows you to export into your shell and run tools expecting the AWS standard environment variables. Namely: \b - AWS_ACCESS_KEY_ID - AWS_SECRET_ACCESS_KEY - AWS_SESSION_TOKEN Your aws credentials file (default: `~/.aws/credentials`) should be configured as follows: A Role profile would look like: \b [roleprofilename] region = us-east-2 role_arn = arn:aws:iam::************:role/<IAM_Role_name> mfa_serial = arn:aws:iam::<AccountID>:mfa/<IAM_user_name> source_profile = default Or a user profile might look like: \b [userprofilename] aws_access_key_id = AKIA**************** aws_secret_access_key = <KEY> mfa_serial = arn:aws:iam::<AccountID>:mfa/<IAM_user_name> region = ap-southeast-2 Notice that the user profile contains `mfa_serial`. This is not standard aws config however it is supported by GAC. If it is available, GAC will use it to get temporary session tokens by requesting your associated MFA code. This is useful for when your user has higher account priviledges that require MFA auth. Returns: commands to copy to your shell terminal -------------------------- ADVANCED USAGE -------------------------------------- Consider appending the following to your `.bashrc` to have the environment variables set automatically: \b gac () { for i in "$( get-aws-creds "$@" --quiet)"; do eval "${i}"; done } Or alternatively, send the variables to a file and source them from your profile so that all terminal sessions receive the same temporary credentials: \b $ get-aws-creds --quiet > /tmp/awscreds $ source /tmp/awscreds """ # Issue #15 - We may be trying to assume another role from a # shell that has previously had its temporary variables populated if 'AWS_SECRET_ACCESS_KEY' in os.environ: del os.environ['AWS_SECRET_ACCESS_KEY'] if 'AWS_ACCESS_KEY_ID' in os.environ: del os.environ['AWS_ACCESS_KEY_ID'] try: session = Session(profile=profile) creds = session.get_session_creds() except Exception as e: print("{}: {}".format(type(e).__name__, e)) sys.exit(1) if not quiet: print("Keys and token for profile: '{profile}'".format(profile=profile)) print("Paste the following into your shell:\n") print("export AWS_ACCESS_KEY_ID={}".format(creds.access_key)) print("export AWS_SECRET_ACCESS_KEY={}".format(creds.secret_key)) # non-temporary keys are returned by the boto3.Session if the # user is not required to use MFA token. Printing out a null # field for this will cause malformed errors when using. if creds.token: print("export AWS_SESSION_TOKEN={}".format(creds.token))
3,610
2021/day21.py
tangarts/advent-of-code
0
2172013
from functools import lru_cache from itertools import product from advent_of_code.core import parse_input raw = """Player 1 starting position: 4 Player 2 starting position: 8""" test = parse_input(raw, parser=lambda s: int(s[-1])) positions = parse_input('data/input21.txt', parser=lambda s: int(s[-1]), test=False) def turn(position, roll): return (position + roll) % 10 def part1(position): # player, starting_pos score = [0, 0] roll = 0 j = 1 while True: for i in [0, 1]: roll += 3 position[i] = turn(position[i], 3 * (j + 1)) j = (j + 3) % 100 score[i] += position[i] if position[i] != 0 else 10 # when statement if score[i] > 999: return min(score) * roll # part1 assert part1(positions) == 926610 all_states = [sum(x) for x in product([1, 2, 3], repeat=3)] @lru_cache(maxsize=None) def play(position1, position2, score1, score2): if score1 >= 21: return 1, 0 if score2 >= 21: return 0, 1 win1 = win2 = 0 for state in all_states: new_position = turn(position1, state) new_score = score1 + (new_position if new_position else 10) w2, w1 = play(position2, new_position, score2, new_score) win1 += w1 win2 += w2 return win1, win2 # part2 assert max(play(6, 2, 0, 0)) == 146854918035875
1,395
server/algos/euler/tests/integration/test_euler.py
yizhang7210/Acre
2
2170543
# pylint: disable=missing-docstring import datetime import math from decimal import Decimal from algos.euler.models import training_samples as ts from algos.euler.models import predictions, predictors from algos.euler.runner import Euler from core.models import instruments from datasource.models import candles from django.test import TestCase from .test_setup import TestSetup TWO_PLACES = Decimal('0.01') class EulerAlgoTest(TestCase): @classmethod def setUpClass(cls): super(EulerAlgoTest, cls).setUpClass() TestSetup.set_up_instruments() TestSetup.set_up_candles() cls.set_up_predictors() @classmethod def set_up_predictors(cls): params = {'max_depth': 3, 'min_samples_split': 2} predictor = predictors.create_one( name='treeRegressor', parameters=params, is_active=True) predictor.save() @classmethod def tearDownClass(cls): super(EulerAlgoTest, cls).tearDownClass() predictors.delete_all() predictions.delete_all() ts.delete_all() candles.delete_all() instruments.delete_all() def test_euler_end_of_day(self): # Given today = datetime.date(2017, 12, 6) # When euler_thread = Euler(today, cv_fold=2) euler_thread.run() # Then new_predictions = predictions.get_all(['date']) self.assertEqual(len(new_predictions), 1) self.assertEqual(new_predictions[0].date, today) self.assertFalse(math.isnan(new_predictions[0].score))
1,560
odoo/base-addons/account/tests/test_invoice_tax_amount_by_group.py
LucasBorges-Santos/docker-odoo
0
2171564
# -*- coding: utf-8 -*- from odoo.addons.account.tests.account_test_savepoint import AccountTestInvoicingCommon from odoo.tests import tagged @tagged('post_install', '-at_install') class TestInvoiceTaxAmountByGroup(AccountTestInvoicingCommon): @classmethod def setUpClass(cls, chart_template_ref=None): super().setUpClass(chart_template_ref=chart_template_ref) cls.tax_group1 = cls.env['account.tax.group'].create({'name': '1'}) cls.tax_group2 = cls.env['account.tax.group'].create({'name': '2'}) def assertAmountByTaxGroup(self, invoice, expected_values): current_values = [(x[6], x[2], x[1]) for x in invoice.amount_by_group] self.assertEqual(current_values, expected_values) def test_multiple_tax_lines(self): tax_10 = self.env['account.tax'].create({ 'name': "tax_10", 'amount_type': 'percent', 'amount': 10.0, 'tax_group_id': self.tax_group1.id, }) tax_20 = self.env['account.tax'].create({ 'name': "tax_20", 'amount_type': 'percent', 'amount': 20.0, 'tax_group_id': self.tax_group2.id, }) invoice = self.env['account.move'].create({ 'type': 'out_invoice', 'partner_id': self.partner_a.id, 'invoice_date': '2019-01-01', 'invoice_line_ids': [ (0, 0, { 'name': 'line', 'account_id': self.company_data['default_account_revenue'].id, 'price_unit': 1000.0, 'tax_ids': [(6, 0, (tax_10 + tax_20).ids)], }), (0, 0, { 'name': 'line', 'account_id': self.company_data['default_account_revenue'].id, 'price_unit': 1000.0, 'tax_ids': [(6, 0, tax_10.ids)], }), (0, 0, { 'name': 'line', 'account_id': self.company_data['default_account_revenue'].id, 'price_unit': 1000.0, 'tax_ids': [(6, 0, tax_20.ids)], }), ] }) self.assertAmountByTaxGroup(invoice, [ (self.tax_group1.id, 2000.0, 200.0), (self.tax_group2.id, 2000.0, 400.0), ]) # Same but both are sharing the same tax group. tax_20.tax_group_id = self.tax_group1 invoice.invalidate_cache(['amount_by_group']) self.assertAmountByTaxGroup(invoice, [ (self.tax_group1.id, 3000.0, 600.0), ]) def test_zero_tax_lines(self): tax_0 = self.env['account.tax'].create({ 'name': "tax_0", 'amount_type': 'percent', 'amount': 0.0, }) invoice = self.env['account.move'].create({ 'type': 'out_invoice', 'partner_id': self.partner_a.id, 'invoice_date': '2019-01-01', 'invoice_line_ids': [ (0, 0, { 'name': 'line', 'account_id': self.company_data['default_account_revenue'].id, 'price_unit': 1000.0, 'tax_ids': [(6, 0, tax_0.ids)], }), ] }) self.assertAmountByTaxGroup(invoice, [ (tax_0.tax_group_id.id, 1000.0, 0.0), ]) def test_tax_affect_base_1(self): tax_10 = self.env['account.tax'].create({ 'name': "tax_10", 'amount_type': 'percent', 'amount': 10.0, 'tax_group_id': self.tax_group1.id, 'price_include': True, 'include_base_amount': True, }) tax_20 = self.env['account.tax'].create({ 'name': "tax_20", 'amount_type': 'percent', 'amount': 20.0, 'tax_group_id': self.tax_group2.id, }) invoice = self.env['account.move'].create({ 'type': 'out_invoice', 'partner_id': self.partner_a.id, 'invoice_date': '2019-01-01', 'invoice_line_ids': [ (0, 0, { 'name': 'line', 'account_id': self.company_data['default_account_revenue'].id, 'price_unit': 1100.0, 'tax_ids': [(6, 0, (tax_10 + tax_20).ids)], }), (0, 0, { 'name': 'line', 'account_id': self.company_data['default_account_revenue'].id, 'price_unit': 1100.0, 'tax_ids': [(6, 0, tax_10.ids)], }), (0, 0, { 'name': 'line', 'account_id': self.company_data['default_account_revenue'].id, 'price_unit': 1000.0, 'tax_ids': [(6, 0, tax_20.ids)], }), ] }) self.assertAmountByTaxGroup(invoice, [ (self.tax_group1.id, 2000.0, 200.0), (self.tax_group2.id, 2100.0, 420.0), ]) # Same but both are sharing the same tax group. tax_20.tax_group_id = self.tax_group1 invoice.invalidate_cache(['amount_by_group']) self.assertAmountByTaxGroup(invoice, [ (self.tax_group1.id, 3000.0, 620.0), ]) def test_tax_affect_base_2(self): tax_10 = self.env['account.tax'].create({ 'name': "tax_10", 'amount_type': 'percent', 'amount': 10.0, 'tax_group_id': self.tax_group1.id, 'include_base_amount': True, }) tax_20 = self.env['account.tax'].create({ 'name': "tax_20", 'amount_type': 'percent', 'amount': 20.0, 'tax_group_id': self.tax_group1.id, }) tax_30 = self.env['account.tax'].create({ 'name': "tax_30", 'amount_type': 'percent', 'amount': 30.0, 'tax_group_id': self.tax_group2.id, 'include_base_amount': True, }) invoice = self.env['account.move'].create({ 'type': 'out_invoice', 'partner_id': self.partner_a.id, 'invoice_date': '2019-01-01', 'invoice_line_ids': [ (0, 0, { 'name': 'line', 'account_id': self.company_data['default_account_revenue'].id, 'price_unit': 1000.0, 'tax_ids': [(6, 0, (tax_10 + tax_20).ids)], }), (0, 0, { 'name': 'line', 'account_id': self.company_data['default_account_revenue'].id, 'price_unit': 1000.0, 'tax_ids': [(6, 0, (tax_30 + tax_10).ids)], }), ] }) self.assertAmountByTaxGroup(invoice, [ (self.tax_group1.id, 2300.0, 450.0), (self.tax_group2.id, 1000.0, 300.0), ]) # Same but both are sharing the same tax group. tax_30.tax_group_id = self.tax_group1 invoice.invalidate_cache(['amount_by_group']) self.assertAmountByTaxGroup(invoice, [ (self.tax_group1.id, 2000.0, 750.0), ])
7,324
object_detector/config.py
vaibhavhariaramani/Object-detector-from-HOG-Linear-SVM-framework
26
2172295
""" Set config variables """ import configparser as cp import json import random import numpy as np config = cp.RawConfigParser() config.read('../data/config/config.cfg') WINDOW_SIZE = json.loads(config.get('hog', 'window_size')) WINDOW_STEP_SIZE = config.getint('hog', 'window_step_size') ORIENTATIONS = config.getint('hog', 'orientations') PIXELS_PER_CELL = json.loads(config.get('hog', 'pixels_per_cell')) CELLS_PER_BLOCK = json.loads(config.get('hog', 'cells_per_block')) VISUALISE = config.getboolean('hog', 'visualise') NORMALISE = config.get('hog', 'normalise') if NORMALISE == 'None': NORMALISE = None THRESHOLD = config.getfloat('nms', 'threshold') MODEL_PATH = config.get('paths', 'model_path') PYRAMID_DOWNSCALE = config.getfloat('general', 'pyramid_downscale') POS_SAMPLES = config.getint('general', 'pos_samples') NEG_SAMPLES = config.getint('general', 'neg_samples') RANDOM_STATE = 31 random.seed(RANDOM_STATE) np.random.seed(RANDOM_STATE)
967
Desafios/desafio017.py
ward910/Python-Aprendizado
2
2171632
from math import hypot ca = float(input('Digite cateto adjacente: ')) co = float(input('Digie cateto oposto: ')) print(f'A Hipotenusa é {hypot(ca, co):.2f}')
160
stackoversight/pipeline/pipelineobject.py
walker76/stackoversight
3
2171233
from pipeline.pipelineoutput import PipelineOutput from taskqueue.redis_queue import RedisQueue from taskqueue.redis_connection import RedisConnection from redis import ConnectionError from rq import Connection, Worker, Queue import time import copy # from queue import Queue class Pipeline(object): __redis_initialized = False # Steps: The pipeline steps to accomplish # Redis_key: unknown # Items: Initial queue of code snippets def __init__(self, steps=None, snippets=None): if steps is None: steps = [] self.steps = steps self.redis_instance = RedisConnection() self.redis_queue = RedisQueue.get_instance() self.__queues = [] self.__raw = snippets for step in steps: self.__queues.append(Queue(name=step.name, connection=self.redis_instance.redis)) # TODO: Make this asynchronous to launch multiple workers at once def setup_workers(self): print(self.__queues) try: with Connection(): workers = [] for ind, step in enumerate(self.steps): work = Worker([self.__queues[ind]], connection=self.redis_instance.redis, name=(self.__queues[ind].name + "_worker")) workers.append(work) for worker in workers: worker.work() print("starting worker " + worker.name) except ConnectionError: print("Could not connect to host") def execute(self, items: list): results = [] for ind, item in enumerate(items): job = self.__queues[0].enqueue(process_in_pipeline, args=[self.steps, [item]]) print("Processing item number [" + str(ind) + "] - queue: " + self.steps[0].name) print(" :|" + item[0:32].replace("\n", "\\n") + "...") while job.get_status() != "finished": time.sleep(0) print(str(job.get_status())) results.append(job.result) print("Num results: " + str(len(results))) print(results) return PipelineOutput(results) def get_redis_instance(self): return self.redis_instance def set_steps(self, steps): self.steps = steps # for i in range(100): # items2.append(items.pop()) def execute_synchronous(self, items): # Feed the item into one step, get the result, feed the # result to the next step and so on. self.__raw = copy.deepcopy(items) for step in self.steps: step.process(items) items = step.get() out = PipelineOutput(items, self.__raw) return out def process_in_pipeline(steps, item: list) -> list: for step in steps: item = step.process(item) return item
2,849
functions_train.py
annikalang/ImageClassifier1
0
2171408
''' 1. Train Train a new network on a data set with train.py Basic usage: python train.py data_directory Prints out training loss, validation loss, and validation accuracy as the network trains Options: Set directory to save checkpoints: python train.py data_dir --save_dir save_directory Choose architecture: python train.py data_dir --arch "vgg13" Set hyperparameters: python train.py data_dir --learning_rate 0.01 --hidden_units 512 --epochs 20 Use GPU for training: python train.py data_dir --gpu ''' import torch from torch import nn from torch import optim import torch.nn.functional as F import torchvision from torchvision import datasets, transforms, models from collections import OrderedDict import json import time from PIL import Image import numpy as np import matplotlib.pyplot as plt from torch.autograd import Variable import argparse # You may have one function for data pre-processing which takes a path as an argument and returns all the loaders. def load_data(data_dir = './flowers'): data_dir = str(data_dir).strip('[]').strip("'") train_dir = data_dir + '/train' valid_dir = data_dir + '/valid' test_dir = data_dir + '/test' # TODO: Define your transforms for the training, validation, and testing sets # For all three sets you'll need to normalize the means [0.485, 0.456, 0.406] and standard deviations [0.229, 0.224, 0.225] # data_transforms = cropped_size = 224 resized_size = 255 means = [0.485, 0.456, 0.406] stds = [0.229, 0.224, 0.225] # random scaling, cropping, and flipping, resized to 224x224 pixels train_transforms = transforms.Compose([transforms.RandomResizedCrop(cropped_size), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(means, stds)]) # resize then crop the images to the appropriate size validate_transforms = transforms.Compose([transforms.RandomResizedCrop(cropped_size), transforms.ToTensor(), transforms.Normalize(means, stds)]) # resize then crop the images to the appropriate size test_transforms = transforms.Compose([transforms.Resize(resized_size), # Why 255 pixels? transforms.CenterCrop(cropped_size), transforms.ToTensor(), transforms.Normalize(means, stds)]) # TODO: Load the datasets with ImageFolder train_data = datasets.ImageFolder(train_dir, transform = train_transforms) validate_data = datasets.ImageFolder(valid_dir, transform = validate_transforms) test_data = datasets.ImageFolder(test_dir, transform = test_transforms) image_data = [train_data, validate_data, test_data] # TODO: Using the image datasets and the trainforms, define the dataloaders batch_size = 60 train_loader = torch.utils.data.DataLoader(train_data, batch_size = batch_size, shuffle = True) validate_loader = torch.utils.data.DataLoader(validate_data, batch_size = batch_size, shuffle = True) test_loader = torch.utils.data.DataLoader(test_data) # train_loader dataloaders = [train_loader, validate_loader, test_loader] return train_loader, validate_loader, test_loader, train_data # One function to load the pre-trained model, build the classifier and define the optimizer. The function will take the architecture name, hidden units etc. def build_model(arch = 'vgg16', middle_features = 1024, learning_rate = 0.01, device = 'gpu'): if arch == 'vgg16': model = models.vgg16(pretrained=True) elif arch == 'densenet121': model = models.densenet121(pretrained=True) elif arch == 'alexnet': model = models.alexnet(pretrained = True) else: print("Im sorry but {} is not a valid model. Did you mean vgg16, densenet121 or alexnet?".format(arch)) for param in model.parameters(): param.requires_grad = False input_features = 25088 # middle_features = 1024 output_number = 102 dropout_probability = 0.5 classifier = nn.Sequential(OrderedDict([ ('fc1', nn.Linear(input_features, middle_features)), ('drop', nn.Dropout(p = dropout_probability)), ('relu', nn.ReLU()), ('fc2', nn.Linear(middle_features, output_number)), ('output', nn.LogSoftmax(dim = 1)) ])) model.classifier = classifier # learning_rate = 0.01 # epochs = 10 criterion = nn.NLLLoss() optimizer = optim.SGD(model.classifier.parameters(), lr = learning_rate) if torch.cuda.is_available() and device == 'gpu': model.cuda() return model, criterion, optimizer # One function to train the model def train_model(model, criterion, optimizer, validate_loader, train_loader, use = 'gpu', epochs = 10): # TODO: Using the image datasets and the trainforms, define the dataloaders #batch_size = 60 #train_loader = torch.utils.data.DataLoader(train_data, batch_size = batch_size, shuffle = True) #validate_loader = torch.utils.data.DataLoader(validate_data, batch_size = batch_size, shuffle = True) #test_loader = torch.utils.data.DataLoader(test_data) # train_loader #dataloaders = [train_loader, validate_loader, test_loader] running_loss = 0 steps = 0 #model.to(device) validation = True device = torch.device('cuda' if torch.cuda.is_available() and use == 'gpu' else 'cpu') model.to(device) start_time = time.time() print('Training starts') for epoch in range(epochs): training_loss = 0 for images, labels in train_loader: # Move data tensors to the default device (gpu or cpu) images, labels = images.to(device), labels.to(device) optimizer.zero_grad() log_probabilities = model(images) loss = criterion(log_probabilities, labels) loss.backward() optimizer.step() training_loss += loss.item() print('Epoch {} of {} ///'.format(epoch + 1, epochs), 'Training loss {:.3f} ///'.format(training_loss / len(train_loader))) if validation == True: validation_loss = 0 validation_accuracy = 0 model.eval() # Turn off gradients for validation, saves memory and computations with torch.no_grad(): for images, labels in validate_loader: # Move data tensors to the default device (gpu or cpu) images, labels = images.to(device), labels.to(device) log_probabilities = model(images) loss = criterion(log_probabilities, labels) validation_loss += loss.item() # accuracy calculation probabilities = torch.exp(log_probabilities) top_probability, top_class = probabilities.topk(1, dim = 1) equals = top_class == labels.view(*top_class.shape) validation_accuracy += torch.mean(equals.type(torch.FloatTensor)).item() model.train() print("Validation loss {:.3f} ///".format(validation_loss / len(validate_loader)), "Validation accuracy {:.3f} ///".format(validation_accuracy / len(validate_loader))) end_time = time.time() print('Training ends') training_time = end_time - start_time print('Training time {:.0f}m {:.0f}s'.format(training_time / 60, training_time % 60)) return model # one function to save the checkpoint file def save_checkpoint(model, optimizer, train_data, arch = 'vgg16', path = 'checkpoint.pth', input_features = 25088, middle_features = 1024, output_number = 102, lr = 0.01, epochs = 10, batch_size = 64): model.class_to_idx = train_data.class_to_idx checkpoint = {'network': 'vgg16', 'input': input_features, 'output': output_number, 'learning_rate': lr, 'batch_size': batch_size, 'classifier' : model.classifier, 'epochs': epochs, 'optimizer': optimizer.state_dict(), 'state_dict': model.state_dict(), 'class_to_idx': model.class_to_idx} torch.save(checkpoint, path)
8,889
RLBotPack/ReliefBotFamily/README/relief_loadout_generator.py
L0laapk3/RLBotPack
13
2171251
import colorsys from pathlib import Path from rlbot.agents.base_loadout_generator import BaseLoadoutGenerator from rlbot.matchconfig.loadout_config import LoadoutConfig, Color class ReliefBotLoadoutGenerator(BaseLoadoutGenerator): def generate_loadout(self, player_index: int, team: int) -> LoadoutConfig: # You could start with a loadout based on a cfg file in the same directory as this generator loadout = self.load_cfg_file(Path('relief_bot_appearance.cfg'), team) if team == 0: paints = [4, 5, 7] hues = [0.55, 0.6, 0.4] else: paints = [1, 6, 10] hues = [0, 0.1, 0.13] paint_id = paints[player_index % 3] rgb = colorsys.hsv_to_rgb(hues[player_index % 3], 1, 0.6) loadout.primary_color_lookup = Color(float_to_byte(rgb[0]), float_to_byte(rgb[1]), float_to_byte(rgb[2]), 255) loadout.paint_config.wheels_paint_id = paint_id loadout.paint_config.trails_paint_id = paint_id return loadout def float_to_byte(value: float) -> int: return int(value * 255)
1,101
Panda/Panda_Merg.py
vibwipro/Machine-Learning-Python
3
2172121
import pandas as pd df1 = pd.DataFrame({ "city": ["new york","chicago","orlando"], "temperature": [21,14,35], }) df2 = pd.DataFrame({ "city": ["chicago","new york","orlando"], "humidity": [65,68,75], }) df3 = pd.merge(df1, df2, on="city") print (df3) #############################################################################3 ######### New DF df1 = pd.DataFrame({ "city": ["new york","chicago","orlando", "baltimore"], "temperature": [21,14,35, 38], }) df2 = pd.DataFrame({ "city": ["chicago","new york","san diego"], "humidity": [65,68,71], }) ############ Inner df3=pd.merge(df1,df2,on="city",how="inner") print (df3) ############ outer df3=pd.merge(df1,df2,on="city",how="outer") print (df3) ############ left (all records of df1) df3=pd.merge(df1,df2,on="city",how="left") print (df3) ############ right (all records of df2) df3=pd.merge(df1,df2,on="city",how="right") print (df3) ########## Indicator about type of join df3=pd.merge(df1,df2,on="city",how="outer",indicator=True) print(df3) ###################################################################### ############# Another DF df1 = pd.DataFrame({ "city": ["new york","chicago","orlando", "baltimore"], "temperature": [21,14,35,38], "humidity": [65,68,71, 75] }) df2 = pd.DataFrame({ "city": ["chicago","new york","san diego"], "temperature": [21,14,35], "humidity": [65,68,71] }) ########## Both DF has Humidety and temp column. ########### Suffix will ass siffix to columns df3= pd.merge(df1,df2,on="city",how="outer", suffixes=('_first','_second')) print(df3) ############# Set city as index and update DF ######## inplace it update DF df1 = pd.DataFrame({ "city": ["new york","chicago","orlando"], "temperature": [21,14,35], }) df1.set_index('city',inplace=True)
1,896
app/main/schema/avg_fare_by_s2id_schema.py
abhishek9sharma/nyctaxiserver
0
2172089
from flask_restplus import Namespace, fields, reqparse class AvgFareByS2ID: """ Class to store Schema information for Average Fare for a S2ID """ ns = Namespace('avg_fare_by_s2id', 'Average Fare Per Pick Up Location (S2ID) for Level 16') model = ns.model('avg_fare_by_s2id', { 's2id': fields.String(required = True, description = 's2id of location at level 16'), 'fare': fields.Float(required= True, description = 'average fare for the given S2ID location') }) parser = reqparse.RequestParser(bundle_errors= True) parser.add_argument('date', type = str, required= True, help = 'Example: 2017-01-01 (DDDD-MM-YY)', location = 'args')
901