xszheng2020/the_stack_dedup_python · Datasets at Hugging Face

da4c1af35b78bb185c69f2e2ce2c1d8ceee1a22d

667

py

Python

chapter03/knock25.py

m-star18/NLP100

e199814f81943f7fb693fd5fe87d6df21da07f5b

[ "MIT" ]

1

2020-07-15T17:21:13.000Z

chapter03/knock25.py

m-star18/NLP100

e199814f81943f7fb693fd5fe87d6df21da07f5b

[ "MIT" ]

1

2021-05-04T01:04:57.000Z

2021-05-04T01:05:32.000Z

chapter03/knock25.py

m-star18/NLP100

e199814f81943f7fb693fd5fe87d6df21da07f5b

[ "MIT" ]

null

import re import pandas as pd df = pd.read_json('jawiki-country.json', lines=True) text = df.query('title=="イギリス"')['text'].values[0].split('\n') memo, flag = [], False template = '基礎情報' check = re.compile('\|(.+?)\s=\s(.+)') check1 = re.compile('\{\{' + template) check2 = re.compile('\}\}') check3 = re.compile('\|') check4 = re.compile('<ref(\s|>).+?(</ref>|$)') for t in text: if flag: if check2.match(t): break if check3.match(t): memo.append(check4.sub('', t.strip())) if check1.match(t): flag = True ans = {} for tmp in [check.match(m) for m in memo]: ans[tmp.group(1)] = tmp.group(2) print(ans)

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null

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null

0

1

0

da4c202f2a4d50150a0f027ce75d19d6e0f3d28d

316

py

Python

constants.py

jaingaurav3/ML_sample

4e53de198f7965fa96f0db44717df27032df4b48

[ "MIT" ]

19

2018-06-08T05:33:47.000Z

2021-04-26T16:19:32.000Z

constants.py

jaingaurav3/ML_sample

4e53de198f7965fa96f0db44717df27032df4b48

[ "MIT" ]

null

constants.py

jaingaurav3/ML_sample

4e53de198f7965fa96f0db44717df27032df4b48

[ "MIT" ]

13

2018-09-24T21:52:06.000Z

2021-02-26T10:40:25.000Z

# Datasets TRAIN = 'trn' VAL = 'val' TEST = 'tst' FULL = 'full' # File extensions JPG = '.jpg' TIF = '.tif' PNG = '.png' GIF = '.gif' BCOLZ = '.bc' CSV = '.csv' # PyTorch MODEL_EXT = '.mdl' WEIGHTS_EXT = '.th' OPTIM_EXT = '.th' # Data Aug IMAGENET_MEAN = [0.485, 0.456, 0.406] IMAGENET_STD = [0.229, 0.224, 0.225]

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null

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null

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1

0

da4ce36d9c8b0451cd19a4276193992895c3a0ed

23,304

py

Python

create_dataset.py

akudan/nephi

83d3ccee0b83b3ca2349e92c454ed178afd1d1fb

[ "MIT" ]

50

2018-04-22T23:12:18.000Z

2022-02-14T15:10:24.000Z

create_dataset.py

akudan/nephi

83d3ccee0b83b3ca2349e92c454ed178afd1d1fb

[ "MIT" ]

1

2019-03-01T02:54:19.000Z

2019-03-01T15:30:12.000Z

create_dataset.py

akudan/nephi

83d3ccee0b83b3ca2349e92c454ed178afd1d1fb

[ "MIT" ]

19

2018-02-07T21:17:13.000Z

2022-02-14T15:11:46.000Z

import os import lmdb # install lmdb by "pip install lmdb" import cv2 import numpy as np from tool.xml_parser import page_images from glob import glob import re import sys import io import argparse from scipy.spatial import distance encoding = 'utf-8' stdout = sys.stdout reload(sys) sys.setdefaultencoding('utf-8') sys.stdout = stdout def checkImageIsValid(imageBin): if imageBin is None: return False imageBuf = np.fromstring(imageBin, dtype=np.uint8) img = cv2.imdecode(imageBuf, cv2.IMREAD_GRAYSCALE) imgH, imgW = img.shape[0], img.shape[1] if imgH * imgW == 0: return False return True # basically "flush the cache to the actual DB" def writeCache(env, cache): with env.begin(write=True) as txn: for k, v in cache.iteritems(): txn.put(k, v) def createDataset(outputPath, imagePathList, labelList, lexiconList=None, checkValid=True): """ Create LMDB dataset for CRNN training. ARGS: outputPath : LMDB output path imagePathList : list of image path labelList : list of corresponding groundtruth texts lexiconList : (optional) list of lexicon lists checkValid : if true, check the validity of every image """ assert(len(imagePathList) == len(labelList)) nSamples = len(imagePathList) env = lmdb.open(outputPath, map_size=1099511627776) cache = {} cnt = 1 for i in xrange(nSamples): imagePath = imagePathList[i] print imagePath label = labelList[i] if not os.path.exists(imagePath): print('%s does not exist' % imagePath) continue with open(imagePath, 'r') as f: imageBin = f.read() if checkValid: if not checkImageIsValid(imageBin): print('%s is not a valid image' % imagePath) continue imageKey = 'image-%09d' % cnt labelKey = 'label-%09d' % cnt cache[imageKey] = imageBin cache[labelKey] = label if lexiconList: lexiconKey = 'lexicon-%09d' % cnt cache[lexiconKey] = ' '.join(lexiconList[i]) if cnt % 1000 == 0: writeCache(env, cache) cache = {} print('Written %d / %d' % (cnt, nSamples)) cnt += 1 nSamples = cnt-1 cache['num-samples'] = str(nSamples) writeCache(env, cache) print('Created dataset with %d samples' % nSamples) # From: https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates #def PolyArea(x,y): # return 0.5*np.abs(np.dot(x,np.roll(y,1))-np.dot(y,np.roll(x,1))) def PolyArea(x,y): correction = x[-1] * y[0] - y[-1]* x[0] main_area = np.dot(x[:-1], y[1:]) - np.dot(y[:-1], x[1:]) return 0.5*np.abs(main_area + correction) # Takes an image read by cv2 and masks out the region of interest (pts) def apply_mask(img, pts, add_pixel = False): pts = np.array(pts, np.int32) xmin = min(pts, key=lambda x: x[0])[0] xmax = max(pts, key=lambda x: x[0])[0] ymin = min(pts, key=lambda x: x[1])[1] ymax = max(pts, key=lambda x: x[1])[1] #if False: if add_pixel: ymin = ymin - add_pixel if ymin < 0: ymin = 0 print("Ymin:") print(ymin) ymax = ymax + add_pixel if ymax >= img.shape[0]: ymax = img.shape[0] - 1 print("Ymax") print(ymax) print("IMage shape:") print(img.shape) # RA: I will probably have to make allowance for the inevitable error that for a first or last line on the page, adding pixels takes us off the page. # RA: I am now just going to use the whole array, given that they are ordered correctly updated_pts = np.array([(p[0] - xmin, p[1] - ymin) for p in pts], np.int32) #if False: #if isinstance(add_pixel, (int, long)): if add_pixel: #x_pts = np.expand_dims(np.array([x[0] for x in updated_pts]), axis=1) #print("Shape and dimensions of x_pts") #print(x_pts.shape) #print(x_pts.ndim) #d_array = distance.cdist(x_pts, x_pts, 'euclidean') # only care about x-distance for i, pt in enumerate(updated_pts): area_poly = PolyArea(updated_pts[:,0], updated_pts[:,1]) up_pts = updated_pts.copy() down_pts = updated_pts.copy() up_pts[i,1] = up_pts[i,1] + add_pixel down_pts[i,1] = down_pts[i,1] - add_pixel if PolyArea(up_pts[:,0], up_pts[:,1]) > area_poly: updated_pts[i,1] = updated_pts[i,1] + add_pixel elif PolyArea(down_pts[:,0], down_pts[:,1]) > area_poly: updated_pts[i,1] = updated_pts[i,1] - add_pixel if updated_pts[i,1] < 0: updated_pts[i,1] = 0 elif updated_pts[i,1] > ymax: updated_pts[i,1] = ymax # First closest point code below: # Find the 7 closest points along the x-axis #closest_x_pts = np.argpartition(d_array[:,i], 8)[:8] # includes index of the first point #print("Indecies of closest_x_pts") #print(closest_x_pts) # k smallest elements #np.argpartition(arr, k)[:k] #closest_pts = pts[np.array(closest_x_pts)] #print("Current point considering") #print(pt) #print("Actual closes_x_pts") #print(closest_pts) # Find whether increasing pixel height or decreasing pixel height adds to the area of the region of interest #area_poly = PolyArea(closest_pts[:,0], closest_pts[:,1]) #print("Area of polygon") #print(area_poly) #up_closest_pts = closest_pts.copy() #down_closest_pts = closest_pts.copy() #pt_idx = np.where(np.all(np.isin(closest_pts, pt), axis=1))[0][0] #print("Point index") #print(pt_idx) #up_closest_pts[pt_idx,1] = up_closest_pts[pt_idx,1] + add_pixel #down_closest_pts[pt_idx,1] = down_closest_pts[pt_idx,1] - add_pixel #if PolyArea(up_closest_pts[:,0], up_closest_pts[:,1]) > area_poly: # updated_pts[i,1] = updated_pts[i,1] + add_pixel #elif PolyArea(down_closest_pts[:,0], down_closest_pts[:,1]) > area_poly: # updated_pts[i,1] = updated_pts[i,1] - add_pixel line_img = img[ymin:ymax, xmin:xmax].copy() mask = np.zeros(line_img.shape, dtype=np.uint8) channel_count = 1 if len(line_img.shape) > 2: channel_count = line_img.shape[2] ignore_mask_color = (255,) * channel_count # Idiosyncrasy of cv2.fillPoly updated_pts = [(p[0], p[1]) for p in updated_pts] roi_corners = np.array([updated_pts], dtype=np.int32) cv2.fillPoly(mask, roi_corners, ignore_mask_color) line_img[mask == 0] = 255 return line_img def simple_dataset_from_dir(image_dir, output_path): # a simple example of generating data (does not generate an alphabet.txt file, generate your own out of band) # pass an image_dir like data/dataset/images/train that contains files like # 25_this is the contents.png imagePathList = [] labelList = [] files = os.listdir(image_dir) for file in files: image_path = file imagePathList.append(os.path.join(image_dir,image_path)) # full path label = os.path.splitext(file.split('_')[1])[0] # "victor" from 25_victor.png print(file, label) labelList.append(label) createDataset(output_path, imagePathList, labelList) def russell_page_journal(data_dir, output_path): env = lmdb.open(output_path, map_size=1099511627776) cache = {} cnt = 1 img_files = glob(os.path.join(data_dir, "*.jpg")) alpha_text = u'' #'0123456789abcdefghijklmnopqrstuvwxyz' alphabet = [] for img_file in img_files: img_c = cv2.imread(img_file) text_file = img_file.partition(".")[0] + ".txt" t_f = io.open(text_file, "r", encoding=encoding) gt = t_f.read() t_f.close() line_img = img_c imageBin = cv2.imencode('.png', line_img)[1].tostring() if not checkImageIsValid(imageBin): print('%s is not a valid image' % img_file) continue annotation = gt label = annotation.encode('utf-8') print("Printing encoded unicode!") print(label) for c in annotation: if not c in alphabet: alphabet.append(c) imageKey = 'image-%09d' % cnt labelKey = 'label-%09d' % cnt fileKey = 'file-%09d' % cnt print imageKey cache[imageKey] = imageBin cache[labelKey] = label cache[fileKey] = os.path.basename(img_file) if cnt % 1000 == 0: writeCache(env, cache) cache = {} print('Written %d' % (cnt)) cnt += 1 nSamples = cnt - 1 cache['num-samples'] = str(nSamples) writeCache(env, cache) print('Created dataset with %d samples' % nSamples) alpha_text = u''.join(alphabet) with io.open("alphabet.txt", "w", encoding=encoding) as text_file: text_file.write(alpha_text) # read into LMDB dataset from ICFHR 2018 def icfhr_dataset_read(data_dir, output_path, include_files=None, binarize = False, howe_dir = False, simplebin_dir = False, test = False): env = lmdb.open(output_path, map_size=1099511627776) cache = {} cnt = 1 img_files = glob(os.path.join(data_dir, "*/*.jpg")) if test else glob(os.path.join(data_dir, "*/*/*.jpg")) for img_file in img_files: img_c = cv2.imread(img_file) info_file = img_file + ".info" if include_files is not None: if ".jpg" not in include_files[0]: include_files = [f + ".jpg" for f in include_files] if os.path.basename(img_file) not in include_files: continue if not test: text_file = img_file + ".txt" if binarize: howe_img = cv2.imread(os.path.join(howe_dir, os.path.basename(img_file).lower().partition(".jpg")[0] + "_howe.jpg")) simplebin_img = cv2.imread(os.path.join(simplebin_dir, os.path.basename(img_file).lower().partition(".jpg")[0] + "_simplebin.jpg")) with open(info_file, "r") as i_f: if not test: t_f = io.open(text_file, "r", encoding=encoding) gt = t_f.read() t_f.close() info = i_f.read() mask = info.partition("MASK\n")[2] myre = re.compile(r"[0-9]+,[0-9]+") mask_p = myre.findall(mask) mask_pts = [tuple(int(x) for x in v.split(',')) for v in mask_p] line_img = apply_mask(img_c, mask_pts) if binarize: howe_line_img = apply_mask(howe_img, mask_pts) # Hopefully this works even though Howe binarization takes out a few pixels simplebin_line_img = apply_mask(simplebin_img, mask_pts) imageBin = cv2.imencode('.png', line_img)[1].tostring() if binarize: howe_imageBin = cv2.imencode('.png', howe_line_img)[1].tostring() simplebin_imageBin = cv2.imencode('.png', simplebin_line_img)[1].tostring() if not checkImageIsValid(imageBin): print('%s is not a valid image' % img_file) continue if binarize: if not (checkImageIsValid(howe_imageBin) and checkImageIsValid(simplebin_imageBin)): print('%s is not a valid image in howe or sauvola binarization' % image['image_file']) continue if not test: annotation = gt label = annotation.encode('utf-8') imageKey = 'image-%09d' % cnt labelKey = 'label-%09d' % cnt fileKey = 'file-%09d' % cnt if binarize: howe_imageKey = 'howe-image-%09d' % cnt simplebin_imageKey = 'simplebin-image-%09d' % cnt print imageKey if binarize: print howe_imageKey print simplebin_imageKey cache[imageKey] = imageBin if binarize: cache[howe_imageKey] = howe_imageBin cache[simplebin_imageKey] = simplebin_imageBin if not test: cache[labelKey] = label cache[fileKey] = os.path.basename(img_file) if cnt % 1000 == 0: writeCache(env, cache) cache = {} print('Written %d' % (cnt)) cnt += 1 nSamples = cnt - 1 cache['num-samples'] = str(nSamples) writeCache(env, cache) print('Created dataset with %d samples' % nSamples) # read into LMDB dataset from XML def lmdb_dataset_read(data_dir, output_path, binarize = False, howe_dir = False, simplebin_dir = False, image_dir = False, add_pixel = False): env = lmdb.open(output_path, map_size=1099511627776) images = page_images(data_dir) # print images cache = {} cnt = 1 alpha_text = u'' #'0123456789abcdefghijklmnopqrstuvwxyz' alphabet = [] for c in alpha_text: alphabet.append(c) for image in images: print image file_image = os.path.join(data_dir,'Images',image.Page.get('imageFilename')) print(file_image) image['data'] = cv2.imread(file_image) page_img = cv2.imread(file_image) if binarize: howe_img = cv2.imread(os.path.join(howe_dir, os.path.basename(file_image).lower().partition(".jpg")[0] + "_howe.jpg")) simplebin_img = cv2.imread(os.path.join(simplebin_dir, os.path.basename(file_image).lower().partition(".jpg")[0] + "_simplebin.jpg")) for region in image.Page.TextRegion: print 'region' print str(region.tag) line_tags = [c.tag.split('}')[1] for c in region.getchildren()] if any('TextLine' in l for l in line_tags): for line in region.TextLine: print 'line '+line.get('id') print str(line.Coords.get('points')) data = line.Coords.get('points') pts = [tuple(int(x) for x in v.split(',')) for v in data.split()] print("Image shape") print(page_img.shape) line_img = apply_mask(page_img, pts, add_pixel) if binarize: howe_line_img = apply_mask(howe_img, pts, add_pixel) # Hopefully this works even though Howe binarization takes out a few pixels simplebin_line_img = apply_mask(simplebin_img, pts, add_pixel) line_file_name = '_'.join([os.path.basename(file_image).partition('.')[0], line.get('id')]) print 'line_file_name: ' + line_file_name if image_dir: cv2.imwrite(os.path.join(image_dir, line_file_name + ".jpg"), line_img) imageBin = cv2.imencode('.png', line_img)[1].tostring() if binarize: howe_imageBin = cv2.imencode('.png', howe_line_img)[1].tostring() simplebin_imageBin = cv2.imencode('.png', simplebin_line_img)[1].tostring() if not checkImageIsValid(imageBin): print('%s is not a valid image' % image['image_file']) continue if binarize: if not (checkImageIsValid(howe_imageBin) and checkImageIsValid(simplebin_imageBin)): print('%s is not a valid image in howe or sauvola binarization' % image['image_file']) continue mini_line_tags = [c.tag.split('}')[1] for c in line.getchildren()] annotation = line.TextEquiv.Unicode.text if any('TextEquiv' in l for l in mini_line_tags) else u'' if annotation is None: annotation = u'' print("Printing apparent unicode!") print(annotation) label = annotation.encode('utf-8') print("Printing encoded unicode!") print(label) for c in annotation: if not c in alphabet: alphabet.append(c) imageKey = 'image-%09d' % cnt fileKey = 'file-%09d' % cnt if binarize: howe_imageKey = 'howe-image-%09d' % cnt simplebin_imageKey = 'simplebin-image-%09d' % cnt labelKey = 'label-%09d' % cnt print imageKey if binarize: print howe_imageKey print simplebin_imageKey cache[imageKey] = imageBin cache[fileKey] = line_file_name if binarize: cache[howe_imageKey] = howe_imageBin cache[simplebin_imageKey] = simplebin_imageBin cache[labelKey] = label if cnt % 1000 == 0: writeCache(env, cache) cache = {} print('Written %d' % (cnt)) line['database_id'] = cnt cnt += 1 nSamples = cnt - 1 cache['num-samples'] = str(nSamples) writeCache(env, cache) print('Created dataset with %d samples' % nSamples) alpha_text = u''.join(alphabet) with io.open("alphabet.txt", "w", encoding=encoding) as text_file: text_file.write(alpha_text) def extract_strips(data_dir, output_path): # example of cutting pieces of images out (unused) # env = lmdb.open(output_path, map_size=1099511627776) images = page_images(data_dir) print images cache = {} cnt = 1 for image in images: print image file_image = os.path.join(data_dir,'Images',image.Page.get('imageFilename')) image['data'] = cv2.imread(file_image) page_img = cv2.imread(file_image) # page_img = image['data'] for region in image.Page.TextRegion: print 'region' print str(region.tag) line_tags = [c.tag.split('}')[1] for c in region.getchildren()] if any('TextLine' in l for l in line_tags): for line in region.TextLine: print 'line '+line.get('id') print str(line.Coords.get('points')) data = line.Coords.get('points') pts = [tuple(int(x) for x in v.split(',')) for v in data.split()] pts = np.array(pts, np.int32) xmin = min(pts, key=lambda x: x[0])[0] xmax = max(pts, key=lambda x: x[0])[0] ymin = min(pts, key=lambda x: x[1])[1] ymax = max(pts, key=lambda x: x[1])[1] updated_pts = [(p[0] - xmin, p[1] - ymin) for p in pts] line_img = page_img[ymin:ymax, xmin:xmax].copy() # http://stackoverflow.com/a/15343106/3479446 mask = np.zeros(line_img.shape, dtype=np.uint8) roi_corners = np.array([updated_pts], dtype=np.int32) channel_count = 1 if len(line_img.shape) > 2: channel_count = line_img.shape[2] ignore_mask_color = (255,) * channel_count cv2.fillPoly(mask, roi_corners, ignore_mask_color) line_img[mask == 0] = 255 line['data'] = line_img imageKey = 'image-%09d' % cnt cv2.imwrite(os.path.join(output_path, imageKey + '.png'), line_img) cnt += 1 if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--data_dir', required=True, help='path to dataset') parser.add_argument('--output_dir', required=True, help='path to lmdb database output') parser.add_argument('--output_image_dir', type=str, default="None", help='path to cropped image output if desired') parser.add_argument('--xml', action='store_true', help='whether the data are organized in /Images and /Pages subdirectories with PAGE segmentation file format') parser.add_argument('--icfhr', action='store_true', help='whether the data are organized according to 2018 ICFHR Handwriting Recognition Competition format') parser.add_argument('--russell', action='store_true', help='whether the data are organized according whole page russell journal') parser.add_argument('--files_include', help='File of filenames to selectively include in the lmdb database from data_dir') parser.add_argument('--binarize', action='store_true', help='whether to include binarized data in lmdb database') parser.add_argument('--howe_dir', help='path to howe binarized dataset') parser.add_argument('--simplebin_dir', help='path to sauvola binarized dataset') parser.add_argument('--test', action='store_true', help='whether to data is a test dataset (includes no ground truth text)') parser.add_argument('--add_pixel', action='store_true', help='whether to include extra pixels along y-axis in line segmentation') parser.add_argument('--n_pixels', type=int, default=0, help='How many extra pixels to include') opt = parser.parse_args() print("Running with options:", opt) if not os.path.isdir(opt.output_dir): os.system('mkdir -p {0}'.format(opt.output_dir)) if not (opt.output_image_dir == "None") and not os.path.isdir(opt.output_image_dir): os.system('mkdir -p {0}'.format(opt.output_image_dir)) if opt.xml: lmdb_dataset_read(opt.data_dir, opt.output_dir, binarize = opt.binarize, howe_dir = opt.howe_dir, simplebin_dir = opt.simplebin_dir, image_dir = opt.output_image_dir if not opt.output_image_dir == "None" else False, add_pixel = opt.n_pixels if opt.add_pixel else False) elif opt.icfhr: if opt.files_include: with open(opt.files_include, "r") as include_file: icfhr_dataset_read(opt.data_dir, opt.output_dir, include_file.read().split(), binarize = opt.binarize, howe_dir = opt.howe_dir, simplebin_dir = opt.simplebin_dir, test=opt.test) else: icfhr_dataset_read(opt.data_dir, opt.output_dir, binarize = opt.binarize, howe_dir = opt.howe_dir, simplebin_dir = opt.simplebin_dir, test=opt.test) elif opt.russell: russell_page_journal(opt.data_dir, opt.output_dir) else: simple_dataset_from_dir(opt.data_dir, opt.output_dir)

40.458333

277

0.573249

2,961

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4.355961

0.133063

0.015196

0.006203

0.011165

0.57947

0.543728

0.498449

0.482633

0.468677

0.435184

0

0.022841

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23,304

576

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40.458333

0.784293

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null

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0

null

0

null

0

1

0

1

da4d25bd823544d3dde8ed32e826fbbb55bcbd80

1,226

py

Python

a10sdk/core/maximum/maximum_paths.py

deepfield/a10sdk-python

bfaa58099f51f085d5e91652d1d1a3fd5c529d5d

[ "Apache-2.0" ]

16

2015-05-20T07:26:30.000Z

2021-01-23T11:56:57.000Z

a10sdk/core/maximum/maximum_paths.py

deepfield/a10sdk-python

bfaa58099f51f085d5e91652d1d1a3fd5c529d5d

[ "Apache-2.0" ]

6

2015-03-24T22:07:11.000Z

2017-03-28T21:31:18.000Z

a10sdk/core/maximum/maximum_paths.py

deepfield/a10sdk-python

bfaa58099f51f085d5e91652d1d1a3fd5c529d5d

[ "Apache-2.0" ]

23

2015-03-29T15:43:01.000Z

2021-06-02T17:12:01.000Z

from a10sdk.common.A10BaseClass import A10BaseClass class MaximumPaths(A10BaseClass): """Class Description:: Set maximum number of route multipaths installed into FIB. Class maximum-paths supports CRUD Operations and inherits from `common/A10BaseClass`. This class is the `"PARENT"` class for this module.` :param path: {"description": "supported multipath numbers", "format": "number", "default": 4, "optional": true, "maximum": 64, "minimum": 1, "type": "number"} :param uuid: {"description": "uuid of the object", "format": "string", "minLength": 1, "modify-not-allowed": 1, "optional": true, "maxLength": 64, "type": "string"} :param DeviceProxy: The device proxy for REST operations and session handling. Refer to `common/device_proxy.py` URL for this object:: `https://<Hostname|Ip address>//axapi/v3/maximum-paths`. """ def __init__(self, **kwargs): self.ERROR_MSG = "" self.required=[] self.b_key = "maximum-paths" self.a10_url="/axapi/v3/maximum-paths" self.DeviceProxy = "" self.path = "" self.uuid = "" for keys, value in kwargs.items(): setattr(self,keys, value)

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null

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0

da4d50c0cd6f0dd5e191b086879be35c23707ff8

331

py

Python

ocun.py

jpcyrino/chunker_dm

1afde2400b81d0fbc351dcb4658546ef018d2640

[ "MIT" ]

1

2022-02-23T12:33:01.000Z

ocun.py

jpcyrino/chunker_dm

1afde2400b81d0fbc351dcb4658546ef018d2640

[ "MIT" ]

null

ocun.py

jpcyrino/chunker_dm

1afde2400b81d0fbc351dcb4658546ef018d2640

[ "MIT" ]

null

import sys filename = sys.argv[1] fileout = sys.argv[2] with open(filename, encoding="utf-8", mode="r") as file: lines = file.read().split("\n") data_lines = [lines[i] for i in range(0,len(lines),3)] print(data_lines) with open(fileout, encoding="utf-8", mode="w") as file: for line in data_lines: file.write(line + '\n')

20.6875

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331

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0.123853

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331

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0.1

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null

0

null

0

1

0

da4f18d031bec1129d069479e75d9c035f860d1d

2,412

py

Python

galaxy/main/urls.py

changelox/galaxy

fc8e11b36de0b78e55c13c05ffc3a3fcaf8b39dc

[ "Apache-2.0" ]

null

galaxy/main/urls.py

changelox/galaxy

fc8e11b36de0b78e55c13c05ffc3a3fcaf8b39dc

[ "Apache-2.0" ]

null

galaxy/main/urls.py

changelox/galaxy

fc8e11b36de0b78e55c13c05ffc3a3fcaf8b39dc

[ "Apache-2.0" ]

null

# (c) 2012-2018, Ansible by Red Hat # # This file is part of Ansible Galaxy # # Ansible Galaxy is free software: you can redistribute it and/or modify # it under the terms of the Apache License as published by # the Apache Software Foundation, either version 2 of the License, or # (at your option) any later version. # # Ansible Galaxy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # Apache License for more details. # # You should have received a copy of the Apache License # along with Galaxy. If not, see <http://www.apache.org/licenses/>. from django.conf.urls import url from django.conf import settings from django.views.decorators.cache import never_cache from django.contrib.staticfiles.views import serve as serve_staticfiles from django.views.static import serve as serve_static from galaxy.main import views urlpatterns = [ # Non-secure URLs url(r'^$', views.home, name='home'), url(r'^explore$', views.explore, name='explore'), url(r'^intro$', views.intro, name='intro'), url(r'^accounts/landing[/]?$', views.accounts_landing, name='accounts-landing'), url(r'^list$', views.list_category, name='list-category'), url(r'^detail$', views.detail_category, name='detail-category'), url(r'^roleadd$', views.role_add_view, name='role-add-category'), url(r'^imports$', views.import_status_view, name='import-status'), url(r'^stars$', views.stars_list_view, name='stars-list'), # Logged in/secured URLs url(r'^accounts/connect/$', views.accounts_connect), url(r'^accounts/connect/success/$', views.accounts_connect_success, name='accounts-connect-success'), url(r'^accounts/profile/$', views.accounts_profile, name='accounts-profile'), url(r'^authors/$', views.NamespaceListView.as_view(), name='namespace-list'), url(r'^([\w\-._+]+)/$', views.RoleListView.as_view(), name='role-list'), url(r'^([\w\-._+]+)/([\w\-._+]+)/$', views.RoleDetailView.as_view(), name='role-detail'), ] # FIX if settings.DEBUG: urlpatterns += [ url(r'^static/(?P<path>.*)$', never_cache(serve_staticfiles)) ] else: urlpatterns += [ url(r'^static/(?P<path>.*)$', serve_static, kwargs={'document_root': settings.STATIC_ROOT}) ]

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64

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0

0.184211

0

null

0

null

0

1

0

da52f7ade412e44099bbd5c88acd3fc976745c19

1,002

py

Python

PROJ/LEVY/RN_CHF/cf_RN_KoBoL.py

mattslezak-shell/PROJ_Option_Pricing_Matlab

6105bd00ba3471802180c122fdf81e90833a91c4

[ "MIT" ]

null

PROJ/LEVY/RN_CHF/cf_RN_KoBoL.py

mattslezak-shell/PROJ_Option_Pricing_Matlab

6105bd00ba3471802180c122fdf81e90833a91c4

[ "MIT" ]

null

PROJ/LEVY/RN_CHF/cf_RN_KoBoL.py

mattslezak-shell/PROJ_Option_Pricing_Matlab

6105bd00ba3471802180c122fdf81e90833a91c4

[ "MIT" ]

1

2022-01-07T15:31:45.000Z

# Generated with SMOP 0.41-beta try: from smop.libsmop import * except ImportError: raise ImportError('File compiled with `smop3`, please install `smop3` to run it.') from None # cf_RN_KoBoL.m @function def cf_RN_KoBoL(u=None,T=None,r=None,c=None,lam_p=None,lam_m=None,nu=None,*args,**kwargs): varargin = cf_RN_KoBoL.varargin nargin = cf_RN_KoBoL.nargin # KoBoL RN CHF - NOTE: params have been # written in correspondence with CGMY, which is a subclass of KoBoL C=copy(c) # cf_RN_KoBoL.m:4 M=copy(lam_p) # cf_RN_KoBoL.m:4 G=- lam_m # cf_RN_KoBoL.m:4 Y=copy(nu) # cf_RN_KoBoL.m:4 m=dot(dot(C,gamma(- Y)),((M - 1) ** Y - M ** Y + (G + 1) ** Y - G ** Y)) # cf_RN_KoBoL.m:5 y=dot(dot(dot(C,T),gamma(- Y)),((M - dot(1j,u)) ** Y - M ** Y + (G + dot(1j,u)) ** Y - G ** Y)) # cf_RN_KoBoL.m:6 y=exp(dot(dot(dot(1j,u),T),(r - m)) + y) # cf_RN_KoBoL.m:7 return y if __name__ == '__main__': pass

27.833333

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null

0

1

0

null

0

1

0

2

da5590e5f9e56a1abcc8575518a715e6c27967cd

161

py

Python

hello-world.py

rmoralesdelgado/example-repo

a0d6f935264ec60a0278ea9fc5b8a694b5e33f0b

[ "MIT" ]

null

hello-world.py

rmoralesdelgado/example-repo

a0d6f935264ec60a0278ea9fc5b8a694b5e33f0b

[ "MIT" ]

null

hello-world.py

rmoralesdelgado/example-repo

a0d6f935264ec60a0278ea9fc5b8a694b5e33f0b

[ "MIT" ]

null

# This is a modified line if __name__ == "__main__": print("Hello World") print("I am a new line") print(hi) # blah blha def myfunc_1(): pass

10.733333

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161

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null

0

null

0

1

0

1

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4

da55b03a123eb70e524ac2c9ba783fe7003f5224

250

py

Python

mundo 1/ex020.py

thiagofreitascarneiro/Curso-de-Python---Curso-em-Video

0342e482780b5a1c6f78cddd51d9bfad785c79fa

[ "MIT" ]

1

2021-08-04T13:21:22.000Z

mundo 1/ex020.py

thiagofreitascarneiro/Curso-de-Python---Curso-em-Video

0342e482780b5a1c6f78cddd51d9bfad785c79fa

[ "MIT" ]

null

mundo 1/ex020.py

thiagofreitascarneiro/Curso-de-Python---Curso-em-Video

0342e482780b5a1c6f78cddd51d9bfad785c79fa

[ "MIT" ]

null

import random n1 = str(input('Primeiro Aluno')) n2 = str(input('Segundo Aluno')) n3 = str(input('Terceiro Aluno')) n4 = str(input('Quarto Aluno')) lista = [n1,n2,n3,n4] Ordem = random.sample(lista,k=4) print(f'A ordem de apresentação será: {Ordem}')

27.777778

47

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0

0.040909

0.12

250

9

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0

0.125

0

null

0

1

0

null

0

1

da58277b5c2af60a518ecbd9a3ef1bdee746623d

1,306

py

Python

python3/ais_sdk/utils.py

MeekoI/ais-sdk

76240abc49795e914988f3cafb6d08f60dbdcb4c

[ "Apache-2.0" ]

null

python3/ais_sdk/utils.py

MeekoI/ais-sdk

76240abc49795e914988f3cafb6d08f60dbdcb4c

[ "Apache-2.0" ]

null

python3/ais_sdk/utils.py

MeekoI/ais-sdk

76240abc49795e914988f3cafb6d08f60dbdcb4c

[ "Apache-2.0" ]

null

# -*- coding:utf-8 -*- import os import base64 import urllib.request import ais_sdk.ais as ais _ENDPOINT = { 'image': { 'cn-north-1':'image.cn-north-1.myhuaweicloud.com', 'ap-southeast-1':'image.ap-southeast-1.myhuaweicloud.com' }, 'moderation': { 'cn-north-1':'moderation.cn-north-1.myhuaweicloud.com', 'ap-southeast-1':'moderation.ap-southeast-1.myhuaweicloud.com' } } def encode_to_base64(filename): """ encoding file to base64 encoded stream text :param filename: :return: """ imgstr = "" with open(filename, 'rb') as file: imgstr = base64.b64encode(file.read()) return imgstr def download_url_base64(url): return base64.b64encode(urllib.request.urlopen(url).read()) def decode_to_wave_file(base64_encoded_str, filename): ''' decode base64 stream to wave file :param base64_encoded_str: :return: ''' wave_data = base64.b64decode(base64_encoded_str) wf = open(filename, 'wb') wf.write(wave_data) wf.close() def get_endpoint(type): region_name = get_region() return _ENDPOINT[type].get(region_name) def get_region(): return os.environ.get(ais.AisService.REGION_MSG) def init_global_env(region): os.environ[ais.AisService.REGION_MSG] = region

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0.060606

0.424242

0

null

0

null

0

1

0

da5863a5ec445793ea40d771aa319962f8ec9010

609

py

Python

GUI/dialogs/propulsion_dialogs/propulsion_dialog.py

StepLogic/Parametric-Drone-Design-Software

be9c537427f85b08c071c2666712fd32643cd439

[ "Unlicense" ]

7

2021-03-17T01:23:28.000Z

2021-05-06T20:41:21.000Z

GUI/dialogs/propulsion_dialogs/propulsion_dialog.py

StepLogic/Parametric-Drone-Design-Software

be9c537427f85b08c071c2666712fd32643cd439

[ "Unlicense" ]

null

GUI/dialogs/propulsion_dialogs/propulsion_dialog.py

StepLogic/Parametric-Drone-Design-Software

be9c537427f85b08c071c2666712fd32643cd439

[ "Unlicense" ]

null

from PyQt5.QtCore import * from PyQt5.QtWidgets import * from GUI.tabs.propulsion_tab.propulsion_tab import propulsion_tab class propulsion_dialog(QDialog): def __init__(self): super().__init__() self.tab = propulsion_tab() self.layout =self.tab.create_widget() self.buttons = QDialogButtonBox( QDialogButtonBox.Ok | QDialogButtonBox.Cancel, Qt.Horizontal, self) self.layout.addWidget(self.buttons) self.buttons.accepted.connect(self.accept) self.buttons.rejected.connect(self.reject) self.setLayout(self.layout)

30.45

65

0.689655

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5.970588

0.470588

0.128079

0.078818

0

0.004175

0.213465

609

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0

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false

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0.2

0

0.333333

0

null

0

null

0

1

0

da58d75367a4513d4ada4db3e0cf52dc127dc010

726

py

Python

blind_75/06_removeNthFromEnd.py

NursultanBeken/leetcode_practice

8aa8a033f95110aafa6acd9ebf842d716fd7552b

[ "MIT" ]

1

2020-09-20T03:55:00.000Z

blind_75/06_removeNthFromEnd.py

NursultanBeken/leetcode_practice

8aa8a033f95110aafa6acd9ebf842d716fd7552b

[ "MIT" ]

null

blind_75/06_removeNthFromEnd.py

NursultanBeken/leetcode_practice

8aa8a033f95110aafa6acd9ebf842d716fd7552b

[ "MIT" ]

null

""" Dummy node, two pointers, swap nodes """ # Definition for singly-linked list. # class ListNode(object): # def __init__(self, val=0, next=None): # self.val = val # self.next = next class Solution(object): def removeNthFromEnd(self, head, n): """ :type head: ListNode :type n: int :rtype: ListNode """ dummy = ListNode(0, head) left = dummy right = head while n>0 and right: right = right.next n -=1 while right: right = right.next left = left.next left.next = left.next.next return dummy.next

22

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0.230769

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null

0

null

0

1

0

da58d89c89872e8d53a290617cb5b532f0d040f3

1,157

py

Python

hard-gists/1023456/snippet.py

jjhenkel/dockerizeme

eaa4fe5366f6b9adf74399eab01c712cacaeb279

[ "Apache-2.0" ]

21

2019-07-08T08:26:45.000Z

2022-01-24T23:53:25.000Z

hard-gists/1023456/snippet.py

jjhenkel/dockerizeme

eaa4fe5366f6b9adf74399eab01c712cacaeb279

[ "Apache-2.0" ]

5

2019-06-15T14:47:47.000Z

2022-02-26T05:02:56.000Z

hard-gists/1023456/snippet.py

jjhenkel/dockerizeme

eaa4fe5366f6b9adf74399eab01c712cacaeb279

[ "Apache-2.0" ]

17

2019-05-16T03:50:34.000Z

2021-01-14T14:35:12.000Z

import scriptcontext import time import System import Rhino rc, view = Rhino.Input.RhinoGet.GetView("select view") print "position mouse where you want" for i in [5,4,3,2,1]: time.sleep(0.5) print i screen_point = System.Windows.Forms.Cursor.Position print "screen_point =", screen_point # convert screen coordinates to the client coordinates of # the active view view = scriptcontext.doc.Views.ActiveView view_screen_rect = view.ScreenRectangle x, y = screen_point.X - view_screen_rect.Left, screen_point.Y - view_screen_rect.Top view_client_point = System.Drawing.Point(x, y) print "view_client_point =", view_client_point # convert the client coordinates of the view to the client # coordinates of the active viewport (there are only multiple # active viewports when working in layouts) viewport = view.ActiveViewport rc, viewport_point = viewport.ClientToScreenPort(view_client_point) print "viewport_point =", viewport_point rc, line = viewport.GetFrustumLine(viewport_point.X, viewport_point.Y) if rc: scriptcontext.doc.Objects.AddPoint(line.From) scriptcontext.doc.Objects.AddPoint(line.To) scriptcontext.doc.Views.Redraw();

33.057143

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null

0.208333

0

null

0

null

0

1

0

1

da590cce76c1f0b75eaa800793b065c606ed64fc

62

py

Python

core/models/abstract/__init__.py

jcquinlan/colophon

96f3eec0a524cb1fe3d655f3cc850b125f4aaff4

[ "MIT" ]

null

core/models/abstract/__init__.py

jcquinlan/colophon

96f3eec0a524cb1fe3d655f3cc850b125f4aaff4

[ "MIT" ]

null

core/models/abstract/__init__.py

jcquinlan/colophon

96f3eec0a524cb1fe3d655f3cc850b125f4aaff4

[ "MIT" ]

null

from .user_document_interaction import UserDocumentInteraction

62

0.935484

6

62

9.333333

1

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62

1

62

0.949153

0

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null

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null

0

1

0

1

0

1

0

5

da5ad4fa6a5854b1d737a7b9cbf69ded01ce0a95

769

py

Python

EasyNN/optimizer/nesterov.py

danielwilczak101/EasyNN

89319e974c324dda228c6ecff7c39d723eda3ca2

[ "MIT" ]

5

2021-01-28T21:19:02.000Z

2022-02-03T05:47:47.000Z

EasyNN/optimizer/nesterov.py

danielwilczak101/EasyNN

89319e974c324dda228c6ecff7c39d723eda3ca2

[ "MIT" ]

1

2021-02-04T20:57:45.000Z

2021-03-03T14:49:44.000Z

EasyNN/optimizer/nesterov.py

danielwilczak101/EasyNN

89319e974c324dda228c6ecff7c39d723eda3ca2

[ "MIT" ]

2

2021-02-12T04:27:40.000Z

2021-12-19T20:11:20.000Z

""" TODO: Not complete. """ from __future__ import annotations from EasyNN.optimizer.momentum_descent import MomentumDescent import EasyNN.model.abc class Nesterov(MomentumDescent): """Nesterov uses parameters -= lr * momentum, where the momentum is computed by looking ahead.""" def get_derivatives(self: Nesterov, model: EasyNN.model.abc.Model) -> Array1D[float]: """Computes the derivatives for the optimizer.""" if model.training.iteration == 0: return super().get_derivatives(model) parameters = model.parameters.copy() self.on_training_start(model, model._derivative_momentum.value) super().get_derivatives(model) model.parameters = parameters return model._derivative_momentum.value

36.619048

101

0.717815

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769

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null

0

null

0

1

0

1

0

2

da5c80138d99d10b2de1007d8d74ecb978a6b876

3,483

py

Python

dl/cifar_python_data_layer.py

zsffq999/DISH

2285747d9a410363ce09778aed5314a2e1b1aed9

[ "MIT" ]

3

2018-09-22T13:13:46.000Z

2020-05-09T07:24:44.000Z

dl/cifar_python_data_layer.py

zsffq999/DISH

2285747d9a410363ce09778aed5314a2e1b1aed9

[ "MIT" ]

null

dl/cifar_python_data_layer.py

zsffq999/DISH

2285747d9a410363ce09778aed5314a2e1b1aed9

[ "MIT" ]

null

# imports import caffe import numpy as np from random import shuffle import cPickle as cp import scipy.io as sio class PythonDataLayer(caffe.Layer): """ This is a simple syncronous datalayer for training a multilabel model on CIFAR. """ def setup(self, bottom, top): self.top_names = ['data', 'label'] # === Read input parameters === # params is a python dictionary with layer parameters. params = eval(self.param_str) # Check the paramameters for validity. # store input as class variables self.phase = params['phase'] self.batch_size = params['batch_size'] # Create a batch loader to load the images. self.batch_loader = BatchLoader(params, None) # === reshape tops === # since we use a fixed input image size, we can shape the data layer # once. Else, we'd have to do it in the reshape call. top[0].reshape( self.batch_size, 3, params['height'], params['width']) # Note the 20 channels (because PASCAL has 20 classes.) top[1].reshape(self.batch_size) print "PythonDataLayer init success", params # print_info("PythonDataLayer", params) def forward(self, bottom, top): """ Load data. """ imgs, labels = self.batch_loader.load_next_batch() top[0].data[...] = imgs top[1].data[...] = labels def reshape(self, bottom, top): """ There is no need to reshape the data, since the input is of fixed size (rows and columns) """ pass def backward(self, top, propagate_down, bottom): """ These layers does not back propagate """ pass class BatchLoader(object): """ This class abstracts away the loading of images. Images can either be loaded singly, or in a batch. The latter is used for the asyncronous data layer to preload batches while other processing is performed. """ def __init__(self, params, result): self.result = result self.batch_size = params['batch_size'] self.height = params['height'] self.width = params['width'] self.is_train = (params['phase']=='TRAIN') # get data self.data = (np.load('cifar10_data/cifar10_data.npy'), np.load('cifar10_data/cifar10_label.npy')) # get list of image indexes. self._cur = 0 # current image self.n_data = 5000 if self.is_train else 10000 self.indexlist = np.arange(self.n_data, dtype=np.int32) if self.is_train else 50000+np.arange(self.n_data, dtype=np.int32) # preprocess: compute img mean self.img_mean = np.load('cifar10_data/cifar10_mean.npy').reshape((1, 3, self.height, self.width)) def load_next_batch(self): """ Load the next image in a batch. """ if self._cur + self.batch_size <= len(self.indexlist): index = self.indexlist[self._cur:self._cur+self.batch_size] self._cur += self.batch_size else: index = np.zeros(self.batch_size, dtype=np.int32) index[:len(self.indexlist)-self._cur] = self.indexlist[self._cur:] if self.is_train: shuffle(self.indexlist) index[len(self.indexlist)-self._cur:] = self.indexlist[:self.batch_size-len(self.indexlist)+self._cur] self._cur = self.batch_size-len(self.indexlist)+self._cur imgs = self.data[0][index].astype(np.float32) imgs[:,:,:,:] = imgs[:,::-1,:,:] imgs -= self.img_mean if self.is_train: flip_ind = np.argwhere(np.random.rand(self.batch_size)>0.5)[:,0] imgs[flip_ind,:,:,:] = imgs[flip_ind,:,:,::-1] labels = self.data[1][index].astype(np.float32) return imgs, labels

29.025

125

0.669825

509

3,483

4.471513

0.328094

0.051406

0.06283

0.052724

0.211336

0.155975

0.131371

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0.019685

0.197818

3,483

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29.268908

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0.018182

0

null

0

null

0

1

0

1

da5d5dda91394d5fcd0bc5d32616b3e16dc5d436

875

py

Python

ethsential/__main__.py

1140251/Ethsential

1de423358f5a0ba8b84d80fa63bce09552bca9fd

[ "Apache-2.0" ]

7

2021-10-11T12:07:08.000Z

2022-01-10T01:19:36.000Z

ethsential/__main__.py

1140251/Ethsential

1de423358f5a0ba8b84d80fa63bce09552bca9fd

[ "Apache-2.0" ]

null

ethsential/__main__.py

1140251/Ethsential

1de423358f5a0ba8b84d80fa63bce09552bca9fd

[ "Apache-2.0" ]

null

import sys from .src.applications.server import ETHSENTIAL from .src.applications.cli import CLI from .src.parser import create_parser def main(): parser = create_parser() args = parser.parse_args() if args.action == 'cli': try: CLI.exec_cmd(args) except Exception as e: if hasattr(e, 'message'): print(getattr(e, 'message', repr(e))) else: print(e) sys.exit(0) elif args.action == 'install': try: CLI.install() except Exception as e: if hasattr(e, 'message'): print(getattr(e, 'message', repr(e))) else: print(e) elif args.action == 'tcp': ETHSENTIAL.start_tcp(args.host, args.port) else: ETHSENTIAL.start_io() if __name__ == '__main__': main()

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null

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null

0

1

0

da5ea178b4528bc2e8ee17e0a8132d23a6388e83

2,322

py

Python

scripts/msig_prods_update_tag.py

xenbo/eosforce

f77a73c2b49f40f8af5c11a13b0a7eb069e02b5f

[ "MIT" ]

117

2018-06-22T08:49:36.000Z

2022-01-30T17:08:29.000Z

scripts/msig_prods_update_tag.py

xenbo/eosforce

f77a73c2b49f40f8af5c11a13b0a7eb069e02b5f

[ "MIT" ]

17

2018-07-05T04:06:47.000Z

2020-09-07T06:19:25.000Z

scripts/msig_prods_update_tag.py

xenbo/eosforce

f77a73c2b49f40f8af5c11a13b0a7eb069e02b5f

[ "MIT" ]

42

2018-06-22T08:57:42.000Z

2022-03-28T13:08:02.000Z

#!/usr/bin/env python3 import argparse import json import os import re import subprocess import sys import time enable_push = True # True to push on chain cleos = '../build/programs/cleos/cleos --wallet-url http://127.0.0.1:6666 --url http://127.0.0.1:8001 ' wallet_password = '' wallet_name = 'testc' active_account = 'testc' funcs_to_open = [ ( 'f.cprod', 10000000 ), ( 'f.votagen', 10000010 ) ] tx_expire_hours = 120 # 5days def jsonArg(a): return " '" + json.dumps(a) + "' " def run(args): print('', args) if subprocess.call(args, shell=True): print(' exiting because of error') sys.exit(1) def runone(args): print('', args) subprocess.call(args, shell=True) def getOutput(args): print('', args) proc = subprocess.Popen(args, shell=True, stdout=subprocess.PIPE) return proc.communicate()[0].decode('utf-8') def getJsonOutput(args): return json.loads(getOutput(args)) def getbps(): bpsa = [] bpsj = getJsonOutput(cleos + " get schedule -j ") for bp in bpsj["active"]["producers"]: bpsa.append(bp["producer_name"]) return bpsa def msigProposeUpdateTag(proposer, bps, func_name, open_block_num, expirehours): requestedPermissions = [] for i in range(0, len(bps)): requestedPermissions.append({'actor': bps[i], 'permission': 'active'}) trxPermissions = [{'actor': 'eosio', 'permission': 'active'}] action_name = 'setconfig' data = { 'typ': func_name, 'num': open_block_num, 'key': '', 'fee': '0.0000 EOS' } run(cleos + 'multisig propose ' + func_name + jsonArg(requestedPermissions) + jsonArg(trxPermissions) + 'eosio ' + action_name + jsonArg(data) + ' ' + proposer + ' ' + str(expirehours) + ' -p ' + proposer) # --------------------------------------------------------------------------------------------------- # msig to update system contract # unlock wallet unlockwallet_str = 'cleos wallet unlock -n ' + wallet_name + ' --password ' + wallet_password runone(unlockwallet_str) # get schedule active bps active_bps = getbps() for ( func_name, func_block_num ) in funcs_to_open: msigProposeUpdateTag(active_account, active_bps, func_name, func_block_num, tx_expire_hours) time.sleep(3)

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103

0.615418

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2,322

5.069091

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0.028694

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0.015782

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null

0

null

0

1

0

da5fc436cce22928bf1e7b8ba50df3169ca33055

7,027

py

Python

maskrcnn/preprocess/download_googlestaticmap.py

JBoshoff/Replicate-night-light

5bdfbb99fe38f98f61f733f4e847be2bb6f559ef

[ "MIT" ]

8

2020-08-26T21:05:32.000Z

2021-08-18T06:55:24.000Z

maskrcnn/preprocess/download_googlestaticmap.py

JBoshoff/Replicate-night-light

5bdfbb99fe38f98f61f733f4e847be2bb6f559ef

[ "MIT" ]

null

maskrcnn/preprocess/download_googlestaticmap.py

JBoshoff/Replicate-night-light

5bdfbb99fe38f98f61f733f4e847be2bb6f559ef

[ "MIT" ]

2

2021-10-20T12:43:00.000Z

2022-01-04T19:40:16.000Z

"""This downloader downloads satellite images from the Google Static Maps API. Usage: $ python download_googlestaticmap.py \ > --log LOG_FILE.csv \ > --initialize INIT_FILE.csv $ nohup python download_googlestaticmap.py \ > --log LOG_FILE.csv \ > --num 3 \ > --download-dir DIR \ > > logs/download_googlestaticmap.log & """ import os import pandas as pd import requests from argparse import ArgumentParser from tqdm import tqdm class Downloader(object): """This class keeps a log of the downloading process, checks for duplicates and manages bad HTTP requests. Args: queue (pandas.DataFrame): Log of the downloaded objects. """ def __init__(self, queue=None): # if downloading for the first time if queue is None: # create an empty queue self.queue = pd.DataFrame(columns=['index', 'url', 'status']) self.queue.set_index('index', inplace=True) self.queue.index.name = 'index' # if not, load previous log else: self.queue = queue def request(self, indices, mapping): """This method requests objects to be downloaded and adds them to the queue. Args: indices (numpy.array): unique id for each object in the queue. mapping (callable): takes in the indices and generates the urls. """ urls = [mapping(index) for index in indices] subqueue = pd.DataFrame( {'url': urls, 'status': False}, index=indices) subqueue.index.name = 'index' try: self.queue = pd.concat([self.queue, subqueue], verify_integrity=True) print('{} new requests initiated.'.format(subqueue.shape[0])) except ValueError: raise Exception('Overlapping new requests with existing requests.') def download(self, num, download_dir, test_page='https://www.google.com', suffix='.png', min_size=20000): """This method downloads objects. Args: num (int): number of downloads to perform. download_dir (str): downloading directory. test_page (str): url to try in order to check internet connection. suffix (str): suffix for saved files. min_size (int): minimum file size. Helps drop NA images. """ # check local directory if not os.path.isdir(download_dir): raise Exception('Download directory does not exist.') # check internet connection _ = requests.get(test_page, timeout=1) # extract items already downloaded mask = self.queue['status'] if not mask.all(): # number of files to be downloaded update_num = min((~mask).sum(), num) print('Preparing to download {} files.'.format(update_num)) idxs = self.queue[~mask].index.copy() idxs = idxs[0:update_num] # downloading starts for idx in tqdm(idxs): # fetch url url = self.queue.loc[idx, 'url'] # construct file names file_name = os.path.join(download_dir, ''.join([idx, suffix])) # check if file exists already if os.path.isfile(file_name): # update status self.queue.loc[idx, 'status'] = True print('{} already exists.'.format(file_name)) else: r = requests.get(url) if int(r.headers['Content-Length']) > min_size: with open(file_name, 'wb') as f: _ = f.write(r.content) # update status self.queue.loc[idx, 'status'] = True print('{} successfully downloaded.'.format(file_name)) else: print('{} skipped - file too small: {} bytes.'.format( file_name, int(r.headers['Content-Length']))) print(url) self.queue.drop(idx, inplace=True) if mask.all(): print('Downloading completed.') def make_url(idx, df, GOOGLE_API_KEY): """Helper function to generate the urls for the Google Static Maps API. Args: index (str): Identifies an image. df (pandas.DataFrame): Stores image info. GOOGLE_API_KEY (str) Returns: url (str): The URL to the image. """ params = { 'center': ('{:.6f},{:.6f}' .format(df.loc[idx, 'lat'], df.loc[idx, 'lon'])), 'zoom': '19', 'size': '640x640', 'scale': '2', 'maptype': 'satellite', 'key': GOOGLE_API_KEY} params_str = '&'.join(['{}={}'.format(k, v) for k, v in params.items()]) return '?'.join(['https://maps.googleapis.com/maps/api/staticmap', params_str]) def run(args): """Runs the script. Args: args (argparse.Namespace): Command line arguments. """ assert args.log is not None, 'Input log file path!' # parse and make url list if args.initialize is not None: downloader = Downloader() # fetch authentication key with open(args.api_key, 'r') as f: GOOGLE_API_KEY = f.read() # read coordinates and index df = pd.read_csv(args.initialize, index_col='index') df = df.filter(items=['lon', 'lat']) downloader.request(indices=df.index.values, mapping=lambda x: make_url(x, df, GOOGLE_API_KEY)) else: queue = pd.read_csv(args.log, index_col='index') downloader = Downloader(queue=queue) # download if args.num is not None: assert args.download_dir is not None, 'Input download directory!' downloader.download(num=args.num, download_dir=args.download_dir) # save the log downloader.queue.to_csv(args.log) if __name__ == '__main__': # parse arguments passed from the command line parser = ArgumentParser( description='Downloads satellite images from Google Statics Maps API.') parser.add_argument('--log', default=None, type=str, help='name of log file (.csv)') # request parser.add_argument('--initialize', default=None, type=str, help='a new list of files to be downloaded') parser.add_argument( '--api-key', default='GOOGLE_API_KEY.txt', help='file that stores the API key, defaults to GOOGLE_API_KEY.txt') # download parser.add_argument( '--num', default=None, type=int, help='number of downloads to perform, this flag turns on downloading') parser.add_argument('--download-dir', default=None, type=str, help='downloading directory') # parse args = parser.parse_args() run(args)

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0.565675

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4.763126

0.295482

0.029992

0.021533

0.011536

0.10869

0.044091

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0

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7,027

194

85

36.221649

0.815679

0.259713

0

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0

0.174419

0

0.019231

1

0.048077

false

0

0.048077

0

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0.067308

0

null

0

null

0

1

0

da5fcd544f0ebf019068c4964041f2d02daca8dc

2,606

py

Python

pyrh/endpoints.py

JamMarHer/pyrh

b5501482974d9a7ba9f34745642d0a2e585154f2

[ "MIT" ]

null

pyrh/endpoints.py

JamMarHer/pyrh

b5501482974d9a7ba9f34745642d0a2e585154f2

[ "MIT" ]

null

pyrh/endpoints.py

JamMarHer/pyrh

b5501482974d9a7ba9f34745642d0a2e585154f2

[ "MIT" ]

null

BASE_API = "https://api.robinhood.com" def login(): return BASE_API + "/oauth2/token/" def logout(): return BASE_API + "/oauth2/revoke_token/" def investment_profile(): return BASE_API + "/user/investment_profile/" def accounts(): return BASE_API + "/accounts/" def ach(option): """ Combination of 3 ACH endpoints. Options include: * iav * relationships * transfers """ return ( BASE_API + "/ach/iav/auth/" if option == "iav" else BASE_API + f"/ach/{option}/" ) def applications(): return BASE_API + "/applications/" def dividends(): return BASE_API + "/dividends/" def edocuments(): return BASE_API + "/documents/" def instruments(instrument_id=None, option=None): """ Return information about a specific instrument by providing its instrument id. Add extra options for additional information such as "popularity" """ url = BASE_API + f"/instruments/" if instrument_id is not None: url += f"{instrument_id}" if option is not None: url += f"{option}" return url def margin_upgrades(): return BASE_API + "/margin/upgrades/" def markets(): return BASE_API + "/markets/" def notifications(): return BASE_API + "/notifications/" def orders(order_id=""): return BASE_API + f"/orders/{order_id}" def password_reset(): return BASE_API + "/password_reset/request/" def portfolios(): return BASE_API + "/portfolios/" def positions(): return BASE_API + "/positions/" def quotes(): return BASE_API + "/quotes/" def historicals(): return BASE_API + "/quotes/historicals/" def document_requests(): return BASE_API + "/upload/document_requests/" def user(): return BASE_API + "/user/" def watchlists(): return BASE_API + "/watchlists/" def news(stock): return BASE_API + f"/midlands/news/{stock}/" def fundamentals(stock): return BASE_API + f"/fundamentals/{stock}/" def tags(tag): """ Returns endpoint with tag concatenated. """ return BASE_API + f"/midlands/tags/tag/{tag}/" def chain(instrument_id): return BASE_API + f"/options/chains/?equity_instrument_ids={instrument_id}" def options(chain_id, dates, option_type): return ( BASE_API + f"/options/instruments/?chain_id={chain_id}&expiration_dates={dates}" + f"&state=active&tradability=tradable&type={option_type}" ) def market_data(option_id): return BASE_API + f"/marketdata/options/{option_id}/" def convert_token(): return BASE_API + "/oauth2/migrate_token/"

18.748201

88

0.653492

320

2,606

5.140625

0.309375

0.12766

0.213374

0.059574

0.103343

0

0.001947

0.211819

2,606

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89

18.884058

0.798929

0.107061

0

0.029412

0

0.29713

0.17351

0

1

0.411765

false

0.029412

0

0.367647

0.823529

0

null

0

1

0

null

0

1

0

1

0

4

da5fdaa047dc2cba3b58d7d4eab745c4fc398500

9,138

py

Python

task.py

zhester/aptask

4fc5c2bfe8dbe373e2ddc5bc15562885bb20b28e

[ "BSD-2-Clause" ]

null

task.py

zhester/aptask

4fc5c2bfe8dbe373e2ddc5bc15562885bb20b28e

[ "BSD-2-Clause" ]

null

task.py

zhester/aptask

4fc5c2bfe8dbe373e2ddc5bc15562885bb20b28e

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python """ User Task Interface This module should be used to implement new task execution drivers. Semantically, a task is the object a worker creates and manipulates to execute long-running code or programs. """ import inspect import data #============================================================================= class NotSupported( NotImplementedError ): """ Exception raised by methods that are not supported by the current task instance. """ #========================================================================= def __str__( self ): """ Convert to string representation. @return A string describing the exception """ return 'Method not supported by this object.' #============================================================================= class Report( data.Data ): """ The object sent to the worker when reporting the status and progress of a task. """ #========================================================================= ERROR = -1 # task encountered an error INIT = 0 # task is initialized RUNNING = 1 # task is executing as normal DONE = 2 # task is done executing #========================================================================= status_strings = ( 'initialized', 'running', 'done' ) #========================================================================= def __init__( self, status = INIT, progress = 0.0, message = None ): """ Constructor. @param status Current task status (ERROR, INIT, RUNNING, DONE) @param progress Current task progress (0.0 to 1.0) @param message User-friendly message about progress (string) """ # load arguments into object state self.super_init( vars() ) #========================================================================= def is_done( self ): """ Informs interested parties if the task has completed. @return True when task has finished executing """ return self.status == self.DONE #============================================================================= class Task( object ): """ Object created and used by a worker process to start and monitor a task. Adding a new task interface requires implementing a child of this class. Child classes may implement the following methods: abort Called to stop the task before completion getargs Used to describe acceptable arguments initialize Called to initialize or start the task process Called iteratively until the task is complete abort, initialize, and process must all return a Report object. """ #========================================================================= def __init__( self, arguments = None ): """ Constructor. @param arguments Argument values requested for task execution """ self.arguments = None self.report = Report() self.valid_arguments = self._load_args( arguments ) #========================================================================= @classmethod def getargs( cls ): """ Retrieves the argument list for this task. An argument list is a list of dicts. Each dict describes an argument with the following keys: name Binding name default Default value if not supplied (implies type) required True if this must always be specified help Brief description of the purpose of this argument type Argument type (if no default is given) (int|float|str) later: (list|dict) """ return [] #========================================================================= @classmethod def gethelp( cls ): """ Retrieves any helpful information that should be sent to the user. """ if cls is Task: return '(Task description unavailable.)' return inspect.getdoc( cls ) #========================================================================= def abort( self ): """ Stops the execution of this task before completion. @throws NotSupported Descendant class does not support this method """ raise NotSupported() #========================================================================= def initialize( self ): """ Initializes or starts the execution of this task. @throws NotSupported Descendant class does not support this method """ raise NotSupported() #========================================================================= def process( self ): """ Called iteratively until the task reports completion. @return Task progress from 0.0 (none) to 1.0 (done) @throws NotSupported Descendant class does not support this method """ raise NotSupported() #========================================================================= def _load_args( self, args ): """ Load given arguments into object state. @param args List or dict of requested argument values """ # flag to indicate valid argument input result = True # build a list of arguments expected by this task driver self._arg_list = self.getargs() # build a lookup table of known arguments self._arg_table = dict( ( a[ 'name' ], a ) for a in self._arg_list ) # create a dictionary to keep the argument values and set defaults self.arguments = dict( ( a[ 'name' ], a[ 'default' ] ) for a in self._arg_list if 'default' in a ) # handle dictionary input if type( args ) is dict: # use our known arguments to extract values into the object for key, arg in self._arg_table.items(): # see if the input specified this argument if key in args: if self._load_arg( key, args[ key ] ) == False: result = False # handle list input elif type( args ) is list: # argument value list index index = 0 # use our known arguments to extract values into the object for key, arg in self._arg_table.items(): # see if the input specified this argument if index < len( args ): if self._load_arg( key, args[ index ] ) == False: result = False index += 1 # make sure all required arguments were specified reqs = [ a[ 'name' ] for a in self._arg_list if 'required' in a ] keys = self.arguments.keys() # the difference between two sets should be an empty set if the entire # first set is a subset of the second set num_diff = len( set( reqs ) - set( keys ) ) if num_diff != 0: result = False # return status of argument loading return result #========================================================================= def _load_arg( self, key, value ): """ Load a given argument into object state. @param key Name of argument to load @param value Value to load @return True if successfully loaded """ # check key against table of arguments if key not in self._arg_table: return False # reference the argument specifier spec = self._arg_table[ key ] # determine argument type if 'default' in spec: type_name = type( spec[ 'default' ] ).__name__ elif 'type' in spec: type_name = spec[ 'type' ] else: type_name = 'str' # pull name of type of value value_type_name = type( value ).__name__ # validate argument type if value_type_name != type_name: return False # store value in object state self.arguments[ key ] = value return True #============================================================================= def main( argv ): """ Script execution entry point @param argv Arguments passed to the script @return Exit code (0 = success) """ import tasks.devtask t = tasks.devtask.DevTask() t._load_args( {} ) print t.arguments #print t.gethelp() #print Task.gethelp() # return success return 0 #============================================================================= if __name__ == "__main__": import sys sys.exit( main( sys.argv ) )

31.402062

78

0.478989

896

9,138

4.804688

0.270089

0.014634

0.012544

0.022997

0.15331

0.129617

0.125668

0.105691

0

0.003051

0.31856

9,138

290

79

31.510345

0.688293

0.258809

0

0.141176

0

0.045244

0

null

0

0.047059

null

0.011765

0

null

0

null

0

1

0

1

da60447f22ba4eba74abcb47b3cadec2e06136d2

9,826

py

Python

NeuroMechFly/experiments/kinematic_replay/kinematic_replay_no_support.py

NeLy-EPFL/NeuroMechFly

69f9e2d86caac561a50e3e060d007dd50a20d481

[ "Apache-2.0" ]

12

2021-05-07T15:27:11.000Z

2022-01-29T04:26:36.000Z

NeuroMechFly/experiments/kinematic_replay/kinematic_replay_no_support.py

NeLy-EPFL/NeuroMechFly

69f9e2d86caac561a50e3e060d007dd50a20d481

[ "Apache-2.0" ]

15

2021-05-07T14:58:04.000Z

2021-11-10T21:30:58.000Z

NeuroMechFly/experiments/kinematic_replay/kinematic_replay_no_support.py

NeLy-EPFL/NeuroMechFly

69f9e2d86caac561a50e3e060d007dd50a20d481

[ "Apache-2.0" ]

1

2022-01-13T16:08:49.000Z

""" Drosophila simulation class for kinematic replay without body support. """ import numpy as np import pandas as pd import pybullet as p from NeuroMechFly.sdf.units import SimulationUnitScaling from NeuroMechFly.simulation.bullet_simulation import BulletSimulation # Random number seed np.random.seed(seed=321) def add_perturbation( size, initial_position, target_position, time, units ): """ Shoot a ball to perturb the target system at a specified velocity Parameters ---------- size: <float> Radius of the ball initial_position: <array> 3D position of the ball target_position: <array> 3D position of the target time: <float> Time before reaching the target position Returns ------- ball : <int> Pybullet ID for the ball """ # Init initial_position = np.asarray(initial_position) * units.meters target_position = np.asarray(target_position) * units.meters # Load ball ball = p.loadURDF( "../data/design/sdf/sphere_1cm.urdf", initial_position, globalScaling=size * units.meters, useMaximalCoordinates=True ) # Change dynamics to remove damping and friction p.changeDynamics( ball, -1, linearDamping=0, angularDamping=0, rollingFriction=0, spinningFriction=0 ) p.changeVisualShape(ball, -1, rgbaColor=[0.8, 0.8, 0.8, 1]) # Compute initial velocity velocity = ( target_position - initial_position - 0.5 * np.asarray([0, 0, -9.81 * units.gravity]) * time**2 ) / time # Reset base velocity p.resetBaseVelocity(ball, velocity) return ball class DrosophilaSimulation(BulletSimulation): """ Drosophila Simulation Class for kinematic replay. Parameters ---------- container: <Container> Instance of the Container class. sim_options: <dict> Dictionary containing the simulation options. kp: <float> Proportional gain of the position controller. kv: <float> Derivative gain of the position controller. position_path: <str> Path of the joint position .pkl file. velocity_path: <str> Path of the joint velocity .pkl file. add_perturbation: <bool> Activate/deactivate the ball perturbation. units: <obj> Instance of SimulationUnitScaling object to scale up the units during calculations. """ def __init__( self, container, sim_options, kp, kv, angles_path, velocity_path, add_perturbation, starting_time=0.0, fixed_positions=None, units=SimulationUnitScaling(meters=1000, kilograms=1000) ): super().__init__(container, units, **sim_options) self.last_draw = [] self.kp = kp self.kv = kv self.pose = [0] * self.num_joints self.vel = [0] * self.num_joints self.angles = self.load_data(angles_path, starting_time) self.velocities = self.load_data(velocity_path, starting_time) self.impulse_sign = 1 self.add_perturbation = add_perturbation self.fixed_positions = fixed_positions self.pball = None self.fixed_positions = fixed_positions def load_data(self, data_path, starting_time): """ Function that loads the pickle format joint angle or velocity gile. Parameters ---------- data_path : <str> Path of the .pkl file. starting_time : <float> Experiment's time from which the simulation will start. Returns ------- dict Returns the joint angles in a dictionary. """ names_equivalence = { 'ThC_pitch': 'Coxa', 'ThC_yaw': 'Coxa_yaw', 'ThC_roll': 'Coxa_roll', 'CTr_pitch': 'Femur', 'CTr_roll': 'Femur_roll', 'FTi_pitch': 'Tibia', 'TiTa_pitch': 'Tarsus1' } converted_dict = {} try: data = pd.read_pickle(data_path) start = int(np.round(starting_time / self.time_step)) for leg, joints in data.items(): for joint_name, val in joints.items(): new_name = 'joint_' + leg[:2] + \ names_equivalence[joint_name] converted_dict[new_name] = val[start:] return converted_dict except BaseException: FileNotFoundError(f"File {data_path} not found!") def controller_to_actuator(self, t): """ Code that glues the controller the actuator in the system. If there are muscles then contoller actuates the muscles. If not then the controller directly actuates the joints. Parameters ---------- t : int Time running in the physics engine. """ # Throw mini balls at the fly during kinematic replay if self.add_perturbation: if ((t + 1) % (0.5 / self.time_step)) == 0: print("Adding perturbation") self.pball = add_perturbation( size=5e-2, initial_position=np.asarray( [0, self.impulse_sign * 2e-3, 0.0]) + self.base_position, target_position=self.base_position, time=20e-3, units=self.units ) self.impulse_sign *= -1 if ((t + 1) % (3.0 / self.time_step) ) == 0 and t < (3.012 / self.time_step): radius = 20e-2 self.pball = add_perturbation( size=radius, initial_position=np.asarray( [radius * 0.05, radius * 0.05, 1e-3]) + self.base_position, target_position=[self.base_position[0], self.base_position[1], 0.0], time=20e-3, units=self.units ) p.changeDynamics(self.pball, -1, 0.3) # Setting the joint angular positions joints # Setting the joint angular positions of the fixed joints if not self.fixed_positions: self.fixed_positions = { 'joint_LAntenna': 35, 'joint_RAntenna': -35, } for joint_name, joint_pos in self.fixed_positions.items(): self.pose[self.joint_id[joint_name]] = np.deg2rad(joint_pos) # Setting the joint angular positions of leg DOFs based on pose estimation for joint_name, joint_pos in self.angles.items(): self.pose[self.joint_id[joint_name]] = joint_pos[t] # Setting the joint angular velocities of leg DOFs based on pose estimation for joint_name, joint_vel in self.velocities.items(): self.vel[self.joint_id[joint_name]] = joint_vel[t] # Control the joints through position controller # Velocity can be discarded if not available and gains can be changed for joint in range(self.num_joints): p.setJointMotorControl2( self.animal, joint, controlMode=p.POSITION_CONTROL, targetPosition=self.pose[joint], targetVelocity=self.vel[joint], positionGain=self.kp, velocityGain=self.kv, maxVelocity=1e8 ) p.changeDynamics(self.animal, joint, maxJointVelocity=1e8) # Change the color of the colliding body segments if self.draw_collisions: draw = [] if self.behavior == 'walking': links_contact = self.get_current_contacts() link_names = list(self.link_id.keys()) link_ids = list(self.link_id.values()) for i in links_contact: link1 = link_names[link_ids.index(i)] if link1 not in draw: draw.append(link1) self.change_color(link1, self.color_collision) for link in self.last_draw: if link not in draw: self.change_color(link, self.color_legs) elif self.behavior == 'grooming': # Don't consider the ground sensors collision_forces = self.contact_normal_force[len( self.ground_contacts):, :] links_contact = np.where( np.linalg.norm(collision_forces, axis=1) > 0 )[0] for i in links_contact: link1 = self.self_collisions[i][0] link2 = self.self_collisions[i][1] if link1 not in draw: draw.append(link1) self.change_color(link1, self.color_collision) if link2 not in draw: draw.append(link2) self.change_color(link2, self.color_collision) for link in self.last_draw: if link not in draw: if 'Antenna' in link: self.change_color(link, self.color_body) else: self.change_color(link, self.color_legs) self.last_draw = draw def change_color(self, identity, color): """ Change color of a given body segment. """ p.changeVisualShape( self.animal, self.link_id[identity], rgbaColor=color) def feedback_to_controller(self): """ Code that glues the sensors/feedback to controller in the system. """ def update_parameters(self, params): """ Update parameters. """ def optimization_check(self): """ Optimization check. """

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da6071c120cc4c6108f42d5833b8ae67a673f55d

3,845

py

Python

hw/ip/otbn/dv/otbnsim/sim/isa.py

wxjstz/opentitan

6ff4397bac9c07373d735bd859c7ef8de39c2af8

[ "Apache-2.0" ]

null

hw/ip/otbn/dv/otbnsim/sim/isa.py

wxjstz/opentitan

6ff4397bac9c07373d735bd859c7ef8de39c2af8

[ "Apache-2.0" ]

null

hw/ip/otbn/dv/otbnsim/sim/isa.py

wxjstz/opentitan

6ff4397bac9c07373d735bd859c7ef8de39c2af8

[ "Apache-2.0" ]

null

# Copyright lowRISC contributors. # Licensed under the Apache License, Version 2.0, see LICENSE for details. # SPDX-License-Identifier: Apache-2.0 from enum import IntEnum import sys from typing import Dict from riscvmodel.types import Immediate # type: ignore from shared.insn_yaml import Insn, load_insns_yaml from .model import OTBNModel # Load the insns.yml file at module load time: we'll use its data while # declaring the classes. The point is that an OTBNInsn below is an instance of # a particular Insn object from shared.insn_yaml, so we want a class variable # on the OTBNInsn that points at the corresponding Insn. try: _INSNS_FILE = load_insns_yaml() except RuntimeError as err: sys.stderr.write('{}\n'.format(err)) sys.exit(1) class DummyInsn(Insn): '''A dummy instruction that will never be decoded. Used for the insn class variable in the OTBNInsn base class. ''' def __init__(self) -> None: fake_yml = { 'mnemonic': 'dummy-insn', 'operands': [] } super().__init__(fake_yml, None) def insn_for_mnemonic(mnemonic: str, num_operands: int) -> Insn: '''Look up the named instruction in the loaded YAML data. As a sanity check, make sure it has the expected number of operands. If we fail to find the right instruction, print a message to stderr and exit (rather than raising a RuntimeError: this happens on module load time, so it's a lot clearer to the user what's going on this way). ''' insn = _INSNS_FILE.mnemonic_to_insn.get(mnemonic) if insn is None: sys.stderr.write('Failed to find an instruction for mnemonic {!r} in ' 'insns.yml.\n' .format(mnemonic)) sys.exit(1) if len(insn.operands) != num_operands: sys.stderr.write('The instruction for mnemonic {!r} in insns.yml has ' '{} operands, but we expected {}.\n' .format(mnemonic, len(insn.operands), num_operands)) sys.exit(1) return insn class OTBNInsn: '''A decoded OTBN instruction. ''' # A class variable that holds the Insn subclass corresponding to this # instruction. insn = DummyInsn() # type: Insn def __init__(self, op_vals: Dict[str, int]): self.op_vals = op_vals def execute(self, model: OTBNModel) -> None: raise NotImplementedError('OTBNInsn.execute') def disassemble(self, pc: int) -> str: '''Generate an assembly listing for this instruction''' return self.insn.disassemble(self.op_vals, 12) class RV32RegReg(OTBNInsn): '''A general class for register-register insns from the RV32I ISA''' def __init__(self, op_vals: Dict[str, int]): super().__init__(op_vals) self.grd = op_vals['grd'] self.grs1 = op_vals['grs1'] self.grs2 = op_vals['grs2'] class RV32RegImm(OTBNInsn): '''A general class for register-immediate insns from the RV32I ISA''' def __init__(self, op_vals: Dict[str, int]): super().__init__(op_vals) self.grd = op_vals['grd'] self.grs1 = op_vals['grs1'] self.imm = op_vals['imm'] class RV32ImmShift(OTBNInsn): '''A general class for immediate shift insns from the RV32I ISA''' def __init__(self, op_vals: Dict[str, int]): super().__init__(op_vals) self.grd = op_vals['grd'] self.grs1 = op_vals['grs1'] self.shamt = op_vals['shamt'] class ShiftType(IntEnum): LSL = 0 # logical shift left LSR = 1 # logical shift right def ShiftReg(reg: int, shift_type: int, shift_bytes: Immediate) -> int: assert 0 <= int(shift_bytes) shift_bits = int(shift_bytes << 3) return (reg << shift_bits if shift_type == ShiftType.LSL else reg >> shift_bits)

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0.024824

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3,845

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0.090909

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0.409091

0

null

0

null

0

1

0

da624c2d151313fbe9db0021e53684c69e6d4b5f

247

py

Python

config.py

TaskeHAMANO/sample_application

628699c62197dd5079e0b600f431c791ac3a301a

[ "BSD-3-Clause" ]

null

config.py

TaskeHAMANO/sample_application

628699c62197dd5079e0b600f431c791ac3a301a

[ "BSD-3-Clause" ]

null

config.py

TaskeHAMANO/sample_application

628699c62197dd5079e0b600f431c791ac3a301a

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # vim:fileencoding=utf-8 # Author: Shinya Suzuki # Created: 2017-11-16 database_path = "/techathon.db" SQLALCHEMY_DATABASE_URI = "sqlite://{0}".format(database_path) SECRET_KEY = 'test' SQLALCHEMY_TRACK_MODIFICATIONS = True

24.7

62

0.761134

34

247

5.323529

0.882353

0.132597

0

0.044843

0.097166

247

9

63

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0

0.183544

0

1

0

false

0

null

0

1

0

null

0

1

da63d798dfe9c2ea59c6459800d52786ae4db56c

2,668

py

Python

tests/features/steps/environment_steps.py

candango/pyclicksign

d709122867cfa5c6fce4322b55a033bc82126e1c

[ "Apache-2.0" ]

null

tests/features/steps/environment_steps.py

candango/pyclicksign

d709122867cfa5c6fce4322b55a033bc82126e1c

[ "Apache-2.0" ]

9

2022-01-15T19:43:46.000Z

2022-03-24T06:04:25.000Z

tests/features/steps/environment_steps.py

candango/pyclicksign

d709122867cfa5c6fce4322b55a033bc82126e1c

[ "Apache-2.0" ]

null

# Copyright 2021-2022 Flávio Gonçalves Garcia # # 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 behave import given, when, then, step from cartola import fs from tornado.escape import json_encode, json_decode import os def get_absolute_path(directory): return os.path.realpath( os.path.join(os.path.dirname(__file__), "..", "..", directory) ) def create_file(path, content, binary=False): real_path = get_absolute_path(path) fs.write(real_path, content, binary) os.chmod(real_path, 0o600) return real_path @then("Podemos converter {index} de dict para texto") def step_arquivo_criado_com_sucesso(context, index): data = getattr(context, index) setattr(context, index, json_encode(data)) @then("Podemos converter {index} de texto para dict") def step_arquivo_criado_com_sucesso(context, index): data = getattr(context, index) setattr(context, index, json_decode(data)) @then("Arquivo de {index} é criado com sucesso em {path}") def step_arquivo_criado_com_sucesso(context, index, path): data = getattr(context, index) if isinstance(data, dict): data = json_encode(data) if isinstance(data, str): data = data.encode() real_path = create_file(path, data, True) context.tester.assertTrue(os.path.exists(real_path)) context.tester.assertTrue(os.path.isfile(real_path)) @given("Arquivo de {index} existe em {path}") def step_arquivo_existe(context, index, path): real_path = get_absolute_path(path) context.tester.assertTrue(os.path.exists(real_path)) context.tester.assertTrue(os.path.isfile(real_path)) setattr(context, index, real_path) print(getattr(context, index)) @given("Ler dados de {index} sucedeu") def step_arquivo_existe(context, index): real_path = getattr(context, index) setattr(context, index, fs.read(real_path)) @then("File at {path} removed") def step_file_at_removed(context, path): real_path = get_absolute_path(path) context.tester.assertTrue(os.path.exists(real_path)) context.tester.assertTrue(os.path.isfile(real_path)) os.remove(real_path) context.tester.assertFalse(os.path.exists(real_path))

33.35

74

0.737631

385

2,668

4.966234

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0.075314

0.072176

0.078452

0.393828

0.348849

0.281381

0.259414

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2,668

79

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0

0.285714

0

0.108134

0

0.142857

1

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false

0

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0.020408

0.285714

0.020408

0

null

0

null

0

1

0

da648cdce4097a5f15c459cc9d3dc08716cd7f4a

2,967

py

Python

photo_album_src/models_bk.py

chrisjen83/k3s-labs

b283c2500b272be0de1025ef541a46d7c4591cc1

[ "MIT" ]

1

2020-04-01T22:05:28.000Z

photo_album_src/models_bk.py

chrisjen83/k3s-labs

b283c2500b272be0de1025ef541a46d7c4591cc1

[ "MIT" ]

null

photo_album_src/models_bk.py

chrisjen83/k3s-labs

b283c2500b272be0de1025ef541a46d7c4591cc1

[ "MIT" ]

5

2020-02-21T22:47:35.000Z

2022-02-03T15:21:39.000Z

#!/usr/bin/env python3 # Import modules required for app import os import boto3 import json from pymongo import MongoClient from werkzeug import secure_filename from PIL import Image from config import ecs_test_drive #Get from K8s ConfigMap values for MongoDB Database MONGO_SERVER = os.environ.get( "MONGO_SERVER", None) DB_NAME = os.environ.get( "DB_NAME", None) client = MongoClient( MONGO_SERVER, 27017) # Get database connection with database name db = client[DB_NAME] # Remove any existing documents in photos collection # db.photos.delete_many({}) # Comment this line if you don't want to remove documents each time you start the app # Retrieve all photos records from database def get_photos(): return db.photos.find({}) # Insert form fields into database def insert_photo(request): title = request.form['title'] comments = request.form['comments'] filename = secure_filename(request.files['photo'].filename) thumbfile = filename.rsplit(".", 1)[0] + "-thumb.jpg" photo_url = "http://" + ecs_test_drive['ecs_access_key_id'].split( '@')[0] + ".public.ecstestdrive.com/" + ecs_test_drive['ecs_bucket_name'] + "/" + filename thumbnail_url = "http://" + ecs_test_drive['ecs_access_key_id'].split( '@')[0] + ".public.ecstestdrive.com/" + ecs_test_drive['ecs_bucket_name'] + "/" + thumbfile db.photos.insert_one({'title': title, 'comments': comments, 'photo': photo_url, 'thumb': thumbnail_url}) def upload_photo(file): # Get ECS credentials from external config file ecs_endpoint_url = ecs_test_drive['ecs_endpoint_url'] ecs_access_key_id = ecs_test_drive['ecs_access_key_id'] ecs_secret_key = ecs_test_drive['ecs_secret_key'] ecs_bucket_name = ecs_test_drive['ecs_bucket_name'] # Open a session with ECS using the S3 API session = boto3.resource(service_name='s3', aws_access_key_id=ecs_access_key_id, aws_secret_access_key=ecs_secret_key, endpoint_url=ecs_endpoint_url) # Remove unsupported characters from filename filename = secure_filename(file.filename) # First save the file locally file.save(os.path.join("uploads", filename)) # Create a thumbnail size = 225, 225 with open("uploads/" + filename, 'rb') as f: img = Image.open(f) img.thumbnail(size) thumbfile = filename.rsplit(".", 1)[0] + "-thumb.jpg" img.save("uploads/" + thumbfile, "JPEG") img.close() # Empty the variables to prevent memory leaks img = None # Upload the original image to ECS session.Object(ecs_bucket_name, filename).put( Body=open("uploads/" + filename, 'rb'), ACL='public-read') # Upload the thumbnail to ECS session.Object(ecs_bucket_name, thumbfile).put( Body=open("uploads/" + thumbfile, 'rb'), ACL='public-read') # Delete the local files os.remove("uploads/" + filename) os.remove("uploads/" + thumbfile)

34.905882

115

0.691271

405

2,967

4.859259

0.34321

0.032012

0.054878

0.060976

0.185976

0.177337

0.164634

0.086382

0

0.009583

0.191102

2,967

84

116

35.321429

0.810417

0.232895

0

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0

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0

1

0.06383

false

0

0.148936

0.021277

0.234043

0

null

0

null

0

1

0

da67084ae53c45931e9876b1394dc1aa92e625de

12,051

py

Python

pymothoa/llvm_backend/backend.py

sklam/pymothoa

330bd70666ccf761f39c75f3acb70aa7e0a92ac6

[ "BSD-2-Clause" ]

2

2017-03-23T19:44:03.000Z

2020-11-28T17:01:49.000Z

pymothoa/llvm_backend/backend.py

sklam/pymothoa

330bd70666ccf761f39c75f3acb70aa7e0a92ac6

[ "BSD-2-Clause" ]

null

pymothoa/llvm_backend/backend.py

sklam/pymothoa

330bd70666ccf761f39c75f3acb70aa7e0a92ac6

[ "BSD-2-Clause" ]

null

# Copyright (c) 2012, Siu Kwan Lam # All rights reserved. import logging import ast from contextlib import contextmanager from pymothoa.util.descriptor import Descriptor, instanceof from pymothoa import dialect from pymothoa.compiler_errors import * from pymothoa.backend import CodeGenerationBase from types import * from values import * import llvm # binding logger = logging.getLogger(__name__) class LLVMCodeGenerator(CodeGenerationBase): retty = Descriptor(constant=True) argtys = Descriptor(constant=True) function = Descriptor(constant=True) entry_block = Descriptor(constant=True) def __init__(self, fnobj, retty, argtys, symbols): super(LLVMCodeGenerator, self).__init__(symbols) self.function = fnobj self.retty = retty self.argtys = argtys @contextmanager def generate_function(self, name): if not self.function.valid(): raise FunctionDeclarationError( self.current_node, self.jit_engine.last_error() ) self.symbols[name] = self.function # make basic block self.entry_block = self.function.append_basic_block("entry") self.__blockcounter = 0 # make instruction builder self.builder = llvm.Builder() bb_body = self.function.append_basic_block("body") self.builder.insert_at(bb_body) yield # wait until args & body are generated # link entry to body bb_last = self.builder.get_basic_block() # remember last block self.builder.insert_at(self.entry_block) # goto entry block self.builder.branch(bb_body) # branch to body self.builder.insert_at(bb_last) # return to last block # close function if not self.builder.is_block_closed(): if isinstance(self.retty, types.Void): # no return self.builder.ret_void() else: raise MissingReturnError(self.current_node) def generate_function_arguments(self, arguments): with self.relocate_to_entry(): fn_args = self.function.arguments() for i, name in enumerate(arguments): try: var = LLVMVariable(name, self.argtys[i], self.builder) except IndexError: raise FunctionDeclarationError( self.current_node, 'Actual number of argument mismatch declaration.') self.builder.store(fn_args[i], var.pointer) self.symbols[name] = var def generate_call(self, fn, args): from function import LLVMFunction if isinstance(fn, LLVMFunction): # another function retty = fn.retty argtys = fn.argtys fn = fn.code_llvm elif fn is self.function: # recursion retty = self.retty argtys = self.argtys else: raise InvalidCall(self.current_node) return self._call_function(fn, args, retty, argtys) def generate_assign(self, from_value, to_target): casted = to_target.type.cast(from_value, self.builder) self.builder.store(casted, to_target.pointer) return casted def generate_compare(self, op_class, lhs, rhs): ty = lhs.type.coerce(rhs.type) lval = ty.cast(lhs, self.builder) rval = ty.cast(rhs, self.builder) fn = getattr(ty, 'op_%s'%op_class.__name__.lower()) pred = fn(lval, rval, self.builder) return LLVMTempValue(pred, LLVMType(types.Bool)) def generate_return(self, value=None): if value is None: # no return value self.builder.ret_void() return if isinstance(self.retty, LLVMVoid): raise InvalidReturnError( self.current_node, 'This function does not return any value.' ) casted = self.retty.cast(value, self.builder) self.builder.ret(casted) def generate_binop(self, op_class, lhs, rhs): ty = lhs.type.coerce(rhs.type) lval = ty.cast(lhs, self.builder) rval = ty.cast(rhs, self.builder) try: fn = getattr(ty, 'op_%s'%op_class.__name__.lower()) except AttributeError as e: raise OperatorError(self.current_node, 'Debug detail: %s'%str(e)) else: return LLVMTempValue(fn(lval, rval, self.builder), ty) def generate_constant_int(self, value): return LLVMConstant(LLVMType(types.Int), value) def generate_constant_real(self, value): return LLVMConstant(LLVMType(types.Double), value) def generate_declare(self, name, ty): with self.relocate_to_entry(): if issubclass(ty, types.GenericBoundedArray): # array return LLVMArrayVariable(name, LLVMType(ty), ty.elemcount.value(self.builder), self.builder) else: # other types realty = LLVMType(ty) return LLVMVariable(name, realty, self.builder) def _call_function(self, fn, args, retty, argtys): arg_values = map(lambda X: LLVMTempValue(X.value(self.builder), X.type), args) # cast types try: for i, argty in enumerate(argtys): arg_values[i] = argty.cast(arg_values[i], self.builder) except IndexError: raise InvalidCall(self.current_node, 'Number of argument mismatch') out = self.builder.call(fn, arg_values) return LLVMTempValue(out, retty) def new_basic_block(self, name='uname'): self.__blockcounter += 1 return self.function.append_basic_block('%s_%d'%(name, self.__blockcounter)) def generate_vector_load_elem(self, ptr, idx): elemval = self.builder.extract_element( ptr.value(self.builder), idx.value(self.builder), ) return LLVMTempValue(elemval, ptr.type.elemtype) def generate_vector_store_elem(self, ptr, idx): zero = self.generate_constant_int(0) indices = map(lambda X: X.value(self.builder), [zero, idx]) addr = self.builder.gep2(ptr.pointer, indices) return LLVMTempPointer(addr, ptr.type.elemtype) def generate_array_load_elem(self, ptr, idx): ptr_val = ptr.value(self.builder) idx_val = idx.value(self.builder) ptr_offset = self.builder.gep(ptr_val, idx_val) return LLVMTempValue(self.builder.load(ptr_offset), ptr.type.elemtype) def generate_array_store_elem(self, ptr, idx): ptr_val = ptr.value(self.builder) idx_val = idx.value(self.builder) ptr_offset = self.builder.gep(ptr_val, idx_val) return LLVMTempPointer(ptr_offset, ptr.type.elemtype) def generate_if(self, test, iftrue, orelse): bb_if = self.new_basic_block('if') bb_else = self.new_basic_block('else') bb_endif = self.new_basic_block('endif') is_endif_reachable = False boolean = self.ensure_boolean(test) self.builder.cond_branch(boolean, bb_if, bb_else) # true branch self.builder.insert_at(bb_if) for stmt in iftrue: self.visit(stmt) else: if not self.builder.is_block_closed(): self.builder.branch(bb_endif) is_endif_reachable=True # false branch self.builder.insert_at(bb_else) for stmt in orelse: self.visit(stmt) else: if not self.builder.is_block_closed(): self.builder.branch(bb_endif) is_endif_reachable=True # endif self.builder.insert_at(bb_endif) if not is_endif_reachable: self.builder.unreachable() def generate_while(self, test, body): bb_cond = self.new_basic_block('loopcond') bb_body = self.new_basic_block('loopbody') bb_exit = self.new_basic_block('loopexit') self.builder.branch(bb_cond) # condition self.builder.insert_at(bb_cond) cond = self.visit(test) self.builder.cond_branch(self.ensure_boolean(cond), bb_body, bb_exit) # body self.builder.insert_at(bb_body) for stmt in body: self.visit(stmt) else: self.builder.branch(bb_cond) # Not sure if it is necessary # if not self.builder.is_block_closed(): # self.builder.branch(bb_cond) # end loop self.builder.insert_at(bb_exit) def generate_for_range(self, counter_ptr, initcount, endcount, step, loopbody): self.builder.store(initcount.value(self.builder), counter_ptr.pointer) bb_cond = self.new_basic_block('loopcond') bb_body = self.new_basic_block('loopbody') bb_incr = self.new_basic_block('loopincr') bb_exit = self.new_basic_block('loopexit') self.builder.branch(bb_cond) # condition self.builder.insert_at(bb_cond) test = self.builder.icmp(llvm.ICMP_SLT, counter_ptr.value(self.builder), endcount.value(self.builder)) self.builder.cond_branch(test, bb_body, bb_exit) # body self.builder.insert_at(bb_body) for stmt in loopbody: self.visit(stmt) else: self.builder.branch(bb_incr) # Not sure if it is necessary # if not self.builder.is_block_closed(): # self.builder.branch(bb_incr) # incr self.builder.insert_at(bb_incr) # counter_next = self.builder.add(counter_ptr.value(self.builder), # step.value(self.builder)) counter_next = counter_ptr.type.op_add(counter_ptr.value(self.builder), step.value(self.builder), self.builder) self.builder.store(counter_next, counter_ptr.pointer) self.builder.branch(bb_cond) # exit self.builder.insert_at(bb_exit) def generate_boolop(self, op_class, lhs, rhs): bb_left = self.builder.get_basic_block() boolty = LLVMType(types.Bool) left = boolty.cast(self.visit(lhs), self.builder) bb_right = self.new_basic_block('bool_right') bb_result = self.new_basic_block('bool_result') if isinstance(op_class, ast.And): self.builder.cond_branch(left, bb_right, bb_result) elif isinstance(op_class, ast.Or): self.builder.cond_branch(left, bb_result, bb_right) else: raise AssertionError('Unknown Boolean operator') self.builder.insert_at(bb_right) right = boolty.cast(self.visit(rhs), self.builder) self.builder.branch(bb_result) self.builder.insert_at(bb_result) pred = self.builder.phi(boolty.type(), [bb_left, bb_right], [left, right]); return LLVMTempValue(pred, boolty) def generate_not(self, operand): boolty = LLVMType(types.Bool) boolval = boolty.cast(operand, self.builder) negated = boolty.op_not(boolval, self.builder) return LLVMTempValue(negated, boolty) def generate_array_slice(self, ptr, lower, upper=None, step=None): assert upper is None assert step is None ptr_val = ptr.value(self.builder) lower_val = lower.value(self.builder) offsetted = self.builder.gep(ptr_val, lower_val) return LLVMTempValue(offsetted, ptr.type) @contextmanager def relocate_to_entry(self): # goto entry block bb_last = self.builder.get_basic_block() self.builder.insert_at(self.entry_block) yield # relocated # pickup at last block self.builder.insert_at(bb_last) def ensure_boolean(self, value): return LLVMType(types.Bool).cast(value, self.builder)

35.759644

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0.617127

1,450

12,051

4.938621

0.170345

0.147465

0.049155

0.045105

0.404552

0.286133

0.232789

0.207234

0.176512

0.168133

0

0.000933

0.288275

12,051

336

111

35.866071

0.833975

0.069538

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0.024257

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0.103306

false

0

0.045455

0.012397

0.247934

0

null

0

null

0

1

0

da68786bead17edb9e00b001f796346815fc35ed

2,412

py

Python

digsby/src/jabber/objects/gmail/mail_thread_info.py

ifwe/digsby

f5fe00244744aa131e07f09348d10563f3d8fa99

[ "Python-2.0" ]

35

2015-08-15T14:32:38.000Z

2021-12-09T16:21:26.000Z

digsby/src/jabber/objects/gmail/mail_thread_info.py

niterain/digsby

16a62c7df1018a49eaa8151c0f8b881c7e252949

[ "Python-2.0" ]

4

2015-09-12T10:42:57.000Z

2017-02-27T04:05:51.000Z

digsby/src/jabber/objects/gmail/mail_thread_info.py

niterain/digsby

16a62c7df1018a49eaa8151c0f8b881c7e252949

[ "Python-2.0" ]

15

2015-07-10T23:58:07.000Z

2022-01-23T22:16:33.000Z

#tid The thread id of this thread. #participation A number indicating the user's participation level in this thread: 0 indicates that the user has not participated; 1 indicates that the user is one of many recipients listed in the thread; 2 indicates that the user is the sole recipient for messages in this thread. #messages The number of messages in the thread. #date A timestamp of the most recent message, in milliseconds since the UNIX epoch. #url The URL linking to this thread # #<senders> #<labels> #<subject> #<snippet> from jabber.objects.gmail.senders import Senders from pyxmpp.utils import from_utf8 from jabber.jabber_util.functions import xpath_eval from pyxmpp.xmlextra import get_node_ns_uri from jabber.objects.gmail import GOOGLE_MAIL_NOTIFY_NS from pyxmpp.objects import StanzaPayloadObject class MailThreadInfo(StanzaPayloadObject): xml_element_name = 'mail-thread-info' xml_element_namespace = GOOGLE_MAIL_NOTIFY_NS def __init__(self, xmlnode): self.__from_xml(xmlnode) def __from_xml(self, node): if node.type!="element": raise ValueError,"XML node is not a %s element (not en element)" % self.xml_element_name ns=get_node_ns_uri(node) if ns and ns!=self.xml_element_namespace or node.name!=self.xml_element_name: raise ValueError,"XML node is not an %s element" % self.xml_element_name labelss = xpath_eval(node, 'g:labels',{'g':GOOGLE_MAIL_NOTIFY_NS}) labels = labelss[0].getContent() if labelss else None self.labels = from_utf8(labels).split('|') if labels else [] senderss = xpath_eval(node, 'g:senders',{'g':GOOGLE_MAIL_NOTIFY_NS}) self.senders = Senders(senderss[0]) if senderss else [] subjects = xpath_eval(node, 'g:subject',{'g':GOOGLE_MAIL_NOTIFY_NS}) self.subject = from_utf8(subjects[0].getContent()) if subjects else None snippets = xpath_eval(node, 'g:snippet',{'g':GOOGLE_MAIL_NOTIFY_NS}) self.snippet = from_utf8(snippets[0].getContent()) if snippets else None self.tid = int(from_utf8(node.prop("tid"))) self.participation = int(from_utf8(node.prop("participation"))) self.messages = int(from_utf8(node.prop("messages"))) self.date = int(from_utf8(node.prop("date"))) self.url = from_utf8(node.prop("date"))

47.294118

285

0.69942

344

2,412

4.726744

0.284884

0.04428

0.059041

0.066421

0.206642

0.075646

0

0.008316

0.202322

2,412

50

286

48.24

0.836798

0.21932

0

0.092755

0

null

0

0.193548

null

0

null

0

null

0

1

0

1

da687c22550da7202f3e33817124a03999dca63a

542

py

Python

cloud/single_stage_detector/pytorch/onnx_demo.py

mgoin/inference

ede5477a2aee72ceb435e9ecd599ffa052417c2a

[ "Apache-2.0" ]

4

2019-07-26T03:00:39.000Z

2021-01-29T16:12:21.000Z

cloud/single_stage_detector/pytorch/onnx_demo.py

mgoin/inference

ede5477a2aee72ceb435e9ecd599ffa052417c2a

[ "Apache-2.0" ]

null

cloud/single_stage_detector/pytorch/onnx_demo.py

mgoin/inference

ede5477a2aee72ceb435e9ecd599ffa052417c2a

[ "Apache-2.0" ]

2

2019-11-12T15:57:29.000Z

2022-03-02T21:26:58.000Z

import onnxruntime import onnx import os from onnx import numpy_helper onnx_model_dir = 'test_ssd_model' onnx_data_dir = 'test_data_set_0' sess = onnxruntime.InferenceSession(os.path.join(onnx_model_dir, 'model.onnx')) img_tensor = onnx.TensorProto() with open(os.path.join(onnx_model_dir, onnx_data_dir, 'input_0.pb'), 'rb') as f: img_tensor.ParseFromString(f.read()) test_img_data = numpy_helper.to_array(img_tensor) out_onnx = sess.run(None, { sess.get_inputs()[0].name: test_img_data }) loc, label, prob = out_onnx print(out_onnx)

30.111111

80

0.778598

91

542

4.307692

0.461538

0.068878

0.091837

0.071429

0.112245

0

0.006148

0.099631

542

18

81

30.111111

0.797131

0

0.093923

0

1

0

false

0

0.285714

0

0.285714

0.071429

0

null

0

null

0

1

0

da69d942b34c7cb188f48d8f571305a3929e1a1b

95

py

Python

abfahrt/unittest/__init__.py

Team-Zugig-zum-Erfolg/InformatiCup

788076ac38bf6d8f462465b7fb96db14d13bed30

[ "MIT" ]

1

2022-01-30T14:30:02.000Z

abfahrt/unittest/__init__.py

Team-Zugig-zum-Erfolg/InformatiCup

788076ac38bf6d8f462465b7fb96db14d13bed30

[ "MIT" ]

null

abfahrt/unittest/__init__.py

Team-Zugig-zum-Erfolg/InformatiCup

788076ac38bf6d8f462465b7fb96db14d13bed30

[ "MIT" ]

null

""" Unittest-Package for testing the most important classes/modules of the abfahrt-Package """

23.75

86

0.778947

13

95

5.692308

0.846154

0

0.126316

95

3

87

31.666667

0.891566

0.905263

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

4

da6a8272e09ed6bcdd72fe1fe0ed6ca276090222

3,519

py

Python

uat/test_uat_CLIParser.py

sorint-lab-us/aws-greengrass-gdk-cli

7508c7f62dcee1638cfc895ea38f3842e0072f0e

[ "Apache-2.0" ]

null

uat/test_uat_CLIParser.py

sorint-lab-us/aws-greengrass-gdk-cli

7508c7f62dcee1638cfc895ea38f3842e0072f0e

[ "Apache-2.0" ]

null

uat/test_uat_CLIParser.py

sorint-lab-us/aws-greengrass-gdk-cli

7508c7f62dcee1638cfc895ea38f3842e0072f0e

[ "Apache-2.0" ]

null

import json import os import subprocess as sp import tempfile from pathlib import Path import gdk.common.exceptions.error_messages as error_messages def test_list_template(): check_list_template = sp.run(["gdk", "component", "list", "--template"], check=True, stdout=sp.PIPE) assert "HelloWorld-python" in check_list_template.stdout.decode() assert "HelloWorld-java" in check_list_template.stdout.decode() def test_list_repository(): check_list_template = sp.run(["gdk", "component", "list", "--repository"], check=True, stdout=sp.PIPE) assert "aws-greengrass-labs-database-influxdb" in check_list_template.stdout.decode() def test_init_template_non_empty_dir(): check_init_template = sp.run(["gdk", "component", "init", "-t", "HelloWorld", "-l", "python"], stdout=sp.PIPE) assert check_init_template.returncode == 1 assert "Try `gdk component init --help`" in check_init_template.stdout.decode() def test_init_template(): dirpath = tempfile.mkdtemp() os.chdir(dirpath) check_init_template = sp.run(["gdk", "component", "init", "-t", "HelloWorld", "-l", "python"], check=True, stdout=sp.PIPE) assert check_init_template.returncode == 0 assert Path(dirpath).joinpath("recipe.yaml").resolve().exists() assert Path(dirpath).joinpath("gdk-config.json").resolve().exists() def test_init_repository(): dirpath = tempfile.mkdtemp() os.chdir(dirpath) check_init_repo = sp.run( ["gdk", "component", "init", "-r", "aws-greengrass-labs-database-influxdb"], check=True, stdout=sp.PIPE ) assert check_init_repo.returncode == 0 assert Path(dirpath).joinpath("recipe.yaml").exists() assert Path(dirpath).joinpath("gdk-config.json").exists() def test_build_template_zip(): dirpath = tempfile.mkdtemp() # Recipe contains HelloWorld.zip artifact. So, create HelloWorld directory inside temporary directory. path_HelloWorld = Path(dirpath).joinpath("HelloWorld") os.mkdir(path_HelloWorld) os.chdir(path_HelloWorld) # Check if init downloads templates with necessary files. check_init_template = sp.run(["gdk", "component", "init", "-t", "HelloWorld", "-l", "python"], check=True, stdout=sp.PIPE) assert check_init_template.returncode == 0 assert Path(path_HelloWorld).joinpath("recipe.yaml").resolve().exists() config_file = Path(path_HelloWorld).joinpath("gdk-config.json").resolve() assert config_file.exists() # Update gdk-config file mandatory field like region. with open(str(config_file), "r") as f: config = json.loads(f.read()) config["component"]["com.example.PythonHelloWorld"]["publish"]["region"] = "us-east-1" with open(str(config_file), "w") as f: f.write(json.dumps(config)) # Check if build works as expected. check_build_template = sp.run(["gdk", "component", "build"], check=True, stdout=sp.PIPE) assert check_build_template.returncode == 0 assert Path(path_HelloWorld).joinpath("zip-build").resolve().exists() assert Path(path_HelloWorld).joinpath("greengrass-build").resolve().exists() artifact_path = ( Path(path_HelloWorld) .joinpath("greengrass-build") .joinpath("artifacts") .joinpath("com.example.PythonHelloWorld") .joinpath("NEXT_PATCH") .joinpath("HelloWorld.zip") .resolve() ) recipes_path = Path(path_HelloWorld).joinpath("greengrass-build").joinpath("recipes").joinpath("recipe.yaml").resolve() assert artifact_path.exists()

40.918605

126

0.700199

443

3,519

5.410835

0.227991

0.045056

0.023363

0.049645

0.577388

0.483521

0.430955

0.396329

0.124322

0

0.002002

0.148338

3,519

85

127

41.4

0.797798

0.06877

0

0.142857

0

0.189181

0.039731

0

0.285714

1

0.095238

false

0

0.095238

0

0.190476

0

null

0

null

0

1

0

da6b4c0eec1b1ed14670ffd508f05ac5d26c2b77

1,326

py

Python

mysite/image/forms.py

HelloTecXin/ZXBlog

60d1f95f541138aa56acbaf4dcfbfe208491d65b

[ "MIT" ]

1

2020-03-17T08:28:48.000Z

mysite/image/forms.py

HelloTecXin/ZXBlog

60d1f95f541138aa56acbaf4dcfbfe208491d65b

[ "MIT" ]

null

mysite/image/forms.py

HelloTecXin/ZXBlog

60d1f95f541138aa56acbaf4dcfbfe208491d65b

[ "MIT" ]

null

from django import forms from django.core.files.base import ContentFile from slugify import slugify from urllib import request from .models import Image class ImageForm(forms.ModelForm): class Meta: model = Image fields = ('title','url','description') def clean_url(self): url = self.cleaned_data['url'] valid_extensions = ['jpg','jpeg','png'] # 规定图片的扩展名 extension = url.rsplit('.',1)[1].lower() # 从得到图片的网址中分解出其扩展名 if extension not in valid_extensions: # 如果属于规定的扩展名，就认为提交的URL对象是一个图片 raise forms.ValidationError('The given url does not match valid image extension.') return url def save(self,force_insert=False,force_update=False,commit=True): # ModelForm类中的save方法，将表单提交的数据保存到数据库 image = super(ImageForm, self).save(commit=False) # 执行父类ModelForm的save()方法，commit=False实例虽然被建立，但并没有保存数据 image_url = self.cleaned_data['url'] image_name = '{0}.{1}'.format(slugify(image.title),image_url.rsplit('.',1)[1].lower()) response = request.urlopen(image_url) # 以get方式访问该图片地址，通过该对象得到所访问URL的数据（图片的ASCII） image.image.save(image_name, ContentFile(response.read()),save=False) #　将上述返回的结果保存到本地，并按照约定的名称给该图片文件命名 if commit: image.save() return image

41.4375

113

0.667421

150

1,326

5.82

0.5

0.024055

0.032073

0.041237

0.084765

0

0.005803

0.220211

1,326

31

114

42.774194

0.838491

0.159879

0

0.088372

0

1

0.08

false

0

0.2

0

0.44

0

null

0

null

0

1

0

da71b1822bbf0d5369bea52d007ca8f6061551f0

128

py

Python

MT-top-perf-cron.py

AdeTheux/ducksboard

339c965dcef448713ed521ba066759f6fb43c2b1

[ "MIT" ]

null

MT-top-perf-cron.py

AdeTheux/ducksboard

339c965dcef448713ed521ba066759f6fb43c2b1

[ "MIT" ]

null

MT-top-perf-cron.py

AdeTheux/ducksboard

339c965dcef448713ed521ba066759f6fb43c2b1

[ "MIT" ]

null

python /homez.144/arnoz/www/dev/MT_ducksboard_top_perf.py -u EMAIL/token -p TOKEN -d mtservicedesk.zendesk.com -a TOKEN -l 72676

128

0.796875

24

128

4.125

0.916667

0

0.068376

0.085938

128

1

128

0.777778

0

null

0

null

0

1

0

null

0

1

0

1

0

null

0

1

0

4

da741a60b0b7e242baf0c8917409303691b189e3

1,110

py

Python

APP/__init__.py

jcyongqin/MerryChristmas2016

f1bfc0f9df33dad474f28bbefa21f320e4ee48e9

[ "MIT" ]

null

APP/__init__.py

jcyongqin/MerryChristmas2016

f1bfc0f9df33dad474f28bbefa21f320e4ee48e9

[ "MIT" ]

null

APP/__init__.py

jcyongqin/MerryChristmas2016

f1bfc0f9df33dad474f28bbefa21f320e4ee48e9

[ "MIT" ]

null

print('Merry Christmas!!!') import sys # # int main(int argc, char* argv[]) { # int n = argc > 1 ? atoi(argv[1]) : 4; # for (int j = 1; j <= n; j++) { # int s = 1 << j, k = (1 << n) - s, x; # for (int y = s - j; y >= 0; y--, putchar('\n')) { # for (x = 0; x < y + k; x++) printf(" "); # for (x = 0; x + y < s; x++) printf("%c ", '!' ^ y & x); # for (x = 1; x + y < s; x++) printf("%c ", '!' ^ y & (s - y - x - 1)); # } # } # } def main(*args): # """上面的是我尝试尽量用最少代码来画一个抽象一点的圣诞树，因此树干都没有.""" if args.__len__() > 1: n = args[1] else: n = 4 for j in range(n): s = 1 << j k = (1 << n) - s x = 0 for y in range(s - j)[::-1]: for x in range(y + k): print(" ", end="") for x in range(s - y): print("%s " % chr(ord('!') ^ y & x), end="") for x in range(1, s - y + 1): print("%s " % chr(ord('!') ^ y & (s - y - x - 1)), end="") print("") if __name__ == "__main__": main(sys.argv)

28.461538

83

0.351351

160

1,110

2.3625

0.23125

0.063492

0.047619

0.087302

0.301587

0.10582

0.042328

0

0.032308

0.414414

1,110

38

84

29.210526

0.549231

0.430631

0

0.058252

0

1

0.047619

false

0

0.047619

0

0.095238

0.238095

0

null

0

null

0

1

0

da760162956fd30fc878df9712168c347b1cba4a

1,013

py

Python

pyday_night_funkin/enums.py

Square789/PydayNightFunkin

8d43daec947202566419a2d5ce63cc191b7b8e3c

[ "Apache-2.0" ]

null

pyday_night_funkin/enums.py

Square789/PydayNightFunkin

8d43daec947202566419a2d5ce63cc191b7b8e3c

[ "Apache-2.0" ]

34

2021-09-10T01:08:14.000Z

2022-03-25T18:10:08.000Z

pyday_night_funkin/enums.py

Square789/PydayNightFunkin

8d43daec947202566419a2d5ce63cc191b7b8e3c

[ "Apache-2.0" ]

null

""" Enums that aren't really too coupled to anything else. """ from enum import IntEnum class DIFFICULTY(IntEnum): EASY = 0 NORMAL = 1 HARD = 2 def to_song_json_suffix(self) -> str: if self is self.EASY: return "-easy" elif self is self.NORMAL: return "" elif self is self.HARD: return "-hard" return "" def to_atlas_prefix(self) -> str: if self is self.EASY: return "EASY" elif self is self.NORMAL: return "NORMAL" elif self is self.HARD: return "HARD" return "" # NOTE: That sucks, but is needed for menu selections etc. DIFFICULTY_REVERSE_MAP = [DIFFICULTY.EASY, DIFFICULTY.NORMAL, DIFFICULTY.HARD] class CONTROL(IntEnum): LEFT = 0 DOWN = 1 UP = 2 RIGHT = 3 ENTER = 4 BACK = 5 DEBUG_DESYNC = 100 DEBUG_WIN = 101 DEBUG_LOSE = 102 class GAME_STATE(IntEnum): LOADING = 0 COUNTDOWN = 1 PLAYING = 2 ENDED = 3 class ANIMATION_TAG(IntEnum): IDLE = 0 SING = 1 MISS = 2 SPECIAL = 3 STORY_MENU = 4 STATIC = 5 PRESSED = 6 CONFIRM = 7 GAME_OVER = 8

15.828125

78

0.672261

158

1,013

4.221519

0.512658

0.053973

0.089955

0.083958

0.278861

0.176912

0

0.039846

0.231984

1,013

63

79

16.079365

0.817481

0.110563

0

0.191489

0

0.026876

0

1

0.042553

false

0

0.021277

0

0.851064

0

null

0

null

0

1

0

da7627eec05c00f505b4e3736efa9cd06fb060ee

946

py

Python

app/restapi/apiMovieDetail.py

DucVinh2609/mtb_admin

0f67faabcda7b6a5bd5f30126b46a5367d00f77b

[ "MIT" ]

null

app/restapi/apiMovieDetail.py

DucVinh2609/mtb_admin

0f67faabcda7b6a5bd5f30126b46a5367d00f77b

[ "MIT" ]

4

2021-06-08T20:42:38.000Z

2022-03-12T00:07:41.000Z

app/restapi/apiMovieDetail.py

DucVinh2609/mtb_admin

0f67faabcda7b6a5bd5f30126b46a5367d00f77b

[ "MIT" ]

null

# import pymysql # from app import app # from flask import jsonify # from flask import flash, request # from flask_restful import Resource, Api # from flaskext.mysql import MySQL # mysql = MySQL() # # MySQL configurations # app.config['MYSQL_DATABASE_USER'] = 'root' # app.config['MYSQL_DATABASE_PASSWORD'] = '' # app.config['MYSQL_DATABASE_DB'] = 'mtb_db' # app.config['MYSQL_DATABASE_HOST'] = 'localhost' # mysql.init_app(app) # class apiMovieDetail(Resource): # def get(self, id): # conn = mysql.connect() # cursor = conn.cursor(pymysql.cursors.DictCursor) # cursor.execute("SELECT id id, name name, movieformat_id movieformat_id, movietype_id movietype_id, duration duration, country_code country_code, start_date start_date, end_date end_date, image image, note note, description description from movies WHERE id=%s", (id,)") # rows = cursor.fetchall() # resp = jsonify(rows) # resp.status_code = 200 # return resp

36.384615

273

0.732558

127

946

5.283465

0.456693

0.053651

0.083458

0.131148

0

0.003727

0.149049

946

25

274

37.84

0.829814

0.948203

0

null

0

null

0

null

0

null

1

null

true

0

null

0

null

0

1

0

null

0

1

0

2

da774b4615481a7592e47b528fe32fca5d803722

56

py

Python

aioface/storages/fsm/types.py

kirillkuzin/aioface

c19f89f3f0f6ccb95832030444f2ece8fda7de62

[ "MIT" ]

1

2020-09-12T21:10:54.000Z

aioface/storages/fsm/types.py

kirillkuzin/aioface

c19f89f3f0f6ccb95832030444f2ece8fda7de62

[ "MIT" ]

null

aioface/storages/fsm/types.py

kirillkuzin/aioface

c19f89f3f0f6ccb95832030444f2ece8fda7de62

[ "MIT" ]

null

import typing State = typing.NewType('State', object)

11.2

39

0.732143

7

56

5.857143

0.714286

0

0.142857

56

4

40

14

0.854167

0

0.089286

0

1

0

false

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

da776d50619a771e6d0a4ec84979dc6f33204a34

291

py

Python

wqxweblib/__main__.py

FlippingBinary/wqxweblib

129ac6d010f5fb726fe29dc9494f90f19a7ec4c0

[ "MIT" ]

null

wqxweblib/__main__.py

FlippingBinary/wqxweblib

129ac6d010f5fb726fe29dc9494f90f19a7ec4c0

[ "MIT" ]

null

wqxweblib/__main__.py

FlippingBinary/wqxweblib

129ac6d010f5fb726fe29dc9494f90f19a7ec4c0

[ "MIT" ]

null

import sys def main(argv:list): print('This module does not yet support direct execution. It should be used as a library.') print('More information is available at https://github.com/FlippingBinary/wqxweblib-python') return 0 if __name__ == "__main__": main(sys.argv[1:])

29.1

95

0.714777

43

291

4.651163

0.883721

0

0.008333

0.175258

291

9

96

32.333333

0.825

0

0.613475

0

1

0.142857

false

0

0.142857

0

0.428571

0.285714

0

null

0

1

0

1

0

null

0

2

da78a73d88c09570047fcaf5e2a501ef100b4dc0

28,780

py

Python

tools/check_cluster.py

jmatuskey/jupyterhub-deploy

6669bb0fa8e6da52f74d4ca015cea9dc96105a34

[ "Unlicense" ]

1

2021-06-02T18:35:05.000Z

tools/check_cluster.py

jmatuskey/jupyterhub-deploy

6669bb0fa8e6da52f74d4ca015cea9dc96105a34

[ "Unlicense" ]

64

2020-05-11T12:35:26.000Z

2022-03-28T16:03:37.000Z

tools/check_cluster.py

jmatuskey/jupyterhub-deploy

6669bb0fa8e6da52f74d4ca015cea9dc96105a34

[ "Unlicense" ]

11

2020-04-07T13:32:07.000Z

2022-02-07T19:16:24.000Z

#! /usr/bin/env python """Check properties of Terraformed resources and/or JupyterHub to verify good deployment. ignore the hub since it may not be delpoyed on the cluster yet. check creation date check for global hammer """ import sys import os import subprocess import argparse import re import json from collections import defaultdict import builtins import functools import traceback import yaml CLUSTER_CHECKS = """ Globals: environment: - DEPLOYMENT_NAME - ENVIRONMENT - JH_HOSTNAME - ADMIN_ARN - ACCOUNT_ID constants: V_K8S: "1.21" MAX_NODE_AGE: 10d MAX_EFS_FILE_SYSTEM_SIZE: 50000000000000 CORE_NODES: 3 NOTEBOOK_EC2_TYPE: r5.xlarge MAX_RESTARTS: 0 LOG_REACH: 30m Groups: - group: Kubernetes Pods command: kubectl get pods -A parser: named_columns assertions: - name: All pods all: STATUS=='Running' and int(RESTARTS)<=MAX_RESTARTS - name: EFS provisioner ok_rows==1: NAMESPACE=='support' and 'efs-provisioner' in NAME - name: Kube Proxy ok_rows>=4: NAMESPACE=='kube-system' and 'kube-proxy' in NAME - name: Autoscaler ok_rows==1: NAMESPACE=='kube-system' and 'cluster-autoscaler' in NAME - name: AWS Pods ok_rows>=4: NAMESPACE=='kube-system' and 'aws-node' in NAME - name: Core DNS ok_rows==2: NAMESPACE=='kube-system' and 'coredns' in NAME - group: JupyterHub Pods command: kubectl get pods -A parser: named_columns assertions: - name: Image puller ok_rows>=1: NAMESPACE=='default' and 'continuous-image-puller' in NAME - name: Hub ok_rows==1: NAMESPACE=='default' and 'hub' in NAME - name: Proxy ok_rows>=1: NAMESPACE=='default' and 'proxy' in NAME - name: User-scheduler ok_rows==2: NAMESPACE=='default' and 'user-scheduler' in NAME - name: User-placeholder ok_rows>=1: NAMESPACE=='default' and 'user-placeholder' in NAME - group: JupyterHub Nodes command: kubectl get nodes -A --show-labels=true parser: named_columns assertions: - name: At least 4 STATUS Ready new Hub AMI ID ok_rows>=4: STATUS=="Ready" # and HUB_AMI_ID in LABELS - name: All Nodes Ready Status all: STATUS=="Ready" or STATUS=="Ready,SchedulingDisabled" - name: Kubernetes Version all: V_K8S in VERSION - name: Node Age all: convert_age(AGE) < convert_age(MAX_NODE_AGE) - name: Core us-east-1a ok_rows==1: "DEPLOYMENT_NAME+'-core' in LABELS and 't3.small' in LABELS and 'zone=us-east-1a' in LABELS" - name: Core us-east-1b ok_rows==1: "DEPLOYMENT_NAME+'-core' in LABELS and 't3.small' in LABELS and 'zone=us-east-1b' in LABELS" - name: Core us-east-1c ok_rows==1: "DEPLOYMENT_NAME+'-core' in LABELS and 't3.small' in LABELS and 'zone=us-east-1c' in LABELS" - name: Notebook nodes ok_rows>=1: "DEPLOYMENT_NAME+'-notebook' in LABELS and NOTEBOOK_EC2_TYPE in LABELS and 'region=us-east-1' in LABELS" - group: EKS Services command: kubectl get services -A parser: named_columns assertions: - name: Datadog Cluster Agent Service ok_rows==1: NAMESPACE=='datadog' and NAME=='datadog-cluster-agent' and TYPE=='ClusterIP' and _['EXTERNAL-IP']=='<none>' and _['PORT(S)']=='5005/TCP' - name: Datadog Kube State Metrics Service ok_rows==1: NAMESPACE=='datadog' and NAME=='datadog-kube-state-metrics' and TYPE=='ClusterIP' and _['EXTERNAL-IP']=='<none>' and _['PORT(S)']=='8080/TCP' - name: Hub Service ok_rows==1: NAMESPACE=='default' and NAME=='hub' and TYPE=='ClusterIP' and _['EXTERNAL-IP']=='<none>' and _['PORT(S)']=='8081/TCP' - name: Kubernetes Service ok_rows==1: NAMESPACE=='default' and NAME=='kubernetes' and TYPE=='ClusterIP' and _['EXTERNAL-IP']=='<none>' and _['PORT(S)']=='443/TCP' - name: Proxy API Service ok_rows==1: NAMESPACE=='default' and NAME=='proxy-api' and TYPE=='ClusterIP' and _['EXTERNAL-IP']=='<none>' and _['PORT(S)']=='8001/TCP' - name: Proxy Public Service ok_rows==1: NAMESPACE=='default' and NAME=='proxy-public' and TYPE=='LoadBalancer' and '.elb.amazonaws.com' in _['EXTERNAL-IP'] and '443:' in _['PORT(S)'] and '80:' in _['PORT(S)'] and 'TCP' in _['PORT(S)'] and 'UDP' not in _['PORT(S)'] - name: Cluster Autoscaler Service ok_rows==1: NAMESPACE=='kube-system' and NAME=='cluster-autoscaler-aws-cluster-autoscaler' and TYPE=='ClusterIP' and _['EXTERNAL-IP']=='<none>' and _['PORT(S)']=='8085/TCP' - name: Kube DNS Service ok_rows==1: NAMESPACE=='kube-system' and NAME=='kube-dns' and TYPE=='ClusterIP' and _['EXTERNAL-IP']=='<none>' and _['PORT(S)']=='53/UDP,53/TCP' - group: EKS Deployments command: kubectl get deployments -A parser: named_columns assertions: - name: Hub Deployment ok_rows==1: NAMESPACE=='default' and NAME=='hub' and READY=='1/1' and _['UP-TO-DATE']=='1' and AVAILABLE=='1' - name: Proxy Deployment ok_rows==1: NAMESPACE=='default' and NAME=='proxy' and READY=='1/1' and _['UP-TO-DATE']=='1' and AVAILABLE=='1' - name: User Scheduler Deployment ok_rows==1: NAMESPACE=='default' and NAME=='user-scheduler' and READY=='2/2' and _['UP-TO-DATE']=='2' and AVAILABLE=='2' - name: Cluster Autoscaler Deployment ok_rows==1: NAMESPACE=='kube-system' and 'cluster-autoscaler' in NAME and READY=='1/1' and _['UP-TO-DATE']=='1' and AVAILABLE=='1' - name: Core DNS Deployment ok_rows==1: NAMESPACE=='kube-system' and 'coredns' in NAME and READY=='2/2' and _['UP-TO-DATE']=='2' and AVAILABLE=='2' - name: EFS Provisioner Deployment ok_rows==1: NAMESPACE=='support' and 'efs-provisioner' in NAME and READY=='1/1' and _['UP-TO-DATE']=='1' and AVAILABLE=='1' - name: Datadog Cluster Agent Deployment ok_rows==1: NAMESPACE=='datadog' and 'datadog-cluster-agent' in NAME and READY=='1/1' and _['UP-TO-DATE']=='1' and AVAILABLE=='1' - name: Datadog Kube Metrics Deployment ok_rows==1: NAMESPACE=='datadog' and 'datadog-kube-state-metrics' in NAME and READY=='1/1' and _['UP-TO-DATE']=='1' and AVAILABLE=='1' - group: Route-53 Host command: "host {JH_HOSTNAME}" parser: raw assertions: - name: DNS Mapping simple: "f'{JH_HOSTNAME} is an alias for' in _" - group: JupyterHub Index Page command: "wget --no-check-certificate -O- {JH_HOSTNAME}" parser: raw assertions: - name: Server Index Page simple: "'HTTP request sent, awaiting response... 200 OK' in _" - group: EFS File Systems command: awsudo {ADMIN_ARN} aws efs describe-file-systems --output yaml --query FileSystems parser: yaml assertions: - name: EFS Home Dirs ok_rows==1: Name==DEPLOYMENT_NAME+'-home-dirs' and LifeCycleState=='available' and Encrypted==True and NumberOfMountTargets==3 and OwnerId==ACCOUNT_ID and aws_kv_dict(Tags)['stsci-backup']=='dmd-2w-sat' - name: EFS Max Size all: int(SizeInBytes['Value']) < MAX_EFS_FILE_SYSTEM_SIZE - group: Daemonsets named rows command: kubectl get daemonsets -A parser: named_rows assertions: - name: datadog - proxy - aws-nodes READY simple: _['datadog']['READY'] == _['kube-proxy']['READY'] == _['aws-node']['READY'] - name: datadog - proxy - aws-nodes DESIRED simple: _['datadog']['DESIRED'] == _['kube-proxy']['DESIRED'] == _['aws-node']['DESIRED'] - name: datadog - proxy - aws-nodes CURRENT simple: _['datadog']['CURRENT'] == _['kube-proxy']['CURRENT'] == _['aws-node']['CURRENT'] - name: datadog - proxy - aws-nodes UP-TO-DATE simple: _['datadog']['UP-TO-DATE'] == _['kube-proxy']['UP-TO-DATE'] == _['aws-node']['UP-TO-DATE'] - name: datadog - proxy - aws-nodes AVAILABLE simple: _['datadog']['AVAILABLE'] == _['kube-proxy']['AVAILABLE'] == _['aws-node']['AVAILABLE'] - name: continuous image puller notebook nodes only simple: int(_['continuous-image-puller']['READY']) == int(_['aws-node']['READY']) - CORE_NODES - group: Daemonsets named columns command: kubectl get daemonsets -A parser: named_columns assertions: - name: continuous-image-puller ok_rows==1: NAMESPACE=='default' and NAME=='continuous-image-puller' - name: datadog ok_rows==1: NAMESPACE=='datadog' and NAME=='datadog' - name: kube-proxy ok_rows==1: NAMESPACE=='kube-system' and NAME=='kube-proxy' - name: ok_rows==1: NAMESPACE=='kube-system' and NAME=='aws-node' - name: matching daemonset states all: READY==DESIRED==CURRENT==AVAILABLE==_['UP-TO-DATE'] - group: EKS AMI Rotation command: awsudo {ADMIN_ARN} aws eks list-nodegroups --cluster-name {DEPLOYMENT_NAME} --query nodegroups --output text parser: raw assertions: - name: Only rotated nodegroup names simple: "functools.reduce(lambda a, b: a and b, [x.count('-')!=1 for x in _.split()])" - group: Log Error Check function: pod_logs(LOG_REACH) parser: yaml assertions: - name: No errors in logs simple: ERRORS==0 - group: Pod to Node Map command: kubectl get pods -A -o wide replace_output: input: NOMINATED NODE output: NOMINATED_NODE parser: node_map print_parsing: true """ # noqa: E501 def convert_age(age_str): """Convert k8s abbreviated-style datetime str e.g. 14d2h to an integer.""" # age_str_org = age_str def age_subst(age_str, letter, factor): parts = age_str.split(letter) if len(parts) == 2: age_str = parts[0] + "*" + factor + "+" + parts[1] return age_str age_str = age_subst(age_str, "d", "60*60*24") age_str = age_subst(age_str, "h", "60*60") age_str = age_subst(age_str, "m", "60") age_str = age_subst(age_str, "s", "1") age_str = age_str[:-1] # print( # f"convert_age({repr(age_str_org)}) --> {repr(age_str)} --> {eval(age_str)}" # nosec # ) # nosec return eval(age_str) # nosec def aws_kv_dict(key_value_dict_list): """Convert AWS dict representation [{ 'Key':k, 'Value':v}, ...] to a Python dict.""" return {item["Key"]: item["Value"] for item in key_value_dict_list} def run(cmd, cwd=".", timeout=10): """Run subprocess `cmd` in dir `cwd` failing if not completed within `timeout` seconds of if `cmd` returns a non-zero exit status. Returns both stdout+stderr from `cmd`. (untested, verify manually if in doubt) """ print(cmd) result = subprocess.run( cmd.split(), stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, check=True, cwd=cwd, timeout=timeout, ) # maybe succeeds return result.stdout def parse_node_map(output): namespaces = parse_named_columns(output) node_map = defaultdict(list) for namespace in namespaces: node_map[namespace["NODE"]].append( namespace["NAMESPACE"] + ":" + namespace["NAME"] ) output = ["Mapping from Node to Pod", "-" * 80, yaml.dump(dict(node_map))] return "\n".join(output) def parse_named_columns(output): """Return rows from a table string `output` as a sequence of dicts. The first row should contain whitespace delimited column names. Each subsequent row should contain whitespace delimited column values. Given tabular `output` as found in many k8s commands: col1_name col2_name ... col1_row1_val col2_row1_val ... col1_row2_val col1_row2_val ... ... Returns [ {col1_name: col1_row1_val, col2_name: col2_row1_val, ...}, {col1_name: col1_row2_val, col2_name: col2_row2_val, ...}, ... ] Each dict in the returned sequence is suitable as a namespace for eval() """ lines = output.splitlines() columns = lines[0].split() rows = [] for line in lines[1:]: d = dict(zip(columns, line.split())) d["_"] = d rows.append(d) return rows def parse_named_rows(output, key="NAME"): return {"_": {row[key]: row for row in parse_named_columns(output)}} def parse_raw(output): """Just return `output` as a single string assigned to dict key '_' for reference in assertion expressions. Returns {'_': output} """ return dict(_=output) def parse_yaml(output): """Return the YAML parsing of `output` string. aws commands can be filtered using the --query parameter to produce more manageable output before YAML parsing. """ return yaml.safe_load(output) def parse_json(output): """Return the JSON parsing of `output` string. aws commands can be filtered using the --query parameter to produce more manageable output before JSON parsing. """ return json.loads(output) def parse_none(output): """Return the input as the output, i.e. no changes.""" return output def test_function(parameters): return yaml.dump(parameters) class Checker: """The Checker class runs a number of tests defined in a `test_spec` string. Commands -------- Each Group includes a subprocess CLI command from which the output is captured, parsed, and checked against various assertions. Output Parsing -------------- The command output is parsed using a parser which can be be one of named_rows, raw, yaml, or json. named_rows is ideal for parsing kubectl output in which each row defines a set of variables as a dict. named_rows requires that column names and values do not contain spaces; generally it is not a problem but not all kubectl output modes work. raw simply returns { "_": cmd_output } so _ is used as a variable in assertions to refer to the entire output string. yaml and json return parsed command output using their respective loaders. The --query parameter of the 'aws' commands can be useful for pre-filtering command output so that a simple direct parsing is usable in assertions. Test Assertions --------------- A series of assertions are evaluated on the parsed output from each group's command. Assertions take the form: simple: <python expression using parsed outputs to define variables, eval must pass.> ok_rows_expr: <python expression using parsed outputs to define row variables, ok_rows_expr must be True.> all: <python expression using parsed outputs to define row variables, each row must pass.> Examples of ok_rows expressions might be: ok_rows==1 ok_rows>=3 Pseudo code for 'all' is: ok_rows==len(total output rows) ok_rows is assigned the number of times the assertion evaluates to True when run against each of the row namespace dicts. Hence overall test success does not require every row to pass the assertion. The `test_spec` specifies a string of YAML which defines: Globals: environment: - env var1 needed in assertion expressions imported from os.environ ... constants: - VAR: VAL a VAR needed in assertion expressions with the spec'd VAL ... Groups: - group: <Command Group Name> command: <UNIX subprocess command string> parser: <named_rows|raw|yaml|json> assertions: - name: <Name defining check> <simple|all|ok_rows_expr>: <python expression> - name: <Name defining check> <simple|all|ok_rows_expr>: <python expression> ... ... NOTE: In the spec, substitions for output vars, env vars, constants, variables, and built-in functions occur in two basic ways: - Using Python's f-string {} formatting. (commands) - Treated as a variable name to be eval'ed. (assertions) This is because commands are "".format()'ed but assertions are eval'ed, each against similar namespaces with the caveat that the command formatting includes no variables derived from it's own output. if `output_file` is specified, commands are run and outputs are stored at the spec'ed path, the checker exits w/o running tests. if `input_file` is specified, it is presumed to be the path to command output YAML stored by `output_file` and replaces running commands, checks are run using the stored outputs. input_file and output_file are mutually exclusive. if `verbose` is specified then additional assertion-by-assertion, row-by-row output is generated. if `groups_regex` is specified, only the group names which can be searched by the regex are checked. (case insensitive substrings of group names work). if `variables` is specified, it should be a comma seperated string of VAR=VAL pairs, i.e. VAR1=VAL1,VAR2=VAL2,... These variables are added to the namespace used for running/eval'ing commands and assertions and override values already defined in Globals. """ # noqa: E501 def __init__( self, test_spec=CLUSTER_CHECKS, output_file=None, input_file=None, verbose=False, groups_regex=".+", exclude_regex="^$", variables=None, ): self._output_file = output_file self._input_file = input_file self._verbose = verbose self._groups_regex = groups_regex self._exclude_regex = exclude_regex print("===> Loading test spec") self.loaded_spec = yaml.safe_load(test_spec) self.variables = ( dict([var.split("=") for var in variables.split(",")]) if variables else [] ) self._outputs = {} self._errors = 0 self._error_msgs = [] @property def groups(self): return self.loaded_spec["Groups"] @property def spec_environment(self): return { var: os.environ[var] for var in self.loaded_spec.get("Globals", {}).get("environment", []) } @property def spec_constants(self): return self.loaded_spec.get("Globals", {}).get("constants", {}) @property def builtins(self): result = { key: getattr(builtins, key) for key in dir(builtins) } # Python builtins result.update( dict( convert_age=convert_age, aws_kv_dict=aws_kv_dict, test_function=test_function, functools=functools, pod_logs=self.pod_logs, ) ) return result @property def combined_environment(self): env = dict() env.update(self.builtins) env.update(self.spec_constants) env.update(self.spec_environment) env.update(self.variables) return env def main(self): self.setup_outputs() for check in self.groups: if re.search( self._groups_regex, check["group"], re.IGNORECASE ) and not re.search(self._exclude_regex, check["group"], re.IGNORECASE): self.run_check(check) if self._output_file: self.store_outputs() return self._errors def setup_outputs(self): """Fetch saved commands ouputs from file rather than running commands.""" if self._input_file: with open(self._input_file) as file: self._outputs = yaml.safe_load(file) else: self._outputs = {} def store_outputs(self): """Store command outputs to file for running offline later.""" print("=" * 80) print("Saving", repr(self._output_file)) with open(self._output_file, "w+") as file: yaml.dump(self._outputs, file) def replace_output(self, check, output): if check.get("replace_output"): input_patt = check.get("replace_output").get("input") output_patt = check.get("replace_output").get("output") output = re.sub(input_patt, output_patt, output, flags=re.MULTILINE) return output def run_check(self, check): print("=" * 80) try: output = self.get_command_output(check) except Exception as exc: self.error( "Failed obtaining command output for group", repr(check.get("group")), ":", str(exc), ) print("=" * 80) return if self._output_file: return if not output.startswith("FAILED"): print("-" * 80) print(output) print("=" * 80) self.process_output(check, output) def process_output(self, check, output): try: output = self.replace_output(check, output) parser = globals()[f"parse_{check['parser']}"] namespaces = parser(output) except Exception as exc: self.error("PARSER failed for", repr(check["group"]), ":", str(exc)) return if check.get("print_parsing"): print(namespaces) for assertion in check.get("assertions", []): try: self.check_assertion(check["group"], assertion, namespaces) except Exception as exc: self.error( "EXECUTION failed for", repr(check["group"]), ":", repr(assertion["name"]), ":", str(exc), ) def get_command_output(self, check): group = check["group"] if not self._input_file: self._outputs[group] = self.compute_outputs(group, check) return self._outputs[group] def compute_outputs(self, group, check): if check.get("command"): command = check.get("command").format(**self.combined_environment) elif check.get("function"): command = check.get("function").format(**self.combined_environment) else: raise RuntimeError(f"Group {group} doesn't define an input command.") print("===> Fetching", repr(group)) print("=" * 80) try: if check.get("command"): outputs = run(command).strip() else: outputs = eval( # nosec command, self.combined_environment, self.combined_environment ) except Exception as exc: traceback.print_exc() outputs = f"FAILED for '{group}': '{command}' : '{str(exc)}'" self.error(outputs) return outputs def check_assertion(self, group_name, assertion, namespaces): assertion = dict(assertion) assertion_name = assertion.pop("name") requirement, condition = list(assertion.items())[0] # condition = condition.format(**self.combined_environment) print(f"Checking assertion '{assertion_name}': {requirement} : {condition}") if requirement == "simple": self.verify_simple(group_name, assertion_name, namespaces, condition) elif requirement.startswith(("ok_rows", "all")): self.verify_rows( group_name, assertion_name, namespaces, requirement, condition ) else: raise ValueError( f"Unhandled requirement: {requirement} for assertion: {assertion}" ) print() def verify_rows(self, group_name, name, namespaces, requirement, condition): rows = [] for i, namespace in enumerate(namespaces): self.verbose(f"Checking '{name}' #{i} : {condition} ... ", end="") if self.eval_condition(namespace, condition): rows.append(namespace) self.verbose("OK") else: self.verbose("FAILED on row:", namespace) if requirement == "all": requirement = f"ok_rows=={len(namespaces)}" if self.eval_condition(dict(ok_rows=len(rows)), requirement): # nosec print(f"===> OK '{group_name}' : '{name}'") else: self.error(f"FAILED '{group_name}' : '{name}' : {condition}") def verify_simple(self, group_name, name, namespace, condition): if self.eval_condition(namespace, condition): print(f"===> OK '{group_name}' : '{name}'") else: self.error(f"FAILED '{group_name}' : '{name}' : {condition}") self.verbose("Namespace:", namespace) def eval_condition(self, namespace, condition): namespace = dict(namespace) # local no-side-effects copy namespace.update(self.combined_environment) return eval(condition, {}, namespace) # nosec def verbose(self, *args, **keys): if self._verbose: print(*args, **keys) def error(self, *args): self._errors += 1 self._error_msgs.append(" ".join(str(arg) for arg in args)) print("===> ERROR: ", *args) def show_error_status(self): print("=" * 80) print("Overall", self._errors, "errors occurred:") for msg in self._error_msgs: print(msg) def pod_logs(self, log_reach="30m"): loaded = yaml.safe_load(run("kubectl get pods -A --output yaml")) pods = [ (pod["metadata"]["namespace"], pod["metadata"]["name"]) for pod in loaded["items"] ] print("=" * 80) print("Fetching", len(loaded["items"]), "pod logs") pod_errors = dict() for i, (namespace, name) in enumerate(pods): pod = f"{namespace}:{name}" print() output = run( f"kubectl logs -n {namespace} {name} --since {log_reach} --all-containers --timestamps=True" ) for line in output.splitlines(): if "error" in line.lower() and "| INFO |" not in line: self.error(f"FAILED Pod {pod} log:", line) if pod not in pod_errors: pod_errors[pod] = [] pod_errors[pod].append(line) print() print("-" * 80) return yaml.dump( { "ERRORS": len(pod_errors), "FAILING_PODS": sorted(list(pod_errors.keys())), "POD_ERRORS": pod_errors, } ) def parse_args(): parser = argparse.ArgumentParser( description="Perform various cluster and hub checks to automatically detect basic anomalies." ) parser.add_argument( "--test-spec", dest="test_spec", action="store", default=None, help="Custom test specification. Defaults to None meaning use built-in spec.", ) parser.add_argument( "--output-file", dest="output_file", action="store", default=None, help="Filepath to store outputs of test commands.", ) parser.add_argument( "--input-file", dest="input_file", action="store", default=None, help="Filepath to load previously stored test command results.", ) parser.add_argument( "--verbose", dest="verbose", action="store_true", help="Include additional output.", ) parser.add_argument( "--groups-regex", dest="groups_regex", action="store", default=".+", help="Select groups to execute based on the specified regex, defaulting to all groups." " Unique group substrings are valid, |-or patterns together. Case is irrelevant.", ) parser.add_argument( "--exclude-regex", dest="exclude_regex", action="store", default="^$", help="Select groups to skip based on the specified regex, defaulting to no groups." " Unique group substrings are valid, |-or patterns together. Case is irrelevant.", ) parser.add_argument( "--variables", dest="variables", action="store", default=None, help="Custom override variables which can be used in commands, assertions, etc." " --variables var1=val1,var2=val2,...", ) return parser.parse_args() def main(): """Parse command line arguments and run the test spec. Return the number of failing tests or 0 if all tests pass. """ args = parse_args() test_spec = ( open(args.test_spec).read().strip() if args.test_spec else CLUSTER_CHECKS ) checker = Checker( test_spec=test_spec, output_file=args.output_file, input_file=args.input_file, verbose=args.verbose, groups_regex=args.groups_regex, exclude_regex=args.exclude_regex, variables=args.variables, ) errors = checker.main() checker.show_error_status() return errors if __name__ == "__main__": sys.exit(main())

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28,780

4.770627

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0.023982

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0.22276

0.18056

0.160613

0.127176

0.100254

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0.011195

0.2582

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null

0

null

0

1

0

da78b0227ad76c6a1e8ba2489ed9c76d00da8725

791

py

Python

tests/in/test_application.py

evereux/catia_python

08948585899b12587b0415ce3c9191a408b34897

[ "MIT" ]

90

2019-02-21T10:05:28.000Z

2022-03-19T01:53:41.000Z

tests/in/test_application.py

Luanee/pycatia

ea5eef8178f73de12404561c00baf7a7ca30da59

[ "MIT" ]

99

2019-05-21T08:29:12.000Z

2022-03-25T09:55:15.000Z

tests/in/test_application.py

Luanee/pycatia

ea5eef8178f73de12404561c00baf7a7ca30da59

[ "MIT" ]

26

2019-04-04T06:31:36.000Z

2022-03-30T07:24:47.000Z

#! /usr/bin/python3.6 from pycatia import catia from tests.source_files import cat_part_measurable def test_application(): caa = catia() assert 'Application(name="CNEXT")' in caa.__repr__() def test_refresh(): caa = catia() documents = caa.documents documents.open(cat_part_measurable) document = caa.active_document caa.refresh_display = False assert caa.refresh_display is False caa.refresh_display = True assert caa.refresh_display is True document.close() def test_visible(): caa = catia() documents = caa.documents documents.open(cat_part_measurable) document = caa.active_document caa.visible = False assert caa.visible is False caa.visible = True assert caa.visible is True document.close()

19.775

56

0.705436

102

791

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0

0.0032

0.209861

791

39

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0.025284

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0

0.032468

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0.2

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false

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0.2

0

null

0

null

0

1

0

da7992586d3c2316d0ce8cb23cf3e01e30ae505b

4,632

py

Python

test/util.py

CarysT/xar

f476c05dec373fcdcd0e884d5a0201501555edb9

[ "BSD-2-Clause" ]

null

test/util.py

CarysT/xar

f476c05dec373fcdcd0e884d5a0201501555edb9

[ "BSD-2-Clause" ]

null

test/util.py

CarysT/xar

f476c05dec373fcdcd0e884d5a0201501555edb9

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python import contextlib import hashlib import os import os.path import shutil import stat import subprocess import sys import xattr class TestCaseSkipError(Exception): pass def skip_if_no_compression_support(type): """ Raises TestCaseSkipError if the type is "lzma" and the test is running on darwin (OS X). In the future, we should add a hidden debugging flag to xar to determine valid compression types. This will skip incorrectly if a custom xar is used on OS X, or if a custom xar on another platform is built without bzip2 or lzma. """ if sys.platform == "darwin" and type == "lzma": raise TestCaseSkipError("{t} support not compiled in".format(t=type)) @contextlib.contextmanager def directory_created(directory_path): """ Creates the named directory and provides the path to the directory to the calling code. Automatically removes the directory when finished. Usage: with directory_created("foobar") as path: do_stuff_with_path """ os.mkdir(directory_path) try: yield os.path.realpath(directory_path) finally: if os.path.exists(directory_path): shutil.rmtree(directory_path) @contextlib.contextmanager def archive_created(archive_path, content_path, *extra_args, **extra_kwargs): """ Creates a named xar archive of the specified content path, returning the path to the archive. Automatically removes the archive when finished. Usage: with archive_created("/bin", "bin.xar") as path: do_stuff_with(path) """ cmd = ["xar", "-c", "-f", archive_path, content_path] if extra_args: cmd += list(extra_args) try: subprocess.check_call(cmd, **extra_kwargs) assert os.path.exists(archive_path), "failed to create archive \"{p}\" but xar did not report an error".format(p=archive_path) yield os.path.realpath(archive_path) finally: if os.path.exists(archive_path): os.unlink(archive_path) HASH_CHUNK_SIZE = 32768 def _md5_path(path): with open(path, "r") as f: h = hashlib.md5() while True: last = f.read(HASH_CHUNK_SIZE) if not last: break h.update(last) return h.digest() def assert_identical_directories(path1, path2): """ Verifies two directories have identical contents. Checks file type (via the high byte of the mode), size, atime, and mtime, but does not check other attributes like uid and gid, since they can be expected to change. """ seen = set([]) for file1 in os.listdir(path1): seen.add(file1) entry1 = os.path.join(path1, file1) entry2 = os.path.join(path2, file1) assert os.path.exists(entry2), "\"{f1}\" exists in \"{p1}\" but not \"{p2}\"".format(f1=file1, p1=path1, p2=path2) # Extended attributes xattr1 = xattr.xattr(entry1) xattr2 = xattr.xattr(entry2) assert set(xattr1.list()) == set(xattr2.list()), "list of extended attributes on \"{f1}\" ({l1}) differs from \"{f2}\" ({l2})".format(f1=entry1, l1=xattr1.list(), f2=entry2, l2=xattr2.list()) for attribute in xattr1.list(): assert xattr1.get(attribute) == xattr2.get(attribute), "extended attribute \"{a1}\" on \"{f1}\" doesn't match value from \"{f2}\"".format(a1=attribute, f1=entry1, f2=entry2) # Why do it this way? We want to lstat() instead of stat(), so we can't use os.path.isdir() and friends stat1 = os.lstat(entry1) stat2 = os.lstat(entry2) # Modes mode1 = stat1.st_mode mode2 = stat2.st_mode if stat.S_ISREG(mode1): assert stat.S_ISREG(mode2) if stat.S_ISDIR(mode1): assert stat.S_ISDIR(mode2) if stat.S_ISLNK(mode1): assert stat.S_ISLNK(mode2) if stat.S_ISCHR(mode1): assert stat.S_ISCHR(mode2) if stat.S_ISBLK(mode1): assert stat.S_ISBLK(mode2) if stat.S_ISFIFO(mode1): assert stat.S_ISFIFO(mode2) if stat.S_ISSOCK(mode1): assert stat.S_ISSOCK(mode2) # Sizes and the like assert stat1.st_size == stat2.st_size, "size mismatch for \"{e1}\" ({s1}) and \"{e2}\" ({s2})".format(e1=entry1, s1=stat1.st_size, e2=entry2, s2=stat2.st_size) assert stat1.st_mtime == stat2.st_mtime, "mtime mismatch for \"{e1}\" and \"{e2}\"".format(e1=entry1, e2=entry2) assert _md5_path(entry1) == _md5_path(entry2), "md5 hash mismatch for \"{e1}\" and \"{e2}\"".format(e1=entry1, e2=entry2) if os.path.isdir(entry1): assert_identical_directories(entry1, entry2) for file2 in os.listdir(path2): assert file2 in seen, "\"{f2}\" exists in \"{p2}\" but not \"{p1}\"".format(f2=file2, p1=path1, p2=path2) def touch(path): if not os.path.exists(path): with open(path, "w"): pass os.utime(path, None) @contextlib.contextmanager def chdir(*args, **kwargs): cwd = os.getcwd() os.chdir(*args, **kwargs) try: yield os.getcwd() finally: os.chdir(cwd)

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false

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0.191489

0

null

0

null

0

1

0

da79b4fcd76b875d0455312cd540c29c3adde2c1

15,083

py

Python

run_game_with_python_arcade.py

LiorAvrahami/fishy-game

e13d71ad04625edffc1ff32f56c918166f6b0bb9

[ "MIT" ]

5

2021-04-24T18:13:36.000Z

2021-08-31T13:54:55.000Z

run_game_with_python_arcade.py

LiorAvrahami/fishy-game

e13d71ad04625edffc1ff32f56c918166f6b0bb9

[ "MIT" ]

null

run_game_with_python_arcade.py

LiorAvrahami/fishy-game

e13d71ad04625edffc1ff32f56c918166f6b0bb9

[ "MIT" ]

null

import arcade import arcade.gui from modifications_to_python_arcade.gui_manager import ModifiedUIManager from modifications_to_python_arcade.resizeable_window import ResizeableWindow from arcade.gui.ui_style import UIStyle import fish from controls import PlayerControlsObject from fish_generator import RandomFishGenerator,WaveFishGenerator,FishGenerator import time import pickle import os from game_sprite_buttons import RestartGameButton,ContinueGameButton,YouWinPoster,ViewHighScoresButton,YouLosePoster import resources GL_NEAREST = 9728 # open_gl scaling filter key for nearest neighbor from game_sprite_buttons import TextureButton SCREEN_TITLE = "Eat or Be eaten" import resources from game_constents import min_computer_fish_size,max_computer_fish_size,min_computer_fish_speed,max_computer_fish_speed,player_win_size,player_start_size all_deltatimes = [] num_of_high_scores = 5 screen_size:list main_game_view:arcade.View game:ResizeableWindow class MainGameView(arcade.View): """ Main application class. """ fish_sprites: arcade.SpriteList ui_manager : ModifiedUIManager player_fish: fish.PlayerFish paused:bool # buttons def restart_button_game_lost:RestartGameButton continue_button_paused:ContinueGameButton continue_button_game_lost:ContinueGameButton you_win_poster: YouWinPoster you_lose_poster: YouLosePoster view_high_scores_button: ViewHighScoresButton time_played:float controls_handler: PlayerControlsObject fish_generator: FishGenerator b_did_win_already : bool FLAG_open_high_scores_menue : int @property def height(self): return screen_size[1] @property def width(self): return screen_size[0] def __init__(self): super().__init__() self.on_resize() self.restart_game() def restart_game(self): """ Set up the game variables. Call to re-start the game. """ # Create your sprites and sprite lists here # set up buttons self.background_texture = resources.background_texture_map["idle"] self.fish_sprites = arcade.SpriteList() self.ui_manager = ModifiedUIManager(self.window) self.player_fish = fish.PlayerFish(self) self.fish_generator = RandomFishGenerator(1.1,self,min_fish_size=min_computer_fish_size,max_fish_size=max_computer_fish_size,min_fish_speed=min_computer_fish_speed,max_fish_speed=max_computer_fish_speed) self.fish_sprites.append(self.player_fish) self.paused = False self.controls_handler = PlayerControlsObject(change_player_direction=self.player_fish.change_movement_direction, reset_game=self.restart_game, pause_game=self.toggle_game_paused) self.restart_button_game_lost = RestartGameButton(self,False) self.restart_button_game_won = self.restart_button_game_lost self.ui_manager.add_ui_element(self.restart_button_game_won) self.continue_button_paused = ContinueGameButton(self,False) self.ui_manager.add_ui_element(self.continue_button_paused) self.you_win_poster = YouWinPoster(self,False) self.you_win_poster.center_y += self.restart_button_game_won.height/2 + self.you_win_poster.height/2 + 10 self.ui_manager.add_ui_element(self.you_win_poster) self.you_lose_poster = YouLosePoster(self,False) self.you_lose_poster.center_y = self.restart_button_game_lost.top + self.you_win_poster.height / 2 + 10 self.ui_manager.add_ui_element(self.you_lose_poster) self.continue_button_game_won = ContinueGameButton(self, False) self.continue_button_game_won.center_y += -self.restart_button_game_won.height / 2 - self.continue_button_game_won.height / 2 - 10 self.ui_manager.add_ui_element(self.continue_button_game_won) self.view_high_scores_button = ViewHighScoresButton(self,True) self.view_high_scores_button.center_x = self.window.width - self.view_high_scores_button.width/2 - 20 self.view_high_scores_button.center_y = self.view_high_scores_button.height / 2 + 20 self.ui_manager.add_ui_element(self.view_high_scores_button) self.time_played = 0 self.b_did_win_already = False self.FLAG_open_high_scores_menue = -1 def on_draw(self): """ Render the screen. """ # This command should happen before we start drawing. It will clear # the screen to the background color, and erase what we drew last frame. arcade.start_render() left, right, bottom, top = self.window.get_viewport() arcade.draw_lrwh_rectangle_textured(0, 0, right, top, self.background_texture) self.fish_sprites.draw(filter=GL_NEAREST) self.ui_manager.on_draw() # draw time arcade.draw_text("time: {:.0f}".format(self.time_played),20,self.height - 40,color=(255,240,200,210),font_size=25,bold=True,anchor_y="bottom",font_name="ariblk") #draw score (only wen game is lost) arcade.draw_text("score: {:.0f}%".format((self.player_fish.size - player_start_size)/(player_win_size-player_start_size)*100), 20, self.height - 40, color=(255, 240, 200, 210), font_size=25, bold=True, anchor_y="top", font_name="ariblk") last_time = None def on_update(self, delta_time): """ All the logic to move, and the game logic goes here. """ # calculate delta_time if self.last_time is not None: delta_time = time.time() - self.last_time self.last_time = time.time() if not self.is_game_lost and not self.b_did_win_already and not self.paused: self.time_played += delta_time # update game if not self.paused: self.fish_sprites.on_update(delta_time) self.fish_generator.update(delta_time) all_deltatimes.append(delta_time) if self.FLAG_open_high_scores_menue == 0: game.show_view(HighScoresView(self.time_played)) self.FLAG_open_high_scores_menue = -1 elif self.FLAG_open_high_scores_menue > 0: self.FLAG_open_high_scores_menue -= 1 @property def is_game_lost(self): return not self.player_fish in self.fish_sprites def unpause(self): self.paused = False self.continue_button_paused.is_visible = False self.you_win_poster.is_visible = False self.restart_button_game_won.is_visible = False self.continue_button_game_won.is_visible = False def toggle_game_paused(self): if not self.is_game_lost: if self.paused: self.unpause() else: self.paused = True self.continue_button_paused.is_visible = True else: self.restart_game() def handle_game_lost(self): self.restart_button_game_lost.is_visible = True self.you_lose_poster.is_visible = True def handle_game_won(self): if not self.b_did_win_already: self.you_win_poster.is_visible = True self.continue_button_game_won.is_visible = True self.restart_button_game_won.is_visible = True self.b_did_win_already = True high_scores = HighScoresView.load_high_scores() if self.time_played < max([HighScoresView.try_parse(s[1]) for s in high_scores]): self.FLAG_open_high_scores_menue = 1 def on_close(self): self.window.on_close() def switch_to_high_scores_view(self): if not ( self.paused or self.b_did_win_already or self.is_game_lost ): self.toggle_game_paused() game.show_view(HighScoresView()) def on_show_view(self): self.last_time = time.time() self.controls_handler.reset_state() def on_resize(self, width: float = 0, height: float = 0): ratio = self.height/self.width self.window.height = int(self.window.width*ratio) return False #UI def on_key_press(self, key, key_modifiers): """ Called whenever a key on the keyboard is pressed. """ self.controls_handler.on_keyboard_press(key, key_modifiers) def on_key_release(self, key, key_modifiers): """ Called whenever the user lets off a previously pressed key. """ self.controls_handler.on_keyboard_release(key, key_modifiers) def on_mouse_motion(self, *args,**kwargs): self.ui_manager.on_mouse_motion(*args,**kwargs) def on_mouse_press(self, *args, **kwargs): self.ui_manager.on_mouse_press(*args,**kwargs) def on_mouse_release(self, *args, **kwargs): self.ui_manager.on_mouse_release(*args,**kwargs) class HighScoresView(arcade.View): text_input_box : arcade.gui.UIInputBox text_output_box : arcade.gui.UILabel high_scores_text_boxes : list ui_manager : arcade.gui.UIManager rectangle_background : arcade.SpriteSolidColor def __init__(self,new_high_score=None): super().__init__() arcade.set_background_color(arcade.color.AZURE) self.ui_manager = arcade.gui.UIManager(self.window) self.uistyle = UIStyle.default_style() font_color = (30, 50, 50) self.uistyle.set_class_attrs("label",font_color=font_color,font_color_hover=font_color,font_color_press=font_color) title_texture = arcade.load_texture(r"resources\high scores.png") self.title_poster = arcade.gui.UIImageButton(center_x=self.width / 2,center_y=self.height,normal_texture=title_texture,hover_texture=title_texture,press_texture=title_texture) self.title_poster.center_y -= self.title_poster.height/2 self.ui_manager.add_ui_element(self.title_poster) self.rectangle_background = arcade.SpriteSolidColor(self.width//2,self.height,(140,150,200)) self.rectangle_background.center_x = self.width / 2 self.rectangle_background.center_y = self.height/ 2 self.line_background = arcade.SpriteSolidColor(10,int(self.title_poster.bottom - 70),(20,30,60)) self.line_background.center_x = self.width / 2 self.line_background.center_y = self.title_poster.bottom - self.line_background.height/2 - 30 # back button: back_button = arcade.gui.UIImageButton(center_x=0, center_y=0, normal_texture=resources.back_button_texture_map["mouse_out"], hover_texture=resources.back_button_texture_map["mouse_in"], press_texture=resources.back_button_texture_map["mouse_pressed"]) back_button.center_x = self.width - back_button.width / 2 - 20 back_button.center_y = self.height - back_button.height / 2 - 20 self.ui_manager.add_ui_element(back_button) @back_button.event("on_click") def on_click(): self.ui_manager.remove_handlers() self.ui_manager.purge_ui_elements() game.show_view(main_game_view) high_scores = self.load_high_scores() if new_high_score is not None: for index in range(len(high_scores)): if new_high_score < self.try_parse(high_scores[index][1]): high_scores.insert(index,(None,"{:.3g}".format(new_high_score))) high_scores.pop() break self.draw_high_scores_table(high_scores) @property def height(self): return screen_size[1] @property def width(self): return screen_size[0] @staticmethod def try_parse(s): try: return float(s) except: return float("inf") def draw_high_scores_table(self,high_scores:list): self.names_boxes = [arcade.gui.UILabel(name,0,0, style=self.uistyle) if name is not None else self.create_input_box() for name,score in high_scores] self.scores_boxes = [arcade.gui.UILabel(score,0,0, style=self.uistyle) for name,score in high_scores] for i in range(len(self.names_boxes)): y = (self.names_boxes[i-1].center_y - self.names_boxes[i-1].height/2 if i > 0 else self.title_poster.bottom - 50)\ - self.names_boxes[i-1].height / 2 - 20 self.names_boxes[i].center_y = y self.names_boxes[i].center_x = self.width/2 - self.names_boxes[i].width/2 - 30 self.scores_boxes[i].center_y = y self.scores_boxes[i].center_x = self.width / 2 + self.scores_boxes[i].width / 2 + 30 self.ui_manager.add_ui_element(self.names_boxes[i]) self.ui_manager.add_ui_element(self.scores_boxes[i]) def create_input_box(self): ret = arcade.gui.UIInputBox(0, 0, (self.line_background.left - self.rectangle_background.left)//1.2, style=self.uistyle) @ret.event("on_enter") def on_enter(): ret.text.replace("\n","\\n") high_scores = [(self.names_boxes[i].text,self.scores_boxes[i].text) for i in range(len(self.names_boxes))] self.save_high_scores(high_scores) # replace text box with label self.ui_manager._ui_elements.remove(ret) new_label = arcade.gui.UILabel(ret.text, 0, 0, style=self.uistyle) new_label.center_y = ret.center_y new_label.center_x = self.width/2 - new_label.width/2 - 30 index = self.names_boxes.index(ret) high_scores[index] = (new_label,high_scores[index][1]) self.ui_manager.add_ui_element(new_label) self.ui_manager.focused_element = ret return ret def save_high_scores(self,high_scores): with open("high_scores.pypickle", "wb+") as file: pickle.dump(high_scores, file) @staticmethod def load_high_scores(): if os.path.exists("high_scores.pypickle"): with open("high_scores.pypickle", "rb") as file: high_scores = pickle.load(file) else: high_scores = [] while len(high_scores) < num_of_high_scores: high_scores.append(("---","---")) return high_scores[:num_of_high_scores] def on_draw(self): """ Render the screen. """ arcade.start_render() self.rectangle_background.draw() self.line_background.draw() def on_resize(self, width: float = 0, height: float = 0): ratio = self.height/self.width self.window.height = int(self.window.width*ratio) return False def main(): """ Main method """ global game,main_game_view,screen_size game = ResizeableWindow(1000, 500, "Fishy Game",resizable=True) game.maximize() game.dispatch_events() screen_size = game.get_size() game.stretch_game_with_window = True # game.set_viewport(0, self.width, 0, self.height) main_game_view = MainGameView() game.show_view(main_game_view) arcade.run() if __name__ == "__main__": main()

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4.712604

0.14615

0.054116

0.028411

0.018316

0.399521

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0.22774

15,083

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0.301818

0

null

0

null

0

1

0

da7b3bc161256bb5501fd5bd641192702f9a7738

2,306

py

Python

pebbles/views/sessions.py

CSCfi/pebbles

24b32e8fc538cc8095fda62c892a8221346c2bce

[ "MIT" ]

4

2017-05-11T14:50:32.000Z

2020-01-10T09:02:27.000Z

pebbles/views/sessions.py

CSCfi/pebbles

24b32e8fc538cc8095fda62c892a8221346c2bce

[ "MIT" ]

145

2017-04-07T11:01:58.000Z

2019-12-11T15:30:23.000Z

pebbles/views/sessions.py

CSCfi/pebbles

24b32e8fc538cc8095fda62c892a8221346c2bce

[ "MIT" ]

3

2017-10-25T12:36:16.000Z

2018-04-26T08:49:34.000Z

from flask_restful import fields, marshal from flask import Blueprint as FlaskBlueprint import logging import json from pebbles.models import User from pebbles.forms import SessionCreateForm from pebbles.server import app, restful from pebbles.views.commons import is_group_manager, update_email # changed sessions = FlaskBlueprint('sessions', __name__) token_fields = { 'token': fields.String, 'user_id': fields.String, 'is_admin': fields.Boolean, 'is_group_owner': fields.Boolean, 'is_group_manager': fields.Boolean, 'icon_value': fields.String } admin_icons = ["Dashboard", "Users", "Groups", "Blueprints", "Configure", "Statistics", "Account"] group_owner_icons = ["Dashboard", "", "Groups", "Blueprints", "", "", "Account"] group_manager_icons = ["Dashboard", "", "", "Blueprints", "", "", "Account"] user_icons = ["Dashboard", "", "", "", "", "", "Account"] class SessionView(restful.Resource): def post(self): form = SessionCreateForm() if not form.validate_on_submit(): logging.warn("validation error on user login") return form.errors, 422 user = User.query.filter_by(eppn=form.eppn.data).first() if user and not user.email_id: # Email and eppn are same because we invite users through emailid user = update_email(eppn=user.eppn, email_id=user.eppn) if user and user.check_password(form.password.data): if user.is_admin: icons = json.dumps(admin_icons) elif user.is_group_owner: icons = json.dumps(group_owner_icons) elif is_group_manager(user): icons = json.dumps(group_manager_icons) else: icons = json.dumps(user_icons) return marshal({ 'token': user.generate_auth_token(app.config['SECRET_KEY']), 'is_admin': user.is_admin, 'is_group_owner': user.is_group_owner, 'is_group_manager': is_group_manager(user), 'user_id': user.id, 'icon_value': icons }, token_fields) logging.warn("invalid login credentials for %s" % form.eppn.data) return { 'message': 'Unauthorized', 'status': 401 }, 401

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0.257155

2,306

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false

0.019231

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0

0.25

0.096154

0

null

0

null

0

1

0

da7bab95ad749f6149016b3bc152e246a371a757

6,138

py

Python

train_rrca.py

deepeshhada/ReXPlug

f6ba1e1707e04f82451fba8ada19c731c8f7c46e

[ "Apache-2.0" ]

6

2021-04-04T05:09:32.000Z

2022-01-21T10:59:20.000Z

train_rrca.py

deepeshhada/ReXPlug

f6ba1e1707e04f82451fba8ada19c731c8f7c46e

[ "Apache-2.0" ]

null

train_rrca.py

deepeshhada/ReXPlug

f6ba1e1707e04f82451fba8ada19c731c8f7c46e

[ "Apache-2.0" ]

1

2021-11-06T05:36:03.000Z

import argparse import os import pickle from copy import deepcopy import pandas as pd import torch.optim as optim from torch.utils.data import DataLoader from collate import CollateTrain, CollateTest from models.RRCA import * from utils.rrca_utils import evaluate, train_one_epoch def get_embeddings(dataset_path): with open(os.path.join(dataset_path, 'true_sentence_embeddings.pkl'), 'rb') as f: true_embeddings = pickle.load(f) return true_embeddings def create_reviews_lists(train_df, true_embeddings): user_reviews_dict = {} item_reviews_dict = {} for idx, row in train_df.iterrows(): if int(row[0]) not in user_reviews_dict: user_reviews_dict[int(row[0])] = [] if int(row[1]) not in item_reviews_dict: item_reviews_dict[int(row[1])] = [] user_reviews_dict[int(row[0])].append(true_embeddings[idx]) item_reviews_dict[int(row[1])].append(true_embeddings[idx]) return user_reviews_dict, item_reviews_dict def create_dataset(df, true_embeddings, mode="Test"): user_item_ratings = {} if mode == "Train": for idx, row in df.iterrows(): user_item_ratings[idx] = [int(row[0]), int(row[1]), true_embeddings[idx], row[3]] else: for idx, row in df.iterrows(): user_item_ratings[idx] = [int(row[0]), int(row[1]), row[3]] return user_item_ratings def train_rrca( dataset_path="./data", model_save_path="./saved_models", model="rrca", batch_size_rrca=256, learning_rate_rrca=0.002, num_epochs_rrca=150, dataset_name="AmazonDigitalMusic" ): with open('./pickled_meta/dataset_meta.pkl', 'rb') as f: dataset_meta = pickle.load(f) num_users = dataset_meta[dataset_name]['num_users'] num_items = dataset_meta[dataset_name]['num_items'] num_factors = 64 num_layers = 3 sentence_embed_dim = 512 embed_dim = num_factors * (2 ** (num_layers - 1)) model_save_path = os.path.join(model_save_path, dataset_name, model + '.pt') dataset_path = os.path.join(dataset_path, dataset_name) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Prepare data_loaders train_df = pd.read_csv(os.path.join(dataset_path, 'train_df.csv')) val_df = pd.read_csv(os.path.join(dataset_path, 'val_df.csv')) test_df = pd.read_csv(os.path.join(dataset_path, 'test_df.csv')) print(f"Train size: {len(train_df)} | Val size: {len(val_df)} | Test size: {len(test_df)}") print("Creating data loaders...") true_embeddings = get_embeddings(dataset_path) user_reviews_dict, item_reviews_dict = create_reviews_lists(train_df, true_embeddings) train_set = create_dataset(train_df, true_embeddings, mode="Train") val_set = create_dataset(val_df, true_embeddings, mode="Val") test_set = create_dataset(test_df, true_embeddings, mode="Test") train_loader = DataLoader( dataset=train_set, batch_size=batch_size_rrca, shuffle=True, collate_fn=CollateTrain(user_reviews_dict, item_reviews_dict) ) val_loader = DataLoader( dataset=val_set, batch_size=batch_size_rrca, shuffle=False, collate_fn=CollateTest(user_reviews_dict, item_reviews_dict) ) test_loader = DataLoader( dataset=test_set, batch_size=batch_size_rrca, shuffle=False, collate_fn=CollateTest(user_reviews_dict, item_reviews_dict) ) print("Creating RRCA modules...") review_regularizer = ReviewRegularizer(num_factors=num_factors).to(device) cross_attention_module = CrossAttention(embed_dim=embed_dim, sentence_embed_dim=sentence_embed_dim).to(device) model = RatingPredictor( review_regularizer=review_regularizer, cross_attention=cross_attention_module, embed_dim=embed_dim, num_users=num_users, num_items=num_items, num_factors=num_factors, num_layers=num_layers ).to(device) optimizer = optim.Adam(model.parameters(), lr=learning_rate_rrca) loss_function = nn.MSELoss() losses_overall, losses_rating_pred, losses_att, losses_reg = [], [], [], [] val_mses, val_maes = [], [] PATIENCE = 15 patience = PATIENCE best_val_mse, best_model = 100, None print("Training...") print("=" * 80) for epoch in range(1, num_epochs_rrca + 1): if patience == 0: break epoch_loss_overall, epoch_loss_rating_pred, epoch_loss_att, epoch_loss_reg, val_mse, val_mae = train_one_epoch( model=model, train_loader=train_loader, val_loader=val_loader, loss_function=loss_function, optimizer=optimizer, epoch=epoch, device=device ) if val_mse < best_val_mse: print("Saving model...") patience = PATIENCE best_val_mse = val_mse best_model = deepcopy(model) torch.save(best_model.state_dict(), model_save_path) else: patience -= 1 losses_overall.append(epoch_loss_overall) losses_rating_pred.append(epoch_loss_rating_pred) losses_att.append(epoch_loss_att) losses_reg.append(epoch_loss_reg) val_mses.append(val_mse) val_maes.append(val_mae) print("=" * 80) print('RRCA trained. Evaluating on the test set.') print("-" * 80) test_mse, test_mae = evaluate(best_model, test_loader, device) print(f"Test MSE: {test_mse:.4f} | Test MAE: {test_mae:.4f}") print("=" * 80) return if __name__ == "__main__": parser = argparse.ArgumentParser(description="Train ReXPlug.") parser.add_argument("--dataset_path", type=str, default="./data", help="Root folder path of preprocessed dataset.") parser.add_argument("--model_save_path", type=str, default="./saved_models", help="Root path to save RRCA's model.") parser.add_argument("--model", type=str, default="rrca", help="Choose from 'rrca' or 'rr'.") parser.add_argument("--batch_size_rrca", type=int, default=256, help="Batch size to train RRCA.") parser.add_argument("--learning_rate_rrca", type=float, default=0.002, help="Learning rate for RRCA.") parser.add_argument("--num_epochs_rrca", type=int, default=150, help="Number of epochs to train RRCA.") parser.add_argument( "--dataset_name", type=str, default="AmazonDigitalMusic", choices=("AmazonDigitalMusic", "AmazonVideoGames", "AmazonClothing", "Yelp_1", "Yelp_2", "BeerAdvocate"), help="Name of the dataset to use." ) args = parser.parse_args() root_path = os.path.join(args.model_save_path, args.dataset_name) if not os.path.exists(root_path): os.makedirs(root_path) train_rrca(**(vars(args)))

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117

0.750244

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0.031264

0.035665

0.229968

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0.064845

0

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0.787565

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0

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false

0

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0

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0.070513

0

null

0

null

0

1

0

da7bd622d1ddbda2597706e2f3a2142e325287e0

4,540

py

Python

SignLAnguage/proyecto.py

val171001/SignLanguage

fc7622e4a31f007791f34fbf0ba08c89138389c5

[ "MIT" ]

null

SignLAnguage/proyecto.py

val171001/SignLanguage

fc7622e4a31f007791f34fbf0ba08c89138389c5

[ "MIT" ]

null

SignLAnguage/proyecto.py

val171001/SignLanguage

fc7622e4a31f007791f34fbf0ba08c89138389c5

[ "MIT" ]

null

#Universidad del Valle de Guatemala #HCI #David valenzuela #Marcos Gutierrez #Fernando Hengstenebrg #Librerias from tkinter import * from tkinter import messagebox as ms #base de datos import sqlite3 import os import webbrowser #Conectamos con base de datos y creamos usuarios with sqlite3.connect('cuentas.db') as db: c = db.cursor() c.execute('CREATE TABLE IF NOT EXISTS user (username TEXT NOT NULL ,password TEX NOT NULL);') db.commit() db.close() #main Class class main: def __init__(self,master): # ventana self.master = master # variables self.Usuario = StringVar() self.password = StringVar() self.usuarioNuevo = StringVar() self.nuevaPassword = StringVar() self.widgets() #Funcion LOGIN def login(self): #establecer coneccion with sqlite3.connect('cuentas.db') as db: c = db.cursor() #buscar usuario en dr find_user = ('SELECT * FROM user WHERE username = ? and password = ?') c.execute(find_user,[(self.Usuario.get()),(self.password.get())]) result = c.fetchall() if result: f = open('alfabeto.html', 'r') mensaje = 'Se abrio el archivo' webbrowser.open_new_tab('alfabeto.html') else: ms.showerror('Oops!','La cuenta no se puede encontrar') def nuevoUsuario(self): with sqlite3.connect('cuentas.db') as db: c = db.cursor() #buscar usuario find_user = ('SELECT * FROM user WHERE username = ?') c.execute(find_user,[(self.Usuario.get())]) if c.fetchall(): ms.showerror('Error!','Ya existe el nombre de usuario Intente de Nuevo.') else: ms.showinfo('Success!','Cuenta Creada!') self.log() #Crear nueva cuenta insert = 'INSERT INTO user(username,password) VALUES(?,?)' c.execute(insert,[(self.usuarioNuevo.get()),(self.nuevaPassword.get())]) db.commit() def log(self): self.Usuario.set('') self.password.set('') self.crf.pack_forget() #self.head['text'] = 'LOGIN' self.logf.pack() def crear(self): self.usuarioNuevo.set('') self.nuevaPassword.set('') self.logf.pack_forget() self.head['text'] = ' CREAR CUENTA ' self.crf.pack() def widgets(self): #Encabezado de la ventana para iniciar sesion self.head = Label(self.master, text = ' INICIAR SESION ', font = ('',20), pady = 10, bg='blue4', fg='white') self.head.pack() #Ventana principal self.logf = Frame(self.master,padx =10,pady = 10, bg='white') #Propiedades principales Label(self.logf,text = 'Usuario: ',font = ('',20),pady=5,padx=5, bg='white', fg='black').grid(sticky = W) Entry(self.logf,textvariable = self.Usuario,bd = 5,font = ('',15)).grid(row=0,column=1) Label(self.logf,text = 'Contraseña: ',font = ('',20),pady=5,padx=5, bg='white', fg='black').grid(sticky = W) Entry(self.logf,textvariable = self.password,bd = 5,font = ('',15),show = '*').grid(row=1,column=1) Button(self.logf,text = ' Crear cuenta ',bd = 3 ,font = ('',15),padx=5,pady=5,command=self.crear).grid() Button(self.logf,text = ' Login ',bd = 3 ,font = ('',15),padx=5,pady=5,command=self.login).grid(row=2,column=1) #Button(self.logf,text = ' Ayuda ',bd = 3 ,font = ('',15),padx=5,pady=5,command=self.login).grid(row=3,column=1) self.logf.pack() #Datos para la ventana de crear usuarios self.crf = Frame(self.master,padx =10,pady = 10, bg='white') #Propiedades para el ingreso de datos Label(self.crf,text = 'Usuario Nuevo: ',font = ('',20),pady=5,padx=5, bg='white', fg='black').grid(sticky = W) Entry(self.crf,textvariable = self.usuarioNuevo,bd = 5,font = ('',15)).grid(row=0,column=1) Label(self.crf,text = 'Contraseña: ',font = ('',20),pady=5,padx=5, bg='white', fg='black').grid(sticky = W) Entry(self.crf,textvariable = self.nuevaPassword,bd = 5,font = ('',15),show = '*').grid(row=1,column=1) Button(self.crf,text = 'Regresar',bd = 3 ,font = ('',15),padx=5,pady=5,command=self.log).grid() Button(self.crf,text = 'Crear cuenta',bd = 3 ,font = ('',15),padx=5,pady=5,command=self.nuevoUsuario).grid(row=2,column=1) #crear la ventana root = Tk() main(root) root.resizable(width=False, height=False) root.mainloop()

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da7e18ea9112e331d9b45cb4a08cb02a217b0d65

53

py

Python

001146StepikPyBegin/Stepik001146PyBeginсh06p01st08C07_fraction_20200419.py

SafonovMikhail/python_000577

739f764e80f1ca354386f00b8e9db1df8c96531d

[ "Apache-2.0" ]

null

001146StepikPyBegin/Stepik001146PyBeginсh06p01st08C07_fraction_20200419.py

SafonovMikhail/python_000577

739f764e80f1ca354386f00b8e9db1df8c96531d

[ "Apache-2.0" ]

null

001146StepikPyBegin/Stepik001146PyBeginсh06p01st08C07_fraction_20200419.py

SafonovMikhail/python_000577

739f764e80f1ca354386f00b8e9db1df8c96531d

[ "Apache-2.0" ]

null

num1 = float(input()) # num1 = 44.45 print(num1 % 1)

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py

Python

data-processing/utils/__init__.py

mark-andrews/BayesianAccountMemoryText

28609a4d3d3924c5082af81359ffc3f78f6eb6da

[ "CC-BY-4.0" ]

2

2020-04-10T17:14:19.000Z

2020-04-10T17:14:26.000Z

data-processing/utils/__init__.py

mark-andrews/BayesianAccountMemoryText

28609a4d3d3924c5082af81359ffc3f78f6eb6da

[ "CC-BY-4.0" ]

18

2020-03-24T17:07:23.000Z

2021-12-13T20:01:11.000Z

data-processing/utils/__init__.py

mark-andrews/BayesianAccountMemoryText

28609a4d3d3924c5082af81359ffc3f78f6eb6da

[ "CC-BY-4.0" ]

null

""" Some general utils. """ ##============================================================================= ## Standard library imports ##============================================================================= #import string #import re #import os #import errno #import hashlib # ##================================ End Imports ================================ # #def deletechars(s, exclude_chars): # ''' Fast deletion of characters from string. # It uses a dummy translation table, and so no mapping is applied, and we # just delete the exclude_chars characters. # ''' # phony_translate_table = string.maketrans("","") # return s.translate(phony_translate_table, exclude_chars) # # #def deletepunctuation(s): # ''' Fast deletion of punctuation from string''' # return deletechars(s,string.punctuation) # # #def tokenize(text, foldcase=True): # ''' # A very cheap and easy tokenization. # First, remove "'s". For example, "dog's" becomes "dog". # Second, zap utf-8 chars. # Then, remove all punctuation and, by default, fold upper and lower case words # and then split by whitespace. # ''' # # text = re.sub(r'\'s','', text) # s = ''.join([s for s in text if s in string.printable]) # # s = str(s) # Got to convert it to str. # s = deletepunctuation(s) # # if foldcase: # s = s.lower() # return s.split() # # #def mkdir_p(path): # ''' # Make a directory, making parents if necessary. # Taken verbatim from # http://stackoverflow.com/a/600612 # ''' # try: # os.makedirs(path) # except OSError as exc: # Python >2.5 # if exc.errno == errno.EEXIST and os.path.isdir(path): # pass # else: raise # # #def checksum(argument, algorithm='sha256'): # ''' # Returns the hash checksum of `argument'. # If `argument' is a name of a file, then perform the checksum on the file. # Otherwise, the checksum is of the string `argument'. # By default, it will be the sha1 checksum (and so equivalent to linux's # sha1sum). Alternatively, the algorithm could be md5 (equivalent to linux's # md5sum), or else sha224, sha256, sha384, sha512. # ''' # # h = hashlib.new(algorithm) # # if os.path.exists(argument) and os.path.isfile(argument): # argument = open(argument,'rb').read() # # h.update(argument) # # return h.hexdigest() # I didn't have anywhere better to put these. hdptm_170617202450_6333_state_checksums = ''' hdptm_170617202450_6333_state_19000.npz 2dadab2c09f54f4d03a1187c8d5db49a8ec0a2bfe7bd5f5630448958ba4f21ac hdptm_170617202450_6333_state_19010.npz 0edcb069ab3e559f62728d372f98fb5c047ca8a47ede262dad05ef236d29615f hdptm_170617202450_6333_state_19020.npz 395b4214a753d811f18f24c6665665bdfc201928c7e661294ab0e991b993b1c5 hdptm_170617202450_6333_state_19030.npz 469e9742d4a508c4b34e5283254041ec34b58ebf1f82a15f845949d7367708d5 hdptm_170617202450_6333_state_19040.npz 036d02b36964f24b4a49465769eb51f46ccbc6f52255797b32207c398c6a31f5 hdptm_170617202450_6333_state_19050.npz 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src/turret/core/cli.py

VanJanssen/turret

a3b87940aa7ed07df7a60e7633f795c0fec26462

[ "MIT" ]

1

2018-01-09T12:40:00.000Z

src/turret/core/cli.py

VanJanssen/turret

a3b87940aa7ed07df7a60e7633f795c0fec26462

[ "MIT" ]

42

2018-01-05T11:45:56.000Z

2019-03-11T09:41:11.000Z

src/turret/core/cli.py

VanJanssen/turret

a3b87940aa7ed07df7a60e7633f795c0fec26462

[ "MIT" ]

1

2017-08-29T15:54:28.000Z

# -*- coding: utf-8 -*- """Entry point for the command line interface of Turret. The command line interface is split across multiple files, to increase modulairity and maintainability. Every component of Turret has its own subcommand, the CLI for this is in the `cli.py` file of this component. This file imports the subcommands for those components and adds them to the main group. """ import click from turret.raw.cli import raw from turret.scout.cli import scout @click.group() @click.version_option(message='Turret %(version)s') def main(): """Entry point for the Turret CLI.""" pass main.add_command(scout) main.add_command(raw)

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py

Python

timekeeper/log.py

jmcph4/timekeeper

1ab850739c7071ebd8a4d1a63795d014bfa9c41b

[ "MIT" ]

null

timekeeper/log.py

jmcph4/timekeeper

1ab850739c7071ebd8a4d1a63795d014bfa9c41b

[ "MIT" ]

5

2017-07-19T10:09:32.000Z

2017-07-30T03:32:56.000Z

timekeeper/log.py

jmcph4/timekeeper

1ab850739c7071ebd8a4d1a63795d014bfa9c41b

[ "MIT" ]

null

from datetime import datetime import sqlite3 from . import slice class Log(object): """ Represents a series of slices, forming a log of how time was spent """ DT_FMT = "%Y-%m-%d %H:%M" _COL_WIDTH = 15 def __init__(self, slices): self._slices = {} for s in slices: self._slices[s.start] = (s, False) @property def slices(self): sl = {} for k, v in self._slices.items(): sl[k] = v[0] return sl def get_slice(self, dt): """ Returns the slice at the specified time """ return self._slices.get(dt)[0] def set_slice(self, s, saved=False): """ Adds s to the log, overwriting any slice previously at that location """ self._slices[s.start] = (s, saved) def __repr__(self): s = "Start | End | Category | Description \n" s += "-----------------+------------------+-----------------+-------------------------------\n" for k, v in self._slices.items(): start_str = v[0].start.strftime(self.DT_FMT) end_str = v[0].end.strftime(self.DT_FMT) if not v[1]: saved_notice = "(!)" else: saved_notice = "" s += saved_notice + start_str + " | " + end_str + " | " + v[0].category + " " * (self._COL_WIDTH - len(v[0].category)) + " | " + v[0].description + "\n" return s def save(self, db_path): """ Saves the log to the specified database file by inserting each slice into the SQL table """ conn = sqlite3.connect(db_path) c = conn.cursor() c.execute('''CREATE TABLE IF NOT EXISTS log (id INTEGER PRIMARY KEY AUTOINCREMENT, start DATETIME, end DATETIME, category VARCHAR, description TEXT)''') for k, v in self._slices.items(): if not v[1]: # if not saved start_str = v[0].start.strftime(self.DT_FMT) end_str = v[0].end.strftime(self.DT_FMT) data = (start_str, end_str, v[0].category, v[0].description) c.execute('''INSERT INTO log (start, end, category, description) VALUES (?, ?, ?, ?)''', data) conn.commit() v = (v[0], True) # set slice as saved conn.close() def load(self, db_path): """ Loads a log from the specified database file by inserting each slice into the log object from the SQL table """ conn = sqlite3.connect(db_path) c = conn.cursor() c.execute('''SELECT * FROM log''') data = c.fetchall() for d in data: self.set_slice(slice.Slice(datetime.strptime(d[1], self.DT_FMT), datetime.strptime(d[2], self.DT_FMT), d[3], d[4]), True) conn.close() def __len__(self): length = 0 for k, v in self._slices.items(): length += len(v[0]) return length def category_aggregate(self): """ Returns a dictionary associating each category in the log with the total number of minutes attributed to it """ categories = {} for k, v in self._slices.items(): categories[v[0].category] = 0 for k, v in self._slices.items(): categories[v[0].category] += len(v[0]) return categories def ranged_category_aggregate(self, start, end): """ Same as category_aggregate() but only applies to slices within the range [start, end] """ new_slices = [] for k, v in self.slices.items(): if k > start and k < end: new_slices.append(v) tmp = Log(new_slices) return tmp.category_aggregate()

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py

Python

tests/test_utils.py

leugimkm/codeseeker

f8a1f8668807a2b02cbaf5c596d26164ba75e366

[ "MIT" ]

1

2022-02-02T04:43:32.000Z

tests/test_utils.py

leugimkm/codeseeker

f8a1f8668807a2b02cbaf5c596d26164ba75e366

[ "MIT" ]

7

2022-02-02T05:25:40.000Z

2022-03-23T17:16:19.000Z

tests/test_utils.py

leugimkm/codeseeker

f8a1f8668807a2b02cbaf5c596d26164ba75e366

[ "MIT" ]

null

import io import unittest from unittest.mock import patch from textwrap import dedent from codeseeker.utils import show class TestCodeSeekerUtils(unittest.TestCase): def test_show(self): data = [ {"path": "repository/path/to/file.py"}, {"path": "repository/path/to/file2.py"}, ] expected = dedent("""\ repository/path/to/file.py repository/path/to/file2.py 2 file(s) found(s).\n""" ) # noqa: E124 with patch("sys.stdout", new_callable=io.StringIO) as mock_stdout: show(data) self.assertEqual(mock_stdout.getvalue(), expected) if __name__ == '__main__': unittest.main()

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py

Python

cryptography/rail_fence_cipher/Python/rail_fence_cipher.py

avi-pal/al-go-rithms

5167a20f1db7b366ff19f2962c1746a02e4f5067

[ "CC0-1.0" ]

1,253

2017-06-06T07:19:25.000Z

2022-03-30T17:07:58.000Z

cryptography/rail_fence_cipher/Python/rail_fence_cipher.py

rishabh99-rc/al-go-rithms

4df20d7ef7598fda4bc89101f9a99aac94cdd794

[ "CC0-1.0" ]

554

2017-09-29T18:56:01.000Z

2022-02-21T15:48:13.000Z

cryptography/rail_fence_cipher/Python/rail_fence_cipher.py

rishabh99-rc/al-go-rithms

4df20d7ef7598fda4bc89101f9a99aac94cdd794

[ "CC0-1.0" ]

2,226

2017-09-29T19:59:59.000Z

2022-03-25T08:59:55.000Z

# used for decryption, take the second element for sorting def takeSecond(elem): return elem[1] def display_rail(lines): depth = len(lines) col = len(lines[0]) # depth is the number of rows of the grid # lines is a tuple where line[i] is the i-th line to print # col is the number of columns = number of characters of the initial string for i in range(0,depth): print( ( ("| %c "*col) + "|") % tuple(lines[i]) ) def encrypt(string,depth): #make sure that string is a string! string = str(string) nChar = len(string) # create a nested list with 'depth' number of items # each item has a number of characters = length of the string to cypher # initialize the list with all spaces: lines = [ [' ',]*nChar for _ in range(depth)] encStrings = list() # encStrings will be a list dynamically filled with the letters of 'string' # each item of the list will represent a row of the rail. # this list will then have 'depth' items encrStrings = ['' for _ in range(depth)] # Define the sequence in which the rows are filled if depth == 2: row_sequence = [0,1] else: row_sequence = [i for i in range(0,depth)] row_sequence.extend(range(depth-2,0,-1) ) # length of the sequence seqLen = len(row_sequence) for i in range(0,nChar): row = row_sequence[i%seqLen] #repeatedly go through the sequence lines[row][i] = string[i] encrStrings[row] = encrStrings[row] + string[i] display_rail(lines) encrString = ''.join(c for c in encrStrings) return encrString def decrypt(encrString,depth): # from depth and the length of the string we can determine the sequence # of places in the rails as they were filled nChar = len(encrString) if depth == 2: row_sequence = [1,2] else: row_sequence = [i for i in range(0,depth)] row_sequence.extend(range(depth-2,0,-1) ) # length of the sequence seqLen = len(row_sequence) sequence = [] # build a list with the indexes of rows and column according to the sequence for i in range(0,nChar): row = row_sequence[i%seqLen] #repeatedly go through the sequence sequence.append([row,i]) # sort according to rows (so in the order the encrypted string is taken) sequence.sort() # now associate the encrypted string to the rail 'coordinates' for i in range(nChar): sequence[i].append(encrString[i]) # finally for decryption we rearrange the list items according to columns and read the result sequence.sort(key=takeSecond) string = ''.join(c[2] for c in sequence) return string # EXAMPLES # check that len(string)>depth print("encryptions with depth 2: ") res = encrypt("rail fence",2) print("rail fence: " + res) res = decrypt(res,2) print("decryption -> " + res) res = encrypt("Github",2) print("Github: " + res) res = decrypt(res,2) print("decryption -> " + res) res = encrypt("I am a test!",2) print("I am a test! -> " + res) res = decrypt(res,2) print("decryption -> " + res) print("encryptions with depth 3: ") res = encrypt("rail fence",3) print("rail fence: " + res) res = decrypt(res,3) print("decryption -> " + res) res = encrypt("Github",3) print("Github: " + res) res = decrypt(res,3) print("decryption -> " + res) res = encrypt("I am a test!",3) print("I am a test! -> " + res) res = decrypt(res,3) print("decryption -> " + res)

27.923077

94

0.685644

520

3,267

4.280769

0.236538

0.049416

0.016173

0.02965

0.340072

0.323001

0.30009

0.287511

0.27044

0.237197

0

0.012864

0.191001

3,267

116

95

28.163793

0.829361

0.367309

0

0.457143

0

0.132713

0

1

0.057143

false

0

0.014286

0.1

0.214286

0

null

0

null

0

1

0

da858baf0350df7bd476677ebd9b47d2ce1cc39c

2,736

py

Python

tools/bin/clearAssignment.py

cpausmit/Tapas

283016cebc036a703609317aafc28675b1c1ea17

[ "MIT" ]

null

tools/bin/clearAssignment.py

cpausmit/Tapas

283016cebc036a703609317aafc28675b1c1ea17

[ "MIT" ]

null

tools/bin/clearAssignment.py

cpausmit/Tapas

283016cebc036a703609317aafc28675b1c1ea17

[ "MIT" ]

null

#!/usr/bin/python #--------------------------------------------------------------------------------------------------- # Clear an existing assignment in the database using the unique task Id. # #--------------------------------------------------------------------------------------------------- import sys,os,re import MySQLdb import Database print " UNTESTED -- CAREFUL NEW SUMMARY TABLES -- Assignments etc." sys.exit(0) EMPTY_EMAIL = "EMPTY@mit.edu" #--------------------------------------------------------------------------------------------------- # H E L P E R #--------------------------------------------------------------------------------------------------- def findAssignment(cursor,semesterId,task): # find person of an existing assignment email = 'EMTPY' results = [] # Prepare SQL query to insert record into the existing table sql = "select * from Assignments where Term = '" + semesterId + "' and Task = '" + task + "';" try: # Execute the SQL command cursor.execute(sql) results = cursor.fetchall() except: print ' ERROR - select failed: ' + sql email = 'ERROR' if len(results) == 1: email = results[0][1] return email #--------------------------------------------------------------------------------------------------- # M A I N #--------------------------------------------------------------------------------------------------- usage = " usage: clearAssignment.py <taskId> [ <execute = no> ]\n\n" usage += " taskId identification string for a specific assignment\n" usage += " execute should we execute the insertion into the database\n" usage += " activate by setting: execute = exec\n\n" if len(sys.argv) < 2: print "\n ERROR - need to specify the taskId.\n" print usage sys.exit(0) # Read command line arguments taskId = sys.argv[1] execute = "no" if len(sys.argv) > 2: execute = sys.argv[2] # Figure out which semester we are talking about semesterId = taskId.split('-')[0] print " Task : " + taskId print " Semester: " + semesterId # Open database connection db = Database.DatabaseHandle() # Prepare a cursor object using cursor() method cursor = db.getCursor() # Prepare SQL query to insert record into the existing table sql = "update Assignments" + \ " set Person = '%s' where Term = '%s' and Task = '%s';"%(EMPTY_EMAIL,semesterId,taskId) try: # Execute the SQL command print " MYSQL> " + sql if execute == "exec": cursor.execute(sql) db.commit() except: print ' ERROR - update failed: ' + sql # disconnect from server db.disco() # exit sys.exit()

31.813953

100

0.482822

277

2,736

4.761733

0.436823

0.018196

0.025777

0.133434

0.078848

0

0.004634

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2,736

85

101

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0

0.375535

0

null

0

0.06383

null

0.170213

0

null

0

null

0

1

0

1

da85979a8a85aecc4d586a12af1e63303a1db9ca

146

py

Python

qna/admin.py

aryaputra28/covidify-PerancanganWeb

34d6d0017f44248c172fc58e6e1b138e23e68a95

[ "Unlicense" ]

null

qna/admin.py

aryaputra28/covidify-PerancanganWeb

34d6d0017f44248c172fc58e6e1b138e23e68a95

[ "Unlicense" ]

null

qna/admin.py

aryaputra28/covidify-PerancanganWeb

34d6d0017f44248c172fc58e6e1b138e23e68a95

[ "Unlicense" ]

null

from django.contrib import admin from .models import * # Register your models here. admin.site.register(pertanyaan) admin.site.register(komentar)

24.333333

32

0.808219

20

146

5.9

0.6

0.152542

0.288136

0

0.10274

146

5

33

29.2

0.900763

0.178082

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

da85a9841500fbd6e450901a3ca02828bbbeb03f

1,443

py

Python

selfdrive/can/tests/test_packer_chrysler.py

matthewklinko/openpilot

b0563a59684d0901f99abbb58ac1fbd729ded1f9

[ "MIT" ]

4

2019-02-12T03:06:31.000Z

2020-07-17T03:54:46.000Z

selfdrive/can/tests/test_packer_chrysler.py

matthewklinko/openpilot

b0563a59684d0901f99abbb58ac1fbd729ded1f9

[ "MIT" ]

3

2020-09-08T07:21:59.000Z

2020-09-08T07:22:07.000Z

selfdrive/can/tests/test_packer_chrysler.py

matthewklinko/openpilot

b0563a59684d0901f99abbb58ac1fbd729ded1f9

[ "MIT" ]

4

2019-05-21T19:02:46.000Z

2020-03-24T14:27:45.000Z

import unittest import random from selfdrive.can.tests.packer_old import CANPacker as CANPackerOld from selfdrive.can.packer import CANPacker import selfdrive.car.chrysler.chryslercan as chryslercan class TestPackerMethods(unittest.TestCase): def setUp(self): self.chrysler_cp_old = CANPackerOld("chrysler_pacifica_2017_hybrid") self.chrysler_cp = CANPacker("chrysler_pacifica_2017_hybrid") def test_correctness(self): # Test all commands, randomize the params. for _ in xrange(1000): gear = ('drive', 'reverse', 'low')[random.randint(0, 3) % 3] lkas_active = (random.randint(0, 2) % 2 == 0) hud_alert = random.randint(0, 6) hud_count = random.randint(0, 65536) lkas_car_model = random.randint(0, 65536) m_old = chryslercan.create_lkas_hud(self.chrysler_cp_old, gear, lkas_active, hud_alert, hud_count, lkas_car_model) m = chryslercan.create_lkas_hud(self.chrysler_cp, gear, lkas_active, hud_alert, hud_count, lkas_car_model) self.assertEqual(m_old, m) apply_steer = (random.randint(0, 2) % 2 == 0) moving_fast = (random.randint(0, 2) % 2 == 0) frame = random.randint(0, 65536) m_old = chryslercan.create_lkas_command(self.chrysler_cp_old, apply_steer, moving_fast, frame) m = chryslercan.create_lkas_command(self.chrysler_cp, apply_steer, moving_fast, frame) self.assertEqual(m_old, m) if __name__ == "__main__": unittest.main()

40.083333

120

0.726265

203

1,443

4.871921

0.315271

0.105157

0.113246

0.051567

0.435794

0.344793

0.293225

0.173913

0.084934

0

0.039037

0.165627

1,443

35

121

41.228571

0.782392

0.02772

0

0.074074

0

0.057816

0.041399

0

0.074074

1

0.074074

false

0

0.185185

0

0.296296

0

null

0

null

0

1

0

da85c358f54be05780771410e2c91e3ce7581a8d

9,156

py

Python

kddg/api/layers.py

Kortemme-Lab/kddg

9fc09172abbefd4fef49261687c60a9bd9b6b29b

[ "MIT" ]

2

2016-06-14T00:32:02.000Z

2020-05-04T03:29:46.000Z

kddg/api/layers.py

Kortemme-Lab/kddg

9fc09172abbefd4fef49261687c60a9bd9b6b29b

[ "MIT" ]

null

kddg/api/layers.py

Kortemme-Lab/kddg

9fc09172abbefd4fef49261687c60a9bd9b6b29b

[ "MIT" ]

null

#!/usr/bin/python2.4 # encoding: utf-8 """ api_layers.py The definition of the layers of the database API and the generic user interface class. Created by Shane O'Connor 2015. Copyright (c) 2015 __UCSF__. All rights reserved. """ import inspect import functools from klab import colortext from kddg.api import settings sys_settings = settings.load() ### API function decorators. These are used to group functions together when printing the help text. functional_layer = { 0 : 'API warnings', 1 : 'Information layer', 2 : 'Prediction layer', 3 : 'Results layer', 4 : 'Analysis layer', 5 : 'Application layer', 6 : 'Consistency layer', 7 : 'Data entry layer', None: 'Miscellanous' } def alien(func): func._helptype = 'Alien functions (these should be moved into another package)' func._layer = 0 func._layer_order = 0 return func def brokenfn(func): func._helptype = 'Broken functions: this need to be fixed/updated' func._layer = 0 func._layer_order = 1 return func def deprecated(func): func._helptype = 'Deprecated functions. These should be removed but exist for now to print errors upon use' func._layer = 0 func._layer_order = 2 return func def informational_misc(func): func._helptype = 'Miscellaneous information API' func._layer = 1 func._layer_order = 0 return func def informational_file(func): func._helptype = 'File information API' func._layer = 1 func._layer_order = 1 return func def informational_pdb(func): func._helptype = 'Structure information API' func._layer = 1 func._layer_order = 2 return func def informational_complex(func): func._helptype = 'Complex information API' func._layer = 1 func._layer_order = 3 return func def informational_job(func): func._helptype = 'Prediction information API' func._layer = 1 func._layer_order = 4 return func def job_creator(func): func._helptype = 'Job creation API' func._layer = 2 func._layer_order = 0 return func def job_input(func): func._helptype = 'Input file generation API' func._layer = 2 func._layer_order = 1 return func def job_execution(func): func._helptype = 'Job execution API' func._layer = 2 func._layer_order = 2 return func def job_completion(func): func._helptype = 'Job completion API' func._layer = 2 func._layer_order = 3 return func def job_results(func): func._helptype = 'Results API' func._layer = 3 func._layer_order = 0 return func def analysis_api(func): func._helptype = 'Analysis API' func._layer = 4 func._layer_order = 0 return func def app_pymol(func): func._helptype = 'PyMOL API' func._layer = 5 func._layer_order = 0 return func def sanity_check(func): func._helptype = 'Data consistency /sanity checks' func._layer = 6 func._layer_order = 0 return func def general_data_entry(func): func._helptype = 'Data entry' func._layer = 7 func._layer_order = 0 return func def ppi_data_entry(func): func._helptype = 'PPI Data entry' func._layer = 7 func._layer_order = 1 return func class GenericUserInterface(object): '''This is the class that should be used to interface with the database. It hides functions that should only be called within this other API functions. The class contains a private copy of the internal API and wraps the public functions of that API so that the functions of GenericUserInterface contain only the public functions of the internal API. Private functions are denoted as such by a leading underscore in the function name. ''' @staticmethod def generate(cls, passwd = None, username = sys_settings.database.username, hostname = sys_settings.database.hostname, rosetta_scripts_path = None, rosetta_database_path = None, port = sys_settings.database.port, file_content_buffer_size = None): return GenericUserInterface(cls, passwd = passwd, username = username, hostname = hostname, rosetta_scripts_path = rosetta_scripts_path, rosetta_database_path = rosetta_database_path, port = port, file_content_buffer_size = file_content_buffer_size) @staticmethod def bind_object_function(fn): @functools.wraps(fn) def wrapper(*args, **kwargs): return fn(*args, **kwargs) return wrapper def __init__(self, cls, passwd = None, username = sys_settings.database.username, hostname = sys_settings.database.hostname, rosetta_scripts_path = None, rosetta_database_path = None, port = sys_settings.database.port, file_content_buffer_size = None): self._ddg_interface = cls(passwd = passwd, username = username, hostname = hostname, rosetta_scripts_path = rosetta_scripts_path, rosetta_database_path = rosetta_database_path, port = port, file_content_buffer_size = file_content_buffer_size) self._api_functions = [] self._api_function_args = {} self.DDG_db = self._ddg_interface.DDG_db self.DDG_db_utf = self._ddg_interface.DDG_db_utf self.cls = cls for m in inspect.getmembers(cls, predicate=inspect.ismethod): if m[0][0] != '_': fn_name = m[0] fn_ref = getattr(self._ddg_interface, fn_name) self._api_function_args[fn_name] = fn_ref.func_code.co_varnames[:fn_ref.func_code.co_argcount] self._api_functions.append(fn_name) self.__dict__[fn_name] = GenericUserInterface.bind_object_function(getattr(self._ddg_interface, fn_name)) def help(self, show_deprecated_functions = False): print(self.get_help(show_deprecated_functions = show_deprecated_functions)) def get_help(self, show_deprecated_functions = False): helpstr = [] title = ' %s API ' % self._ddg_interface.__class__.__name__ l = len(title) helpstr.append(colortext.mcyan('\n' + ('*' * (l + 10)) + '\n' + ('*' * 5) + title + ('*' * 5) + '\n' + ('*' * (l + 10)) + '\n')) doc_strings = {} for fn_name in sorted(self._api_functions): fn = self.__dict__[fn_name] function_layer, function_layer_order, function_class = None, None, None try: function_layer = fn._layer assert(function_layer in functional_layer) function_layer_order = fn._layer_order except: function_layer = None function_layer_order = 0 try: function_class = fn._helptype except: function_class = 'Miscellanous' if function_class.startswith('Deprecated functions') and not show_deprecated_functions: continue doc_strings[function_layer] = doc_strings.get(function_layer, {}) doc_strings[function_layer][function_layer_order] = doc_strings[function_layer].get(function_layer_order, {}) doc_strings[function_layer][function_layer_order][function_class] = doc_strings[function_layer][function_layer_order].get(function_class, {}) doc_strings[function_layer][function_layer_order][function_class][fn_name] = self._get_fn_docstring(fn, fn_name) for function_layer, function_layer_components in sorted(doc_strings.iteritems()): function_layer_name = functional_layer[function_layer] prefix = '' if function_layer != None: prefix = 'Layer %d: ' % function_layer helpstr.append(colortext.mcyan('-------- %s%s --------\n' % (prefix, function_layer_name))) for function_layer_order, function_classes in sorted(function_layer_components.iteritems()): for function_class, fn_names in sorted(function_classes.iteritems()): helpstr.append(colortext.mlightpurple(' %s\n' % function_class)) for fn_name, docstr in sorted(fn_names.iteritems()): helpstr.append(colortext.mgreen(' %s(%s)' % (fn_name, ', '.join(self._api_function_args[fn_name])))) if docstr: helpstr.append(colortext.myellow(' %s' % ('\n '.join([s.strip() for s in docstr.split('\n') if s.strip()])))) else: helpstr.append(colortext.mred(' <not documented>')) helpstr.append('') return '\n'.join(helpstr) def _get_fn_docstring(self, fn, fn_name, default_name = ''): '''Returns the docstring for a function, winding up the inheritance tree until we find a non-empty docstring. If no docstring is found, default_name is returned.''' if fn.__doc__: return fn.__doc__ # Wind up the hierarchy until we find the class where this function was last defined for parent in self.cls.__mro__[1:]: overridden = getattr(parent, fn_name, None) if overridden and overridden.__doc__: return overridden.__doc__ return default_name

34.292135

257

0.662844

1,152

9,156

4.978299

0.212674

0.056495

0.050218

0.020924

0.356582

0.33531

0.296774

0.207149

0.150654

0.122406

0

0.009556

0.245631

9,156

266

258

34.421053

0.820762

0.108126

0

0.302198

0

0.093132

0

0.005495

1

0.137363

false

0.021978

0.010989

0.296703

0.010989

0

null

0

null

0

1

0

da8712cd2ff361045352f744da703fa2ec6f82df

3,142

py

Python

fb_api.py

wing3s/shop_bot

4c6a34538ac8de9999edae190f6269bc6a63c2cf

[ "BSD-3-Clause" ]

1

2016-04-11T01:18:53.000Z

fb_api.py

wing3s/shop_bot

4c6a34538ac8de9999edae190f6269bc6a63c2cf

[ "BSD-3-Clause" ]

null

fb_api.py

wing3s/shop_bot

4c6a34538ac8de9999edae190f6269bc6a63c2cf

[ "BSD-3-Clause" ]

null

import os import requests import time import ConfigParser import logging import logging.config from requests.exceptions import RequestException from helper import get_logger, base_path config = ConfigParser.ConfigParser() config.read(os.path.join(base_path, 'config.ini')) logger = get_logger('fb_api', __file__) __author__ = "Wen-Hao Lee" __email__ = "wing3s@gmail.com" __copyright__ = "Copyright 2014, Numnum" class FBBot(object): graph_url = "https://graph.facebook.com" cooldown = 120 # sec search_radius = 500 # m def search_restaurant(self, lat, lon): restaurants = self._search_place('restaurant', lat, lon) steakhouses = self._search_place('steakhouse', lat, lon) bars = self._search_place('bar', lat, lon) return restaurants + steakhouses + bars def _search_place(self, query, lat, lon): params = { 'q': query, 'type': 'place', 'center': '%s,%s' % (lat, lon), 'distance': self.search_radius, 'limit': 500, 'offset': 0 } return self.search(params) def search(self, params): params['access_token'] = "{app_id}|{app_key}".format( app_key=config.get('fbAPI', 'key'), app_id=config.get('fbAPI', 'id')) try: r = requests.get( "%s/%s" % (self.graph_url, 'search'), params=params) resp = r.json() if r.status_code != 200: resp_err = resp.get('error') err_code = resp_err.get('code') if err_code == 4: logger.warning( 'Reach limit, cooldown %ds' % self.cooldown) time.sleep(self.cooldown) return self.search(params) else: logger.error(resp) return None return resp['data'] except RequestException as err: logger.error(err) def fetch(self, fbid): try: r = requests.get("%s/%s" % (self.graph_url, fbid)) resp = r.json() if r.status_code != 200: resp_err = resp.get('error') err_code = resp_err.get('code') if err_code == 4: logger.warning( 'Reach limit, cooldown %ds' % self.cooldown) time.sleep(self.cooldown) return self.fetch(fbid) elif err_code == 21: err_msg = resp_err.get('message') new_fbid_pt = 'page ID' new_fbid = err_msg[ err_msg.index(new_fbid_pt)+len(new_fbid_pt)+1: err_msg.index('.')] logger.warning( 'Get new fbid %s for %s' % (new_fbid, fbid)) return self.fetch(new_fbid) else: logger.error([resp, r.url]) return None return resp except RequestException as err: logger.error(err)

34.152174

70

0.510185

341

3,142

4.510264

0.316716

0.03186

0.029259

0.028609

0.287386

0.23407

0.196359

0

0.013306

0.378103

3,142

92

71

34.152174

0.773797

0.001591

0

0.349398

0

0.103349

0

1

0.048193

false

0

0.096386

0

0.301205

0

null

0

null

0

1

0

da89128d24114037df1325dfa4587c3b0ac3e279

6,409

py

Python

examples/formula_library.py

bherbruck/plend

55271d79c983cc3b3307661833c5a7dcc11efc32

[ "MIT" ]

5

2020-02-21T09:22:58.000Z

2021-09-07T16:39:47.000Z

examples/formula_library.py

bherbruck/plend

55271d79c983cc3b3307661833c5a7dcc11efc32

[ "MIT" ]

null

examples/formula_library.py

bherbruck/plend

55271d79c983cc3b3307661833c5a7dcc11efc32

[ "MIT" ]

1

2022-01-26T20:00:47.000Z

""" This example shows how to statically define formulas, add them to a formula library, optimize them, and output the results. "Statically in this context means we are manually setting the attribures (min and max) for each ingredient and nutrient rather defining them dynamically (which is where plend really shines) TODO: make an example with dynamic formulas """ from plend import Nutrient, Ingredient, Formula, FormulaLibrary from plend.presets.poultry import * # initialize the starter formula starter = Formula(name='Starter', code='B1', batch_size=100) # add ingredients to starter from presets starter.add_ingredient(corn) starter.add_ingredient(soybean_meal) starter.add_ingredient(oil, maximum=10) # add nutrients to grower from presets starter.add_ingredient(limestone) starter.add_ingredient(meat_meal, maximum=10) starter.add_nutrient(energy, minimum=3010) starter.add_nutrient(protein, minimum=24) starter.add_nutrient(fiber) starter.add_nutrient(calcium, minimum=1) # initialize the grower formula grower = Formula(name='Grower', code='B2', batch_size=100) # add ingredients to grower from presets grower.add_ingredient(corn) grower.add_ingredient(soybean_meal) grower.add_ingredient(oil, maximum=10) # add nutrients to grower from presets grower.add_ingredient(limestone) grower.add_ingredient(meat_meal, maximum=10) grower.add_nutrient(energy, minimum=3175) grower.add_nutrient(protein, minimum=22) grower.add_nutrient(fiber) grower.add_nutrient(calcium, minimum=0.9) # initialize the finisher formula finisher = Formula(name='Finisher', code='B3', batch_size=100) # add ingredients to finisher from presets finisher.add_ingredient(corn) finisher.add_ingredient(soybean_meal) finisher.add_ingredient(oil, maximum=10) finisher.add_ingredient(limestone) finisher.add_ingredient(meat_meal, maximum=10) # add nutrients to finisher from presets finisher.add_nutrient(energy, minimum=3225) finisher.add_nutrient(protein, minimum=20) finisher.add_nutrient(fiber) finisher.add_nutrient(calcium, minimum=0.85) formulas = FormulaLibrary(name='Broiler') formulas.add_formulas(starter, grower, finisher) formulas.optimize() print(formulas.to_csv()) formulas.save_csv('examples/formulas.csv') """ this will have the output (this output has been aligned for readability): library_name ,formula_name ,formula_code ,formula_cost ,formula_status ,item_type ,item_name ,item_code ,item_amount ,item_minimum ,item_maximum Broiler ,Starter ,B1 ,68.312016841 ,Optimal ,ingredient ,Corn , ,58.587658 ,0 , Broiler ,Starter ,B1 ,68.312016841 ,Optimal ,ingredient ,Soybean Meal , ,30.429012 ,0 , Broiler ,Starter ,B1 ,68.312016841 ,Optimal ,ingredient ,Oil , ,0.63258515 ,0 ,10 Broiler ,Starter ,B1 ,68.312016841 ,Optimal ,ingredient ,Limestone , ,0.35074529 ,0 , Broiler ,Starter ,B1 ,68.312016841 ,Optimal ,ingredient ,Meat Meal , ,10.0 ,0 ,10 Broiler ,Starter ,B1 ,68.312016841 ,Optimal ,nutrient ,Energy , ,3010.0000132 ,3010 , Broiler ,Starter ,B1 ,68.312016841 ,Optimal ,nutrient ,Protein , ,24.000000110000002 ,24 , Broiler ,Starter ,B1 ,68.312016841 ,Optimal ,nutrient ,Fiber , ,2.37756181 ,0 , Broiler ,Starter ,B1 ,68.312016841 ,Optimal ,nutrient ,Calcium , ,1.0 ,1 , Broiler ,Grower ,B2 ,68.284483722 ,Optimal ,ingredient ,Corn , ,61.16353 ,0 , Broiler ,Grower ,B2 ,68.284483722 ,Optimal ,ingredient ,Soybean Meal , ,25.859865 ,0 , Broiler ,Grower ,B2 ,68.284483722 ,Optimal ,ingredient ,Oil , ,2.8656471 ,0 ,10 Broiler ,Grower ,B2 ,68.284483722 ,Optimal ,ingredient ,Limestone , ,0.11095768 ,0 , Broiler ,Grower ,B2 ,68.284483722 ,Optimal ,ingredient ,Meat Meal , ,10.0 ,0 ,10 Broiler ,Grower ,B2 ,68.284483722 ,Optimal ,nutrient ,Energy , ,3174.9999923 ,3175 , Broiler ,Grower ,B2 ,68.284483722 ,Optimal ,nutrient ,Protein , ,21.999999950000003 ,22 , Broiler ,Grower ,B2 ,68.284483722 ,Optimal ,nutrient ,Fiber , ,2.3048842 ,0 , Broiler ,Grower ,B2 ,68.284483722 ,Optimal ,nutrient ,Calcium , ,0.9000000014 ,0.9 , Broiler ,Finisher ,B3 ,66.00538196504 ,Optimal ,ingredient ,Corn , ,66.023255 ,0 , Broiler ,Finisher ,B3 ,66.00538196504 ,Optimal ,ingredient ,Soybean Meal , ,20.933866 ,0 , Broiler ,Finisher ,B3 ,66.00538196504 ,Optimal ,ingredient ,Oil , ,3.038852 ,0 ,10 Broiler ,Finisher ,B3 ,66.00538196504 ,Optimal ,ingredient ,Limestone , ,0.0040261626 ,0 , Broiler ,Finisher ,B3 ,66.00538196504 ,Optimal ,ingredient ,Meat Meal , ,10.0 ,0 ,10 Broiler ,Finisher ,B3 ,66.00538196504 ,Optimal ,nutrient ,Energy , ,3224.9999740000003 ,3225 , Broiler ,Finisher ,B3 ,66.00538196504 ,Optimal ,nutrient ,Protein , ,19.999999805 ,20 , Broiler ,Finisher ,B3 ,66.00538196504 ,Optimal ,nutrient ,Fiber , ,2.278597355 ,0 , Broiler ,Finisher ,B3 ,66.00538196504 ,Optimal ,nutrient ,Calcium , ,0.849999999288 ,0.85 , """

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da89cac67e3dd9455f993529126f6ea3e387def3

1,278

py

Python

sstmap/scripts/dtr_to_netcdf.py

ssabrii/SSTMap

f4f3fb72ed632f00b9f519ae9eab4a41b6c69db9

[ "MIT" ]

23

2017-12-12T17:59:26.000Z

2022-02-01T20:19:56.000Z

sstmap/scripts/dtr_to_netcdf.py

ssabrii/SSTMap

f4f3fb72ed632f00b9f519ae9eab4a41b6c69db9

[ "MIT" ]

45

2017-05-03T14:05:19.000Z

2022-03-02T07:28:39.000Z

sstmap/scripts/dtr_to_netcdf.py

ssabrii/SSTMap

f4f3fb72ed632f00b9f519ae9eab4a41b6c69db9

[ "MIT" ]

24

2017-04-28T19:49:56.000Z

2021-11-05T17:57:02.000Z

from argparse import ArgumentParser import mdtraj as md def parse_args(): """Parse the command line arguments and perform some validation on the arguments Returns ------- args : argparse.Namespace The namespace containing the arguments """ parser = ArgumentParser( description='''Run GIST calculations through command-line.''') parser.add_argument('-i', '--input_parm', required=False, type=str, help='''Input toplogy File.''') parser.add_argument('-t', '--input_traj', required=True, type=str, help='''Input trajectory file.''') parser.add_argument('-o', '--output_prefix', required=False, type=str, help='''Prefix for all the results files.''') args = parser.parse_args() return args def main(): args = parse_args() print("Reading in trajectory ...") traj = md.load_dtr(args.input_traj, top=args.input_parm) print(traj) print("Outputting NETCDF ...") traj.save_netcdf(args.output_prefix + "_converted.nc") print("Outputting PDB file of frame 1 ...") traj[0].save_pdb(args.output_prefix + "_converted.pdb") print("Done") def entry_point(): main() if __name__ == '__main__': entry_point()

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da8aea2d0e2cc438f1e2afe2e6c0015770e13ef8

6,106

py

Python

tpDcc/abstract/scene.py

tpDcc/tpDccLib

a4f77a3fdd981eac494331e429c92bd3e4a87d3b

[ "MIT" ]

6

2021-03-02T00:31:53.000Z

2021-03-30T09:02:54.000Z

tpDcc/abstract/scene.py

tpDcc/tpDccLib

a4f77a3fdd981eac494331e429c92bd3e4a87d3b

[ "MIT" ]

1

2021-03-02T08:43:34.000Z

2021-03-04T01:36:02.000Z

tpDcc/abstract/scene.py

tpDcc/tpDccLib

a4f77a3fdd981eac494331e429c92bd3e4a87d3b

[ "MIT" ]

1

2021-03-03T21:01:51.000Z

#! /usr/bin/env python # -*- coding: utf-8 -*- """ Module that contains DCC scene abstract class implementation """ from __future__ import print_function, division, absolute_import from tpDcc import dcc from tpDcc.dcc import sceneobject from tpDcc.libs.python import python, decorators class AbstractScene(object): # ============================================================================================== # ABSTRACT FUNCTIONS # ============================================================================================== @decorators.abstractmethod def _dcc_objects(self, from_selection=False, wildcard='', object_type=None): """ Internal function that returns DCC objects from current scene :param from_selection: bool, Whether to return only selected DCC objects or all objects in the scene :param wildcard: str, filter objects by its name :param object_type: int :return: list(variant) """ raise NotImplementedError('Abstract Scene _dcc_objects function not implemented!') @decorators.abstractmethod def _rename_dcc_objects(self, dcc_native_objects, names, display=True): """ Rename given DCC objects with the given new names :param dcc_native_objects: variant or list(variant) :param names: list(str) :param display: bool, Whether or not we want to rename internal dcc name or display name :return: bool, True if the operation is successful; False otherwise """ raise NotImplementedError('Abstract Scene _remove_dcc_objects function not implemented!') # ============================================================================================== # BASE # ============================================================================================== def objects(self, wildcard='', object_type=None): """ Returns a list of scene objects as SceneObjects :param wildcard: str, filter objects by its name :param object_type: :return: list(SceneObject) """ return [sceneobject.SceneObject(self, obj) for obj in self._dcc_objects( from_selection=False, wildcard=wildcard, object_type=object_type)] def selected_objects(self, wildcard='', object_type=None): """ Returns a list of selected objects in current scene as SceneObjects :param wildcard: str, filter objects by its name :param object_type: int :return: list(SceneObject) """ return [sceneobject.SceneObject(self, obj) for obj in self._dcc_objects( from_selection=True, wildcard=wildcard, object_type=object_type)] def root_object(self): """ Returns the DCC root object of the scene as SceneObject :return: SceneObject or None """ dcc_root = self._dcc_root_object() if not dcc_root: return None return dcc.SceneObject(self, dcc_root) def remove_objects(self, objects): """ Removes the given objects from the this scene :param objects: list(SceneObject) :return: bool, True if the operation was successful; False otherwise """ objects = python.force_list(objects) return self._remove_dcc_objects([obj.dcc_native_object() for obj in objects if not obj.is_deleted()]) def rename_objects(self, objects, names, display=True): """ Rename given objects with the given new names :param objects: SceneObject or list(SceneObject) :param names: list(str) :param display: bool, Whether or not we want to rename internal dcc name or display name :return: bool, True if the operation is successful; False otherwise """ objects = python.force_list(objects) names = python.force_list(names) if len(objects) != len(names): return False return self._rename_dcc_objects( [obj.dcc_native_object() for obj in objects if not obj.is_deleted()], names, display=display) def find_object_by_name(self, name): """ Looks for an individual node for its name :param name: str, name of the object to find :return: SceneObject or None """ dcc_object = self._find_dcc_object_by_name(name) if not dcc_object: return None return sceneobject.SceneObject(self, dcc_object) def find_object_by_id(self, unique_id): """ Looks for an individual node for its name :param unique_id: unique identifier of the object to find in current scene :return: SceneObject or None """ dcc_object = self._find_dcc_object_by_id(unique_id) if not dcc_object: return None return sceneobject.SceneObject(self, dcc_object) # ============================================================================================== # INTERNAL # ============================================================================================== def _dcc_root_object(self): """ Internal function that returns DCC root object from current scene :return: variant """ return dcc.root_node() def _remove_dcc_objects(self, dcc_native_objects): """ Internal function that removes given DCC objects from current scene :param dcc_native_objects: variant or list(variant) :return: bool, True if the operation is successful; False otherwise """ return dcc.delete_node(dcc_native_objects) def _find_dcc_object_by_name(self, name): """ Internal function that returns a valid a DCC object by its name :param name: str :return: variant """ return dcc.find_node_by_name(name) def _find_dcc_object_by_id(self, unique_id): """ Internal function that returns a valid DCC object its ID :param unique_id: str :return: variant """ return dcc.find_node_by_id(unique_id)

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da8e614ab7b081bdccebe5c0752328dc5769b689

11,303

py

Python

fpga/lib/pcie/tb/test_dma_client_axis_sink_512_64.py

totuwei/corundum

e983ad519fb4523d0ffca32f5e436195bcfc945c

[ "BSD-2-Clause-FreeBSD" ]

544

2019-08-12T03:45:32.000Z

2022-03-19T14:17:20.000Z

fpga/lib/pcie/tb/test_dma_client_axis_sink_512_64.py

akira2009999/corundum

cdc14769c33186c6d45fcd79b95c70889febff2b

[ "BSD-2-Clause-FreeBSD" ]

78

2020-08-20T20:06:33.000Z

2022-03-30T23:44:37.000Z

fpga/lib/pcie/tb/test_dma_client_axis_sink_512_64.py

akira2009999/corundum

cdc14769c33186c6d45fcd79b95c70889febff2b

[ "BSD-2-Clause-FreeBSD" ]

142

2019-07-15T04:23:23.000Z

2022-03-29T01:25:33.000Z

#!/usr/bin/env python """ Copyright (c) 2019 Alex Forencich Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from myhdl import * import os import dma_ram import axis_ep module = 'dma_client_axis_sink' testbench = 'test_%s_512_64' % module srcs = [] srcs.append("../rtl/%s.v" % module) srcs.append("%s.v" % testbench) src = ' '.join(srcs) build_cmd = "iverilog -o %s.vvp %s" % (testbench, src) def bench(): # Parameters SEG_COUNT = 4 SEG_DATA_WIDTH = 128 SEG_ADDR_WIDTH = 12 SEG_BE_WIDTH = int(SEG_DATA_WIDTH/8) RAM_ADDR_WIDTH = SEG_ADDR_WIDTH+(SEG_COUNT-1).bit_length()+(SEG_BE_WIDTH-1).bit_length() AXIS_DATA_WIDTH = 64 AXIS_KEEP_ENABLE = (AXIS_DATA_WIDTH>8) AXIS_KEEP_WIDTH = (AXIS_DATA_WIDTH/8) AXIS_LAST_ENABLE = 1 AXIS_ID_ENABLE = 0 AXIS_ID_WIDTH = 8 AXIS_DEST_ENABLE = 0 AXIS_DEST_WIDTH = 8 AXIS_USER_ENABLE = 1 AXIS_USER_WIDTH = 1 LEN_WIDTH = 20 TAG_WIDTH = 8 # Inputs clk = Signal(bool(0)) rst = Signal(bool(0)) current_test = Signal(intbv(0)[8:]) s_axis_write_desc_ram_addr = Signal(intbv(0)[RAM_ADDR_WIDTH:]) s_axis_write_desc_len = Signal(intbv(0)[LEN_WIDTH:]) s_axis_write_desc_tag = Signal(intbv(0)[TAG_WIDTH:]) s_axis_write_desc_valid = Signal(bool(0)) s_axis_write_data_tdata = Signal(intbv(0)[AXIS_DATA_WIDTH:]) s_axis_write_data_tkeep = Signal(intbv(0)[AXIS_KEEP_WIDTH:]) s_axis_write_data_tvalid = Signal(bool(0)) s_axis_write_data_tlast = Signal(bool(0)) s_axis_write_data_tid = Signal(intbv(0)[AXIS_ID_WIDTH:]) s_axis_write_data_tdest = Signal(intbv(0)[AXIS_DEST_WIDTH:]) s_axis_write_data_tuser = Signal(intbv(0)[AXIS_USER_WIDTH:]) ram_wr_cmd_ready = Signal(intbv(0)[SEG_COUNT:]) enable = Signal(bool(0)) abort = Signal(bool(0)) # Outputs s_axis_write_desc_ready = Signal(bool(0)) m_axis_write_desc_status_len = Signal(intbv(0)[LEN_WIDTH:]) m_axis_write_desc_status_tag = Signal(intbv(0)[TAG_WIDTH:]) m_axis_write_desc_status_id = Signal(intbv(0)[AXIS_ID_WIDTH:]) m_axis_write_desc_status_dest = Signal(intbv(0)[AXIS_DEST_WIDTH:]) m_axis_write_desc_status_user = Signal(intbv(0)[AXIS_USER_WIDTH:]) m_axis_write_desc_status_valid = Signal(bool(0)) s_axis_write_data_tready = Signal(bool(0)) ram_wr_cmd_be = Signal(intbv(0)[SEG_COUNT*SEG_BE_WIDTH:]) ram_wr_cmd_addr = Signal(intbv(0)[SEG_COUNT*SEG_ADDR_WIDTH:]) ram_wr_cmd_data = Signal(intbv(0)[SEG_COUNT*SEG_DATA_WIDTH:]) ram_wr_cmd_valid = Signal(intbv(0)[SEG_COUNT:]) # PCIe DMA RAM dma_ram_inst = dma_ram.PSDPRam(2**16) dma_ram_pause = Signal(bool(0)) dma_ram_port0 = dma_ram_inst.create_write_ports( clk, ram_wr_cmd_be=ram_wr_cmd_be, ram_wr_cmd_addr=ram_wr_cmd_addr, ram_wr_cmd_data=ram_wr_cmd_data, ram_wr_cmd_valid=ram_wr_cmd_valid, ram_wr_cmd_ready=ram_wr_cmd_ready, pause=dma_ram_pause, name='port0' ) # sources and sinks write_desc_source = axis_ep.AXIStreamSource() write_desc_source_pause = Signal(bool(False)) write_desc_source_logic = write_desc_source.create_logic( clk, rst, tdata=(s_axis_write_desc_ram_addr, s_axis_write_desc_len, s_axis_write_desc_tag), tvalid=s_axis_write_desc_valid, tready=s_axis_write_desc_ready, pause=write_desc_source_pause, name='write_desc_source' ) write_desc_status_sink = axis_ep.AXIStreamSink() write_desc_status_sink_logic = write_desc_status_sink.create_logic( clk, rst, tdata=(m_axis_write_desc_status_len, m_axis_write_desc_status_tag, m_axis_write_desc_status_id, m_axis_write_desc_status_dest, m_axis_write_desc_status_user), tvalid=m_axis_write_desc_status_valid, name='write_desc_status_sink' ) write_data_source = axis_ep.AXIStreamSource() write_data_source_pause = Signal(bool(False)) write_data_source_logic = write_data_source.create_logic( clk, rst, tdata=s_axis_write_data_tdata, tkeep=s_axis_write_data_tkeep, tvalid=s_axis_write_data_tvalid, tready=s_axis_write_data_tready, tlast=s_axis_write_data_tlast, tid=s_axis_write_data_tid, tdest=s_axis_write_data_tdest, tuser=s_axis_write_data_tuser, pause=write_data_source_pause, name='write_data_source' ) # DUT if os.system(build_cmd): raise Exception("Error running build command") dut = Cosimulation( "vvp -m myhdl %s.vvp -lxt2" % testbench, clk=clk, rst=rst, current_test=current_test, s_axis_write_desc_ram_addr=s_axis_write_desc_ram_addr, s_axis_write_desc_len=s_axis_write_desc_len, s_axis_write_desc_tag=s_axis_write_desc_tag, s_axis_write_desc_valid=s_axis_write_desc_valid, s_axis_write_desc_ready=s_axis_write_desc_ready, m_axis_write_desc_status_len=m_axis_write_desc_status_len, m_axis_write_desc_status_tag=m_axis_write_desc_status_tag, m_axis_write_desc_status_id=m_axis_write_desc_status_id, m_axis_write_desc_status_dest=m_axis_write_desc_status_dest, m_axis_write_desc_status_user=m_axis_write_desc_status_user, m_axis_write_desc_status_valid=m_axis_write_desc_status_valid, s_axis_write_data_tdata=s_axis_write_data_tdata, s_axis_write_data_tkeep=s_axis_write_data_tkeep, s_axis_write_data_tvalid=s_axis_write_data_tvalid, s_axis_write_data_tready=s_axis_write_data_tready, s_axis_write_data_tlast=s_axis_write_data_tlast, s_axis_write_data_tid=s_axis_write_data_tid, s_axis_write_data_tdest=s_axis_write_data_tdest, s_axis_write_data_tuser=s_axis_write_data_tuser, ram_wr_cmd_be=ram_wr_cmd_be, ram_wr_cmd_addr=ram_wr_cmd_addr, ram_wr_cmd_data=ram_wr_cmd_data, ram_wr_cmd_valid=ram_wr_cmd_valid, ram_wr_cmd_ready=ram_wr_cmd_ready, enable=enable, abort=abort ) @always(delay(4)) def clkgen(): clk.next = not clk def wait_normal(): while write_desc_status_sink.empty(): yield clk.posedge def wait_pause_ram(): while write_desc_status_sink.empty(): dma_ram_pause.next = True yield clk.posedge yield clk.posedge yield clk.posedge dma_ram_pause.next = False yield clk.posedge def wait_pause_source(): while write_desc_status_sink.empty(): write_data_source_pause.next = True yield clk.posedge yield clk.posedge yield clk.posedge write_data_source_pause.next = False yield clk.posedge @instance def check(): yield delay(100) yield clk.posedge rst.next = 1 yield clk.posedge rst.next = 0 yield clk.posedge yield delay(100) yield clk.posedge # testbench stimulus cur_tag = 1 enable.next = 1 yield clk.posedge print("test 1: write") current_test.next = 1 addr = 0x00000000 test_data = b'\x11\x22\x33\x44' write_desc_source.send([(addr, len(test_data), cur_tag)]) write_data_source.send(axis_ep.AXIStreamFrame(test_data, id=cur_tag)) yield write_desc_status_sink.wait(2000) status = write_desc_status_sink.recv() print(status) assert status.data[0][0] == len(test_data) assert status.data[0][1] == cur_tag assert status.data[0][2] == cur_tag data = dma_ram_inst.read_mem(addr, 32) for i in range(0, len(data), 16): print(" ".join(("{:02x}".format(c) for c in bytearray(data[i:i+16])))) assert dma_ram_inst.read_mem(addr, len(test_data)) == test_data cur_tag = (cur_tag + 1) % 256 yield delay(100) yield clk.posedge print("test 2: various writes") current_test.next = 2 for length in list(range(1,66))+[128]: for offset in list(range(8,65,8))+list(range(4096-64,4096,8)): for diff in [-16, -2, -1, 0, 1, 2, 16]: if length+diff < 1: continue for wait in wait_normal, wait_pause_ram, wait_pause_source: print("length %d, offset %d, diff %d"% (length, offset, diff)) #addr = length * 0x100000000 + offset * 0x10000 + offset addr = offset test_data = bytearray([x%256 for x in range(length)]) test_data2 = bytearray([x%256 for x in range(length+diff)]) dma_ram_inst.write_mem(addr & 0xffff80, b'\xaa'*(len(test_data)+256)) write_desc_source.send([(addr, len(test_data), cur_tag)]) write_data_source.send(axis_ep.AXIStreamFrame(test_data2, id=cur_tag)) yield wait() yield clk.posedge yield clk.posedge status = write_desc_status_sink.recv() print(status) assert status.data[0][0] == min(len(test_data), len(test_data2)) assert status.data[0][1] == cur_tag assert status.data[0][2] == cur_tag data = dma_ram_inst.read_mem(addr&0xfffff0, 64) for i in range(0, len(data), 16): print(" ".join(("{:02x}".format(c) for c in bytearray(data[i:i+16])))) if len(test_data) <= len(test_data2): assert dma_ram_inst.read_mem(addr-8, len(test_data)+16) == b'\xaa'*8+test_data+b'\xaa'*8 else: assert dma_ram_inst.read_mem(addr-8, len(test_data2)+16) == b'\xaa'*8+test_data2+b'\xaa'*8 cur_tag = (cur_tag + 1) % 256 yield delay(100) raise StopSimulation return instances() def test_bench(): sim = Simulation(bench()) sim.run() if __name__ == '__main__': print("Running test...") test_bench()

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166

0.657348

1,674

11,303

4.038829

0.158303

0.101168

0.076912

0.066262

0.56042

0.4841

0.327171

0.308978

0.291229

0.221269

0

0.02679

0.253649

11,303

320

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0.774656

0.107228

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0.030043

false

0

0.017167

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0.034335

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null

0

null

0

1

0

da8e6b5d27ab3ab699761ec5dcf3eadc2360c2c0

5,713

py

Python

scrape_votes.py

purrcat259/reddit-vote-grapher

0a0f1dccee7befc6e94e856d09eb61b546b34644

[ "MIT" ]

1

2016-05-18T06:30:26.000Z

scrape_votes.py

purrcat259/reddit-vote-grapher

0a0f1dccee7befc6e94e856d09eb61b546b34644

[ "MIT" ]

null

scrape_votes.py

purrcat259/reddit-vote-grapher

0a0f1dccee7befc6e94e856d09eb61b546b34644

[ "MIT" ]

null

#!/usr/bin/python3 import time import os import csv import praw import OAuth2Util from pprint import pprint class SubmissionCSV: def __init__(self, file_name='', csv_directory='data'): self.file_name = file_name + '.csv' self.file_path = os.path.join(os.getcwd(), csv_directory, self.file_name) def run(self, data_row=None): self.create_csv() if data_row is not None: self.write_row(row=data_row) def create_csv(self): # create the CSV if it does not exist if not os.path.isfile(self.file_path): with open(self.file_path, mode='w', newline='') as csvfile: csvfile.flush() time.sleep(1) def write_row(self, row=None): if row is not None: with open(self.file_path, mode='a', newline='') as csvfile: writer = csv.writer(csvfile, quotechar='"') writer.writerow(row) csvfile.flush() class VoteScraper: def __init__(self, user_agent='vote-grapher-v1-by-Always_SFW', subreddit='EliteDangerous', verbose=True): self.user_agent = user_agent self.subreddit_name = subreddit self.verbose = verbose self.r = None self.o = None self.subreddit = None self.submission_limit = 50 self.start_time = time.time() # holds the objects for cached submissions. self.cached_submissions = [] def run(self): self.connect() while True: print('Retrieving/Removing submissions') self.cache_new_submissions() self.remove_old_submissions() self.store_submissions_data() self.show_time_elapsed() self.print('Pausing for 120 seconds') time.sleep(120) def print(self, string=''): if self.verbose: print(string) def connect(self): # initialise a connection to reddit self.print('Initialising connection to Reddit') try: self.r = praw.Reddit(self.user_agent) self.o = OAuth2Util.OAuth2Util(self.r) # force re-validating the access token self.o.refresh(force=True) self.print('Successfully connected to Reddit') except Exception as e: print('Unable to connect to Reddit: {}'.format(e)) quit() self.subreddit = self.r.get_subreddit(subreddit_name=self.subreddit_name) def get_latest_submissions(self): # self.print('Getting latest submissions') try: new_submissions = self.subreddit.get_new(limit=self.submission_limit) except Exception as e: print(e) return [] return new_submissions def cache_new_submissions(self): new_submissions = self.get_latest_submissions() # self.print('Caching new submissions') previous_count = len(self.cached_submissions) for submission in new_submissions: if submission not in self.cached_submissions: self.cached_submissions.append(submission) self.print('{} new submissions recorded'.format(len(self.cached_submissions) - previous_count)) def remove_old_submissions(self): # self.print('Removing old submissions') current_time = time.time() to_remove = [] previous_count = len(self.cached_submissions) for submission in self.cached_submissions: if (current_time - submission.created_utc) > (12 * 60 * 60): # self.print('Removing Submission with ID: {} as it is older than 12 hours'.format(submission.id)) to_remove.append(submission) # remove the old submissions from the cached submissions list self.cached_submissions = [sub for sub in self.cached_submissions if sub not in to_remove] self.print('{} old submissions removed'.format(previous_count - len(self.cached_submissions))) # append '_complete' to the old submission file names for submission in to_remove: file_name = str(submission.id) + '.csv' new_file_name = str(submission.id) + '_complete.csv' path = os.path.join(os.getcwd(), 'data', file_name) # only perform this if the file actually exists if os.path.isfile(path): os.rename(src=path, dst=os.path.join(os.getcwd(), 'data', new_file_name)) def store_submissions_data(self): for i, sub in enumerate(self.cached_submissions): try: sub.refresh() ratio = self.r.get_submission(sub.permalink).upvote_ratio except Exception as e: print(e) continue ups = int(round((ratio*sub.score)/(2*ratio - 1)) if ratio != 0.5 else round(sub.score/2)) downs = ups - sub.score self.print('[{}] ID: {} S/U/D: {}/{}/{} Ratio: {} Age: {} hours Link: {}'.format( i, sub.id, sub.score, ups, downs, ratio, abs(round((time.time() - sub.created_utc) / (60 * 60), 1)), sub.short_link)) subcsv = SubmissionCSV(file_name=sub.id) subcsv.run(data_row=[time.time(), sub.score, ups, downs, ratio]) time.sleep(2) def show_time_elapsed(self): # convert to hours time_elapsed = (time.time() - self.start_time) / (60 * 60) self.print('{} hours passed since start of script'.format(round(time_elapsed, 1))) def main(): v = VoteScraper() v.run() if __name__ == '__main__': main()

37.585526

114

0.593033

688

5,713

4.765988

0.252907

0.067399

0.070448

0.029277

0.165294

0.093626

0.031717

0

0.009512

0.300718

5,713

151

115

37.834437

0.811264

0.097322

0

0.10084

0

0.008403

0.075233

0.005638

0

1

0.117647

false

0.008403

0.05042

0

0.201681

0.117647

0

null

0

null

0

1

0

da8e99c09aabca3db1bc0e1af11da10e940286d6

2,549

py

Python

tests/fixtures/dict_list/docket_list_with_homicide.py

SimmonsRitchie/court_docket_scraper

f467d59c4ea8dbddb4fd7545dc36656a4b30e46d

[ "MIT" ]

1

2021-10-29T20:12:44.000Z

tests/fixtures/dict_list/docket_list_with_homicide.py

SimmonsRitchie/court_docket_scraper

f467d59c4ea8dbddb4fd7545dc36656a4b30e46d

[ "MIT" ]

2

2019-07-19T20:13:16.000Z

tests/fixtures/dict_list/docket_list_with_homicide.py

SimmonsRitchie/court_docket_scraper

f467d59c4ea8dbddb4fd7545dc36656a4b30e46d

[ "MIT" ]

null

docket_list = [ { "county": "Dauphin", "docketnum": 1, "case_caption": "Commonwealth V. Smith, John A.", "arresting_agency": "Harrisburg PD", "municipality": "Harrisburg", "defendant": "John A. Smith", "defendant_race": "white", "defendant_gender": "Male", "dob": "01/01/1986", "filing_date": "03/03/2019", "charges": "Receiving Stolen Property; Driving W/O A License", "bail": 25000, "url": "https://ujsportal.pacourts.us/DocketSheets/MDJReport.ashx?docketNumber=MJ-12302-CR-0000110-2019&dnh=zj8BkxXzkOi23xMzscQ6hw%3d%3d", }, { "county": "Dauphin", "docketnum": 2, "case_caption": "Commonwealth V. Smith, Duke A.", "arresting_agency": "Harrisburg PD", "municipality": "Harrisburg", "defendant": "Duke A. Smith", "defendant_race": "white", "defendant_gender": "Male", "dob": "01/01/1986", "filing_date": "03/03/2019", "charges": "Receiving Stolen Property; Driving W/O A License", "bail": 25000, "url": "https://ujsportal.pacourts.us/DocketSheets/MDJReport.ashx?docketNumber=MJ-12302-CR-0000110-2019&dnh=zj8BkxXzkOi23xMzscQ6hw%3d%3d", }, { "county": "Dauphin", "docketnum": 3, "case_caption": "Commonwealth V. Smith, John A.", "arresting_agency": "Harrisburg PD", "municipality": "Harrisburg", "defendant": "John A. Smith", "defendant_race": "white", "defendant_gender": "Male", "dob": "01/01/1986", "filing_date": "03/03/2019", "charges": "Receiving Stolen Property; homicide; Driving W/O A " "License", "bail": 25000, "url": "https://ujsportal.pacourts.us/DocketSheets/MDJReport.ashx?docketNumber=MJ-12302-CR-0000110-2019&dnh=zj8BkxXzkOi23xMzscQ6hw%3d%3d", }, { "county": "Dauphin", "docketnum": 4, "case_caption": "Commonwealth V. Smith, John A.", "arresting_agency": "Harrisburg PD", "municipality": "Harrisburg", "defendant": "John A. Smith", "defendant_race": "white", "defendant_gender": "Male", "dob": "01/01/1986", "filing_date": "03/03/2019", "charges": "Receiving Stolen Property; Driving W/O A License; Murder", "bail": 25000, "url": "https://ujsportal.pacourts.us/DocketSheets/MDJReport.ashx?docketNumber=MJ-12302-CR-0000110-2019&dnh=zj8BkxXzkOi23xMzscQ6hw%3d%3d", } ]

39.828125

146

0.582581

264

2,549

5.545455

0.234848

0.020492

0.060109

0.065574

0.960383

0.940574

0.900273

0

0.092098

0.250294

2,549

63

147

40.460317

0.673993

0

0.698413

0

0.063492

0.634759

0

1

0

false

0

null

0

1

0

1

0

null

0

7

da9203437dccc2b66c4d623067b94d8a0a97c3de

3,488

py

Python

DeleteBook.py

saurabhmaurya45/library-management-system

2e489728068cca87ed58f493ac2524b6586f66cf

[ "Apache-2.0" ]

null

DeleteBook.py

saurabhmaurya45/library-management-system

2e489728068cca87ed58f493ac2524b6586f66cf

[ "Apache-2.0" ]

null

DeleteBook.py

saurabhmaurya45/library-management-system

2e489728068cca87ed58f493ac2524b6586f66cf

[ "Apache-2.0" ]

null

from tkinter import * import pymysql as ms from tkinter import messagebox # Add your own database name and password here to reflect in the code mypass = "saurabh" mydatabase = "library" con = ms.connect(host="localhost", user="root", password=mypass, database=mydatabase) cur = con.cursor() # Enter Table Names here bookTable = "books" # Book Table def deleteBook(): bid = en1.get() try: a = int(bid) type1 = type(a) if type1 == int: print(True) cur.execute('select Book_Id from books') print(True) list = [] for i in cur: getId = i[0] list.append(getId) print(True) if a in list: deleteSql = "delete from " + bookTable + " where Book_Id = '" + bid + "'" cur.execute(deleteSql) print(True) con.commit() print(True) # messagebox.showinfo('success',"Successfully deleted Book Id "+bid+" ") lb6 = Label(labelFrame, text="Successfully deleted book ", bg='black', fg='white', font=("times new roman", 18, "bold")) lb6.place(relx=0.3, rely=0.75) print(True) else: lb6 = Label(labelFrame, text="Book deletion failed ", bg='black', fg='white', font=("times new roman", 18, "bold")) lb6.place(relx=0.3, rely=0.75) # messagebox.showinfo('Error', "Please insert correct Book ID") except: messagebox.showinfo('Error', 'Invalid Book ID, must be number') print(bid) def delete(): global en1, con, cur, bookTable, root, labelFrame root = Tk() root.title("Library") root.minsize(width=400, height=400) root.geometry("1350x700+0+0") root.config(bg='#0099cc') title = Label(root, text="Welcome to Sterling's Library", bd=15, relief=GROOVE, font=("algerian", 40, "bold"), bg="red", fg="white") title.pack(side=TOP, fill=X) labelFrame = Frame(root, bg='#333945', bd=10, relief=GROOVE) labelFrame.place(relx=0.1, rely=0.35, relwidth=0.8, relheight=0.35) headingFrame1 = Frame(root, bg="blue", bd=10, relief=GROOVE) headingFrame1.place(relx=0.25, rely=0.15, relwidth=0.60, relheight=0.13) headingLabel = Label(headingFrame1, text="DELETE BOOK", bg='blue', fg='white', font=("bookman old style", 34, "bold")) headingLabel.place(relx=0.25, rely=0.15, relwidth=0.5, relheight=0.5) # Book ID to Delete lb2 = Label(labelFrame, text="Book ID : ", bg='black', fg='white', font=("bookman old style", 20, "bold")) lb2.place(relx=0.1, rely=0.33) en1 = Entry(labelFrame) en1.place(relx=0.3, rely=0.35, relwidth=0.62, relheight=0.15) # Submit Button SubmitBtn = Button(root, text="SUBMIT", bg='#d1ccc0', fg='black', font=("times new roman", 18, "bold"), relief=GROOVE, bd=10, command=deleteBook) SubmitBtn.place(relx=0.28, rely=0.75, relwidth=0.18, relheight=0.08) quitBtn = Button(root, text="Quit", bg='#f7f1e3', fg='black', font=("times new roman", 18, "bold"), relief=GROOVE, bd=10, command=root.quit) quitBtn.place(relx=0.53, rely=0.75, relwidth=0.18, relheight=0.08) root.mainloop()

35.591837

119

0.556479

443

3,488

4.376975

0.363431

0.041774

0.046416

0.03507

0.240846

0.22589

0.174317

0.114492

0

0.062041

0.297592

3,488

97

120

35.958763

0.729388

0.076261

0

0.149254

0

0.15061

0

1

0.029851

false

0.029851

0.044776

0

0.074627

0.104478

0

null

0

null

0

1

0

da920dcd51ae362c5f26a3360b65c16937f31fe7

8,474

py

Python

asdf/extension.py

larrybradley/asdf

b1e0fe6ab7aa319d5939ec2aa78d23822abf6bd4

[ "BSD-2-Clause", "BSD-3-Clause" ]

null

asdf/extension.py

larrybradley/asdf

b1e0fe6ab7aa319d5939ec2aa78d23822abf6bd4

[ "BSD-2-Clause", "BSD-3-Clause" ]

null

asdf/extension.py

larrybradley/asdf

b1e0fe6ab7aa319d5939ec2aa78d23822abf6bd4

[ "BSD-2-Clause", "BSD-3-Clause" ]

null

# Licensed under a 3-clause BSD style license - see LICENSE.rst # -*- coding: utf-8 -*- import os import abc import warnings from pkg_resources import iter_entry_points import six import importlib from . import types from . import resolver from .util import get_class_name from .type_index import AsdfTypeIndex from .version import version as asdf_version from .exceptions import AsdfDeprecationWarning __all__ = ['AsdfExtension', 'AsdfExtensionList'] ASDF_TEST_BUILD_ENV = 'ASDF_TEST_BUILD' @six.add_metaclass(abc.ABCMeta) class AsdfExtension: """ Abstract base class defining an extension to ASDF. """ @classmethod def __subclasshook__(cls, C): if cls is AsdfExtension: return (hasattr(C, 'types') and hasattr(C, 'tag_mapping') and hasattr(C, 'url_mapping')) return NotImplemented @abc.abstractproperty def types(self): """ A list of `asdf.CustomType` subclasses that describe how to store custom objects to and from ASDF. """ pass @abc.abstractproperty def tag_mapping(self): """ A list of 2-tuples or callables mapping YAML tag prefixes to JSON Schema URL prefixes. For each entry: - If a 2-tuple, the first part of the tuple is a YAML tag prefix to match. The second part is a string, where case the following are available as Python formatting tokens: - ``{tag}``: the complete YAML tag. - ``{tag_suffix}``: the part of the YAML tag after the matched prefix. - ``{tag_prefix}``: the matched YAML tag prefix. - If a callable, it is passed the entire YAML tag must return the entire JSON schema URL if it matches, otherwise, return `None`. Note that while JSON Schema URLs uniquely define a JSON Schema, they do not have to actually exist on an HTTP server and be fetchable (much like XML namespaces). For example, to match all YAML tags with the ``tag:nowhere.org:custom` prefix to the ``http://nowhere.org/schemas/custom/`` URL prefix:: return [('tag:nowhere.org:custom/', 'http://nowhere.org/schemas/custom/{tag_suffix}')] """ pass @abc.abstractproperty def url_mapping(self): """ A list of 2-tuples or callables mapping JSON Schema URLs to other URLs. This is useful if the JSON Schemas are not actually fetchable at their corresponding URLs but are on the local filesystem, or, to save bandwidth, we have a copy of fetchable schemas on the local filesystem. If neither is desirable, it may simply be the empty list. For each entry: - If a 2-tuple, the first part is a URL prefix to match. The second part is a string, where the following are available as Python formatting tokens: - ``{url}``: The entire JSON schema URL - ``{url_prefix}``: The matched URL prefix - ``{url_suffix}``: The part of the URL after the prefix. - If a callable, it is passed the entire JSON Schema URL and must return a resolvable URL pointing to the schema content. If it doesn't match, should return `None`. For example, to map a remote HTTP URL prefix to files installed alongside as data alongside Python module:: return [('http://nowhere.org/schemas/custom/1.0.0/', asdf.util.filepath_to_url( os.path.join(SCHEMA_PATH, 'stsci.edu')) + '/{url_suffix}.yaml' )] """ pass class AsdfExtensionList: """ Manage a set of extensions that are in effect. """ def __init__(self, extensions): tag_mapping = [] url_mapping = [] validators = {} self._type_index = AsdfTypeIndex() for extension in extensions: if not isinstance(extension, AsdfExtension): raise TypeError( "Extension must implement asdf.types.AsdfExtension " "interface") tag_mapping.extend(extension.tag_mapping) url_mapping.extend(extension.url_mapping) for typ in extension.types: self._type_index.add_type(typ, extension) validators.update(typ.validators) for sibling in typ.versioned_siblings: self._type_index.add_type(sibling, extension) validators.update(sibling.validators) self._tag_mapping = resolver.Resolver(tag_mapping, 'tag') self._url_mapping = resolver.Resolver(url_mapping, 'url') self._validators = validators @property def tag_to_schema_resolver(self): """Deprecated. Use `tag_mapping` instead""" warnings.warn( "The 'tag_to_schema_resolver' property is deprecated. Use " "'tag_mapping' instead.", AsdfDeprecationWarning) return self._tag_mapping @property def tag_mapping(self): return self._tag_mapping @property def url_mapping(self): return self._url_mapping @property def type_index(self): return self._type_index @property def validators(self): return self._validators class BuiltinExtension: """ This is the "extension" to ASDF that includes all the built-in tags. Even though it's not really an extension and it's always available, it's built in the same way as an extension. """ @property def types(self): return types._all_asdftypes @property def tag_mapping(self): return resolver.DEFAULT_TAG_TO_URL_MAPPING @property def url_mapping(self): return resolver.DEFAULT_URL_MAPPING class _DefaultExtensions: def __init__(self): self._extensions = [] self._extension_list = None self._package_metadata = {} def _load_installed_extensions(self, group='asdf_extensions'): for entry_point in iter_entry_points(group=group): with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always', category=AsdfDeprecationWarning) ext = entry_point.load() if not issubclass(ext, AsdfExtension): warnings.warn("Found entry point {}, from {} but it is not a " "subclass of AsdfExtension, as expected. It is " "being ignored.".format(ext, entry_point.dist)) continue dist = entry_point.dist name = get_class_name(ext, instance=False) self._package_metadata[name] = (dist.project_name, dist.version) self._extensions.append(ext()) for warning in w: warnings.warn('{} (from {})'.format(warning.message, name), AsdfDeprecationWarning) @property def extensions(self): # This helps avoid a circular dependency with external packages if not self._extensions: # If this environment variable is defined, load the default # extension. This allows the package to be tested without being # installed (e.g. for builds on Debian). if os.environ.get(ASDF_TEST_BUILD_ENV): # Fake the extension metadata name = get_class_name(BuiltinExtension, instance=False) self._package_metadata[name] = ('asdf', asdf_version) self._extensions.append(BuiltinExtension()) self._load_installed_extensions() return self._extensions @property def extension_list(self): if self._extension_list is None: self._extension_list = AsdfExtensionList(self.extensions) return self._extension_list @property def package_metadata(self): return self._package_metadata def reset(self): """This will be used primarily for testing purposes.""" self._extensions = [] self._extension_list = None self._package_metadata = {} def resolver(self, uri): tag_mapping = self.extension_list.tag_mapping url_mapping = self.extension_list.url_mapping return url_mapping(tag_mapping(uri)) default_extensions = _DefaultExtensions()

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0

null

0

null

0

1

0

da923abab5b7e2cb6e8f37c23f2fa4cc9504aff5

2,153

py

Python

source/setup.py

Sylvain-Barde/mic-toolbox

10d9d930a1a359aaa831f2f917eff357a3d5282e

[ "BSD-3-Clause" ]

4

2019-06-28T20:36:33.000Z

2022-01-04T21:49:52.000Z

source/setup.py

Sylvain-Barde/mic-toolbox

10d9d930a1a359aaa831f2f917eff357a3d5282e

[ "BSD-3-Clause" ]

1

2019-06-27T14:52:52.000Z

2019-07-04T14:14:14.000Z

source/setup.py

Sylvain-Barde/mic-toolbox

10d9d930a1a359aaa831f2f917eff357a3d5282e

[ "BSD-3-Clause" ]

1

2019-06-27T13:33:42.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """ Created on Wed Sep 12 14:48:07 2018 @author: sb636 """ import os import sys from setuptools import setup, Extension, find_packages from distutils.errors import DistutilsModuleError # Check for cython installation try: from Cython.Distutils import build_ext as _build_ext HAVE_CYTHON = True except ImportError: # As a fallback import the standard setuptools build_ext, and raise # error about Cython later from setuptools.command.build_ext import build_ext as _build_ext HAVE_CYTHON = False def scandir(dir, files=[]): for file in os.listdir(dir): path = os.path.join(dir, file) if os.path.isfile(path) and path.endswith(".pyx"): files.append(path.replace(os.path.sep, ".")[:-4]) elif os.path.isdir(path): scandir(path, files) return files def makeExtension(extName): extPath = extName.replace(".", os.path.sep)+".pyx" return Extension(extName, [extPath]) class build_ext(_build_ext): def initialize_options(self): if not HAVE_CYTHON: raise DistutilsModuleError( 'Cython is required to compile the package.\n' 'Cython can be obtained at www.cython.org or installed with ' 'conda or pip.') super(build_ext, self).initialize_options() def finalize_options(self): try: import numpy except ImportError: raise DistutilsModulesError('Building extension modules requires numpy') for ext in self.distribution.ext_modules: ext.include_dirs.extend([numpy.get_include(), '.']) ext.cython_directives = { "cdivision": True, "cdivision_warnings": False } super(build_ext, self).finalize_options() setup( name="mic-toolbox", version="0.1.0a1", packages=find_packages(), ext_modules=[makeExtension(name) for name in scandir('mic')], cmdclass={'build_ext': build_ext}, options = {'build_ext': {'inplace': True, 'force': True}} )

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0

0.326531

0

null

0

null

0

1

0

da92ca41103ff60b1a50e24d1900c7aae0620a32

4,049

py

Python

api-reconstruction/ipython_analysis.py

eurecom-s3/syscall2api

2f2c72c759b0fd803fe1302c3b6717cda1906916

[ "MIT" ]

10

2019-09-24T13:36:15.000Z

2021-11-01T02:40:10.000Z

api-reconstruction/ipython_analysis.py

eurecom-s3/syscall2api

2f2c72c759b0fd803fe1302c3b6717cda1906916

[ "MIT" ]

2

2020-10-19T11:51:08.000Z

2021-04-17T01:08:23.000Z

api-reconstruction/ipython_analysis.py

eurecom-s3/syscall2api

2f2c72c759b0fd803fe1302c3b6717cda1906916

[ "MIT" ]

null

#!/usr/local/bin/ipython3 -i import sys from analysis import * import analysis.classes as classes import nwalign as nw kb = {} apis = {} syscalls = {} regexes = {} models = {} models2 = {} kb_file = 'kb_no_empties.pickle' regex_file = 'new_regex.pickle' models_file = 'models.pickle' models2_file = 'models2.pickle' symbols_file = 'symbols.pickle' leaf_models = {} def first_run(): global kb global apis global syscalls global regexes global models global symbols_file kb_file = "pruned_db.pickle" if not Path(kb_file).is_file(): print("Error: No KB file found", file=sys.stderr) sys.exit(1) with open(kb_file, "rb") as pf: d = pickle.load(pf) syscalls = pickle.load(pf) d = prune_kb_from_signals(d) print("Finding leaf apis") leaves = find_leaves(d) print("Finding strong polymorph apis") polymorph = find_polymorph(d) print("Finding empty apis") empties = find_empties(d) print("Finding 0Sys apis") no_sys = find_no_syscall_apis(d) print("Finding 0IndSys apis") no_ind_sys = find_no_indirect_sys(d) apis = set(d.keys()) print("Finding no-leaf apis") no_leaves = apis - leaves print("Finding weak monomorph apis") monomorph = apis - polymorph print("Finding 1+Sys apis") sys = apis - no_sys print("Finding 1+IndSys apis") ind_sys = apis - no_ind_sys print("Finding weak polymorph") weak_polymorph = find_weak_polymorph(d) print("Finding strong monomorph apis") strong_monomorph = apis - weak_polymorph print("Building models for strong monomorph apis") precise_models = build_precise_models(d, strong_monomorph) print("Building models for implicit monomorph apis") implicit_precise_models = find_implicit_monomorph_models(d, precise_models) print("Finding empty/non-empty models") empty_models = {api for api, model in implicit_precise_models.items() if len(model) == 0} non_empty_models = {api: model for api, model in implicit_precise_models.items() if api not in empty_models} strong_monomorph |= set(implicit_precise_models.keys()) # checks that no_ind_sys is a subset of no_sys check_0sys(no_sys, no_ind_sys) check_polymorph(weak_polymorph, polymorph) check_empties_have_precise_model(empties, precise_models) check_implicit_precise_models(implicit_precise_models, precise_models) check_empties_have_empty_model(empties, empty_models) kb = prune_kb_from_empties(d, empty_models) with open('kb_no_empties.pickle', 'wb') as pf: pickle.dump(kb, pf) pickle.dump(syscalls, pf) with open(symbols_file, 'wb') as pf: pickle.dump(set(kb.keys()), pf) pickle.dump(syscalls, pf) def load_kb_no_empties(): global kb global syscalls global apis global regexes global regexes_test global test_results global models global kb_file global regex_file global models_file global symbols_file global leaf_models global models2 print("Loading KB") with open(kb_file, "rb") as pf: sys.modules['classes'] = classes kb= pickle.load(pf) syscalls = pickle.load(pf) print("Loading symbols") apis, syscalls = load_symbols(symbols_file) kb = prune_kb_from_signals(kb) # print("Loading regexes") # f = open(regex_file, 'rb') # regexes_test = pickle.load(f) # f.close() # regexes, test_results = regexes_split_test_results(regexes_test) print("Loading generic models") models = load_models(models_file) print("Loading not-so-generic models") models2 = load_models(models2_file) symbols_generator(apis | syscalls.keys()) leaf_models = find_leaves_models(models, syscalls) if __name__ == '__main__': if (not Path(kb_file).is_file() or not Path(models_file).is_file() or not Path(symbols_file).is_file()): first_run() else: load_kb_no_empties()

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551

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0.029942

0.013052

0.127447

0.098273

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0.031478

0

0.004456

0.224006

4,049

140

80

28.921429

0.824634

0.056557

0

0.160714

0

0.153987

0

1

0.017857

false

0

0.035714

0

0.053571

0.169643

0

null

0

null

0

1

0

da92e435dd529ef7c6adb64e5afe197974724936

660

py

Python

tests/client/test_spread_sheet.py

shin-hama/git-svn-monitor

acb793c2da63d6802efa8e0e6c99482f4fad0f80

[ "MIT" ]

null

tests/client/test_spread_sheet.py

shin-hama/git-svn-monitor

acb793c2da63d6802efa8e0e6c99482f4fad0f80

[ "MIT" ]

36

2021-07-12T00:08:03.000Z

2022-03-25T11:19:39.000Z

tests/client/test_spread_sheet.py

shin-hama/git-svn-monitor

acb793c2da63d6802efa8e0e6c99482f4fad0f80

[ "MIT" ]

null

from datetime import datetime from git_svn_monitor.client import spread_seat from git_svn_monitor.core.config import TIMESTAMP_FORMAT def test__convert_datetime_to_str() -> None: timestamp = "2021-01-01 12:34:56" _timestamp = datetime.strptime(timestamp, TIMESTAMP_FORMAT) converted = spread_seat._convert_to_str(_timestamp) assert timestamp == converted def test__convert_int_to_str() -> None: num = 1 converted = spread_seat._convert_to_str(num) assert converted == "1" def test__convert_str_with_new_line() -> None: text = "new line" converted = spread_seat._convert_to_str(text+"\n") assert converted == text

27.5

63

0.75

92

660

4.978261

0.380435

0.054585

0.091703

0.170306

0.203057

0

0.028933

0.162121

660

23

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0.799277

0

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0

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0.1875

false

0

0.1875

0

0.375

0

null

0

1

0

null

0

1

16f0b6155221bd21f39e5a25133a8324a5286c72

3,785

py

Python

accessdata/api/extensions.py

AccessDataOps/FTK-API-SDK

34e689a55eadacc51e6ff585e9126799f80e269a

[ "MIT" ]

2

2021-12-10T10:20:08.000Z

2022-01-06T11:15:43.000Z

accessdata/api/extensions.py

AccessDataOps/FTK-API-SDK

34e689a55eadacc51e6ff585e9126799f80e269a

[ "MIT" ]

null

accessdata/api/extensions.py

AccessDataOps/FTK-API-SDK

34e689a55eadacc51e6ff585e9126799f80e269a

[ "MIT" ]

null

## /api/extensions.py """ Maintains the API endpoint URI extensions. """ ## Declaring __all__ __all__ = ( "status_check_ext", "site_server_status_check_ext", "case_create_ext", "case_list_ext", "case_create_portable_ext", "evidence_list_ext", "evidence_processed_list_ext", "evidence_process_ext", "object_page_list_ext", "label_create_ext" "label_list_ext" "label_objects_job_ext" "label_objects_list_ext" "label_objects_count_ext" "label_objects_sync_ext" "search_report_ext", "export_natives_ext", "agent_push_ext", "agent_collection_ext", "agent_disk_acquisition_ext", "agent_memory_acquisition_ext", "agent_remediation_ext", "agent_software_inventory_ext", "agent_volatile_analysis_ext", "agent_volatile_import_ext", "job_status_ext", "attribute_list_ext", "attribute_list_by_case_ext", "child_file_categories_ext", "processing_case_ext", "server_setting_ext", "yara_ioc_rule_import_ext", ) ## Predefined Constants DELETE = "delete" GET = "get" PATCH = "patch" POST = "post" PUT = "put" ## Status Extensions base_ext = "api/v2/enterpriseapi" status_check_ext = GET, base_ext + "/statuscheck" site_server_status_check_ext = GET, base_ext + "/agent/getsiteserverstatus" ## Case Management Extensions case_create_ext = POST, base_ext + "/core/createcase" case_list_ext = GET, base_ext + "/core/getcaselist" case_create_portable_ext = POST, base_ext + "/core/{caseid}/createportablecase" ## Evidence Management Extensions evidence_list_ext = GET, base_ext + "/core/{caseid}/getevidencelist" evidence_processed_list_ext = GET, base_ext + "/core/{caseid}/getprocessedevidencelist" evidence_process_ext = POST, base_ext + "/core/{caseid}/processdata" ## Object Management Extensions object_page_list_ext = POST, base_ext + "/core/{caseid}/getobjectlist/{pagenumber}/{pagesize}" ## Label Management Extensions label_create_ext = POST, base_ext + "/core/{caseid}/createlabel" label_list_ext = GET, base_ext + "/core/{caseid}/getlabellist" label_objects_job_ext = POST, base_ext + "/jobs/{caseid}/labelobjects" label_objects_list_ext = GET, base_ext + "/core/cases/{caseid}/label/{labelid}/evidenceobjects" label_objects_count_ext = GET, base_ext + "/core/cases/{caseid}/label/{labelid}/objectscount" label_objects_sync_ext = POST, base_ext + "/{caseid}/labelobjectssync" ## Search Extensions search_report_ext = POST, base_ext + "/jobs/{caseid}/createsearchcountreport" ## Export Extenstions export_natives_ext = POST, base_ext + "/jobs/{caseid}/dumpnativeobjects" ## Agent Management Extensions agent_push_ext = POST, base_ext + "/agent/{caseid}/runagentpush" agent_collection_ext = POST, base_ext + "/agent/{caseid}/collectiononagent" agent_disk_acquisition_ext = POST, base_ext + "/agent/{caseid}/diskacquistion" agent_memory_acquisition_ext = POST, base_ext + "/agent/{caseid}/memoryacquistion" agent_remediation_ext = POST, base_ext + "/agent/{caseid}/remediate" agent_software_inventory_ext = POST, base_ext + "/agent/{caseid}/softwareinventory" agent_volatile_analysis_ext = POST, base_ext + "/agent/{caseid}/volatile" agent_volatile_import_ext = GET, base_ext + "/agent/{caseid}/importvolatile/{jobid}" ## Generic Job Extensions job_status_ext = GET, base_ext + "/core/{caseid}/getjobstatus/{jobid}" ## Utility Extensions attribute_list_ext = GET, base_ext + "/core/getallattributes" attribute_list_by_case_ext = GET, base_ext + "/core/{caseid}/getallattributesbycaseid" child_file_categories_ext = GET, base_ext + "/core/getchildrenfilecategories" processing_case_ext = GET, base_ext + "/processingcaseid" server_setting_ext = GET, base_ext + "/core/getserversetting/{setting}" yara_ioc_rule_import_ext = POST, base_ext + "/agent/importiocandyara"

33.495575

97

0.763804

476

3,785

5.638655

0.22479

0.086066

0.069672

0.088674

0.335693

0.259314

0.086811

0.029806

0

0.000298

0.113606

3,785

113

98

33.495575

0.799702

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0

0.491501

0.382474

0

1

0

false

0

0.055556

0

0.055556

0

null

0

null

0

1

0

16f0f93ca79ae51931ef205e9c059a600e80445c

1,982

py

Python

evaluators/dialog/state/distinct.py

kaniblu/vhda

35941097ef552568c29f66cc55d8ce1927f34978

[ "MIT" ]

3

2021-01-12T05:43:20.000Z

2021-03-05T17:03:06.000Z

evaluators/dialog/state/distinct.py

kaniblu/vhda

35941097ef552568c29f66cc55d8ce1927f34978

[ "MIT" ]

null

evaluators/dialog/state/distinct.py

kaniblu/vhda

35941097ef552568c29f66cc55d8ce1927f34978

[ "MIT" ]

null

__all__ = ["DistinctStateEvaluator"] from dataclasses import dataclass from typing import Sequence, Optional import torch import utils from utils import TensorMap from datasets import VocabSet from ...evaluator import DialogEvaluator @dataclass class DistinctStateEvaluator(DialogEvaluator): vocabs: VocabSet _values: dict = utils.private_field(default_factory=dict) def reset(self): self._values.clear() @property def speakers(self): return set(spkr for spkr in self.vocabs.speaker.f2i if spkr != "<unk>") @staticmethod def compute_distinct(tokens): if len(tokens) == 0: return torch.tensor(0.0) return torch.tensor(len(set(tokens)) / len(tokens)) def compute(self, samples: Sequence, spkr=None): return {i: [self.compute_distinct(turn.text, i) for sample in samples for turn in sample.output.turns if spkr is None or turn.speaker == spkr] for i in self.ngrams} def update(self, samples: Sequence) -> Optional[TensorMap]: for sample in samples: asvs = [asv for turn in sample.output if turn.speaker != "<unk>" for asv in turn.state] spkr_asvs = {spkr: [asv for turn in sample.output if turn.speaker != "<unk>" for asv in turn.state] for spkr in self.speakers} stats = {"dist-a": self.compute_distinct(asvs)} stats.update({ f"dist-a-{spkr}": self.compute_distinct(spkr_asvs[spkr]) for spkr in self.speakers }) for k, v in stats.items(): if k not in self._values: self._values[k] = list() self._values[k].append(v.item()) return def get(self) -> Optional[TensorMap]: return {k: torch.tensor(v).mean() for k, v in self._values.items()}

33.59322

79

0.584258

239

1,982

4.769874

0.309623

0.031579

0.023684

0.034211

0.173684

0.1

0

0.002952

0.316347

1,982

58

80

34.172414

0.838376

0

0.042553

0

0.028254

0.0111

0

1

0.12766

false

0

0.148936

0.06383

0.468085

0

null

0

null

0

1

0

16f2f95568be402c343f83e95bd816466c4a6dd1

1,788

py

Python

src/dataset/manually_labeled_bases.py

yullidias/AutomaticIronyDetection

3297ddc4ecc97e840b00df4ba4f9e6b8e710fdb9

[ "MIT" ]

null

src/dataset/manually_labeled_bases.py

yullidias/AutomaticIronyDetection

3297ddc4ecc97e840b00df4ba4f9e6b8e710fdb9

[ "MIT" ]

1

2020-12-05T14:22:03.000Z

src/dataset/manually_labeled_bases.py

yullidias/AutomaticIronyDetection

3297ddc4ecc97e840b00df4ba4f9e6b8e710fdb9

[ "MIT" ]

null

import src.utils.constants as cns from src.utils.files import write_list import pandas as pd import glob import os def read_sheets(): manually_labeled_df = pd.DataFrame() for sheet in glob.glob(cns.PATH_LABELED + '*'): manually_labeled_df = manually_labeled_df.append( pd.read_excel(sheet, index_col=0), ignore_index=True) return manually_labeled_df def rename_columns(dataset): return dataset.rename(columns={ "pathOriginal": "path_ask", "tweet 'Pergunta'": "reply_response_tweet", "pathTweet": "id", "tweet a ser avaliado": "tweet", "rotulo": "label" }) def parser_label(label): if label == "Irônico": return cns.IRONIC_LABEL elif label == "Não irônico": return cns.NOT_IRONIC_LABEL else: return cns.DONT_KNOW_LABLE def update_label(df, col): df[col] = df[col].apply(parser_label) def path_to_id(df, col): df[col] = df[col].apply(lambda x: os.path.basename(x) .split('.json')[0]) def get_by_label(df, label): return df[df["label"] == label] def generate_manually_bases(): labled_df = read_sheets() path_to_id(labled_df, "pathTweet") labled_df = rename_columns(labled_df) labled_df = labled_df[["id", "label"]] update_label(labled_df, "label") print("Generate base manually labeled as ironic ...") write_list(cns.B_M_IRONIC, get_by_label(labled_df, cns.IRONIC_LABEL)["id"].to_list()) print("Generate base manually labeled as not ironic ...") write_list(cns.B_M_NOT_IRONIC, get_by_label(labled_df, cns.NOT_IRONIC_LABEL)["id"].to_list()) return labled_df if __name__ == "__main__": generate_manually_bases()

26.294118

77

0.644295

243

1,788

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null

0

null

0

1

0

16f3b2b6831a8b521ba1a14a11fa6b224ec5222f

1,785

py

Python

cryptotest.py

xebia/django-DefectDojo

7bc6695bd8fb93e23b0d8ed8326f5d01283eadaa

[ "BSD-3-Clause" ]

null

cryptotest.py

xebia/django-DefectDojo

7bc6695bd8fb93e23b0d8ed8326f5d01283eadaa

[ "BSD-3-Clause" ]

null

cryptotest.py

xebia/django-DefectDojo

7bc6695bd8fb93e23b0d8ed8326f5d01283eadaa

[ "BSD-3-Clause" ]

1

2017-09-22T20:39:39.000Z

import binascii, os from Crypto.Cipher import AES KEY = 'a0b8c7398c9363b3216ff1d001a1308e5f96a77dbf6bda2367f87519d80995fb' IV = os.urandom(16) def encrypt(key, iv, plaintext): aes = AES.new(key, AES.MODE_CBC, iv, segment_size=128) plaintext = _pad_string(plaintext) encrypted_text = aes.encrypt(plaintext) return binascii.b2a_hex(encrypted_text).rstrip() def decrypt(key, iv, encrypted_text): aes = AES.new(key, AES.MODE_CBC, iv, segment_size=128) encrypted_text_bytes = binascii.a2b_hex(encrypted_text) decrypted_text = aes.decrypt(encrypted_text_bytes) decrypted_text = _unpad_string(decrypted_text) return decrypted_text def _pad_string(value): length = len(value) pad_size = 16 - (length % 16) return value.ljust(length + pad_size, '\x00') def _unpad_string(value): while value[-1] == '\x00': value = value[:-1] return value def prepare_for_save(IV, encrypted_value): binascii.b2a_hex(encrypted_text).rstrip() stored_value = "AES.1:" + binascii.b2a_hex(IV).rstrip() + ":" + encrypted_value return stored_value def prepare_for_view(encrypted_value): encrypted_values = encrypted_value.split(":") type = encrypted_values[0] iv = binascii.a2b_hex(encrypted_values[1]).rstrip() value = encrypted_values[2] return decrypt(KEY, iv, value) if __name__ == '__main__': input_plaintext = 'The answer is no' encrypted_text = encrypt(KEY, IV, input_plaintext) print encrypted_text decrypted_text = decrypt(KEY, IV, encrypted_text) print decrypted_text print prepare_for_save(IV, encrypted_text) print "*****" print prepare_for_view("AES.1:fff2e6659bef045f25f8249d36f58789:178e6f316b680b486e4e6b8cc79f589e") assert decrypted_text == input_plaintext

31.875

101

0.729412

228

1,785

5.412281

0.276316

0.115883

0.034036

0.019449

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0.115073

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0.075067

0.164146

1,785

55

102

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0

null

0

null

0

1

0

1

16f4d90e8a8de6335b4d40090aa8cb9b83b7e850

871

py

Python

Larry/preprocess.py

NCBI-Hackathons/ClusterDuck

1d5478500dffea973f96affd969783278193aa8a

[ "MIT" ]

7

2019-02-19T15:10:24.000Z

2020-05-31T00:41:13.000Z

Larry/preprocess.py

NCBI-Hackathons/ClusterDuck

1d5478500dffea973f96affd969783278193aa8a

[ "MIT" ]

11

2018-03-21T20:01:32.000Z

2022-03-11T23:19:40.000Z

Larry/preprocess.py

NCBI-Hackathons/DiseaseClusters

1d5478500dffea973f96affd969783278193aa8a

[ "MIT" ]

3

2018-03-19T13:14:23.000Z

2018-03-20T14:13:38.000Z

from nltk.corpus import stopwords from nltk.tokenize import RegexpTokenizer from nltk.stem import WordNetLemmatizer STOPWORDS = set(stopwords.words('english')) # Instantiate Lemmanizer WNL = WordNetLemmatizer() def preprocess(abstract, keywords=None): """ Convert an abstract to word tokens. This is done by lowering the case of the text, tokenizing the text, removing english stopwords and punctuation,and finally lemmatizing the words. Args: abstract: (str) Return: str """ # Lowercase all words abstract = abstract.lower() # tokenize words, remove punctuation tokenizer = RegexpTokenizer(r'\w[\w-]+') tokens = tokenizer.tokenize(abstract) # Remove stopwords and lemmatize tokens words = [WNL.lemmatize(word) for word in tokens if word not in STOPWORDS] return words

26.393939

77

0.6969

102

871

5.95098

0.539216

0.039539

0

0.228473

871

32

78

27.21875

0.903274

0.394948

0

0.031513

0

1

0.090909

false

0

0.272727

0

0.454545

0

null

0

null

0

1

0

16f520db46f1b8b8a53e17ff7a93ac14fed25f00

16,848

py

Python

Codes/env.py

zongdaoming/Reinforcement-Learning

426b646b1184e96d8a0f6c6341e53b13ef89ea12

[ "Apache-2.0" ]

1

2021-04-20T13:49:55.000Z

Codes/env.py

zongdaoming/Reinforcement-Learning

426b646b1184e96d8a0f6c6341e53b13ef89ea12

[ "Apache-2.0" ]

1

2021-04-18T18:27:49.000Z

Codes/env.py

zongdaoming/Reinforcement-Learning

426b646b1184e96d8a0f6c6341e53b13ef89ea12

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # @author : naive dormin # @time : 2021/04/19 02:17:43 # @version : 1.0.0 import os import time import numpy as np import random from utils import * import pickle from ConvE import ConvE_double import torch import torch.autograd as autograd import torch.nn as nn import torch.nn.functional as F import torch.optim as optim USE_CUDA = torch.cuda.is_available() if USE_CUDA: longTensor = torch.cuda.LongTensor floatTensor = torch.cuda.FloatTensor byteTensor = torch.cuda.ByteTensor else: longTensor = torch.LongTensor floatTensor = torch.FloatTensor byteTensor = torch.ByteTensor class Env(object): """knowledge graph environment definition""" def __init__(self, dataPath, task=None, model="TransE"): f1 = open(dataPath + 'entity2id.txt') f2 = open(dataPath + 'relation2id.txt') self.entity2id = f1.readlines() self.relation2id = f2.readlines() f1.close() f2.close() self.entity2id_ = {} self.relation2id_ = {} self.id2entity_ = {} self.id2relation_ = {} self.relations = [] for line in self.entity2id: self.entity2id_[line.split()[0]] = int(line.split()[1]) self.id2entity_[int(line.split()[1])] = line.split()[0] for line in self.relation2id: self.relation2id_[line.split()[0]] = int(line.split()[1]) self.id2relation_[int(line.split()[1])] = line.split()[0] self.relations.append(line.split()[0]) # Which model to compute pretrained embedding of entities and relations? (The definition of states) if model == "TransH": print("Uses TransH") self.entity2vec = np.loadtxt( dataPath + 'NELL-995_100_1.0_TransH_entity_embedding.txt') self.relation2vec = np.loadtxt( dataPath + 'NELL-995_100_1.0_TransH_relation_embedding.txt') self.norm2vec = np.loadtxt( dataPath + 'NELL-995_100_1.0_TransH_norm_embedding.txt') if task is not None: relation = task.strip().split()[2].replace('_', ':') w_r = self.norm2vec[self.relation2id_[relation]] new_entity2vec = self.entity2vec - \ np.sum(self.entity2vec * w_r, axis=1, keepdims=True) * w_r self.entity2vec = new_entity2vec elif model == "TransR": print("Uses TransR") self.entity2vec = np.loadtxt( dataPath + 'NELL-995_100_1.0_TransR_entity_embedding.txt') self.relation2vec = np.loadtxt( dataPath + 'NELL-995_100_1.0_TransR_relation_embedding.txt') self.projection2vec = np.loadtxt( dataPath + "NELL-995_100_1.0_TransR_norm_embedding.txt") dim = int(np.sqrt(self.projection2vec.shape[1])) # By default, entities and relations share the same dimension # This is not the main point of research self.projection2vec = self.projection2vec.reshape([-1, dim, dim]) if task is not None: relation = task.strip().split()[2].replace('_', ':') M_vec = self.projection2vec[self.relation2id_[relation], :, :] new_entity2vec = np.matmul(M_vec, self.entity2vec.T).T self.entity2vec = new_entity2vec elif model == "TransD": print("Uses TransD") self.entity2vec = np.loadtxt( dataPath + 'NELL-995_100_1.0_TransD_entity_embedding.txt') self.relation2vec = np.loadtxt( dataPath + 'NELL-995_100_1.0_TransD_relation_embedding.txt') self.ent_norm2vec = np.loadtxt( dataPath + "NELL-995_100_1.0_TransD_ent_norm_embedding.txt") self.rel_norm2vec = np.loadtxt( dataPath + "NELL-995_100_1.0_TransD_rel_norm_embedding.txt") if task is not None: relation = task.strip().split()[2].replace('_', ':') rel_proj = self.rel_norm2vec[self.relation2id_[relation]] new_entity2vec = self.entity2vec + \ np.sum(self.entity2vec * self.ent_norm2vec, axis=1, keepdims=True) * rel_proj self.entity2vec = new_entity2vec elif model == "ProjE": print("Uses ProjE") self.entity2vec = np.loadtxt( dataPath + 'NELL-995_100_ProjE_entity_embedding.txt') self.relation2vec = np.loadtxt( dataPath + 'NELL-995_100_ProjE_relation_embedding.txt') self.simple_hr_combination_weights = np.loadtxt( dataPath + "NELL-995_100_ProjE_simple_hr_combination_weights.txt") self.simple_tr_combination_weights = np.loadtxt( dataPath + "NELL-995_100_ProjE_simple_tr_combination_weights.txt") self.combination_bias_hr = np.loadtxt( dataPath + "NELL-995_100_ProjE_combination_bias_hr.txt") self.combination_bias_tr = np.loadtxt( dataPath + "NELL-995_100_ProjE_combination_bias_tr.txt") if task is not None: relation = task.strip().split()[2].replace('_', ':') dim = self.entity2vec.shape[1] r = self.relation2vec[[self.relation2id_[relation]]] # ent_mat = np.transpose(self.entity2vec) hr = self.entity2vec * \ self.simple_hr_combination_weights[:dim] + \ r * self.simple_hr_combination_weights[dim:] new_entity2vec = np.tanh(hr + self.combination_bias_hr) self.entity2vec = new_entity2vec elif model == "ConvE": print("Uses ConvE") start_time = time.time() self.entity2vec = np.loadtxt( dataPath + 'NELL-995_100_ConvE_entity_embedding.txt') self.relation2vec = np.loadtxt( dataPath + 'NELL-995_100_ConvE_relation_embedding.txt') self.TransE_to_ConvE_id_entity = {} with open(dataPath + "TransE_to_ConvE_entity_id.txt") as fr: for line in fr: line_list = line.strip().split() self.TransE_to_ConvE_id_entity[int( line_list[0])] = int(line_list[1]) self.TransE_to_ConvE_id_relation = {} with open(dataPath + "TransE_to_ConvE_relation_id.txt") as fr: for line in fr: line_list = line.strip().split() self.TransE_to_ConvE_id_relation[int( line_list[0])] = int(line_list[1]) homepath = os.path.expanduser('~') token2idx_ent, idx2token_ent, label2idx_ent, idx2label_ent = pickle.load( open(homepath + "/.data/NELL-995/vocab_e1", 'rb')) token2idx_rel, idx2token_rel, label2idx_rel, idx2label_rel = pickle.load( open(homepath + "/.data/NELL-995/vocab_rel", 'rb')) self.ConvE_model = ConvE_double( len(token2idx_ent), len(token2idx_rel)) model_params = torch.load( dataPath + "NELL-995_ConvE_0.2_0.3_100.model") self.ConvE_model.load_state_dict(model_params) for parameter in self.ConvE_model.parameters(): parameter.requires_grad = False if USE_CUDA: self.ConvE_model.cuda() if task is not None: relation = task.strip().split()[2].replace('_', ':') rel_id = token2idx_rel[relation] ConvE_ent_id_list = [self.TransE_to_ConvE_id_entity[i] for i in range(len(self.TransE_to_ConvE_id_entity))] new_entity2vec_list = [] bs = self.ConvE_model.batch_size batch_count = len(ConvE_ent_id_list) // bs for i in range(batch_count): x_middle, output = self.ConvE_model(longTensor( ConvE_ent_id_list[i * bs: (i + 1) * bs]), longTensor([rel_id] * bs)) new_entity2vec_list.append(x_middle.cpu()) if len(ConvE_ent_id_list) % bs != 0: input_ent_list = ConvE_ent_id_list[batch_count * bs:] + [ 0] * (bs - len(ConvE_ent_id_list) % bs) x_middle, output = self.ConvE_model(longTensor( input_ent_list), longTensor([rel_id] * bs)) new_entity2vec_list.append( x_middle[: len(ConvE_ent_id_list) % bs].cpu()) self.entity2vec = torch.cat(new_entity2vec_list).numpy() torch.cuda.empty_cache() """ else: if USE_CUDA: self.ConvE_model.cuda() """ end_time = time.time() print("Embedding calculation time: ", end_time - start_time) else: print("Default. Uses TransE") self.entity2vec = np.loadtxt(dataPath + 'entity2vec.bern') self.relation2vec = np.loadtxt(dataPath + 'relation2vec.bern') if task is None: self.embedding_precomputed_flag = False else: self.embedding_precomputed_flag = True self.model = model self.path = [] self.path_relations = [] # Knowledge Graph for path finding f = open(dataPath + 'kb_env_rl.txt') kb_all = f.readlines() f.close() self.kb = [] if task != None: relation = task.split()[2] # Remove query relation and its inverse for line in kb_all: rel = line.split()[2] if rel != relation and rel != relation + '_inv': self.kb.append(line) else: for line in kb_all: self.kb.append(line) self.entity2link = {} # Build the dictionary. Attention: they are all represented with numbers! for line in self.kb: line_list = line.strip().split() head = self.entity2id_[line_list[0]] tail = self.entity2id_[line_list[1]] rel = self.relation2id_[line_list[2]] if head not in self.entity2link: self.entity2link[head] = {rel: [tail]} elif rel not in self.entity2link[head]: self.entity2link[head][rel] = [tail] else: self.entity2link[head][rel].append(tail) self.die = 0 # record how many times does the agent choose an invalid action self.banned_action_list = [] def interact(self, state, action): # state and action are all represented with numbers # print("Die: ", self.die) ''' This function process the interact from the agent state: is [current_position, target_position, die] action: an integer return: (reward, [new_position, target_position, die], done) ''' done = 0 # Whether the episode has finished curr_pos = state[0] target_pos = state[1] if action in self.banned_action_list: # print("Type 1") choices = [] elif curr_pos not in self.entity2link: # print("Type 2", curr_pos) choices = [] elif action not in self.entity2link[curr_pos]: # print("Type 3") choices = [] else: # print("Type 4") choices = self.entity2link[curr_pos][action] """ chosed_relation = self.relations[action] choices = [] for line in self.kb: triple = line.rsplit() e1_idx = self.entity2id_[triple[0]] if curr_pos == e1_idx and triple[2] == chosed_relation and triple[1] in self.entity2id_: choices.append(triple) """ if len(choices) == 0: # doesn't find a successful path # print("No proper path! ") reward = -1 self.die += 1 next_state = state # stay in the initial state next_state[-1] = self.die # Total failure times # print(next_state) return (reward, next_state, done) else: # find a valid step # print("Proper path exists! ") # Randomly choose one from multiple choices chose_entity = random.choice(choices) # path[2]: relation；path[1]: tail entity（the next entity） self.path.append(self.id2relation_[ action] + ' -> ' + self.id2entity_[chose_entity]) self.path_relations.append(self.id2relation_[action]) # Relation # print 'Find a valid step', path # print 'Action index', action self.die = 0 new_pos = chose_entity # Using the next entity as the new position reward = 0 # Reward is zero means the action is valid new_state = [new_pos, target_pos, self.die] if new_pos == target_pos: print('Find a path:', self.path) done = 1 # episode finished reward = 0 # reward is 0 means the episode is successful new_state = None # print(new_state) return (reward, new_state, done) def idx_state(self, idx_list, relation=None): # Calculate state vector if idx_list != None: curr = self.entity2vec[idx_list[0], :] targ = self.entity2vec[idx_list[1], :] if self.embedding_precomputed_flag == True or relation is None: pass else: if self.model == "TransH": w_r = self.norm2vec[relation] curr = curr - np.sum(curr * w_r) * w_r targ = targ - np.sum(targ * w_r) * w_r elif self.model == "TransR": M_vec = self.projection2vec[relation, :, :] curr = np.matmul(M_vec, curr.T).T targ = np.matmul(M_vec, targ.T).T elif self.model == "TransD": rel_proj = self.rel_norm2vec[relation] curr = curr + \ np.sum( curr * self.ent_norm2vec[idx_list[0]]) * rel_proj targ = targ + \ np.sum( targ * self.ent_norm2vec[idx_list[1]]) * rel_proj elif self.model == "ProjE": dim = self.entity2vec.shape[1] r = self.relation2vec[relation] curr = curr * \ self.simple_hr_combination_weights[:dim] + \ r * self.simple_hr_combination_weights[dim:] curr = np.tanh(curr + self.combination_bias_hr) targ = targ * \ self.simple_hr_combination_weights[:dim] + \ r * self.simple_hr_combination_weights[dim:] targ = np.tanh(targ + self.combination_bias_hr) elif self.model == "ConvE": curr_id = self.TransE_to_ConvE_id_entity[idx_list[0]] targ_id = self.TransE_to_ConvE_id_entity[idx_list[1]] rel_id = self.TransE_to_ConvE_id_relation[relation] bs = self.ConvE_model.batch_size curr = [curr_id] + [0] * (bs - 1) curr, output = self.ConvE_model( longTensor(curr), longTensor([rel_id] * bs)) curr = curr[0].cpu().numpy() targ = [targ_id] + [0] * (bs - 1) targ, output = self.ConvE_model( longTensor(targ), longTensor([rel_id] * bs)) targ = targ[0].cpu().numpy() else: # Default, TransE pass return np.expand_dims(np.concatenate((curr, targ - curr)), axis=0) else: return None def get_valid_actions(self, entityID): # Get the valid action actions = set() for line in self.kb: triple = line.split() e1_idx = self.entity2id_[triple[0]] if e1_idx == entityID: actions.add(self.relation2id_[triple[2]]) return np.array(list(actions)) # A path's embedding is calculated as summing all the relational vectors def path_embedding(self, path): embeddings = [self.relation2vec[self.relation2id_[relation], :] for relation in path] embeddings = np.reshape(embeddings, (-1, embedding_dim)) path_encoding = np.sum(embeddings, axis=0) return np.reshape(path_encoding, (-1, embedding_dim))

42.014963

107

0.553063

1,927

16,848

4.623249

0.149974

0.034572

0.038164

0.042429

0.393086

0.328656

0.275564

0.234706

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0.165563

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0.032135

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16,848

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42.12

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false

0.006601

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0.026403

0

null

0

null

0

1

0

16f718d511a624ff6bafcf060c184c7b35cb49f0

2,608

py

Python

txrtpengine/NGCPProxy.py

braams/txrtpengine

5511cf79d7fc338b28d927c19e5ff3b88e66a5be

[ "MIT" ]

null

txrtpengine/NGCPProxy.py

braams/txrtpengine

5511cf79d7fc338b28d927c19e5ff3b88e66a5be

[ "MIT" ]

null

txrtpengine/NGCPProxy.py

braams/txrtpengine

5511cf79d7fc338b28d927c19e5ff3b88e66a5be

[ "MIT" ]

null

import json from twisted.internet import reactor from twisted.python import log from twisted.web.resource import Resource from twisted.web.server import NOT_DONE_YET from twisted.web.server import Site from txrtpengine.NGCP import NGCPClient class NGCPProxy(Resource): def __init__(self, addr): self.c = NGCPClient(addr) self.isLeaf = True Resource.__init__(self) def _onResponse(self, response, request): request.write(json.dumps(response).encode('utf-8')) request.finish() def _onError(self, error, request): request.write(json.dumps({'error': str(error)}).encode('utf-8')) request.finish() def render_POST(self, request): request.setHeader('Content-Type', 'application/json; charset=utf-8') # copy-paste from https://stackoverflow.com/a/33571117 def _byteify(data, ignore_dicts=False): # if this is a unicode string, return its string representation if isinstance(data, unicode): return data.encode('utf-8') # if this is a list of values, return list of byteified values if isinstance(data, list): return [_byteify(item, ignore_dicts=True) for item in data] # if this is a dictionary, return dictionary of byteified keys and values # but only if we haven't already byteified it if isinstance(data, dict) and not ignore_dicts: return { _byteify(key, ignore_dicts=True): _byteify(value, ignore_dicts=True) for key, value in data.iteritems() } # if it's anything else, return it in its original form return data try: content = request.content.read().decode("utf-8") cmd = json.loads(content, object_hook=_byteify) d = self.c.command(cmd) d.addCallback(self._onResponse, request) d.addErrback(self._onError, request) return NOT_DONE_YET except Exception as e: return json.dumps({'error': str(e)}, ensure_ascii=False, indent=1).encode('utf-8') if __name__ == '__main__': import sys from twisted.web.client import getPage log.startLogging(sys.stdout) def test(): reactor.listenTCP(1222, Site(NGCPProxy(('127.0.0.1', 16222)))) def onResponse(data): log.msg("response: %s" % data) getPage('http://localhost:1222/', method='POST', postdata='{"command":"ping"}').addBoth(onResponse) reactor.callWhenRunning(test) reactor.run()

33.435897

107

0.625383

321

2,608

4.965732

0.417445

0.041405

0.035132

0.016939

0.100376

0.032622

0

0.017792

0.267255

2,608

77

108

33.87013

0.816327

0.132285

0

0.039216

0

0.066933

0

1

0.137255

false

0

0.176471

0

0.45098

0

null

0

null

0

1

0

16f73a2d16ee1a1b4874c0d6207d250cd9f1609d

6,980

py

Python

ise_session_gui.py

ComtecSystem-dev/ise_session

299bf47b7584094c7722a27a5cbec704e8acc084

[ "Apache-2.0" ]

null

ise_session_gui.py

ComtecSystem-dev/ise_session

299bf47b7584094c7722a27a5cbec704e8acc084

[ "Apache-2.0" ]

null

ise_session_gui.py

ComtecSystem-dev/ise_session

299bf47b7584094c7722a27a5cbec704e8acc084

[ "Apache-2.0" ]

null

import sys import requests import xmltodict from functools import partial from PyQt5.QtWidgets import * from PyQt5.QtCore import Qt from PyQt5 import uic #Link : Qt5 UI File # - condition : The UI file should be located in the sam directory as the this file form_class = uic.loadUiType("./ise_session.ui")[0] # Class Define : UI Open class ISE_Session(): def __init__(self, ip, id, pwd): self.ip = ip self.id = id self.pwd = pwd def getActiveSession(self): url = "https://%s/admin/API/mnt/Session/ActiveList" % self.ip ret_state, ret_val = self.request_action("get", url, self.id, self.pwd) return ret_state, ret_val def deleteSessionByMAC(self, MAC): url = "https://%s/admin/API/mnt/Session/Delete/MACAddress/%s" % (self.ip, MAC) ret_state, ret_val = self.request_action("delete", url, self.id, self.pwd) return ret_state, ret_val def request_action(self, request_type, url, id, pwd, ): print("\t Request URL : %s %s" % (request_type, url)) print("\t Request ID/PWD : [%s][%s]" % (id, pwd)) session = requests.Session() session.auth = (id, pwd) if request_type == "get": response = session.get(url, verify=False) elif request_type == "delete": response = session.delete(url, verify=False) else: return 000, "unknow error" ret_val = None if response.status_code == 401: ret_val = "Auth failed" elif response.status_code != 200: ret_val = "Error code %s " % (response.status_code) else: ret_val = xmltodict.parse(response.text) return response.status_code, ret_val class MyWindow(QMainWindow, form_class) : def __init__(self) : super().__init__() self.setupUi(self) self.lineEdit_IP.setText("10.200.150.212") self.lineEdit_ID.setText("admin") self.lineEdit_PWD.setText("Comtec123") # Linking functions to buttons self.pushButton.clicked.connect(self.button1Function) def button1Function(self): ISE_IP = self.lineEdit_IP.text() ISE_ID = self.lineEdit_ID.text() ISE_PWD = self.lineEdit_PWD.text() print("[MyWindow] button1Function() - [%s][%s][%s]" % (ISE_IP, ISE_ID, ISE_PWD)) ise_session = ISE_Session(ISE_IP, ISE_ID, ISE_PWD) ret_state, ret_val = ise_session.getActiveSession() if ret_state != 200: QMessageBox.about(self, "에러", "%s" % (ret_val) ) else: print("[MyWindow] button1Function() - %s" % (ret_state)) session_count = 0 session_list = [] if ret_val is not None and "activeList" in ret_val: session_count = ret_val['activeList']['@noOfActiveSession'] if session_count == "1": ret = ret_val['activeList']['activeSession'] session = {} session['user_name'] = ret['user_name'] if 'user_name' in ret else '!!!' session['mac'] = ret['calling_station_id'] if 'calling_station_id' in ret else '!!!' session['ip'] = ret['framed_ip_address'] if 'framed_ip_address' in ret else '!!!' session['sw_ip'] = ret['nas_ip_address'] if 'nas_ip_address' in ret else '!!!' session_list.append(session) print("\t%s" % (session)) else: for ret in ret_val['activeList']['activeSession']: session = {} session['user_name'] = ret['user_name'] if 'user_name' in ret else '!!!' session['mac'] = ret['calling_station_id'] if 'calling_station_id' in ret else '!!!' session['ip'] = ret['framed_ip_address'] if 'framed_ip_address' in ret else '!!!' session['sw_ip'] = ret['nas_ip_address'] if 'nas_ip_address' in ret else '!!!' session_list.append(session) print("\t%s" % (session)) self.Set_Table(["user_name", "mac", "ip", "sw_ip"], session_list) def click_btn(self, btnClass, MAC): msgBox = QMessageBox() msgBox.setIcon(QMessageBox.Warning) msgBox.setText("Are you soure you want to delete session on MAC(%s)" % (MAC)) msgBox.setWindowTitle("warring") msgBox.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel) returnValue = msgBox.exec() if returnValue == QMessageBox.Ok: ISE_IP = self.lineEdit_IP.text() ISE_ID = self.lineEdit_ID.text() ISE_PWD = self.lineEdit_PWD.text() ise_session = ISE_Session(ISE_IP, ISE_ID, ISE_PWD) ret_state, ret_val = ise_session.deleteSessionByMAC(MAC) if ret_state == 200: if ret_val is not None and "mnt-rest-result" in ret_val: if "status" in ret_val["mnt-rest-result"]: btnClass.setEnabled(False) return QMessageBox.about(self, "Error[%s]" % ret_state, "%s" % (ret_val) ) pass def Set_Table(self, head_list, data_list): self.tableWidget.setRowCount(len(data_list)) self.tableWidget.setColumnCount(len(head_list)+1) self.tableWidget.setHorizontalHeaderLabels([" "]+head_list) self.tableWidget.setColumnWidth(0, 50) self.tableWidget.setColumnWidth(1, 130) self.tableWidget.setColumnWidth(2, 150) self.tableWidget.setColumnWidth(3, 130) self.tableWidget.setColumnWidth(4, 130) col_count = 0 row_count = 0 for table_data in data_list: col_count = 0 btnDelete = QPushButton("Delete") btnDelete.MAC = table_data['mac'] btnDelete.clicked.connect(partial(self.click_btn, btnDelete, table_data['mac'])) #btnDelete.clicked.connect(self.click_btn) self.tableWidget.setCellWidget(row_count, col_count, btnDelete) col_count = 1 for column_name in head_list: column_val = table_data[column_name] if column_name in table_data else '!!!' tableitem = QTableWidgetItem(column_val) tableitem.setFlags(Qt.ItemIsEnabled) self.tableWidget.setItem(row_count, col_count, tableitem) col_count = col_count + 1 row_count = row_count + 1 if __name__ == "__main__" : #QApplication : run the servic app = QApplication(sys.argv) #created the instance to WindowClass myWindow = MyWindow() #show UI myWindow.show() #Run Program app.exec_()

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109

0.570487

807

6,980

4.72119

0.22057

0.033071

0.018898

0.033596

0.298163

0.275591

0.234646

0

0.013834

0.316476

6,980

170

110

41.058824

0.784741

0.039398

0

0.227273

0

0.13174

0

1

0.060606

false

0.007576

0.05303

0

0.166667

0.045455

0

null

0

null

0

1

0

16fa4d94e2d23cb6b84df2ab54d37fba71a56141

1,597

py

Python

examples/zegar.py

BrownEmmett/8digit

aa9ab5e6673ec0fa3764510bd845a94ac37c4c1e

[ "MIT" ]

null

examples/zegar.py

BrownEmmett/8digit

aa9ab5e6673ec0fa3764510bd845a94ac37c4c1e

[ "MIT" ]

null

examples/zegar.py

BrownEmmett/8digit

aa9ab5e6673ec0fa3764510bd845a94ac37c4c1e

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2017-18 Richard Hull and contributors # See LICENSE.rst for details. """ Example for seven segment displays. """ import time from datetime import datetime from luma.led_matrix.device import max7219 from luma.core.interface.serial import spi, noop from luma.core.virtual import viewport, sevensegment def date(seg): """ Display current date on device. """ now = datetime.now() seg.text = now.strftime("%d-%m-%y") def clock(seg, seconds): """ Display current time on device. """ interval = 0.5 for i in range(int(seconds / interval)): now = datetime.now() seg.text = now.strftime("%H-%M-%S") # calculate blinking dot if i % 2 == 0: seg.text = now.strftime("%H-%M-%S") else: seg.text = now.strftime("%H %M %S") time.sleep(interval) def show_message_vp(device, msg, delay=0.1): # Implemented with virtual viewport width = device.width padding = " " * width msg = padding + msg + padding n = len(msg) virtual = viewport(device, width=n, height=8) sevensegment(virtual).text = msg for i in reversed(list(range(n - width))): virtual.set_position((i, 0)) time.sleep(delay) def show_message_alt(seg, msg, delay=0.1): # Does same as above but does string slicing itself width = seg.device.width padding = " " * width msg = padding + msg + padding for i in range(len(msg)): seg.text = msg[i:i + width] time.sleep(delay) else: pass

22.492958

55

0.612398

222

1,597

4.378378

0.441441

0.036008

0.041152

0.074074

0.200617

0.088477

0

0.017677

0.256105

1,597

70

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0.800505

0.14402

0

0.324324

0

0.0278

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null

0.027027

0.135135

null

0

null

0

null

0

1

0

1

16fa7014b2509e362e1a19500f13adaa6c41db09

1,109

py

Python

caption_generation/sub_json.py

Collapsar-G/clevr-dataset-gen

a09b0559b53891bf4f4771190e4ad361406c67fe

[ "BSD-3-Clause" ]

1

2021-05-23T13:48:59.000Z

caption_generation/sub_json.py

Collapsar-G/clevr-dataset-gen

a09b0559b53891bf4f4771190e4ad361406c67fe

[ "BSD-3-Clause" ]

null

caption_generation/sub_json.py

Collapsar-G/clevr-dataset-gen

a09b0559b53891bf4f4771190e4ad361406c67fe

[ "BSD-3-Clause" ]

null

import argparse import json import os import ijson parser = argparse.ArgumentParser() # /questions/CLEVR_test_questions.json # Inputs parser.add_argument('--all_scene_paths', default='../data/CLEVR_v1.0/scenes', help="JSON file containing questions information for all images " + "from generate_questions.py") parser.add_argument('--output_scene_file', default='../data/CLEVR_v1.0/CLEVR_train_scenes.json', help="Directory containing JSON templates for captions") if __name__ == "__main__": all_scenes = [] args = parser.parse_args() paths = os.listdir(args.all_scene_paths) for scene_path in paths: # print(scene_path) with open(args.all_scene_paths + "/" + scene_path, 'r') as f: all_scenes.append(json.load(f)) output = { 'info': {"split": "train", "license": "Creative Commons Attribution (CC BY 4.0)", "version": "1.0", "date": "2/14/2017"}, 'scenes': all_scenes } with open(args.output_scene_file, 'w') as f: json.dump(output, f)

34.65625

103

0.627592

140

1,109

4.728571

0.485714

0.036254

0.058912

0.054381

0.057402

0

0.017794

0.239856

1,109

31

104

35.774194

0.767497

0.055005

0

0.321839

0.084291

0

1

0

false

0

0.16

0

0.16

0

null

0

null

0

1

0

16fd36971459752eacaa3008f88c6855b286e881

1,439

py

Python

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/commerce/tests/factories.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

3

2021-12-15T04:58:18.000Z

2022-02-06T12:15:37.000Z

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/commerce/tests/factories.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

null

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/commerce/tests/factories.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

1

2019-01-02T14:38:50.000Z

""" Factories for generating fake commerce-related data. """ import factory from factory.fuzzy import FuzzyText class OrderFactory(factory.Factory): """ Factory for stubbing orders resources from Ecommerce (v2). """ class Meta: model = dict number = factory.Sequence(lambda n: 'edx-%d' % n) date_placed = '2016-01-01T10:00:00Z' status = 'Complete' currency = 'USD' total_excl_tax = '100.00' lines = [] class OrderLineFactory(factory.Factory): """ Factory for stubbing order lines resources from Ecommerce (v2). """ class Meta: model = dict title = FuzzyText(prefix='Seat in ') quantity = 1 description = FuzzyText() status = 'Complete' line_price_excl_tax = '100.00' unit_price_excl_tax = '100.00' product = {} class ProductFactory(factory.Factory): """ Factory for stubbing Product resources from Ecommerce (v2). """ class Meta: model = dict id = factory.Sequence(lambda n: n) # pylint: disable=invalid-name url = 'http://test/api/v2/products/' + str(id) product_class = 'Seat' title = FuzzyText(prefix='Seat in ') price = '100.00' attribute_values = [] class ProductAttributeFactory(factory.Factory): """ Factory for stubbing product attribute resources from Ecommerce (v2). """ class Meta: model = dict name = FuzzyText() code = FuzzyText() value = FuzzyText()

24.810345

75

0.645587

166

1,439

5.53012

0.445783

0.122004

0.091503

0.104575

0.431373

0.267974

0.183007

0

0.036397

0.236275

1,439

57

76

25.245614

0.798908

0.23558

0

0.333333

0

0.110377

0

1

0

false

0

0.055556

0

0.888889

0

null

0

null

0

1

0

1

16fee0714125b907c565a7460bda1a63c75c9808

3,682

py

Python

boundlexx/boundless/migrations/0002_create_item_timeseries.py

AngellusMortis/boundlexx

407f5e38e8e0f067cbcb358787fc9af6a9be9b2a

[ "MIT" ]

1

2021-04-23T11:49:50.000Z

boundlexx/boundless/migrations/0002_create_item_timeseries.py

AngellusMortis/boundlexx

407f5e38e8e0f067cbcb358787fc9af6a9be9b2a

[ "MIT" ]

1

2021-04-17T18:17:12.000Z

boundlexx/boundless/migrations/0002_create_item_timeseries.py

AngellusMortis/boundlexx

407f5e38e8e0f067cbcb358787fc9af6a9be9b2a

[ "MIT" ]

null

# Generated by Django 3.0.8 on 2020-07-21 17:44 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('boundless', '0001_initial'), ] operations = [ migrations.CreateModel( name='ItemShopStandPrice', fields=[ ('time', models.DateTimeField(auto_now=True, primary_key=True, serialize=False)), ('location_x', models.IntegerField()), ('location_y', models.IntegerField()), ('location_z', models.IntegerField()), ('price', models.DecimalField(decimal_places=2, max_digits=10)), ('item_count', models.IntegerField()), ('beacon_name', models.CharField(db_index=True, max_length=64)), ('guild_tag', models.CharField(max_length=8)), ('shop_activity', models.IntegerField()), ('active', models.BooleanField(db_index=True, default=True)), ('world', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='boundless.World')), ('item', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='boundless.Item')), ], options={ 'abstract': False, 'unique_together': {('time', 'world', 'location_x', 'location_y', 'item', 'price', 'item_count')}, }, ), migrations.CreateModel( name='ItemRequestBasketPrice', fields=[ ('time', models.DateTimeField(auto_now=True, primary_key=True, serialize=False)), ('location_x', models.IntegerField()), ('location_y', models.IntegerField()), ('location_z', models.IntegerField()), ('price', models.DecimalField(decimal_places=2, max_digits=10)), ('item_count', models.IntegerField()), ('beacon_name', models.CharField(db_index=True, max_length=64)), ('guild_tag', models.CharField(max_length=8)), ('shop_activity', models.IntegerField()), ('active', models.BooleanField(db_index=True, default=True)), ('world', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='boundless.World')), ('item', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='boundless.Item')), ], options={ 'abstract': False, 'unique_together': {('time', 'world', 'location_x', 'location_y', 'item', 'price', 'item_count')}, }, ), migrations.RunSQL( "CREATE EXTENSION IF NOT EXISTS timescaledb CASCADE", reverse_sql=migrations.RunSQL.noop ), migrations.RunSQL( 'ALTER TABLE "boundless_itemshopstandprice" DROP CONSTRAINT "boundless_itemshopstandprice_pkey"', reverse_sql=migrations.RunSQL.noop ), migrations.RunSQL( "SELECT create_hypertable('boundless_itemshopstandprice', 'time', chunk_time_interval => 86400000000, migrate_data => true, create_default_indexes => false)", reverse_sql=migrations.RunSQL.noop ), migrations.RunSQL( 'ALTER TABLE "boundless_itemrequestbasketprice" DROP CONSTRAINT "boundless_itemrequestbasketprice_pkey"', reverse_sql=migrations.RunSQL.noop ), migrations.RunSQL( "SELECT create_hypertable('boundless_itemrequestbasketprice', 'time', chunk_time_interval => 86400000000, migrate_data => true, create_default_indexes => false)", reverse_sql=migrations.RunSQL.noop ), ]

51.138889

209

0.605921

348

3,682

6.215517

0.281609

0.083218

0.032362

0.050855

0.784096

0

0.0194

0.258012

3,682

71

210

51.859155

0.772328

0.012222

0

0.738462

1

0

0.27923

0.084182

0

1

0

false

0

0.030769

0

0.076923

0

null

0

1

0

1

0

null

0

7

e50054bcfcc58e68ad2fe236a5c12539ae5190f0

39,700

py

Python

det3d/models/bbox_heads/clear_mg_ohs_head.py

Lelin-HUNUST/VISTA

7bf34132d719cb0e5e803b92cd15451df58a9a5d

[ "MIT" ]

47

2022-03-21T02:41:39.000Z

2022-03-30T17:25:29.000Z

det3d/models/bbox_heads/clear_mg_ohs_head.py

Lelin-HUNUST/VISTA

7bf34132d719cb0e5e803b92cd15451df58a9a5d

[ "MIT" ]

1

2022-03-28T15:11:26.000Z

2022-03-28T16:27:40.000Z

det3d/models/bbox_heads/clear_mg_ohs_head.py

Lelin-HUNUST/VISTA

7bf34132d719cb0e5e803b92cd15451df58a9a5d

[ "MIT" ]

2

2022-03-23T12:56:14.000Z

2022-03-27T14:25:50.000Z

# Copyright (c) Gorilla-Lab. All rights reserved. import logging from functools import partial from collections import defaultdict from typing import Dict, List, Optional, Sequence import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from ..losses.ohs_loss_clear import OHSLossClear from ..losses.attention_constrain_loss import AttentionConstrainedLoss from ..registry import HEADS from ..builder import build_loss from ...core.bbox import box_torch_ops from ...core.bbox.geometry import points_in_convex_polygon_torch from ...core.bbox.box_coders import BoxCoder, GroundBox3dCoderAF from ipdb import set_trace def multi_apply(func, *args, **kwargs): pfunc = partial(func, **kwargs) if kwargs else func map_results = map(pfunc, *args) return tuple(map(list, zip(*map_results))) def _get_pos_neg_loss(cls_loss, labels, label_weights): # cls_loss: [N, num_anchors, num_class] # labels: [N, num_anchors] batch_size = cls_loss.shape[0] if cls_loss.shape[-1] == 1 or len(cls_loss.shape) == 2: cls_pos_loss = (labels > 0).type_as(cls_loss) * cls_loss.view(batch_size, -1) cls_neg_loss = ((labels == 0) & (label_weights > 0)).type_as( cls_loss) * cls_loss.view(batch_size, -1) cls_pos_loss = cls_pos_loss.sum() / batch_size cls_neg_loss = cls_neg_loss.sum() / batch_size else: cls_pos_loss = cls_loss[..., 1:].sum() / batch_size cls_neg_loss = cls_loss[..., 0].sum() / batch_size return cls_pos_loss, cls_neg_loss @HEADS.register_module class OHSHeadClear(nn.Module): def __init__(self, box_coder: GroundBox3dCoderAF, num_input: int, num_pred: int, num_cls: int, header: bool = True, name: str = "", **kwargs,): super().__init__() self.box_coder = box_coder self.conv_cls = nn.Conv2d(num_input, num_cls, 1) self.mode = kwargs.get("mode", "bev") if self.box_coder.center == "direct": self.conv_xy = nn.Conv2d(num_input, 2, 1) elif self.box_coder.center == "soft_argmin": self.conv_xy = nn.Conv2d(num_input, 2 * self.box_coder.kwargs["xy_bin_num"], 1) self.loc_bins_x = torch.linspace(self.box_coder.kwargs["x_range"][0], self.box_coder.kwargs["x_range"][1], self.box_coder.kwargs["xy_bin_num"]).reshape(1, 1, -1, 1, 1) self.loc_bins_y = torch.linspace(self.box_coder.kwargs["y_range"][0], self.box_coder.kwargs["y_range"][1], self.box_coder.kwargs["xy_bin_num"]).reshape(1, 1, -1, 1, 1) self.loc_bins = torch.cat([self.loc_bins_x, self.loc_bins_y], 1) else: raise NotImplementedError if "direct" in self.box_coder.height: self.conv_z = nn.Conv2d(num_input, 1, 1) elif "soft_argmin" in self.box_coder.height: self.conv_z = nn.Conv2d(num_input, self.box_coder.kwargs["z_bin_num"], 1) self.z_loc_bins = torch.linspace(self.box_coder.kwargs["z_range"][0], self.box_coder.kwargs["z_range"][1], self.box_coder.kwargs["z_bin_num"]).reshape(1, self.box_coder.kwargs["z_bin_num"], 1, 1) else: raise NotImplementedError if "soft_argmin" in self.box_coder.dim: self.conv_dim = nn.Conv2d(num_input, 3 * self.box_coder.kwargs["dim_bin_num"], 1) self.dim_loc_bins = torch.linspace(self.box_coder.kwargs["dim_range"][0], self.box_coder.kwargs["dim_range"][1], self.box_coder.kwargs["dim_bin_num"]).reshape(1, self.box_coder.kwargs[ "dim_bin_num"], 1, 1) self.dim_bins = torch.cat([self.dim_loc_bins, self.dim_loc_bins, self.dim_loc_bins], 1) else: self.conv_dim = nn.Conv2d(num_input, 3, 1) if self.box_coder.velocity: self.conv_velo = nn.Conv2d(num_input, 2, 1) if self.box_coder.rotation == "vector": self.conv_r = nn.Conv2d(num_input, 2, 1) elif self.box_coder.rotation == "soft_argmin": self.conv_r = nn.Conv2d(num_input, self.box_coder.kwargs["r_bin_num"], 1) self.r_loc_bins = torch.linspace(-np.pi, np.pi, self.box_coder.kwargs["r_bin_num"]).reshape( 1, self.box_coder.kwargs["r_bin_num"], 1, 1) else: self.conv_r = nn.Conv2d(num_input, 1, 1) def forward(self, x, return_loss): x_bev = x ret_dict = {} cls_preds = self.conv_cls(x_bev).permute(0, 2, 3, 1).contiguous() # predict bounding box xy = self.conv_xy(x_bev) z = self.conv_z(x_bev) dim = self.conv_dim(x_bev) # encode as bounding box if self.box_coder.center == "soft_argmin": xy = xy.view( (xy.shape[0], 2, self.box_coder.kwargs["xy_bin_num"], xy.shape[2], xy.shape[3])) xy = F.softmax(xy, dim=2) xy = xy * self.loc_bins.to(xy.device) xy = torch.sum(xy, dim=2, keepdim=False) if "soft_argmin" in self.box_coder.height: z = F.softmax(z, dim=1) z = z * self.z_loc_bins.to(z.device) z = torch.sum(z, dim=1, keepdim=True) if "soft_argmin" in self.box_coder.dim: dim = dim.view( (dim.shape[0], 3, self.box_coder.kwargs["dim_bin_num"], dim.shape[2], dim.shape[3])) dim = F.softmax(dim, dim=2) dim = dim * self.dim_loc_bins.to(dim.device) dim = torch.sum(dim, dim=2, keepdim=False) xy = xy.permute(0, 2, 3, 1).contiguous() z = z.permute(0, 2, 3, 1).contiguous() dim = dim.permute(0, 2, 3, 1).contiguous() if self.box_coder.dim == "direct": dim = F.relu(dim) if self.box_coder.velocity: velo = self.conv_velo(x_bev).permute(0, 2, 3, 1).contiguous() r_preds = self.conv_r(x_bev) if self.box_coder.rotation == "vector": #import pdb; pdb.set_trace() r_preds = F.normalize(r_preds, p=2, dim=1) elif self.box_coder.rotation == "soft_argmin": r_preds = F.softmax(r_preds, dim=1) r_preds = r_preds * self.r_loc_bins.to(r_preds.device) r_preds = torch.sum(r_preds, dim=1, keepdim=True) r_preds = r_preds.permute(0, 2, 3, 1).contiguous() if self.box_coder.velocity: box_preds = torch.cat([xy, z, dim, velo, r_preds], -1) else: box_preds = torch.cat([xy, z, dim, r_preds], -1) ret_dict.update({"box_preds": box_preds, "cls_preds": cls_preds}) return ret_dict @HEADS.register_module class MultiGroupOHSHeadClear(nn.Module): def __init__(self, mode: str = "3d", in_channels: List[int] = [128, ], norm_cfg=None, tasks: List[Dict] = [], weights=[], box_coder: BoxCoder = None, with_cls: bool = True, with_reg: bool = True, encode_background_as_zeros: bool = True, use_sigmoid_score: bool = True, loss_norm: Dict = dict(type="NormByNumPositives", pos_class_weight=1.0, neg_class_weight=1.0,), loss_cls: Dict = dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0,), loss_bbox: Dict = dict(type="SmoothL1Loss", beta=1.0, loss_weight=1.0,), atten_res: Sequence[int] = None, assign_cfg: Optional[dict] = dict(), name="rpn",): super().__init__() assert with_cls or with_reg # read tasks and analysis the classes for tasks num_classes = [len(t["class_names"]) for t in tasks] self.class_names = [t["class_names"] for t in tasks] self.num_anchor_per_locs = [1] * len(num_classes) self.targets = tasks # define the essential paramters self.box_coder = box_coder self.with_cls = with_cls self.with_reg = with_reg self.in_channels = in_channels self.num_classes = num_classes self.encode_background_as_zeros = encode_background_as_zeros self.use_sigmoid_score = use_sigmoid_score self.box_n_dim = self.box_coder.n_dim self.mode = mode self.assign_cfg = assign_cfg self.pc_range = np.asarray(self.box_coder.pc_range) # [6] self.dims = self.pc_range[3:] - self.pc_range[:3] # [3] # initialize loss self.loss_norm = loss_norm self.loss_cls = build_loss(loss_cls) self.loss_reg = build_loss(loss_bbox) self.atten_res = atten_res # initialize logger logger = logging.getLogger("MultiGroupHead") self.logger = logger # check box_coder assert isinstance( box_coder, GroundBox3dCoderAF), "OHSLoss must comes with an anchor-free box coder" assert box_coder.code_size == len( loss_bbox.code_weights), "code weights does not match code size" # set multi-tasks heads # split each head num_clss = [] num_preds = [] box_code_sizes = [self.box_coder.n_dim] * len(self.num_classes) for num_c, num_a, box_cs in zip( self.num_classes, self.num_anchor_per_locs, box_code_sizes ): if self.encode_background_as_zeros: num_cls = num_a * num_c else: num_cls = num_a * (num_c + 1) num_clss.append(num_cls) num_pred = num_a * box_cs num_preds.append(num_pred) self.logger.info( f"num_classes: {self.num_classes}, num_preds: {num_preds}" ) # construct each task head self.tasks = nn.ModuleList() for task_id, (num_pred, num_cls) in enumerate(zip(num_preds, num_clss)): self.tasks.append( OHSHeadClear( self.box_coder, self.in_channels, num_pred, num_cls, header=False, mode=self.mode, ) ) def set_train_cfg(self, cfg): self.ohs_loss = [] self.atten_loss = [] for task_id, target in enumerate(self.targets): self.ohs_loss.append( OHSLossClear(self.box_coder, target.num_class, self.loss_cls, self.loss_reg, self.encode_background_as_zeros, cfg, self.loss_norm, task_id, self.mode)) self.atten_loss.append( AttentionConstrainedLoss( self.box_coder, target.num_class, task_id, self.atten_res) ) self.logger.info("Finish Attention Constrained Loss Initialization") self.logger.info("Finish MultiGroupOHSHeadClear Initialization") def forward(self, x, return_loss=False): ret_dicts = [] for task in self.tasks: ret_dicts.append(task(x, return_loss)) return ret_dicts def loss(self, example, preds_dicts, **kwargs): annos = example["annos"] batch_size_device = example["num_voxels"].shape[0] batch_labels = [anno["gt_classes"] for anno in annos] batch_boxes = [anno["gt_boxes"] for anno in annos] batch_atten_map = kwargs.get('atten_map', None) rets = [] for task_id, preds_dict in enumerate(preds_dicts): box_preds = preds_dict["box_preds"] cls_preds = preds_dict["cls_preds"] bs_per_gpu = len(cls_preds) batch_task_boxes = [batch_box[task_id] for batch_box in batch_boxes] batch_task_labels = [batch_label[task_id] for batch_label in batch_labels] attention_loss = defaultdict(list) for index, bam in enumerate(batch_atten_map): temp_attention_loss = self.atten_loss[task_id]( bam, batch_task_boxes, batch_task_labels) for ke, va in temp_attention_loss.items(): attention_loss[ke].append(va) targets = self.assign_hotspots(cls_preds, batch_task_boxes, batch_task_labels) labels, label_weights, bbox_targets, bbox_locs, num_total_pos, num_total_neg = targets # process assign targets labels = torch.stack(labels, 0).view(bs_per_gpu, -1) # [B, H*W] label_weights = torch.stack(label_weights, 0).view(bs_per_gpu, -1) # [B, H*W] kwargs = {} # calculate ohs loss for each task loc_loss, cls_loss = self.ohs_loss[task_id]( box_preds, cls_preds, labels, label_weights, bbox_targets, bbox_locs, **kwargs ) if self.loss_norm["type"] == "NormByNumExamples": normalizer = num_total_pos + num_total_neg elif self.loss_norm["type"] == "NormByNumPositives": normalizer = max(num_total_pos, 1.0) elif self.loss_norm["type"] == "NormByNumPosNeg": normalizer = self.loss_norm["pos_cls_weight"] * num_total_pos + \ self.loss_norm["neg_cls_weight"] * num_total_neg elif self.loss_norm["type"] == "dont_norm": # support ghm loss normalizer = batch_size_device else: raise ValueError(f"unknown loss norm type") loc_loss_reduced = loc_loss.sum() / normalizer loc_loss_reduced *= self.loss_reg._loss_weight cls_pos_loss, cls_neg_loss = _get_pos_neg_loss(cls_loss, labels, label_weights) cls_pos_loss /= self.loss_norm["pos_cls_weight"] cls_neg_loss /= self.loss_norm["neg_cls_weight"] cls_loss_reduced = cls_loss.sum() / normalizer cls_loss_reduced *= self.loss_cls._loss_weight loss = loc_loss_reduced + cls_loss_reduced atten_loss = 0.0 for value in attention_loss.values(): if type(value) == list: temp_loss = 0.0 norm_fac = len(value) for temp_atten_loss in value: temp_loss = temp_loss + temp_atten_loss value = temp_loss * 1.0 / norm_fac atten_loss = atten_loss + value loss = loss + atten_loss loc_loss_elem = [ loc_loss[:, :, i].sum() / num_total_pos for i in range(loc_loss.shape[-1]) ] ret = { "loss": loss, "cls_pos_loss": cls_pos_loss.detach().cpu(), "cls_neg_loss": cls_neg_loss.detach().cpu(), "cls_loss_reduced": cls_loss_reduced.detach().cpu().mean(), "loc_loss_reduced": loc_loss_reduced.detach().cpu().mean(), "loc_loss_elem": [elem.detach().cpu() for elem in loc_loss_elem], "num_pos": torch.tensor([num_total_pos]), "num_neg": torch.tensor([num_total_neg]), } for key, value in attention_loss.items(): if type(value) == list: temp_loss = 0.0 norm_fac = len(value) for temp_atten_loss in value: temp_loss = temp_loss + temp_atten_loss value = temp_loss * 1.0 / norm_fac ret.update({key: value.detach().cpu()}) rets.append(ret) rets_merged = defaultdict(list) for ret in rets: for k, v in ret.items(): rets_merged[k].append(v) return rets_merged def assign_hotspots(self, cls_scores: torch.Tensor, gt_bboxes: List[np.ndarray], gt_labels: List[np.ndarray]): """ assign hotspots(generate targets) Args: cls_scores (torch.Tensor, [B, H, W, C]): classification prediction score map gt_bboxes (List[np.ndarray], [[M, ndim], [K, ndim], ...]): ground truth bounding box for each batch gt_labels (List[np.ndarray], [[M], [K], ...]): ground truth bounding box id for each batch cls_scores (torch.Tensor, [B, H, D, C], optional): classification prediction score map for RV. Default to None. """ bs_per_gpu = len(gt_bboxes) # Get the batch size device = cls_scores.device # Get the current device gt_bboxes = [torch.tensor(box, device=device).float() for box in gt_bboxes] # [M, 9], all gt_boxes # [M] all gt_classes,start from 1,( 0 means background) gt_labels = [torch.tensor(label, device=device).long() for label in gt_labels] labels_list, label_weights_list, bbox_targets_list, bbox_locs_list, num_pos_list, num_neg_list = \ multi_apply(self.assign_hotspots_bev_single, cls_scores, gt_bboxes, gt_labels) for i in range(bs_per_gpu): bbox_locs_list[i][:, 0] = i num_total_pos = sum([max(num, 1) for num in num_pos_list]) num_total_neg = sum([max(num, 1) for num in num_neg_list]) targets = (labels_list, label_weights_list, bbox_targets_list, bbox_locs_list, num_total_pos, num_total_neg) return targets def assign_hotspots_bev_single(self, cls_scores: torch.Tensor, gt_bboxes: torch.Tensor, gt_labels: torch.Tensor): r""" assign hotspots(generate targets) of BEV for a single batch. Args: cls_scores (torch.Tensor, [H, W, C]): classification prediction score map gt_bboxes (torch.Tensor, [M, ndim]): ground truth bounding box gt_labels_list (torch.Tensor, [M]): ground truth bounding box id """ h, w = cls_scores.size()[:2] # Get the size of the feature map of bev view (262,64) # initialize relate labels labels = torch.zeros_like(cls_scores[:, :, 0], dtype=torch.long) # Set up the bev labels label_weights = torch.ones_like( cls_scores[:, :, 0], dtype=torch.float) * self.loss_norm["neg_cls_weight"] # Initialize all weights to neg weights # initialized to record the positive bbx's location in grid map bbox_locs = cls_scores.new_zeros((0, 3), dtype=torch.long) # initialized to record the positive bbx's regression targets bbox_targets = cls_scores.new_zeros((0, self.box_coder.code_size), dtype=torch.float) # scan gt_bboxes self.effective_ratio = self.assign_cfg.get("effective_ratio", [1.0, 6.0]) if len(gt_bboxes > 0): effective_boxes = gt_bboxes[:, [0, 1, 3, 4]].clone().detach() # [M, 4] effective_ratio_l = (self.dims[0] / w) / effective_boxes[:, 2] # [M] effective_ratio_w = (self.dims[1] / h) / effective_boxes[:, 3] # [M] effective_ratio_l = effective_ratio_l.clamp(min=self.effective_ratio[0], # [M] max=self.effective_ratio[1]) # [M] effective_ratio_w = effective_ratio_w.clamp(min=self.effective_ratio[0], # [M] max=self.effective_ratio[1]) # [M] # expand the box'area into a grid if the box is too small, # so that this box label can match the center of the correspond box # the expanded box called `effective_boxes` effective_boxes[:, 2] *= effective_ratio_l effective_boxes[:, 3] *= effective_ratio_w # get the corners angles = gt_bboxes[:, -1] # [num_box] effective_boxes = box_torch_ops.center_to_corner_box2d( effective_boxes[:, :2], effective_boxes[:, 2:4], angles) ignore_boxes_out = effective_boxes # transfer the hybrid coordinate system to Cartesian coordinate system self.box_coder.layout(w, h) # read necessary parameters from box_coder # center cartesian coordinate, grid coordinate index in hybrid coordinate # grid_real_centers - [W * H, 2] # w_indices - [W * H] # h_indices - [W * H] grid_real_centers = self.box_coder.grids_sensor w_indices = self.box_coder.ww_l h_indices = self.box_coder.hh_l # scan bounding boxes for i in range(len(gt_bboxes)): # get the points(hotspots) cover by the bounding box pos_mask = points_in_convex_polygon_torch( grid_real_centers, effective_boxes[i].unsqueeze(0)) # [num_points, 8] # get the raw hotspots pos_ind = pos_mask.nonzero()[:, 0] # NOTE: fix the bugs of targets assignment in bev, while using hybird coordinates, # the `effective_boxes` may not expand enough to cover a grid center, # so we nearest search a grid center as hotspots for this situation gt_center = gt_bboxes[i: i + 1, :2] # [1, 2] dist_to_grid_center = torch.norm(grid_real_centers - gt_center, dim=1) # [W * H] min_ind = torch.argmin(dist_to_grid_center) if min_ind not in pos_ind: pos_ind = torch.cat([pos_ind.reshape(-1, 1), min_ind.reshape(-1, 1)], dim=0).reshape(-1) num_hotspots = self.assign_cfg.get("num_hotspots", 28) if self.assign_cfg.get("select_hotspots", True): # filter out the verbose hotspots if len(pos_ind) > num_hotspots: # if the hotspots are too many for the instance # select the num_hotspots-th nearest as valid hotspots points = grid_real_centers[pos_ind, :] diff = gt_bboxes[i, :2] - points diff = torch.norm(diff, dim=1) sorted_ind = torch.argsort(diff)[:num_hotspots] pos_ind = pos_ind[sorted_ind] # get the indices of hotspots pos_h_indices = h_indices[pos_ind] # [num_pos] pos_w_indices = w_indices[pos_ind] # [num_pos] # scan the positive hotspots if len(pos_h_indices): if not (labels[pos_h_indices, pos_w_indices] == 0).all(): unique_pos_h_indices = pos_h_indices.new_zeros((0,)) unique_pos_w_indices = pos_w_indices.new_zeros((0,)) unique_pos_ind = pos_ind.new_zeros((0,)) # NOTE: assert that each grid's gradient just be affected by one label # if a grid was covered by other label, eliminate its effects for ph, pw, pi in zip(pos_h_indices, pos_w_indices, pos_ind): if labels[ph, pw] == 0: unique_pos_h_indices = torch.cat( (unique_pos_h_indices, ph.view((1)))) unique_pos_w_indices = torch.cat( (unique_pos_w_indices, pw.view((1)))) unique_pos_ind = torch.cat((unique_pos_ind, pi.view((1)))) else: label_weights[ph, pw] = 0 pos_h_indices = unique_pos_h_indices pos_w_indices = unique_pos_w_indices pos_ind = unique_pos_ind # fullfill `labels` and `label_weights` labels[pos_h_indices, pos_w_indices] = gt_labels[i] label_weights[pos_h_indices, pos_w_indices] = self.loss_norm["pos_cls_weight"] # get the overlap hotspots and set the `label_weights` as 0 ig_mask = points_in_convex_polygon_torch( grid_real_centers, ignore_boxes_out[i].unsqueeze(0)) ig_mask = (ig_mask & (~pos_mask)).reshape(-1) # Get the overlapped grid ig_h = h_indices[ig_mask] ig_w = w_indices[ig_mask] # 1 for hspots in gtbbx, 0 for non-hspots in gtbbx label_weights[ig_h, ig_w] = 0 centers = grid_real_centers[pos_ind] shifts = torch.zeros((len(centers), self.box_coder.code_size), device=cls_scores.device, dtype=torch.float) # Got the encode bbx target for each positive grid shifts = self.box_coder._encode(centers, shifts, gt_bboxes[i]) zeros = torch.zeros_like(pos_w_indices) locs = torch.stack((zeros, pos_h_indices, pos_w_indices), dim=-1) # get the corresponding bounding boxes bbox_locs = torch.cat((bbox_locs, locs), dim=0) bbox_targets = torch.cat((bbox_targets, shifts), dim=0) # get the ratio os positive and negative examples num_pos = bbox_targets.size(0) num_neg = label_weights.nonzero().size(0) - num_pos return (labels, label_weights, bbox_targets, bbox_locs, num_pos, num_neg) def predict(self, example, preds_dicts, test_cfg, **kwargs): rets = [] double_flip = test_cfg.get('double_flip', False) for task_id, preds_dict in enumerate(preds_dicts): batch_size_device = example['num_voxels'].shape[0] if "metadata" not in example or len(example["metadata"]) == 0: meta_list = [None] * batch_size_device else: meta_list = example["metadata"] if double_flip: assert batch_size_device % 4 == 0, f"batch_size_device: {batch_size_device}" batch_size_device = int(batch_size_device / 4) meta_list = meta_list[:4 * int(batch_size_device):4] batch_box_preds_all = preds_dict["box_preds"] batch_cls_preds_all = preds_dict["cls_preds"] _, H, W, C = batch_box_preds_all.shape batch_box_preds_all = batch_box_preds_all.reshape( int(batch_size_device), 4, H, W, C) batch_box_preds_sincos_all = batch_box_preds_all[:, :, :, :, 8:10].clone() _, H, W, C = batch_cls_preds_all.shape batch_cls_preds_all = batch_cls_preds_all.reshape( int(batch_size_device), 4, H, W, C) batch_cls_preds_all[:, 1] = torch.flip(batch_cls_preds_all[:, 1], dims=[1]) batch_cls_preds_all[:, 2] = torch.flip(batch_cls_preds_all[:, 2], dims=[2]) batch_cls_preds_all[:, 3] = torch.flip(batch_cls_preds_all[:, 3], dims=[1, 2]) batch_cls_preds_all = batch_cls_preds_all.sigmoid() batch_cls_preds = batch_cls_preds_all.mean(dim=1) batch_box_preds_sincos_all[:, 1] = torch.flip( batch_box_preds_sincos_all[:, 1], dims=[1]) batch_box_preds_sincos_all[:, 2] = torch.flip( batch_box_preds_sincos_all[:, 2], dims=[2]) batch_box_preds_sincos_all[:, 3] = torch.flip( batch_box_preds_sincos_all[:, 3], dims=[1, 2]) num_class_with_bg = self.num_classes[task_id] if not self.encode_background_as_zeros: num_class_with_bg = self.num_classes[task_id] + 1 batch_cls_preds = batch_cls_preds.contiguous() batch_cls_preds = batch_cls_preds.view( batch_size_device, -1, num_class_with_bg) batch_reg_preds = torch.zeros( (int(batch_size_device), 4, H * W, 9), dtype=batch_box_preds_all.dtype, device=batch_box_preds_all.device) for i in range(4): batch_box_preds = batch_box_preds_all[:, i, :, :, :] box_ndim = self.box_n_dim h, w = batch_box_preds.size()[1:3] batch_box_preds = batch_box_preds.contiguous() batch_box_preds = batch_box_preds.view(batch_size_device, -1, box_ndim) if i == 1: # theta = pi-theta batch_box_preds[:, :, -2] = -batch_box_preds[:, :, -2] batch_box_preds_sincos_all[:, i, :, :, 0] = - \ batch_box_preds_sincos_all[:, i, :, :, 0] elif i == 2: # x=-x, theta = 2pi-theta, vx = -vx batch_box_preds[:, :, -1] = -batch_box_preds[:, :, -1] batch_box_preds_sincos_all[:, i, :, :, 1] = - \ batch_box_preds_sincos_all[:, i, :, :, 1] elif i == 3: # x=-x,y=-y, theta=theta-pi, vx=-vx, vy=-vy batch_box_preds[:, :, -1] = -batch_box_preds[:, :, -1] batch_box_preds[:, :, -2] = -batch_box_preds[:, :, -2] batch_box_preds_sincos_all[:, i, :, :, 0] = - \ batch_box_preds_sincos_all[:, i, :, :, 0] batch_box_preds_sincos_all[:, i, :, :, 1] = - \ batch_box_preds_sincos_all[:, i, :, :, 1] #import pdb; pdb.set_trace() # -pi/2 #batch_box_preds[:, :, -2], batch_box_preds[:, :, -1] = batch_box_preds[:, :, -1], -batch_box_preds[:, :, -2] # # +pi/2 #batch_box_preds[:, :, -2], batch_box_preds[:, :, -1] = -batch_box_preds[:, :, -1], batch_box_preds[:, :, -2] batch_reg_preds_temp = self.box_coder._decode( batch_box_preds[:, :, :self.box_coder.code_size], w, h ) if i == 1: # y=-y, vy = -vy batch_reg_preds_temp[:, :, 1] = -batch_reg_preds_temp[:, :, 1] batch_reg_preds_temp[:, :, 7] = -batch_reg_preds_temp[:, :, 7] elif i == 2: # x=-x, vx = -vx batch_reg_preds_temp[:, :, 0] = -batch_reg_preds_temp[:, :, 0] batch_reg_preds_temp[:, :, 6] = -batch_reg_preds_temp[:, :, 6] elif i == 3: # x=-x,y=-y, vx=-vx, vy=-vy batch_reg_preds_temp[:, :, 1] = -batch_reg_preds_temp[:, :, 1] batch_reg_preds_temp[:, :, 0] = -batch_reg_preds_temp[:, :, 0] batch_reg_preds_temp[:, :, 7] = -batch_reg_preds_temp[:, :, 7] batch_reg_preds_temp[:, :, 6] = -batch_reg_preds_temp[:, :, 6] batch_reg_preds[:, i, :, :] = batch_reg_preds_temp batch_box_preds_sincos_all = batch_box_preds_sincos_all.mean(dim=1) batch_box_preds_sincos_all = batch_box_preds_sincos_all.reshape( batch_size_device, -1, 2) batch_box_preds_rads = torch.atan2( batch_box_preds_sincos_all[:, :, 1], batch_box_preds_sincos_all[:, :, 0]) batch_reg_preds = batch_reg_preds.reshape(batch_size_device, 4, H, W, 9) batch_reg_preds[:, 1] = torch.flip(batch_reg_preds[:, 1], dims=[1]) batch_reg_preds[:, 2] = torch.flip(batch_reg_preds[:, 2], dims=[2]) batch_reg_preds[:, 3] = torch.flip(batch_reg_preds[:, 3], dims=[1, 2]) batch_reg_preds = batch_reg_preds.mean(dim=1) batch_reg_preds = batch_reg_preds.reshape(batch_size_device, -1, 9) batch_reg_preds[:, :, -1] = batch_box_preds_rads else: batch_box_preds = preds_dict["box_preds"] batch_cls_preds = preds_dict["cls_preds"].sigmoid() box_ndim = self.box_n_dim h, w = batch_box_preds.size()[1:3] batch_box_preds = batch_box_preds.view(batch_size_device, -1, box_ndim) num_class_with_bg = self.num_classes[task_id] if not self.encode_background_as_zeros: num_class_with_bg = self.num_classes[task_id] + 1 batch_cls_preds = batch_cls_preds.contiguous() batch_cls_preds = batch_cls_preds.view(batch_size_device, -1, num_class_with_bg) batch_reg_preds = self.box_coder._decode( batch_box_preds[:, :, :self.box_coder.code_size], w, h ) batch_dir_preds = [None] * batch_size_device rets.append( self.get_task_detections( task_id, num_class_with_bg, test_cfg, batch_cls_preds, batch_reg_preds, batch_dir_preds, meta_list, ) ) num_samples = len(rets[0]) ret_list = [] for i in range(num_samples): ret = {} for k in rets[0][i].keys(): if k in ["box3d_lidar", "scores"]: ret[k] = torch.cat([ret[i][k] for ret in rets]) elif k in ["label_preds"]: flag = 0 for j, num_class in enumerate(self.num_classes): rets[j][i][k] += flag flag += num_class ret[k] = torch.cat([ret[i][k] for ret in rets]) elif k == "metadata": # metadata ret[k] = rets[0][i][k] ret_list.append(ret) return ret_list def get_task_detections( self, task_id, num_class_with_bg, test_cfg, batch_cls_preds, batch_reg_preds, batch_dir_preds=None, meta_list=None, ): predictions_dicts = [] post_center_range = test_cfg.post_center_limit_range if len(post_center_range) > 0: post_center_range = torch.tensor( post_center_range, dtype=batch_reg_preds.dtype, device=batch_reg_preds.device, ) for box_preds, cls_preds, dir_preds, meta in zip( batch_reg_preds, batch_cls_preds, batch_dir_preds, meta_list, ): box_preds = box_preds.float() cls_preds = cls_preds.float() if self.encode_background_as_zeros: # this don't support softmax assert self.use_sigmoid_score is True total_scores = cls_preds #total_scores = cls_preds else: # encode background as first element in one-hot vector if self.use_sigmoid_score: total_scores = cls_preds[..., 1:] else: total_scores = F.softmax(cls_preds, dim=-1)[..., 1:] feature_map_size_prod = ( batch_reg_preds.shape[1] // self.num_anchor_per_locs[task_id] ) # get highest score per prediction, than apply nms # to remove overlapped box. if num_class_with_bg == 1: top_scores = total_scores.squeeze(-1) top_labels = torch.zeros( total_scores.shape[0], device=total_scores.device, dtype=torch.long, ) else: top_scores, top_labels = torch.max(total_scores, dim=-1) if test_cfg.score_threshold > 0.0: thresh = torch.tensor( [test_cfg.score_threshold], device=total_scores.device ).type_as(total_scores) top_scores_keep = top_scores >= thresh top_scores = top_scores.masked_select(top_scores_keep) if top_scores.shape[0] != 0: if test_cfg.score_threshold > 0.0: box_preds = box_preds[top_scores_keep] assert (box_preds[:, 3:6] > 0).cpu().numpy().any() top_labels = top_labels[top_scores_keep] boxes_for_nms = box_torch_ops.boxes3d_to_bevboxes_lidar_torch(box_preds) selected = box_torch_ops.rotate_nms_pcdet(boxes_for_nms, top_scores, thresh=test_cfg.nms.nms_iou_threshold, pre_maxsize=test_cfg.nms.nms_pre_max_size, post_max_size=test_cfg.nms.nms_post_max_size) else: selected = [] # if selected is not None: selected_boxes = box_preds[selected] selected_labels = top_labels[selected] selected_scores = top_scores[selected] # finally generate predictions. if selected_boxes.shape[0] != 0: box_preds = selected_boxes scores = selected_scores label_preds = selected_labels final_box_preds = box_preds final_scores = scores final_labels = label_preds if post_center_range is not None: mask = (final_box_preds[:, :3] >= post_center_range[:3]).all(1) mask &= (final_box_preds[:, :3] <= post_center_range[3:]).all(1) predictions_dict = { "box3d_lidar": final_box_preds[mask], "scores": final_scores[mask], "label_preds": label_preds[mask], "metadata": meta, } else: predictions_dict = { "box3d_lidar": final_box_preds, "scores": final_scores, "label_preds": final_labels, "metadata": meta, } else: dtype = batch_reg_preds.dtype device = batch_reg_preds.device predictions_dict = { "box3d_lidar": torch.zeros([0, box_preds.shape[1]], dtype=dtype, device=device), "scores": torch.zeros([0], dtype=dtype, device=device), "label_preds": torch.zeros( [0], dtype=top_labels.dtype, device=device ), "metadata": meta, } predictions_dicts.append(predictions_dict) return predictions_dicts

46.541618

133

0.540227

4,925

39,700

4.033503

0.096041

0.033828

0.038611

0.019935

0.420992

0.349711

0.26821

0.225925

0.169444

0.152882

0

0.015431

0.358463

39,700

852

134

46.596244

0.764537

0.093325

0

0.204511

0

0.036144

0.000615

0

0.009023

1

0.018045

false

0

0.02406

0

0.058647

0

null

0

null

0

1

0

e501d8d323b13656dcc8aa99310855c82f2554fb

4,267

py

Python

run_full_dataset.py

TimoK93/ApLift

732070175ab6bf76db5b0c793cdb4a1fb5d235d7

[ "MIT" ]

4

2021-09-23T17:44:01.000Z

2022-01-10T07:14:25.000Z

run_full_dataset.py

TimoK93/ApLift

732070175ab6bf76db5b0c793cdb4a1fb5d235d7

[ "MIT" ]

1

2021-10-18T07:41:31.000Z

run_full_dataset.py

TimoK93/ApLift

732070175ab6bf76db5b0c793cdb4a1fb5d235d7

[ "MIT" ]

null

""" A script to run the main script with all sequences of a dataset. To use the script a config.yaml needs to be specified. Example usage: python3 main.py config/example_config.yaml if "pretrained_model_path" is passed as an argument in the config, training is skipped and pretrained models are used for the inference. """ import os import shutil from copy import copy os.environ["CUDA_VISIBLE_DEVICES"] = "" # GPUs are not necessary! from main import run_pipeline from src.utilities.config_reader import main_function def copyanything(src, dst): for root, dirs, files in os.walk(src): for name in files: dir = root.replace(src, dst) dst_file = os.path.join(dir, name) if os.path.exists(dst_file): print("Model", dst_file, "is already existing!") os.makedirs(dir, exist_ok=True) shutil.copy(os.path.join(root, name), os.path.join(dir, name)) @main_function def main(working_dir, dataset: str, pretrained_models_path=None, **kwargs): """ Runs the main pipeline on all sequences of a dataset """ ''' Create directory an copy pretrained models ''' os.makedirs(working_dir, exist_ok=True) if pretrained_models_path is not None: copyanything(os.path.join(pretrained_models_path), working_dir) ''' Creates a list of jobs to be executed''' jobs = list() if dataset == "MOT17": detectors = ["FRCNN", "DPM", "SDP"] train_sequences = [2, 4, 5, 9, 10, 11, 13] test_sequences = [1, 3, 6, 7, 8, 12, 14] for d in detectors: for t in train_sequences: train = ["MOT17-%s-%s" % (str(_).rjust(2, "0"), d) for _ in train_sequences if _ != t] val = ["MOT17-%s-%s" % (str(t).rjust(2, "0"), d)] jobs.append(dict(train=train, val=val, detector=d)) for t in test_sequences: train = ["MOT17-%s-%s" % (str(_).rjust(2, "0"), d) for _ in train_sequences] val = ["MOT17-%s-%s" % (str(t).rjust(2, "0"), d)] jobs.append(dict(train=train, val=val, detector=d)) elif dataset == "MOT20": test_sequences = [4, 6, 7, 8] train_sequences = [1, 2, 3, 5] for t in train_sequences: train = ["MOT20-%s" % str(_).rjust(2, "0") for _ in train_sequences if _ != t] val = ["MOT20-%s" % str(t).rjust(2, "0")] jobs.append(dict(train=train, val=val, detector="FRCNN")) for t in test_sequences: train = ["MOT20-%s" % str(_).rjust(2, "0") for _ in train_sequences] val = ["MOT20-%s" % str(t).rjust(2, "0")] jobs.append(dict(train=train, val=val, detector="FRCNN")) elif dataset == "MOT15": test_sequences = [ 'Venice-1', 'TUD-Crossing', 'PETS09-S2L2', 'KITTI-19', 'KITTI-16', 'ETH-Jelmoli', 'ETH-Linthescher', 'ETH-Crossing', 'AVG-TownCentre', 'ADL-Rundle-3', 'ADL-Rundle-1' ] train_sequences = [ 'Venice-2', 'KITTI-17', 'KITTI-13', 'ETH-Sunnyday', 'ETH-Pedcross2', 'ETH-Bahnhof', 'ADL-Rundle-8', 'TUD-Stadtmitte', 'TUD-Campus', 'ADL-Rundle-6', 'PETS09-S2L1' ] for t in train_sequences: train = [_ for _ in train_sequences if _ != t] val = [t] jobs.append(dict(train=train, val=val, detector="FRCNN")) for t in test_sequences: train = [_ for _ in train_sequences if _ != t] val = [t] jobs.append(dict(train=train, val=val, detector="FRCNN")) ''' Runs the jobs sequentially ''' features = copy(kwargs["data_config"]["edge_features"]) for job in jobs: print("Run Job:", job) if os.path.exists(os.path.join(working_dir, job["val"][0], job["val"][0] + ".txt")): print("... Result file already existing!") continue kwargs["data_config"]["edge_features"] = copy(features) kwargs["data_config"]["dataset"]["detector"] = job["detector"] kwargs["training_config"]["sequences_for_training"] = job["train"] kwargs["training_config"]["sequences_for_inference"] = job["val"] run_pipeline(working_dir=working_dir, **kwargs) if __name__ == "__main__": main()

40.638095

117

0.588704

578

4,267

4.207612

0.261246

0.069079

0.059211

0.046875

0.362253

0.280839

0.263158

0.260691

0

0.028779

0.258964

4,267

104

118

41.028846

0.740354

0.093743

0

0.266667

0

0.172127

0.012084

0

1

0.026667

false

0

0.066667

0

0.093333

0.04

0

null

0

null

0

1

0

e5033a61e857baa0aed9b97b41edbcf668557962

298

py

Python

_Training_/RegEx - HackerRank/2. Character Class/Excluding Specific Characters.py

JUD210/Study-Note

2add9db3f11d99370f49878f0c19e9caa60d2d02

[ "MIT" ]

null

_Training_/RegEx - HackerRank/2. Character Class/Excluding Specific Characters.py

JUD210/Study-Note

2add9db3f11d99370f49878f0c19e9caa60d2d02

[ "MIT" ]

null

_Training_/RegEx - HackerRank/2. Character Class/Excluding Specific Characters.py

JUD210/Study-Note

2add9db3f11d99370f49878f0c19e9caa60d2d02

[ "MIT" ]

null

# https://www.hackerrank.com/challenges/excluding-specific-characters/problem import re # Inputs standard_input = """think?""" Regex_Pattern = ( r"^[^\d][^aeiou][^bcDF][^\r\n\t\f\s][^AEIOU][^.,]$" ) # Do not delete 'r'. print(str(bool(re.search(Regex_Pattern, input()))).lower()) # true

18.625

77

0.64094

41

298

4.585366

0.829268

0.12766

0

0.114094

298

15

78

19.866667

0.712121

0.355705

0

0.28877

0.256684

0

1

0

false

0

0.166667

0

0.166667

0

null

0

null

0

1

0

e503d96de70079ddac429727f7983b8a0fcdef59

354

py

Python

toroid/toroid/pairs.py

LeoTindall/corewar32

c29891ca67c01dd65d01d120364a010eb12eb255

[ "Apache-2.0" ]

null

toroid/toroid/pairs.py

LeoTindall/corewar32

c29891ca67c01dd65d01d120364a010eb12eb255

[ "Apache-2.0" ]

1

2016-08-06T23:20:56.000Z

toroid/toroid/pairs.py

SilverWingedSeraph/corewar32

c29891ca67c01dd65d01d120364a010eb12eb255

[ "Apache-2.0" ]

null

def make_pairings(warriors): if len(warriors) == 0: return False, False pairings = [] for (warrior1, warrior2) in zip(warriors[0::2], warriors[1::2]): pairings.append((warrior1, warrior2)) if len(warriors) % 2 == 0: odd_one_out = False else: odd_one_out = warriors[-1] return pairings, odd_one_out

29.5

68

0.610169

47

354

4.446809

0.446809

0.086124

0.129187

0

0.045802

0.259887

354

11

69

32.181818

0.751908

0

1

0.090909

false

0

0.272727

0

null

0

null

0

1

0

e503f5404abfe29fbefb2c59950b6c8386b55b14

5,814

py

Python

Nets/TRNetShared.py

AndresOtero/TensorDecompositionMachineLearning

455f16b405ec9d031999b0ebf9c5a68d3c20b233

[ "MIT" ]

3

2021-06-11T02:46:06.000Z

2021-08-17T02:59:30.000Z

Nets/TRNetShared.py

AndresOtero/TensorDecompositionMachineLearning

455f16b405ec9d031999b0ebf9c5a68d3c20b233

[ "MIT" ]

null

Nets/TRNetShared.py

AndresOtero/TensorDecompositionMachineLearning

455f16b405ec9d031999b0ebf9c5a68d3c20b233

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from Nets.TRNetSerialized import TRNetSerialized from Nets.TTNetParallel import FirstKernelTensorTrain, FeatureMap, TTKernel from Nets.TTNetShared import KernelSharedTensorTrain from Utils import Constant from Utils.RanksFactory import RanksFactory from Utils.TensorTools import group_divisions from torch.nn import Parameter class LastKernelSharedTensorRing(nn.Module): def __init__(self, categories, first_rank, m, second_rank, init_value): super(LastKernelSharedTensorRing, self).__init__() self.categories = categories self.first_rank = first_rank self.m = m self.second_rank = second_rank self.weight = Parameter(torch.randn(categories, first_rank, m, second_rank)) self.init_value = init_value self.reset_parameters() def reset_parameters(self): nn.init.xavier_uniform_(self.weight,gain=self.init_value ) def forward(self, input, state): x = torch.einsum('bj,rbi->rbji', [input, state]) # OuterProduct x = torch.einsum('cijk,rbji->cbrk', [self.weight, x]) # Mutiply by core return x class KernelSharedTensorRing(nn.Module): def __init__(self, first_rank, m, second_rank, init_value): super(KernelSharedTensorRing, self).__init__() self.first_rank = first_rank self.m = m self.second_rank = second_rank self.weight = Parameter(torch.randn(first_rank, m, second_rank)) self.init_value = init_value self.reset_parameters() def reset_parameters(self): nn.init.xavier_uniform_(self.weight,gain=self.init_value ) def forward(self, input, state): x = torch.einsum('bj,rbi->rbji', [input, state]) # OuterProduct x = torch.einsum('ijk,rbji->rbk', [self.weight, x]) # Mutiply by core return x class TRNetShared(nn.Module): def __init__(self, net_params): super(TRNetShared, self).__init__() self.ranks = RanksFactory.create_tensor_ring_shared_ranks(net_params) self.kernels = [] self.m = net_params.get_m() self.n = net_params.get_n() self.amount_of_divisions = net_params.get_amount_of_divisions() self.batch_size = net_params.get_batch_size() self.feature_map = FeatureMap(self.n, self.m, self.amount_of_divisions, self.batch_size) self.amount_of_divisions = net_params.amount_of_divisions self.categories = net_params.categories self.last_kernel = LastKernelSharedTensorRing(self.categories, self.ranks[Constant.SECOND], self.m, self.ranks[Constant.THIRD], net_params.init_value) self.shared_kernel = KernelSharedTensorRing(self.ranks[Constant.SECOND], self.m, self.ranks[Constant.THIRD], net_params.init_value) def forward(self, tensor): featured_tensor = self.feature_map(tensor) division_divided_tensors = group_divisions(featured_tensor, self.amount_of_divisions) batch_size = tensor.size()[Constant.FIRST] state = torch.ones(self.ranks[Constant.FIRST], batch_size, self.ranks[Constant.SECOND]) times = division_divided_tensors.size()[Constant.FIRST] for t in range(0, times): division_divided_input = division_divided_tensors[t] state = self.shared_kernel(division_divided_input, state) pad_input = torch.ones(batch_size, self.m) state = self.last_kernel(pad_input, state) state = TRNetSerialized.calculate_traces_serialized(state) return F.log_softmax(state, dim=1) def extra_repr(self): return 'ranks={}'.format( self.ranks ) def get_number_of_parameters(self): for p in self.parameters(): print(p.numel()) self.number = sum(p.numel() for p in self.parameters()) return self.number class TRNetSharedWithoutFeatureMap(nn.Module): def __init__(self, net_params): super(TRNetSharedWithoutFeatureMap, self).__init__() self.ranks = RanksFactory.create_tensor_ring_shared_ranks(net_params) self.kernels = [] self.m = net_params.get_m() self.n = net_params.get_n() self.amount_of_divisions = net_params.get_amount_of_divisions() self.batch_size = net_params.get_batch_size() self.amount_of_divisions = net_params.amount_of_divisions self.categories = net_params.categories self.last_kernel = LastKernelSharedTensorRing(self.categories, self.ranks[Constant.SECOND], self.m, self.ranks[Constant.LAST], net_params.init_value) self.shared_kernel = KernelSharedTensorRing(self.ranks[Constant.SECOND], self.n, self.ranks[Constant.THIRD], net_params.init_value) def forward(self, tensor): division_divided_tensors = tensor.transpose(0,1) batch_size = tensor.size()[Constant.FIRST] state = torch.ones(self.ranks[Constant.FIRST], batch_size, self.ranks[Constant.SECOND]) times = division_divided_tensors.size()[Constant.FIRST] for t in range(0, times): division_divided_input = division_divided_tensors[t] state = self.shared_kernel(division_divided_input, state) pad_input = torch.ones(batch_size, self.m) state = self.last_kernel(pad_input, state) state = TRNetSerialized.calculate_traces_serialized(state) return state def extra_repr(self): return 'ranks={}'.format( self.ranks ) def get_number_of_parameters(self): self.number = sum(p.numel() for p in self.parameters()) return self.number

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5,814

5.278642

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0.721597

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null

0

1

0

null

0

5

e504a4528ab13dc7037b0bc87eb63a2bf6c3d4cb

2,550

py

Python

tests/test_deny_mime_type_validator.py

fastmonkeys/pontus

6542190aae896cd79c55f7f43e98a6bf3cbc613b

[ "MIT" ]

4

2017-04-24T10:17:28.000Z

2020-05-28T06:25:03.000Z

tests/test_deny_mime_type_validator.py

fastmonkeys/pontus

6542190aae896cd79c55f7f43e98a6bf3cbc613b

[ "MIT" ]

9

2015-02-23T14:27:37.000Z

2021-02-24T13:23:41.000Z

tests/test_deny_mime_type_validator.py

fastmonkeys/pontus

6542190aae896cd79c55f7f43e98a6bf3cbc613b

[ "MIT" ]

1

2017-08-14T16:40:44.000Z

# -*- coding: utf-8 -*- import os import pytest import boto3 from pontus.exceptions import ValidationError from pontus.validators import DenyMimeType class TestDenyMimeTypeValidator(object): @pytest.fixture def jpeg_key(self, bucket): with open(os.path.join( os.path.dirname(__file__), 'data', 'example.jpg' ), 'rb') as image: key_name = 'example.jpg' obj = boto3.resource('s3').Object(bucket.name, key_name) obj.put( Body=image ) return obj def test_raises_validation_error_if_invalid_mime_type( self, jpeg_key ): validator = DenyMimeType(mime_type='image/jpeg') with pytest.raises(ValidationError) as e: validator(jpeg_key) assert str(e.value) == ( "Invalid file: File MIME type image/jpeg is in denied list " "image/jpeg." ) def test_does_not_raise_validation_error_if_valid_mime_type( self, jpeg_key ): validator = DenyMimeType(mime_type='image/png') validator(jpeg_key) def test_repr(self): assert repr(DenyMimeType(mime_type='image/png')) == ( u"<DenyMimeType mime_types='image/png'>" ) def test_raises_validation_error_if_mime_type_not_in_valid_mime_types( self, jpeg_key ): validator = DenyMimeType(mime_types=['image/jpeg', 'application/csv']) with pytest.raises(ValidationError) as e: validator(jpeg_key) assert str(e.value) == ( "Invalid file: File MIME type image/jpeg is in denied list " "['image/jpeg', 'application/csv']." ) def test_doesnt_raise_validation_error_if_mime_type_in_valid_mime_types( self, jpeg_key ): validator = DenyMimeType(mime_types=['image/png', 'application/csv']) validator(jpeg_key) def test_raises_validation_error_if_mime_type_doesnt_match_regex( self, jpeg_key ): validator = DenyMimeType(regex=r'image\/.*') with pytest.raises(ValidationError) as e: validator(jpeg_key) assert str(e.value) == ( "Invalid file: File MIME type image/jpeg matches denied regex " "r'image\/.*'." ) def test_doesnt_raise_validation_error_if_mime_type_matches_regex( self, jpeg_key ): validator = DenyMimeType(regex=r'application\/.*') validator(jpeg_key)

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0.608627

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2,550

5.034247

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0.061905

0.069388

0.081633

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0.444218

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2,550

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0.202703

0

null

0

null

0

1

0

e5052a97fcc4c82072c34cd78471734f05a4fd9c

1,339

py

Python

proyecto utilizando pewee migrations/models.py

Raul-Flores/ORM-example

ff289f74f858514cebefe7070c3688ad773a0e2a

[ "MIT" ]

null

proyecto utilizando pewee migrations/models.py

Raul-Flores/ORM-example

ff289f74f858514cebefe7070c3688ad773a0e2a

[ "MIT" ]

null

proyecto utilizando pewee migrations/models.py

Raul-Flores/ORM-example

ff289f74f858514cebefe7070c3688ad773a0e2a

[ "MIT" ]

null

import pymysql import sqlite3 import psycopg2 from peewee import * db = "postgres" class BaseModel(Model): class Meta: global db secretvar= "Secret" while True: try: #db = input("BD Options: [mariadb|postgres|sqlite] :") db_migration = db if db == "mariadb": db = MySQLDatabase("TEST", host="localhost", port=3306, user="root", password=secretvar) break elif db == "postgres": db = PostgresqlDatabase("TEST", host="localhost", port=5432, user="postgres", password=secretvar) break elif db == "sqlite": db = SqliteDatabase('TEST.db') break except: print ("Los valores introducidos no son correctos") database = db class interface(BaseModel): device_ip = CharField(max_length=40) intf_name = CharField(max_length=40) description = CharField(max_length=90) is_enabled = BooleanField() mac_address = CharField(max_length=30) mtu = IntegerField() speed = IntegerField() status_date = DateTimeField() validation1 = BooleanField(default=True) detalles = CharField(max_length=50) #class Meta: # db_table = 'interface'

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5.740458

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1,339

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false

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null

0

null

0

1

0

1

e50744ae0a91afd75f5121562b3f88c9fadcfea8

1,966

py

Python

MyModel/signLanguageTranslator.py

rahulmishra11/Sign-Language-Translator

83b6907f722324d01142ab25e9e9cf806c51b0d3

[ "Apache-2.0" ]

null

MyModel/signLanguageTranslator.py

rahulmishra11/Sign-Language-Translator

83b6907f722324d01142ab25e9e9cf806c51b0d3

[ "Apache-2.0" ]

null

MyModel/signLanguageTranslator.py

rahulmishra11/Sign-Language-Translator

83b6907f722324d01142ab25e9e9cf806c51b0d3

[ "Apache-2.0" ]

null

import tensorflow as tf import pandas as pd import numpy as np import tensorflow.keras as keras train = pd.read_csv("./sign_mnist_train/sign_mnist_train.csv") test = pd.read_csv("./sign_mnist_test/sign_mnist_test.csv") # put labels into y_train variable Y_train = train["label"] # Drop 'label' column X_train = train.drop(labels = ["label"],axis = 1) # put labels into y_test variable Y_test = test["label"] # Drop 'label' column X_test = test.drop(labels = ["label"],axis = 1) # Normalize the data X_train = X_train / 255.0 X_test = X_test / 255.0 print("x_train shape: ",X_train.shape) print("x_test shape: ",X_test.shape) #Reshape X_train = X_train.values.reshape(-1,28,28,1) X_test = X_test.values.reshape(-1,28,28,1) print("x_train shape: ",X_train.shape) print("x_test shape: ",X_test.shape) model = keras.models.Sequential([ keras.layers.Conv2D(filters=64, kernel_size=3, input_shape=[28, 28, 1]), keras.layers.MaxPooling2D(pool_size=2), keras.layers.Conv2D(filters=128, kernel_size=3, activation='relu', padding='same'), keras.layers.Conv2D(filters=128, kernel_size=3, activation='relu', padding='same'), keras.layers.MaxPooling2D(pool_size=2), keras.layers.Conv2D(filters=128, kernel_size=3, activation='relu', padding='same'), keras.layers.Conv2D(filters=128, kernel_size=3, activation='relu', padding='same'), keras.layers.MaxPooling2D(pool_size=2), keras.layers.Flatten(), keras.layers.Dense(units=128, activation='relu'), keras.layers.Dropout(0.5), keras.layers.Dense(units=64, activation='relu'), keras.layers.Dropout(0.5), keras.layers.Dense(units=25, activation='softmax'), ]) model.summary() model.compile( loss="sparse_categorical_crossentropy", optimizer = 'adam', metrics=['accuracy'] ) history = model.fit(X_train,Y_train, epochs=10,) pd.DataFrame(history.history).plot() model.save("sign_mnist_train.h5") print(model.evaluate(X_test,Y_test))

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65

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0.111111

0

null

0

null

0

1

0

e507533d51e8ae8cde7f283f5c299ebd345bec98

5,817

py

Python

gammapy/detect/tests/test_kernel.py

grburgess/gammapy

609e460698caca7223afeef5e71826c7b32728d1

[ "BSD-3-Clause" ]

3

2019-01-28T12:21:14.000Z

2019-02-10T19:58:07.000Z

gammapy/detect/tests/test_kernel.py

grburgess/gammapy

609e460698caca7223afeef5e71826c7b32728d1

[ "BSD-3-Clause" ]

null

gammapy/detect/tests/test_kernel.py

grburgess/gammapy

609e460698caca7223afeef5e71826c7b32728d1

[ "BSD-3-Clause" ]

null

# Licensed under a 3-clause BSD style license - see LICENSE.rst from __future__ import absolute_import, division, print_function, unicode_literals import numpy as np from numpy.testing import assert_allclose from astropy.io import fits from astropy.units import Quantity from astropy.coordinates.angles import Angle from ...utils.testing import requires_dependency, requires_data from ...image import SkyImage from ...stats import significance from ...datasets import FermiGalacticCenter from ..kernel import KernelBackgroundEstimatorData, KernelBackgroundEstimator @requires_dependency('scipy') def test_KernelBackgroundEstimatorData(): """Tests compute correlated maps in KernelBackgroundEstimatorData. This is the only method in KernelBackgroundEstimatorData that actually calculates anything. """ # Set up test counts and background counts_hdu = SkyImage.empty(nxpix=10, nypix=10, binsz=1, fill=42).to_image_hdu() counts_hdu.data[4][4] = 1000 counts = counts_hdu.data background_data = 42 * np.ones_like(counts, dtype=float) # Single unit pixel kernel so should actually be no change. background_kernel = np.ones((1, 1)) images = KernelBackgroundEstimatorData(counts, background_data) images.compute_correlated_maps(background_kernel) # Test significance image against Li & Ma significance value expected = significance(counts, background_data) actual = images.significance assert_allclose(actual, expected) @requires_dependency('scipy') @requires_data('gammapy-extra') class TestKernelBackgroundEstimator(object): def setup_class(self): """Prepares appropriate input and defines inputs for test cases. """ from scipy.ndimage import convolve # Load/create example model images counts_hdu = SkyImage.empty(nxpix=10, nypix=10, binsz=1, fill=42).to_image_hdu() counts_hdu.data[4][4] = 1000 counts = counts_hdu.data # Initial counts required by one of the tests. self.counts = counts psf = FermiGalacticCenter.psf() psf = psf.table_psf_in_energy_band(Quantity([10, 500], 'GeV')) kernel_array = psf.kernel(pixel_size=Angle(1, 'deg'), offset_max=Angle(3, 'deg'), normalize=True) counts_blob = convolve(counts, kernel_array, mode='constant') self.counts_blob = counts_blob # Start with flat background estimate # Background must be provided as an ImageHDU images = KernelBackgroundEstimatorData(counts=counts, header=counts_hdu.header) images_blob = KernelBackgroundEstimatorData(counts=counts_blob, header=counts_hdu.header) source_kernel = np.ones((1, 3)) background_kernel = np.ones((5, 3)) significance_threshold = 4 mask_dilation_radius = 1 # Loads prepared inputs into estimator self.kbe = KernelBackgroundEstimator( images, source_kernel, background_kernel, significance_threshold, mask_dilation_radius ) self.kbe2 = KernelBackgroundEstimator( images, source_kernel, background_kernel, significance_threshold, mask_dilation_radius ) self.kbe_blob = KernelBackgroundEstimator( images_blob, source_kernel, background_kernel, significance_threshold, mask_dilation_radius ) def test_run_iteration_point(self): """Asserts that mask and background are as expected according to input.""" # Call the run_iteration code as this is what is explicitly being tested self.kbe.run_iteration() # Should be run twice to update the mask self.kbe.run_iteration() mask = self.kbe.mask_image_hdu.data background = self.kbe.background_image_hdu.data # Check mask matches expectations expected_mask = np.ones_like(self.counts) expected_mask[4][3] = 0 expected_mask[4][4] = 0 expected_mask[4][5] = 0 assert_allclose(mask.astype(int), expected_mask) # Check background, should be 42 uniformly assert_allclose(background.astype(float), 42 * np.ones((10, 10))) def test_run_iteration_blob(self): """Asserts that mask and background are as expected according to input.""" # Call the run_iteration code as this is what is explicitly being tested self.kbe_blob.run_iteration() # Should be run twice to update the mask self.kbe_blob.run_iteration() background = self.kbe_blob.background_image_hdu.data # Check background, should be 42 uniformly within 10% assert_allclose(background, 42 * np.ones((10, 10)), rtol=0.15) def test_run(self): """Tests run script.""" mask, background = self.kbe2.run() assert_allclose(mask.sum(), 97) assert_allclose(background, 42 * np.ones((10, 10))) def test_save_files(self, tmpdir): """Tests that files are saves, and checks values within them.""" # Create temporary file to write output into self.kbe.run_iteration(1) self.kbe.save_files(base_dir=str(tmpdir), index=0) filename = tmpdir / '00_mask.fits' mask = fits.open(str(filename))[1].data filename = tmpdir / '00_significance.fits' significance = fits.open(str(filename))[1].data filename = tmpdir / '00_background.fits' background = fits.open(str(filename))[1].data # Checks values in files against known results for one iteration. assert_allclose(mask.sum(), 97) assert_allclose(significance.sum(), 157.316195729298) assert_allclose(background.sum(), 4200)

36.130435

97

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5.47851

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0.013598

0.021967

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0.275366

0.252877

0.234048

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0

0.026203

0.238955

5,817

160

98

36.35625

0.837588

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0

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1

0.061856

false

0

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0.010309

0

null

0

null

0

1

0

e508b0cb043508fe01e3e1d06e6baa67a2130ba3

4,772

py

Python

morsesmale.py

scotthellman/discrete-topology

6182fe607868d88c462c185be8629a35ad2d7c37

[ "MIT" ]

null

morsesmale.py

scotthellman/discrete-topology

6182fe607868d88c462c185be8629a35ad2d7c37

[ "MIT" ]

null

morsesmale.py

scotthellman/discrete-topology

6182fe607868d88c462c185be8629a35ad2d7c37

[ "MIT" ]

null

import networkx as nx import numpy as np import scipy import graph import itertools from collections import defaultdict def calculate_persistence(crystal, other, minimum_value, G, function_vals): minimums = [] min_vertices = [] other = set(other) for vertex in crystal: neighbors = set(G.neighbors(vertex)) & other if len(neighbors) == 0: continue value = function_vals[vertex] worst_case = minimum_value - value minimum_dist = None minimum_node = None for n in neighbors: diff = minimum_value - function_vals[n] if minimum_dist is None or diff > worst_case and diff < minimum_dist: minimum_dist = diff minimum_node = n if minimum_dist < worst_case: minimum_dist = worst_case minimum_node = vertex minimums.append(minimum_dist) min_vertices.append(minimum_node) try: chosen_index = np.argmin(minimums) return minimums[chosen_index], min_vertices[chosen_index] except ValueError: return float("inf"), None def find_filtrations(G, function_vals, msc): #TODO: throw exception when 2 values are the same # minkP(X) mines(pa,pk) maxxiekamin − xik. crystals = defaultdict(list) for i,label in enumerate(msc): crystals[label].append(i) filtration = [crystals] while len(crystals) > 1: #find the crystal with the smalled persistence best_pair = None best_persistence = None for crystal in crystals: minimum_val = function_vals[crystal[0]] for other in crystals: if other != crystal: persistence = calculate_persistence(crystals[crystal], crystals[other], minimum_val, G, function_vals)[0] if best_persistence is None or persistence < best_persistence: best_pair = (crystal, other) best_persistence = persistence new_crystals = defaultdict(list) for crystal,values in crystals.items(): if crystal != best_pair[0]: new_crystals[crystal].extend(values) else: new_crystals[best_pair[1]].extend(values) filtration.append(new_crystals) crystals = new_crystals return filtration def generate_morse_smale(G, pdist, function_vals): maxima, minima, ascent, descent = find_extrema(G, pdist, function_vals) max_labels = assign_extrema(G, maxima, ascent) min_labels = assign_extrema(G, minima, descent) return list(zip(min_labels, max_labels)) def assign_extrema(G, extrema, path): assignments = [0] * len(G.nodes()) for node in G: traverser = node while traverser not in extrema: traverser = path[traverser] assignments[node] = traverser return assignments def find_extrema(G, pdist, function_vals): ascent = {} descent = {} maxima = [] minima = [] for i,value in enumerate(function_vals): neighbors = np.array(G.neighbors(i)) distances = np.array([d for n,d in enumerate(pdist[i]) if n in neighbors]) differences = np.array([function_vals[n] - value for n in neighbors]) normalized = differences / distances ordered = np.argsort(normalized) if np.all(differences < 0): maxima.append(i) ascent[i] = i descent[i] = neighbors[ordered[0]] elif np.all(differences > 0): minima.append(i) ascent[i] = neighbors[ordered[-1]] descent[i] = i else: ascent[i] = neighbors[ordered[-1]] descent[i] = neighbors[ordered[0]] return maxima, minima, ascent, descent def get_filtrations(pdist, function_vals, k=2): if k is None: G = graph.generate_gabriel_graph(pdist) else: G = graph.generate_knn_graph(pdist, k) msc = generate_morse_smale(G, pdist, function_vals) filtrations = find_filtrations(G, function_vals, msc) return filtrations if __name__ == "__main__": import scipy.spatial values = np.array(range(20)).reshape(20,1) pairs = scipy.spatial.distance.pdist(values) pdist = scipy.spatial.distance.squareform(pairs) G = graph.generate_knn_graph(pdist, 2) func_vals = values % 5 maxs, mins, ascent, descent = find_extrema(G, pdist, func_vals) msc = generate_morse_smale(G, pdist, func_vals) print(msc) filtration = find_filtrations(G, func_vals, msc) print("-"*20) for f in filtration: print(f) get_filtrations(pdist, func_vals)

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0.165905

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0

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4,772

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92

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0

0.169492

0.025424

0

null

0

null

0

1

0

e508ffc9689183a1879c3556805bdcd71560a700

4,390

py

Python

Packs/ZeroTrustAnalyticsPlatform/Integrations/ZeroTrustAnalyticsPlatform/test_data/xsoar_data.py

mazmat-panw/content

024a65c1dea2548e2637a9cbbe54966e9e34a722

[ "MIT" ]

2

2021-12-06T21:38:24.000Z

2022-01-13T08:23:36.000Z

Packs/ZeroTrustAnalyticsPlatform/Integrations/ZeroTrustAnalyticsPlatform/test_data/xsoar_data.py

mazmat-panw/content

024a65c1dea2548e2637a9cbbe54966e9e34a722

[ "MIT" ]

87

2022-02-23T12:10:53.000Z

2022-03-31T11:29:05.000Z

Packs/ZeroTrustAnalyticsPlatform/Integrations/ZeroTrustAnalyticsPlatform/test_data/xsoar_data.py

henry-sue-pa/content

043c6badfb4f9c80673cad9242fdea72efe301f7

[ "MIT" ]

2

2022-01-05T15:27:01.000Z

2022-02-01T19:27:43.000Z

def event_response(): return [ { "ata_event_count": 1, "datetime_created": "2021-05-11T20:11:30Z", "fields": [ { "key": "auto_run", "label": "Auto Run", "value": "False", "order": 0, }, { "key": "event_name", "label": "Event Name", "value": "threat_quarantined", "order": 1, }, { "key": "event_timestamp", "label": "Event Timestamp", "value": "2021-05-11T20:11:30.728667", "order": 2, }, ], "trigger": True, }, ] def alert_response(): return [ { "datetime_created": "2021-05-11T20:11:31Z", "datetime_closed": None, "datetime_firstevent": "2021-05-11T20:11:30Z", "datetime_events_added": "2021-05-11T20:11:31Z", "datetime_org_assigned": "2021-05-11T20:11:31Z", "id": 1, "status": "assigned", "description": "Test Alert 1", "url": "http://some_mock_url/#/incidents/1", "xsoar_trigger_events": event_response(), "xsoar_trigger_kv": trigger_event_kv(), "xsoar_mirror_direction": "Both", "xsoar_mirror_instance": "dummy_instance", "xsoar_mirror_id": "1", "xsoar_mirror_tags": ["comment_tag", "escalate_tag"], }, { "datetime_created": "2021-05-11T20:09:50Z", "datetime_closed": None, "datetime_firstevent": "2021-05-11T20:09:48Z", "datetime_events_added": "2021-05-11T20:09:50Z", "datetime_org_assigned": "2021-05-11T20:09:50Z", "id": 2, "status": "assigned", "description": "Test Alert 2", "url": "http://some_mock_url/#/incidents/2", "xsoar_trigger_events": event_response(), "xsoar_trigger_kv": trigger_event_kv(), "xsoar_mirror_direction": "Both", "xsoar_mirror_instance": "dummy_instance", "xsoar_mirror_id": "2", "xsoar_mirror_tags": ["comment_tag", "escalate_tag"], }, ] def alert_response_remote(): return { "datetime_created": "2021-05-11T20:11:31Z", "datetime_closed": None, "datetime_firstevent": "2021-05-11T20:11:30Z", "datetime_events_added": "2021-05-11T20:11:31Z", "datetime_org_assigned": "2021-05-11T20:11:31Z", "id": 1, "status": "assigned", "description": "Test Alert 1", "url": "http://some_mock_url/#/incidents/1", "xsoar_trigger_events": event_response(), "in_mirror_error": "", } def comment_response(): return [ { "comment": "Test comment", "datetime_created": "2021-05-10T19:36:48Z", "id": 1, "user": user_response(), }, { "comment": "Closing alert due to duplicate.", "datetime_created": "2021-05-10T19:50:18Z", "id": 2, "user": user_response(), }, ] def user_response(): return { "id": 1, "name": "Active User", "email": "test@test", "organization": {"id": 1, "name": "dummy_org", "psa_id": "dummy_id"}, } def trigger_event_kv(): return { "auto_run": "False", "event_name": "threat_quarantined", "event_timestamp": "2021-05-11T20:11:30.728667", } def comment_entries(): return [ { "Type": 1, "ContentsFormat": "json", "Contents": comment_response()[0], "HumanReadable": "Test comment\n\nSent by Active User (test@test) via ZTAP", "ReadableContentsFormat": "text", "Note": True, "Tags": [], }, { "Type": 1, "ContentsFormat": "json", "Contents": comment_response()[1], "HumanReadable": "Closing alert due to duplicate.\n\nSent by Active User (test@test) via ZTAP", "ReadableContentsFormat": "text", "Note": True, "Tags": [], }, ]

32.043796

107

0.479499

413

4,390

4.861985

0.227603

0.050797

0.082171

0.071215

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0.098096

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136

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32.279412

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0

null

0

null

0

1

0

1

e5090e4da04384e2fbcd5b4f114deb2087c6f5f4

288

py

Python

postgres/scripts/test-db-connection.py

tcalmant/ldbc_snb_interactive

baf4a8150ffd0b193ba2c6d1dc7cdc3a99edeedf

[ "Apache-2.0" ]

null

postgres/scripts/test-db-connection.py

tcalmant/ldbc_snb_interactive

baf4a8150ffd0b193ba2c6d1dc7cdc3a99edeedf

[ "Apache-2.0" ]

null

postgres/scripts/test-db-connection.py

tcalmant/ldbc_snb_interactive

baf4a8150ffd0b193ba2c6d1dc7cdc3a99edeedf

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import os import psycopg2 con = psycopg2.connect( host="localhost", user=os.environ.get("POSTGRES_USER", "postgres"), password=os.environ.get("POSTGRES_PASSWORD", "mysecretpassword"), port=int(os.environ.get("POSTGRES_PORT", 5432)), ) con.close()

22.153846

69

0.704861

37

288

5.405405

0.567568

0.135

0.18

0.3

0

0.027778

0.125

288

12

70

24

0.765873

0.072917

0

0.285714

0

1

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false

0.111111

0.222222

0

0.222222

0

null

0

1

0

null

0

1

0

2

e50969a938fad964949586d116b12c2990a0ae87

2,054

py

Python

pyxll_jupyter/widget.py

TanKingsley/pyxll-jupyter

4f7b3eb361079b74683d89340dfff9576fb2ff41

[ "MIT" ]

1

2020-12-28T10:40:38.000Z

pyxll_jupyter/widget.py

TanKingsley/pyxll-jupyter

4f7b3eb361079b74683d89340dfff9576fb2ff41

[ "MIT" ]

null

pyxll_jupyter/widget.py

TanKingsley/pyxll-jupyter

4f7b3eb361079b74683d89340dfff9576fb2ff41

[ "MIT" ]

null

""" JupyterQtWidget is the widget that gets embedded in Excel and hosts a tabbed browser widget containing the Jupyter notebook. """ from .kernel import start_kernel, launch_jupyter from .browser import Browser from .qtimports import QWidget, QVBoxLayout import subprocess import ctypes class JupyterQtWidget(QWidget): def __init__(self, parent=None, scale=None, initial_path=None): super().__init__(parent) # proc gets set to the subprocess when the jupyter is started self.proc = None # Get the scale from the window DPI if scale is None: LOGPIXELSX = 88 hwnd = self.winId() if isinstance(hwnd, str): hwnd = int(hwnd, 16 if hwnd.startswith("0x") else 10) hwnd = ctypes.c_size_t(hwnd) screen = ctypes.windll.user32.GetDC(hwnd) try: scale = ctypes.windll.gdi32.GetDeviceCaps(screen, LOGPIXELSX) / 96.0 finally: ctypes.windll.user32.ReleaseDC(hwnd, screen) # Create the browser widget self.browser = Browser(self, scale=scale) self.browser.closed.connect(self.close) # Add the browser to the widgets layout layout = QVBoxLayout() layout.addWidget(self.browser) self.setLayout(layout) # Start the kernel and open Jupyter in a new tab app = start_kernel() self.proc, url = launch_jupyter(app.connection_file, cwd=initial_path) self.browser.create_tab(url) def closeEvent(self, event): # Kill the Jupyter subprocess using taskkill (just killing the process using POpen.kill # doesn't terminate any child processes) if self.proc is not None: while self.proc.poll() is None: si = subprocess.STARTUPINFO(wShowWindow=subprocess.SW_HIDE) subprocess.check_call(['taskkill', '/F', '/T', '/PID', str(self.proc.pid)], startupinfo=si, shell=True)

36.678571

95

0.619279

246

2,054

5.089431

0.47561

0.031949

0.028754

0

0.011096

0.297955

2,054

55

96

37.345455

0.857143

0.221519

0

0.011356

0

1

0.057143

false

0

0.142857

0

0.228571

0

null

0

null

0

1

0

e50c65e44676b2b7cbe06fd4c5deb5f102a8415d

621

py

Python

py/A Rule Of Divisibility By 13.py

aadithpm/code-a-day

18d7c1847e14d32d33d09d29f8847b6252c6e9e6

[ "Unlicense" ]

3

2018-03-16T14:52:40.000Z

2020-12-04T10:12:07.000Z

py/A Rule Of Divisibility By 13.py

aadithpm/code-a-day

18d7c1847e14d32d33d09d29f8847b6252c6e9e6

[ "Unlicense" ]

null

py/A Rule Of Divisibility By 13.py

aadithpm/code-a-day

18d7c1847e14d32d33d09d29f8847b6252c6e9e6

[ "Unlicense" ]

5

2017-06-30T05:35:00.000Z

2019-07-13T08:05:30.000Z

""" https://www.codewars.com/kata/564057bc348c7200bd0000ff/train/python """ def thirt(n): seq = [1,10,9,12,3,4] n = list(int(i) for i in reversed(str(n))) if len(seq) < len(n): compute1 = [i for i in seq[0:len(n)-len(seq)]] seq.extend(compute1) compute1 = sum(i * j for i,j in zip(n,seq)) compute1 = list(int(i) for i in reversed(str(compute1))) compute2 = sum(i * j for i,j in zip(compute1,seq)) if compute1 == compute2: return compute2 else: compute1 = list(int(i) for i in reversed(str(compute2))) return sum(i * j for i,j in zip(compute1,seq))

34.5

67

0.602254

106

621

3.528302

0.339623

0.074866

0.053476

0.074866

0.42246

0.315508

0.139037

0

0.082278

0.236715

621

17

68

36.529412

0.706751

0.107891

0

1

0.071429

false

0

0.214286

0

null

0

null

0

1

0

e50ca21b180f2193d02c55bbba259bcd1c32234b

5,292

py

Python

lonely/system.py

LonelyPale/lonely

328d0ef12b98a6d208ef8cf75e69f0cc421a0b2b

[ "Apache-2.0" ]

null

lonely/system.py

LonelyPale/lonely

328d0ef12b98a6d208ef8cf75e69f0cc421a0b2b

[ "Apache-2.0" ]

null

lonely/system.py

LonelyPale/lonely

328d0ef12b98a6d208ef8cf75e69f0cc421a0b2b

[ "Apache-2.0" ]

null

import os as _os import platform from lonely.cmd import command def lsb_release(): ret = command("cat /etc/openEuler-release", capture_output=True, print_out=False, print_err=False) if ret.returncode == 0: #print("success:", ret) if ret.stdout.lower().find("openeuler") > -1: return "openeuler" else: return "unknown" else: #print("failure:", ret) return "unknown" os = platform.system().lower() arch = platform.machine().lower() release = lsb_release() shell = _os.getenv('SHELL') home = _os.getenv('HOME') base = "/usr/local" temp = "/tmp" env_file = { "linux": "%s/.bashrc" % home, "darwin": "%s/.bash_profile" % home, } source_file = { "/bin/bash": env_file.get(os), "/bin/zsh": "%s/.zshrc" % home, } # todo: 无效的，子进程内 source，不会作用于父进程。 def source(file=None): if file is not None and isinstance(file, str) and len(file) > 0: return command("source %s" % file) else: sf = source_file.get(shell) if sf is not None: return command("source %s" % sf) else: return None def env_add(conf): if conf is None or type(conf) not in (type(()), type([])) or len(conf) < 2: return False if os not in env_file: print("Unsupported os: %s" % os) return False env_file_path = env_file[os] temp_file = env_file_path + ".tmp" start = conf[0] end = conf[len(conf)-1] flag = 0 with open(env_file_path, "r", encoding="utf-8") as f1, open(temp_file, "w", encoding="utf-8") as f2: lines = f1.readlines() start_idx = -1 end_idx = -1 for i, line in enumerate(lines): if flag == 0 and start in line: flag = 1 start_idx = i elif flag == 1 and end in line: #已存在，修改 flag = 2 end_idx = i break if flag == 0: #不存在，新增 new_lines = [] lines.reverse() first_none = True for line in lines: if first_none: if line != '\n': first_none = False new_lines.append(line) else: new_lines.append(line) new_lines.reverse() f2.writelines(new_lines + ['\n'] + conf) elif flag == 2: new_lines1 = [] lines1 = lines[:start_idx] lines1.reverse() first_none = True for line in lines1: if first_none: if line != '\n': first_none = False new_lines1.append(line) else: new_lines1.append(line) new_lines1.reverse() new_lines2 = [] lines2 = lines[end_idx+1:] first_none = True for line in lines2: if first_none: if line != '\n': first_none = False new_lines2.append(line) else: new_lines2.append(line) f2.writelines(new_lines1 + ['\n'] + conf + ['\n'] + new_lines2) elif flag == 1: raise("env file syntax error, missing end. %s" % env_file_path) _os.remove(env_file_path) _os.rename(temp_file, env_file_path) # source() return True def env_del(conf): if conf is None or type(conf) not in (type(()), type([])) or len(conf) < 2: return False if os not in env_file: print("Unsupported os: %s" % os) return False env_file_path = env_file[os] temp_file = env_file_path + ".tmp" start = conf[0] end = conf[len(conf)-1] flag = 0 with open(env_file_path, "r", encoding="utf-8") as f1, open(temp_file, "w", encoding="utf-8") as f2: lines = f1.readlines() start_idx = -1 end_idx = -1 for i, line in enumerate(lines): if flag == 0 and start in line: flag = 1 start_idx = i elif flag == 1 and end in line: #已存在，删除 flag = 2 end_idx = i break if flag == 1: raise("env file syntax error, missing end. %s" % env_file_path) elif flag == 0: #不存在，退出 return True new_lines1 = [] lines1 = lines[:start_idx] lines1.reverse() first_none = True for line in lines1: if first_none: if line != '\n': first_none = False new_lines1.append(line) else: new_lines1.append(line) new_lines1.reverse() new_lines2 = [] lines2 = lines[end_idx+1:] first_none = True for line in lines2: if first_none: if line != '\n': first_none = False new_lines2.append(line) else: new_lines2.append(line) f2.writelines(new_lines1 + ['\n'] + new_lines2) _os.remove(env_file_path) _os.rename(temp_file, env_file_path) # source() return True

28.76087

104

0.493386

647

5,292

3.876352

0.185471

0.055821

0.052632

0.031898

0.665869

0.654306

0.63756

0

0.022194

0.395503

5,292

183

105

28.918033

0.761801

0.022298

0

0.714286

0

0.06273

0.004259

0

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0

1

0.025974

false

0

0.019481

0

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0.019481

0

null

0

1

0

1

0

null

0

2

e50e8e3032d7d7837365ea7b6780cf4d9b0c82b7

5,722

py

Python

entityfactssheetsharvester/entityfactssheetsharvester.py

zazi/entityfactssheetsharvester

150e702a763d73356adba112c0e1c1141df4884c

[ "Apache-2.0" ]

1

2019-08-13T07:44:32.000Z

entityfactssheetsharvester/entityfactssheetsharvester.py

zazi/entityfactssheetsharvester

150e702a763d73356adba112c0e1c1141df4884c

[ "Apache-2.0" ]

null

entityfactssheetsharvester/entityfactssheetsharvester.py

zazi/entityfactssheetsharvester

150e702a763d73356adba112c0e1c1141df4884c

[ "Apache-2.0" ]

1

2019-08-13T07:44:32.000Z

#!/usr/bin/python3 # -*- coding: utf-8 -*- import argparse import json import os import socket import sys import requests from threading import current_thread from rx import create, of from rx import operators as op from rx.scheduler import ThreadPoolScheduler USER_AGENT_HTTP_HEADER_KEY = 'user-agent' USER_AGENT_PATTERN = "entityfactssheetsharvester-bot-from-{0}/0.0.1 (https://github.com/slub/entityfactssheetsharvester; zazi@smiy.org) entityfactssheetsharvester/0.0.1" HOSTNAME = socket.getfqdn() USER_AGENT = USER_AGENT_PATTERN.format(HOSTNAME) HTTP_HEADERS = {USER_AGENT_HTTP_HEADER_KEY: USER_AGENT} ENTITYFACTS_BASE_URI = "http://hub.culturegraph.org/entityfacts/" UTF8_CHARSET_ID = 'utf-8' LINEBREAK = "\n" THREAD_POOL_SCHEDULER = ThreadPoolScheduler(10) def eprint(*args, **kwargs): print(*args, file=sys.stderr, **kwargs) def get_gnd_identifier(line): gnd_identifier = line # remove line break lastchar = line[-1] oslinebreak = os.linesep if lastchar == oslinebreak: gnd_identifier = line[0:-1] eprint("GND identifier '{0}' (thread = '{1}')".format(gnd_identifier, current_thread().name)) return gnd_identifier def entityfacts_request(request_uri, gnd_identifier): eprint("try to retrieve EntityFacts sheet for GND identifier '{0}' (thread = '{1}')".format(gnd_identifier, current_thread().name)) response = requests.get(request_uri, headers=HTTP_HEADERS, timeout=60) if response.status_code != 200: eprint("couldn't fetch EntityFacts sheet for GND identifier '{0}', got a '{1}' (thread = '{2}')".format( gnd_identifier, response.status_code, current_thread().name)) return None response_body = response.content.decode(UTF8_CHARSET_ID) eprint("retrieved EntityFacts sheet for GND identifier '{0}' (thread = '{1}')".format(gnd_identifier, current_thread().name)) return response_body def retrieve_entityfacts_sheet_obs(gnd_identifier): return of(gnd_identifier).pipe(op.map(lambda gndid: retrieve_entityfacts_sheet(gnd_identifier)), op.filter(lambda value: value is not None)) def retrieve_entityfacts_sheet(gnd_identifier): entityfacts_sheets_uri = ENTITYFACTS_BASE_URI + gnd_identifier response_tuple = entityfacts_request(entityfacts_sheets_uri, gnd_identifier) if response_tuple is None: return None entityfacts_sheet_tuple = (response_tuple, gnd_identifier) return entityfacts_sheet_tuple def format_entityfacts_sheet_obs(entityfacts_sheet_tuple_obs): return entityfacts_sheet_tuple_obs.pipe(op.map(lambda ef_sheet_tuple: format_entityfacts_sheet(ef_sheet_tuple))) def format_entityfacts_sheet(entityfacts_sheet_tuple): gnd_identifier = entityfacts_sheet_tuple[1] eprint("format EntityFacts sheet for GND identifier '{0}' (thread = '{1}')".format(gnd_identifier, current_thread().name)) entityfacts_sheet_json = json.loads(entityfacts_sheet_tuple[0]) flat_entityfacts_sheet_json = json.dumps(entityfacts_sheet_json, indent=None) return flat_entityfacts_sheet_json, gnd_identifier def write_entityfacts_sheet_obs(flat_entityfacts_sheet_json_tuple_obs): return flat_entityfacts_sheet_json_tuple_obs.pipe(op.map(lambda flat_ef_sheet_json_tuple: write_entityfacts_sheet( flat_ef_sheet_json_tuple))) def write_entityfacts_sheet(flat_entityfacts_sheet_json_tuple): gnd_identifier = flat_entityfacts_sheet_json_tuple[1] eprint("write EntityFacts sheet for GND identifier '{0}' (thread = '{1}')".format(gnd_identifier, current_thread().name)) sys.stdout.write(flat_entityfacts_sheet_json_tuple[0] + LINEBREAK) return gnd_identifier def push_input(observer, scheduler): for line in sys.stdin: observer.on_next(line) return observer.on_completed() def run(): parser = argparse.ArgumentParser(prog='entityfactssheetsharvester', description='Retrieves EntityFacts sheets from a given CSV with GND identifiers and returns them as line-delimited JSON records.', epilog='example: entityfactssheetsharvester < [INPUT CSV FILE WITH GND IDENTIFIERS] > [PATH TO THE OUTPUT LINE-DELIMITED JSON RECORDS FILE]', formatter_class=argparse.ArgumentDefaultsHelpFormatter) args = parser.parse_args() if hasattr(args, 'help') and args.help: parser.print_usage(sys.stderr) exit(-1) source = create(push_input) all_in_one = source.pipe(op.map(lambda line: get_gnd_identifier(line)), op.map(lambda gnd_identifier: retrieve_entityfacts_sheet_obs(gnd_identifier)), op.map(lambda ef_sheet_tuple_obs: format_entityfacts_sheet_obs(ef_sheet_tuple_obs)), op.map(lambda flat_ef_sheet_json_tuple_obs: write_entityfacts_sheet_obs( flat_ef_sheet_json_tuple_obs)), op.flat_map(lambda x: x)) all_in_one.subscribe( on_next=lambda gnd_identifier: eprint( "PROCESSED GND identifier '{0}': {1}".format(gnd_identifier, current_thread().name)), on_error=lambda e: eprint(e), on_completed=lambda: eprint("PROCESS done!"), scheduler=THREAD_POOL_SCHEDULER) if __name__ == "__main__": run()

41.463768

178

0.67791

677

5,722

5.420975

0.243722

0.120436

0.049046

0.045777

0.320163

0.236785

0.145504

0.118529

0.101635

0

0.009574

0.23331

5,722

137

179

41.766423

0.826989

0.009962

0

0.082474

0

0.030928

0.164959

0.022783

0

1

0.113402

false

0

0.103093

0.030928

0.329897

0.123711

0

null

0

null

0

1

0

e50f03b5ad643f99fc6faba88c3fc2cee5a3768e

473

py

Python

mayan/apps/quotas/icons.py

CMU-313/fall-2021-hw2-451-unavailable-for-legal-reasons

0e4e919fd2e1ded6711354a0330135283e87f8c7

[ "Apache-2.0" ]

2

2021-09-12T19:41:19.000Z

2021-09-12T19:41:20.000Z

mayan/apps/quotas/icons.py

CMU-313/fall-2021-hw2-451-unavailable-for-legal-reasons

0e4e919fd2e1ded6711354a0330135283e87f8c7

[ "Apache-2.0" ]

37

2021-09-13T01:00:12.000Z

2021-10-02T03:54:30.000Z

mayan/apps/quotas/icons.py

CMU-313/fall-2021-hw2-451-unavailable-for-legal-reasons

0e4e919fd2e1ded6711354a0330135283e87f8c7

[ "Apache-2.0" ]

1

2021-09-22T13:17:30.000Z

from mayan.apps.appearance.classes import Icon icon_quota_create = Icon( driver_name='fontawesome-dual', primary_symbol='tachometer-alt', secondary_symbol='plus' ) icon_quota_delete = Icon(driver_name='fontawesome', symbol='times') icon_quota_edit = Icon(driver_name='fontawesome', symbol='pencil-alt') icon_quota_list = Icon(driver_name='fontawesome', symbol='tachometer-alt') icon_quota_setup = Icon(driver_name='fontawesome', symbol='tachometer-alt')

43

76

0.77167

61

473

5.704918

0.409836

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e51032e8f05343cce31308455d21b22aca3ea53e

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py

Python

pyner/util/optimizer.py

chantera/pyner

6de19713871e923c997495c07e2ec249bded8671

[ "MIT" ]

1

2019-06-16T00:52:26.000Z

pyner/util/optimizer.py

chantera/pyner

6de19713871e923c997495c07e2ec249bded8671

[ "MIT" ]

null

pyner/util/optimizer.py

chantera/pyner

6de19713871e923c997495c07e2ec249bded8671

[ "MIT" ]

null

from chainer import optimizer_hooks from chainer import optimizers from chainer import training import numpy import logging logger = logging.getLogger(__name__) def create_optimizer(configs): """ :param optimizer_config: dict, 学習のパラメータを含む辞書 """ if 'optimizer' not in configs: raise Exception('Optimizer configurations are not found') optimizer_configs = configs['optimizer'] optimizer_ = optimizer_configs['name'] optimizer_ = optimizer_.lower() if optimizer_ == 'sgd': optimizer = optimizers.SGD(lr=optimizer_configs['learning_rate']) elif optimizer_ == 'momentumsgd': optimizer = optimizers.MomentumSGD( lr=optimizer_configs['learning_rate']) elif optimizer_ == 'adadelta': optimizer = optimizers.AdaDelta() elif optimizer_ == 'adam': optimizer = optimizers.Adam(alpha=optimizer_configs['alpha'], beta1=optimizer_configs['beta1'], beta2=optimizer_configs['beta2']) elif optimizer_ == 'adabound': optimizer = optimizers.Adam(alpha=optimizer_configs['alpha'], beta1=optimizer_configs['beta1'], beta2=optimizer_configs['beta2'], adabound=True, final_lr=optimizer_configs['final_lr']) # NOQA else: raise Exception return optimizer def add_hooks(optimizer, configs): """ :param optimizer: chainer.Optimizer, chainerのオプティマイザ :param configs: pyner.util.config.ConfigParser """ if 'optimizer' not in configs: raise Exception('Optimizer configurations are not found') optimizer_configs = configs['optimizer'] if optimizer_configs.get('weight_decay'): logger.debug('\x1b[31mSet weight decay\x1b[0m') optimizer.add_hook(optimizer_hooks.WeightDecay( optimizer_configs['weight_decay'])) if 'gradient_clipping' in optimizer_configs: clipping_threshold = optimizer_configs['gradient_clipping'] msg = 'Enable gradient clipping:' msg += f' threshold \x1b[31m{clipping_threshold}\x1b[0m' logger.debug(msg) optimizer.add_hook( optimizer_hooks.GradientClipping(clipping_threshold) ) return optimizer class LearningRateDecay(training.extension.Extension): """Exception to decay learning rate as in Ma+ (http://www.aclweb.org/anthology/P16-1101) Learning rate would be updated to ``rate * / (1 + (1 + iteration)) * decay`` This extension is also called before the training loop starts by default. Args: attr (str): Name of the attribute to shift. rate (float): Exponent of polynomial shift. max_count (int): Number of this extension to be invoked. init (float): Initial value of the attribute. If it is ``None``, the extension extracts the attribute at the first call and uses it as the initial value. target (float): Target value of the attribute. If the attribute reaches this value, the shift stops. optimizer (~chainer.Optimizer): Target optimizer to adjust the attribute. If it is ``None``, the main optimizer of the updater is used. """ invoke_before_training = True def __init__(self, attr, rate, decay, target=None, optimizer=None): self._attr = attr self._rate = rate self._decay = decay self._target = target self._optimizer = optimizer self._t = 0 self._last_value = None def initialize(self, trainer): optimizer = self._get_optimizer(trainer) if self._last_value is not None: # resuming from a snapshot self._update_value(optimizer, self._last_value) else: self._update_value(optimizer, self._rate) def __call__(self, trainer): self._t += 1 optimizer = self._get_optimizer(trainer) value = self._rate / (1 + (self._decay * self._t)) if self._target is not None: if self._rate > 0: # almost same as value = min(value, self._target), but this # line supports negative values, too if self._target / value > 1: value = self._target else: # ditto if self._target / value < 1: value = self._target self._update_value(optimizer, value) def serialize(self, serializer): self._t = serializer('_t', self._t) self._last_value = serializer('_last_value', self._last_value) if isinstance(self._last_value, numpy.ndarray): self._last_value = self._last_value.item() def _get_optimizer(self, trainer): return self._optimizer or trainer.updater.get_optimizer('main') def _update_value(self, optimizer, value): setattr(optimizer, self._attr, value) self._last_value = value

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e510c7426f5e3c38449cb80f147daf9524ba1a19

4,553

py

Python

5th_pipeline.py

Jose-Oton/airflow_project

1b65a83975be63ad15cab95ad2947f6526400368

[ "Apache-2.0" ]

1

2021-07-08T12:29:34.000Z

5th_pipeline.py

Jose-Oton/airflow_project

1b65a83975be63ad15cab95ad2947f6526400368

[ "Apache-2.0" ]

null

5th_pipeline.py

Jose-Oton/airflow_project

1b65a83975be63ad15cab95ad2947f6526400368

[ "Apache-2.0" ]

null

#1. Documentación de un DAG """ ## PYSPARK DAG Este pipeline toma data de Covid compartida de forma pública por Google y calcula unos KPIs. """ from airflow import DAG from datetime import timedelta, datetime from airflow.utils.dates import days_ago from airflow.models import Variable from airflow.operators.bash_operator import BashOperator from airflow.operators.python_operator import BranchPythonOperator from airflow.providers.google.cloud.operators.dataproc import DataprocCreateClusterOperator from airflow.providers.google.cloud.operators.dataproc import DataprocDeleteClusterOperator from airflow.providers.google.cloud.operators.dataproc import DataprocSubmitPySparkJobOperator from airflow.providers.google.cloud.operators.dataproc import DataprocSubmitJobOperator from airflow.utils import trigger_rule # DataprocSubmitPySparkJobOperator( # task_id="store_stock", # main="gs://your_bucket/datapipelines/pyspark/pyspark_transformation_joseOton.py", # cluster_name="spark-cluster-{{ ds_nodash }}", # dataproc_jars=["gs://spark-lib/bigquery/spark-bigquery-latest.jar"], #JAR para que Spark pueda leer de BigQuery # region='us-central1', # gcp_conn_id='google_cloud_default' # ).generate_job() #2. Utilizar Variables PROJECT_ID = Variable.get("project") STORAGE_BUCKET = Variable.get("storage_bucket") default_dag_args = { "start_date": days_ago(1), "owner": "José Otón" } def is_weekend(execution_date=None): date = datetime.strptime(execution_date, "%Y-%m-%d") if date.isoweekday() < 6: return "store_stock" return "weekend" # DEFINIMOS DAG with DAG( dag_id='5th_exercise', description='Running a PySpark Job on GCP', schedule_interval='@daily', default_args=default_dag_args, max_active_runs=1, user_defined_macros={"project": PROJECT_ID},#5. Macros en Airflow ) as dag: dag.doc_md = __doc__ #Para documentar un DAG create_dataproc = DataprocCreateClusterOperator( task_id="create_dataproc", project_id='{{ project }}', cluster_name="spark-cluster-{{ ds_nodash }}", num_workers=2, storage_bucket=STORAGE_BUCKET, region="us-central1" ) create_dataproc.doc_md = """## Crear cluster de Dataproc Crea un cluster de Dataproc en el proyecto de GCP """ # 3. Agregar elementos de lógica para ejecutar uno u otro pipeline do_analytics = BranchPythonOperator( task_id="do_analytics", python_callable=is_weekend, op_kwargs={"execution_date": "{{ ds }}"}, # 4. Jinja Templating ) do_analytics.doc_md = """## Evalua que dia de la semana es Crea un cluster de Dataproc en el proyecto de GCP. """ store_stock = DataprocSubmitJobOperator( task_id="store_stock", project_id=PROJECT_ID, location='us-central1', job={ 'reference': {'project_id': '{{ project }}', 'job_id': '{{task.task_id}}_{{ds_nodash}}_2446afcc_joseOton'}, ## si puede haber cambio. 'placement': {'cluster_name': 'spark-cluster-{{ ds_nodash }}'}, 'labels': {'airflow-version': 'v2-1-0'}, 'pyspark_job': { 'jar_file_uris': ['gs://spark-lib/bigquery/spark-bigquery-latest_2.12.jar'], 'main_python_file_uri': 'gs://your_bucket/datapipelines/pyspark/pyspark_transformation_joseOton.py' } }, gcp_conn_id='google_cloud_default' ) store_stock.doc_md = """## Spark Transformation Ejecuta las transformaciones con Spark. """ weekend = BashOperator( task_id="weekend", bash_command='echo "\'$TODAY\' is weekend so the pipeline hasnt been executed."', env={'TODAY': '2021-06-20'}, ) weekend.doc_md = """## Imprime el día de la semana Se ejecuta en caso sea fin de semana. """ delete_cluster = DataprocDeleteClusterOperator( task_id="delete_cluster", project_id=PROJECT_ID, cluster_name="spark-cluster-{{ ds_nodash }}", trigger_rule="all_done", region='us-central1' #zone='us-central1-a' ) delete_cluster.doc_md = """## Borrar Cluster de Dataproc Elimina el cluster de Dataproc. """ # SETEAR LAS DEPEDENDENCIAS DEL DAG (create_dataproc >> do_analytics >> [ store_stock, weekend, ] >> delete_cluster)

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null

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e510db97dd6ed101594891f1033e5947097f3261

127

py

Python

muve/sumo_server/__init__.py

muve-traffic/sumo-server

0a857ba9555569db1c118367668a507600c12cdf

[ "MIT" ]

null

muve/sumo_server/__init__.py

muve-traffic/sumo-server

0a857ba9555569db1c118367668a507600c12cdf

[ "MIT" ]

null

muve/sumo_server/__init__.py

muve-traffic/sumo-server

0a857ba9555569db1c118367668a507600c12cdf

[ "MIT" ]

null

"""Muve Traffic SUMO server. Server for simulating traffic and relaying traffic information programatically through SUMO. """

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2,613

py

Python

tests/unit/sagemaker/cli/compatibility/v2/modifiers/test_shuffle_config.py

LastRemote/sagemaker-python-sdk

fddf29d9e4383cd3f939253eef47ee79a464dd37

[ "Apache-2.0" ]

1,690

2017-11-29T20:13:37.000Z

2022-03-31T12:58:11.000Z

tests/unit/sagemaker/cli/compatibility/v2/modifiers/test_shuffle_config.py

LastRemote/sagemaker-python-sdk

fddf29d9e4383cd3f939253eef47ee79a464dd37

[ "Apache-2.0" ]

2,762

2017-12-04T05:18:03.000Z

2022-03-31T23:40:11.000Z

tests/unit/sagemaker/cli/compatibility/v2/modifiers/test_shuffle_config.py

LastRemote/sagemaker-python-sdk

fddf29d9e4383cd3f939253eef47ee79a464dd37

[ "Apache-2.0" ]

961

2017-11-30T16:44:03.000Z

2022-03-30T23:12:09.000Z

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file 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 __future__ import absolute_import import pasta import pytest from sagemaker.cli.compatibility.v2.modifiers import training_input from tests.unit.sagemaker.cli.compatibility.v2.modifiers.ast_converter import ast_call, ast_import @pytest.fixture def constructors(): return ( "sagemaker.session.ShuffleConfig(seed)", "session.ShuffleConfig(seed)", ) @pytest.fixture def modified_constructors(constructors): return [c.replace("session", "inputs") for c in constructors] def test_constructor_node_should_be_modified(constructors): modifier = training_input.ShuffleConfigModuleRenamer() for constructor in constructors: node = ast_call(constructor) assert modifier.node_should_be_modified(node) def test_constructor_node_should_be_modified_random_call(): modifier = training_input.ShuffleConfigModuleRenamer() node = ast_call("FileSystemInput()") assert not modifier.node_should_be_modified(node) def test_constructor_modify_node(constructors, modified_constructors): modifier = training_input.ShuffleConfigModuleRenamer() for before, expected in zip(constructors, modified_constructors): node = ast_call(before) modifier.modify_node(node) assert expected == pasta.dump(node) def test_import_from_node_should_be_modified_training_input(): modifier = training_input.ShuffleConfigImportFromRenamer() node = ast_import("from sagemaker.session import ShuffleConfig") assert modifier.node_should_be_modified(node) def test_import_from_node_should_be_modified_random_import(): modifier = training_input.ShuffleConfigImportFromRenamer() node = ast_import("from sagemaker.session import Session") assert not modifier.node_should_be_modified(node) def test_import_from_modify_node(): modifier = training_input.ShuffleConfigImportFromRenamer() node = ast_import("from sagemaker.session import ShuffleConfig") modifier.modify_node(node) assert "from sagemaker.inputs import ShuffleConfig" == pasta.dump(node)

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