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2,431
py
Python
lmnet/lmnet/datasets/cifar100_distribute.py
toohsk/blueoil
596922caa939db9c5ecbac3286fbf6f703865ee6
[ "Apache-2.0" ]
null
null
null
lmnet/lmnet/datasets/cifar100_distribute.py
toohsk/blueoil
596922caa939db9c5ecbac3286fbf6f703865ee6
[ "Apache-2.0" ]
1
2018-11-21T07:06:17.000Z
2018-11-21T07:06:17.000Z
lmnet/lmnet/datasets/cifar100_distribute.py
toohsk/blueoil
596922caa939db9c5ecbac3286fbf6f703865ee6
[ "Apache-2.0" ]
null
null
null
# -*- coding: utf-8 -*- # Copyright 2018 The Blueoil Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================= import functools import numpy as np from lmnet.datasets.cifar100 import Cifar100 from lmnet.datasets.base import DistributionInterface from lmnet.utils.random import shuffle
31.571429
106
0.626903
e9f22bb1ea67ab94d6fe17f1e1dc1a68f58ceef8
3,149
py
Python
gridengine/functional.py
MiqG/gridengine
457c34b16f2c43b9be985cd822f30305d68afd91
[ "BSD-3-Clause" ]
20
2015-01-31T16:52:15.000Z
2019-03-22T20:09:50.000Z
gridengine/functional.py
MiqG/gridengine
457c34b16f2c43b9be985cd822f30305d68afd91
[ "BSD-3-Clause" ]
1
2021-11-27T16:33:59.000Z
2021-11-27T16:33:59.000Z
gridengine/functional.py
MiqG/gridengine
457c34b16f2c43b9be985cd822f30305d68afd91
[ "BSD-3-Clause" ]
7
2015-10-27T16:49:52.000Z
2021-09-22T10:16:25.000Z
import inspect import functools from gridengine import job, dispatch, schedulers # ---------------------------------------------------------------------------- # Partial # ---------------------------------------------------------------------------- def isexception(x): """Test whether the value is an Exception instance""" return isinstance(x, Exception) def isnumeric(x): """Test whether the value can be represented as a number""" try: float(x) return True except: return False def partial(f, *args, **kwargs): """Return a callable partially closed over the input function and arguments partial is functionally equivalent to functools.partial, however it also applies a variant of functools.update_wrapper, with: __doc__ = f.__doc__ __module__ = f.__module__ __name__ = f.__name__ + string_representation_of_closed_arguments This is useful for running functions with different parameter sets, whilst being able to identify the variants by name """ g = functools.partial(f, *args, **kwargs) g.__doc__ = f.__doc__ g.__module__ = f.__module__ g.__name__ = '_'.join([f.__name__] + [name(arg) for arg in list(args)+list(kwargs.values())]) return g # ---------------------------------------------------------------------------- # Map # ---------------------------------------------------------------------------- def map(f, args, scheduler=schedulers.best_available, reraise=True): """Perform a functional-style map operation Apply a function f to each argument in the iterable args. This is equivalent to y = [f(x) for x in args] or y = map(f, args) except that each argument in the iterable is assigned to a separate Job and scheduled to run via the scheduler. The default scheduler is a schedulers.ProcessScheduler instance. To run map on a grid engine, simply pass a schedulers.GridEngineScheduler instance. Args: f (func): A picklable function args (iterable): An iterable (list) of arguments to f Keyword Args: scheduler: A schedulers.Scheduler instance or class. By default, the system tries to return the best_available() scheduler. Use this if you want to set a scheduler specifically. reraise (bool): Reraise exceptions that occur in any of the jobs. Set this to False if you want to salvage any good results. Returns: List of return values equivalent to the builtin map function Raises: Any exception that would occur when applying [f(x) for x in args] """ # setup the dispatcher dispatcher = dispatch.JobDispatcher(scheduler) # allocate the jobs jobs = [job.Job(target=f, args=(arg,)) for arg in args] # run the jobs (guaranteed to return in the same order) dispatcher.dispatch(jobs) results = dispatcher.join() # check for exceptions if reraise: for exception in filter(isexception, results): # an error occurred during execution of one of the jobs, reraise it raise exception return results
32.463918
97
0.64719
e9f24ec99f076ba98908603ffa1d50f5644d6aa7
31,441
py
Python
Bio/Prosite/__init__.py
nuin/biopython
045d57b08799ef52c64bd4fa807629b8a7e9715a
[ "PostgreSQL" ]
2
2016-05-09T04:20:06.000Z
2017-03-07T10:25:53.000Z
Bio/Prosite/__init__.py
nuin/biopython
045d57b08799ef52c64bd4fa807629b8a7e9715a
[ "PostgreSQL" ]
null
null
null
Bio/Prosite/__init__.py
nuin/biopython
045d57b08799ef52c64bd4fa807629b8a7e9715a
[ "PostgreSQL" ]
1
2019-08-19T22:05:14.000Z
2019-08-19T22:05:14.000Z
# Copyright 1999 by Jeffrey Chang. All rights reserved. # Copyright 2000 by Jeffrey Chang. All rights reserved. # Revisions Copyright 2007 by Peter Cock. All rights reserved. # This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. """ This module provides code to work with the prosite dat file from Prosite. http://www.expasy.ch/prosite/ Tested with: Release 15.0, July 1998 Release 16.0, July 1999 Release 17.0, Dec 2001 Release 19.0, Mar 2006 Functions: parse Iterates over entries in a Prosite file. scan_sequence_expasy Scan a sequence for occurrences of Prosite patterns. index_file Index a Prosite file for a Dictionary. _extract_record Extract Prosite data from a web page. _extract_pattern_hits Extract Prosite patterns from a web page. Classes: Record Holds Prosite data. PatternHit Holds data from a hit against a Prosite pattern. Dictionary Accesses a Prosite file using a dictionary interface. RecordParser Parses a Prosite record into a Record object. Iterator Iterates over entries in a Prosite file; DEPRECATED. _Scanner Scans Prosite-formatted data. _RecordConsumer Consumes Prosite data to a Record object. """ from types import * import re import sgmllib from Bio import File from Bio import Index from Bio.ParserSupport import * # There is probably a cleaner way to write the read/parse functions # if we don't use the "parser = RecordParser(); parser.parse(handle)" # approach. Leaving that for the next revision of Bio.Prosite. def scan_sequence_expasy(seq=None, id=None, exclude_frequent=None): """scan_sequence_expasy(seq=None, id=None, exclude_frequent=None) -> list of PatternHit's Search a sequence for occurrences of Prosite patterns. You can specify either a sequence in seq or a SwissProt/trEMBL ID or accession in id. Only one of those should be given. If exclude_frequent is true, then the patterns with the high probability of occurring will be excluded. """ from Bio import ExPASy if (seq and id) or not (seq or id): raise ValueError("Please specify either a sequence or an id") handle = ExPASy.scanprosite1(seq, id, exclude_frequent) return _extract_pattern_hits(handle) def _extract_pattern_hits(handle): """_extract_pattern_hits(handle) -> list of PatternHit's Extract hits from a web page. Raises a ValueError if there was an error in the query. """ p = parser() p.feed(handle.read()) if p.broken_message: raise ValueError(p.broken_message) return p.hits def index_file(filename, indexname, rec2key=None): """index_file(filename, indexname, rec2key=None) Index a Prosite file. filename is the name of the file. indexname is the name of the dictionary. rec2key is an optional callback that takes a Record and generates a unique key (e.g. the accession number) for the record. If not specified, the id name will be used. """ import os if not os.path.exists(filename): raise ValueError("%s does not exist" % filename) index = Index.Index(indexname, truncate=1) index[Dictionary._Dictionary__filename_key] = filename handle = open(filename) records = parse(handle) end = 0L for record in records: start = end end = long(handle.tell()) length = end - start if rec2key is not None: key = rec2key(record) else: key = record.name if not key: raise KeyError("empty key was produced") elif key in index: raise KeyError("duplicate key %s found" % key) index[key] = start, length # This function can be deprecated once Bio.Prosite.ExPASyDictionary # is removed. def _extract_record(handle): """_extract_record(handle) -> str Extract PROSITE data from a web page. Raises a ValueError if no data was found in the web page. """ # All the data appears between tags: # <pre width = 80>ID NIR_SIR; PATTERN. # </PRE> p = parser() p.feed(handle.read()) if not p.data: raise ValueError("No data found in web page.") return "".join(p.data)
35.326966
256
0.580675
e9f29e0f95ccd2b1945aff6967594472289887d8
21,120
py
Python
build/lib/mrgaze/pupilometry.py
jmtyszka/mrgaze
29217eab9ea431686fd200f08bddd6615c45d0d3
[ "MIT" ]
18
2016-01-22T02:47:45.000Z
2021-09-23T18:37:51.000Z
build/lib/mrgaze/pupilometry.py
jmtyszka/mrgaze
29217eab9ea431686fd200f08bddd6615c45d0d3
[ "MIT" ]
7
2015-05-26T21:33:16.000Z
2020-05-26T11:47:54.000Z
build/lib/mrgaze/pupilometry.py
jmtyszka/mrgaze
29217eab9ea431686fd200f08bddd6615c45d0d3
[ "MIT" ]
7
2016-02-06T00:17:52.000Z
2021-02-22T03:51:55.000Z
#!/usr/bin/env python # # Video pupilometry functions # - takes calibration and gaze video filenames as input # - controls calibration and gaze estimation workflow # # USAGE : mrgaze.py <Calibration Video> <Gaze Video> # # AUTHOR : Mike Tyszka # PLACE : Caltech # DATES : 2014-05-07 JMT From scratch # 2016-02-22 JMT Update print for python3. Remove unused vars, imports # # This file is part of mrgaze. # # mrgaze is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # mrgaze is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with mrgaze. If not, see <http://www.gnu.org/licenses/>. # # Copyright 2014 California Institute of Technology. import os import time import getpass import cv2 from mrgaze import media, utils, config, calibrate, report, engine def LivePupilometry(data_dir, live_eyetracking=False): """ Perform pupil boundary ellipse fitting on camera feed Arguments ---- data_dir : string Root data directory path. cfg : Analysis configuration parameters Returns ---- pupils : boolean Completion status (True = successful) """ # If user did not provide a root data directory, we use HOME/mrgaze if data_dir == '': data_dir = os.path.join(os.getenv("HOME"), 'mrgaze') # Full video file paths hostname = os.uname()[1] username = getpass.getuser() ss_dir = os.path.join(data_dir, "%s_%s_%s" % (hostname, username, int(time.time()))) else: ss_dir = data_dir # Load Configuration cfg = config.LoadConfig(data_dir) cfg_ts = time.time() # Output flags verbose = cfg.getboolean('OUTPUT', 'verbose') overwrite = cfg.getboolean('OUTPUT', 'overwrite') # Video information # vin_ext = cfg.get('VIDEO', 'inputextension') vout_ext = cfg.get('VIDEO' ,'outputextension') # vin_fps = cfg.getfloat('VIDEO', 'inputfps') # Flag for freeze frame freeze_frame = False vid_dir = os.path.join(ss_dir, 'videos') res_dir = os.path.join(ss_dir, 'results') vout_path = os.path.join(vid_dir, 'gaze' + vout_ext) cal_vout_path = os.path.join(vid_dir, 'cal' + vout_ext) # if we do live eye-tracking, we read in what would be the output of the live eye-tracking if not live_eyetracking: vin_path = vout_path cal_vin_path = cal_vout_path else: vin_path = 0 # Raw and filtered pupilometry CSV file paths cal_pupils_csv = os.path.join(res_dir, 'cal_pupils.csv') pupils_csv = os.path.join(res_dir, 'gaze_pupils.csv') # Check that output directory exists if not os.path.isdir(res_dir): os.makedirs(res_dir) print('* %s does not exist - creating' % res_dir) if not os.path.isdir(vid_dir): os.makedirs(vid_dir) print('* %s does not exist - creating' % vid_dir) # Set up the LBP cascade classifier LBP_path = os.path.join(utils._package_root(), 'Cascade/cascade.xml') print(' Loading LBP cascade') cascade = cv2.CascadeClassifier(LBP_path) if cascade.empty(): print('* LBP cascade is empty - mrgaze installation problem') return False # Check for output CSV existance and overwrite flag if os.path.isfile(pupils_csv): print('+ Pupilometry output already exists - checking overwrite flag') if overwrite: print('+ Overwrite allowed - continuing') else: print('+ Overwrite forbidden - skipping pupilometry') return True # # Camera Input # print(' Opening camera stream') try: if not live_eyetracking: vin_stream = cv2.VideoCapture(vin_path) cal_vin_stream = cv2.VideoCapture(cal_vin_path) else: vin_stream = cv2.VideoCapture(vin_path) cal_vin_stream = vin_stream except: print('* Problem opening input video stream - skipping pupilometry') return False while not vin_stream.isOpened(): print("Waiting for Camera.") key = utils._waitKey(500) if key == 'ESC': print("User Abort.") break if not vin_stream.isOpened(): print('* Video input stream not opened - skipping pupilometry') return False if not cal_vin_stream.isOpened(): print('* Calibration video input stream not opened - skipping pupilometry') return False # Video FPS from metadata # TODO: may not work with Quicktime videos # fps = vin_stream.get(cv2.cv.CV_CAP_PROP_FPS) # fps = cfg.getfloat('CAMERA', 'fps') # Desired time between frames in milliseconds # time_bw_frames = 1000.0 / fps vin_stream.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 320) vin_stream.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 240) vin_stream.set(cv2.cv.CV_CAP_PROP_FPS, 30) # Total number of frames in video file # nf = vin_stream.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT) # print(' Video has %d frames at %0.3f fps' % (nf, vin_fps)) # Read first preprocessed video frame from stream keep_going, frame_orig = media.LoadVideoFrame(vin_stream, cfg) if keep_going: frame, art_power = media.Preproc(frame_orig, cfg) else: art_power = 0.0 # Get size of preprocessed frame for output video setup nx, ny = frame.shape[1], frame.shape[0] # By default we start in non-calibration mode # switch between gaze/cal modes by pressing key "c" do_cal = False while keep_going: if do_cal == False: # # Output video # if live_eyetracking: print(' Opening output video stream') # Output video codec (MP4V - poor quality compression) # TODO : Find a better multiplatform codec fourcc = cv2.cv.CV_FOURCC('m','p','4','v') try: vout_stream = cv2.VideoWriter(vout_path, fourcc, 30, (nx, ny), True) except: print('* Problem creating output video stream - skipping pupilometry') return False if not vout_stream.isOpened(): print('* Output video not opened - skipping pupilometry') return False # Open pupilometry CSV file to write try: pupils_stream = open(pupils_csv, 'w') except: print('* Problem opening pupilometry CSV file - skipping pupilometry') return False # # Main Video Frame Loop # # Print verbose column headers if verbose: print('') print(' %10s %10s %10s %10s %10s' % ( 'Time (s)', 'Area', 'Blink', 'Artifact', 'FPS')) # Init frame counter fc = 0 # Init processing timer t0 = time.time() t = t0 while keep_going: # check whether config file has been updated, reload of that is the case if fc % 30 == 0: cfg_mtime = os.path.getmtime(os.path.join(data_dir, 'mrgaze.cfg')) if cfg_mtime > cfg_ts: print("Updating Configuration") cfg = config.LoadConfig(data_dir) cfg_ts = time.time() # Current video time in seconds t = time.time() # ------------------------------------- # Pass this frame to pupilometry engine # ------------------------------------- # b4_engine = time.time() pupil_ellipse, roi_rect, blink, glint, frame_rgb = engine.PupilometryEngine(frame, cascade, cfg) # print "Enging took %s ms" % (time.time() - b4_engine) # Derive pupilometry parameters px, py, area = engine.PupilometryPars(pupil_ellipse, glint, cfg) # Write data line to pupilometry CSV file pupils_stream.write( '%0.4f,%0.3f,%0.3f,%0.3f,%d,%0.3f,\n' % (t, area, px, py, blink, art_power) ) if live_eyetracking: # Write output video frame vout_stream.write(frame_orig) # Read next frame, unless we want to figure out the correct settings for this frame if not freeze_frame: keep_going, frame_orig = media.LoadVideoFrame(vin_stream, cfg) if keep_going: frame, art_power = media.Preproc(frame_orig, cfg) else: art_power = 0.0 # Increment frame counter fc = fc + 1 # Report processing FPS if verbose: if fc % 100 == 0: pfps = fc / (time.time() - t0) print(' %10.1f %10.1f %10d %10.3f %10.1f' % ( t, area, blink, art_power, pfps)) t0 = time.time() fc = 0 # wait whether user pressed esc to exit the experiment key = utils._waitKey(1) if key == 'ESC': # Clean up if live_eyetracking: vout_stream.release() pupils_stream.close() keep_going = False elif key == 'c': # Clean up if live_eyetracking: vout_stream.release() pupils_stream.close() do_cal = True print("Starting calibration.") break elif key == 'f': freeze_frame = not freeze_frame else: # do calibration # # Output video # if live_eyetracking: print(' Opening output video stream') # Output video codec (MP4V - poor quality compression) # TODO : Find a better multiplatform codec fourcc = cv2.cv.CV_FOURCC('m','p','4','v') try: cal_vout_stream = cv2.VideoWriter(cal_vout_path, fourcc, 30, (nx, ny), True) except: print('* Problem creating output video stream - skipping pupilometry') return False if not cal_vout_stream.isOpened(): print('* Output video not opened - skipping pupilometry') return False # Open pupilometry CSV file to write try: cal_pupils_stream = open(cal_pupils_csv, 'w') except: print('* Problem opening pupilometry CSV file - skipping pupilometry') return False # # Main Video Frame Loop # # Print verbose column headers if verbose: print('') print(' %10s %10s %10s %10s %10s' % ( 'Time (s)', 'Area', 'Blink', 'Artifact', 'FPS')) # Init frame counter fc = 0 # Init processing timer t0 = time.time() t = t0 while keep_going: # check whether config file has been updated, reload of that is the case if fc % 30 == 0: cfg_mtime = os.path.getmtime(os.path.join(data_dir, 'mrgaze.cfg')) if cfg_mtime > cfg_ts: print("Updating Configuration") cfg = config.LoadConfig(data_dir) cfg_ts = time.time() # Current video time in seconds t = time.time() # ------------------------------------- # Pass this frame to pupilometry engine # ------------------------------------- # b4_engine = time.time() pupil_ellipse, roi_rect, blink, glint, frame_rgb = engine.PupilometryEngine(frame, cascade, cfg) # print "Engine took %s ms" % (time.time() - b4_engine) # Derive pupilometry parameters px, py, area = engine.PupilometryPars(pupil_ellipse, glint, cfg) # Write data line to pupilometry CSV file cal_pupils_stream.write( '%0.4f,%0.3f,%0.3f,%0.3f,%d,%0.3f,\n' % (t, area, px, py, blink, art_power) ) # Write output video frame if live_eyetracking: cal_vout_stream.write(frame_orig) # Read next frame (if available) # if verbose: # b4_frame = time.time() keep_going, frame_orig = media.LoadVideoFrame(vin_stream, cfg) if keep_going: frame, art_power = media.Preproc(frame_orig, cfg) else: art_power = 0.0 #if verbose: # print "Time to load frame: %s" % (time.time() - b4_frame) # Increment frame counter fc = fc + 1 # Report processing FPS if verbose: if fc % 100 == 0: pfps = fc / (time.time() - t0) print(' %10.1f %10.1f %10d %10.3f %10.1f' % ( t, area, blink, art_power, pfps)) t0 = time.time() fc = 0 # wait whether user pressed esc to exit the experiment key = utils._waitKey(1) if key == 'ESC': keep_going = False # Clean up if live_eyetracking: cal_vout_stream.release() cal_pupils_stream.close() elif key == 'v' or not keep_going: do_cal = False print("Stopping calibration.") # Clean up if live_eyetracking: cal_vout_stream.release() cal_pupils_stream.close() break print(' Create calibration model') C, central_fix = calibrate.AutoCalibrate(res_dir, cfg) if not C.any(): print('* Empty calibration matrix detected - skipping') try: print(' Calibrate pupilometry') calibrate.ApplyCalibration(ss_dir, C, central_fix, cfg) except UnboundLocalError: print(' No calibration data found') cv2.destroyAllWindows() vin_stream.release() print('') print(' Generate Report') print(' ---------------') report.WriteReport(ss_dir, cfg) # Return pupilometry timeseries return t, px, py, area, blink, art_power def VideoPupilometry(data_dir, subj_sess, v_stub, cfg): """ Perform pupil boundary ellipse fitting on entire video Arguments ---- data_dir : string Root data directory path. subj_sess : string Subject/Session name used for subdirectory within data_dir v_stub : string Video filename stub, eg 'cal' or 'gaze' cfg : Analysis configuration parameters Returns ---- pupils : boolean Completion status (True = successful) """ # Output flags verbose = cfg.getboolean('OUTPUT', 'verbose') overwrite = cfg.getboolean('OUTPUT','overwrite') # Video information vin_ext = cfg.get('VIDEO', 'inputextension') vout_ext = cfg.get('VIDEO' ,'outputextension') vin_fps = cfg.getfloat('VIDEO', 'inputfps') # Full video file paths ss_dir = os.path.join(data_dir, subj_sess) vid_dir = os.path.join(ss_dir, 'videos') res_dir = os.path.join(ss_dir, 'results') vin_path = os.path.join(vid_dir, v_stub + vin_ext) vout_path = os.path.join(res_dir, v_stub + '_pupils' + vout_ext) # Raw and filtered pupilometry CSV file paths pupils_csv = os.path.join(res_dir, v_stub + '_pupils.csv') # Check that input video file exists if not os.path.isfile(vin_path): print('* %s does not exist - returning' % vin_path) return False # Set up the LBP cascade classifier LBP_path = os.path.join(utils._package_root(), 'Cascade/cascade.xml') print(' Loading LBP cascade') cascade = cv2.CascadeClassifier(LBP_path) if cascade.empty(): print('* LBP cascade is empty - mrgaze installation problem') return False # Check for output CSV existance and overwrite flag if os.path.isfile(pupils_csv): print('+ Pupilometry output already exists - checking overwrite flag') if overwrite: print('+ Overwrite allowed - continuing') else: print('+ Overwrite forbidden - skipping pupilometry') return True # # Input video # print(' Opening input video stream') try: vin_stream = cv2.VideoCapture(vin_path) except: print('* Problem opening input video stream - skipping pupilometry') return False if not vin_stream.isOpened(): print('* Video input stream not opened - skipping pupilometry') return False # Video FPS from metadata # TODO: may not work with Quicktime videos # fps = vin_stream.get(cv2.cv.CV_CAP_PROP_FPS) # Total number of frames in video file nf = vin_stream.get(cv2.CAP_PROP_FRAME_COUNT) print(' Video has %d frames at %0.3f fps' % (nf, vin_fps)) # Read first preprocessed video frame from stream keep_going, frame_orig = media.LoadVideoFrame(vin_stream, cfg) if keep_going: frame, art_power = media.Preproc(frame_orig, cfg) else: art_power = 0.0 # Get size of preprocessed frame for output video setup nx, ny = frame.shape[1], frame.shape[0] # # Output video # print(' Opening output video stream') # Output video codec (MP4V - poor quality compression) fourcc = cv2.VideoWriter_fourcc('m','p','4','v') try: vout_stream = cv2.VideoWriter(vout_path, fourcc, 30, (nx, ny), True) except: print('* Problem creating output video stream - skipping pupilometry') return False if not vout_stream.isOpened(): print('* Output video not opened - skipping pupilometry') return False # Open pupilometry CSV file to write try: pupils_stream = open(pupils_csv, 'w') except: print('* Problem opening pupilometry CSV file - skipping pupilometry') return False # # Main Video Frame Loop # # Print verbose column headers if verbose: print('') print(' %10s %10s %10s %10s %10s %10s' % ( 'Time (s)', '% Done', 'Area', 'Blink', 'Artifact', 'FPS')) # Init frame counter fc = 0 # Init processing timer t0 = time.time() while keep_going: # Current video time in seconds t = fc / vin_fps # ------------------------------------- # Pass this frame to pupilometry engine # ------------------------------------- pupil_ellipse, roi_rect, blink, glint, frame_rgb = engine.PupilometryEngine(frame, cascade, cfg) # Derive pupilometry parameters px, py, area = engine.PupilometryPars(pupil_ellipse, glint, cfg) # Write data line to pupilometry CSV file pupils_stream.write( '%0.3f,%0.3f,%0.3f,%0.3f,%d,%0.3f,\n' % (t, area, px, py, blink, art_power) ) # Write output video frame vout_stream.write(frame_rgb) # Read next frame (if available) keep_going, frame_orig = media.LoadVideoFrame(vin_stream, cfg) if keep_going: frame, art_power = media.Preproc(frame_orig, cfg) else: art_power = 0.0 # Increment frame counter fc = fc + 1 # Report processing FPS if verbose: if fc % 100 == 0: perc_done = fc / float(nf) * 100.0 pfps = fc / (time.time() - t0) print(' %10.1f %10.1f %10.1f %10d %10.3f %10.1f' % ( t, perc_done, area, blink, art_power, pfps)) # Clean up cv2.destroyAllWindows() vin_stream.release() vout_stream.release() pupils_stream.close() # Return pupilometry timeseries return t, px, py, area, blink, art_power
33.051643
112
0.550284
e9f462dbb1b4b480ae079d20eb179ca06f53f704
1,927
py
Python
aws_glue/combine_csv_files/combine_csv_files.py
veben/aws_python_snippets
39fa3cda8290fb097a5b9e8168829b62ab1af41e
[ "MIT" ]
1
2020-09-08T09:22:25.000Z
2020-09-08T09:22:25.000Z
aws_glue/combine_csv_files/combine_csv_files.py
veben/aws_python_snippets
39fa3cda8290fb097a5b9e8168829b62ab1af41e
[ "MIT" ]
null
null
null
aws_glue/combine_csv_files/combine_csv_files.py
veben/aws_python_snippets
39fa3cda8290fb097a5b9e8168829b62ab1af41e
[ "MIT" ]
1
2020-09-08T09:26:58.000Z
2020-09-08T09:26:58.000Z
from lib_combination.aws_client.aws_client import get_session_for_profile, run_job, get_job, create_job from lib_combination.aws_client.aws_client import upload_file_to_s3_bucket from lib_combination.conf_utils.conf_utils import get_job_name, get_profile_name, get_bucket_name, get_database_name from lib_combination.file_utils.file_utils import get_local_script_folder_path if __name__ == "__main__": main()
41.891304
116
0.70576
e9f4dc1139fdd0b79eb9f6a5670984a538e5b297
1,062
py
Python
p850h/rectangle_area.py
l33tdaima/l33tdaima
0a7a9573dc6b79e22dcb54357493ebaaf5e0aa90
[ "MIT" ]
1
2020-02-20T12:04:46.000Z
2020-02-20T12:04:46.000Z
p850h/rectangle_area.py
l33tdaima/l33tdaima
0a7a9573dc6b79e22dcb54357493ebaaf5e0aa90
[ "MIT" ]
null
null
null
p850h/rectangle_area.py
l33tdaima/l33tdaima
0a7a9573dc6b79e22dcb54357493ebaaf5e0aa90
[ "MIT" ]
null
null
null
from typing import List # TESTS for rectangles, expected in [ ([[0, 0, 2, 2], [1, 0, 2, 3], [1, 0, 3, 1]], 6), ([[0, 0, 1000000000, 1000000000]], 49), ]: sol = Solution() actual = sol.rectangleArea(rectangles) print("Total area covered by rectangles", rectangles, "->", actual) assert actual == expected
32.181818
88
0.508475
e9f668b9ca060060d4949971143a55425febaef0
1,323
py
Python
hack/examples/python/sentiments/sentiments.py
margarytaSadovets/nuclio
37bf21900d543a6340edf9374475b104ea963459
[ "Apache-2.0" ]
1
2018-01-02T18:48:27.000Z
2018-01-02T18:48:27.000Z
hack/examples/python/sentiments/sentiments.py
ilaykav/nuclio
23a65b9f5c9e00afccbfbc62cd2a4dd2cc8a75dd
[ "Apache-2.0" ]
null
null
null
hack/examples/python/sentiments/sentiments.py
ilaykav/nuclio
23a65b9f5c9e00afccbfbc62cd2a4dd2cc8a75dd
[ "Apache-2.0" ]
null
null
null
# Copyright 2017 The Nuclio Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # uses vader lib (will be installed automatically via build commands) to identify sentiments in the body string # return score result in the form of: {'neg': 0.0, 'neu': 0.323, 'pos': 0.677, 'compound': 0.6369} # # @nuclio.configure # # function.yaml: # apiVersion: "nuclio.io/v1beta1" # kind: "Function" # spec: # runtime: "python" # # build: # commands: # - "pip install requests vaderSentiment" # from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
30.767442
111
0.721844
e9f7cea197f517cec2cbd809a57d3dcde8bc48fa
1,636
py
Python
crawler/src/map_client/kakao_map_client.py
HVHO/holiday-pharmacy
e641dca93ed0cc0e3ffa28f54a1da6a86c1cfe22
[ "MIT" ]
null
null
null
crawler/src/map_client/kakao_map_client.py
HVHO/holiday-pharmacy
e641dca93ed0cc0e3ffa28f54a1da6a86c1cfe22
[ "MIT" ]
null
null
null
crawler/src/map_client/kakao_map_client.py
HVHO/holiday-pharmacy
e641dca93ed0cc0e3ffa28f54a1da6a86c1cfe22
[ "MIT" ]
null
null
null
import requests
33.387755
94
0.590465
e9f9eaf439178a9738f5c3bed675e41c46a5be64
404
py
Python
main.py
Alenx58/python-mysql-elasticsearch
a5deb16dcfce6d37c9c4a076f7ec6ff84ca967c3
[ "MIT" ]
1
2021-04-27T06:32:18.000Z
2021-04-27T06:32:18.000Z
main.py
Alenx58/python-mysql-elasticsearch
a5deb16dcfce6d37c9c4a076f7ec6ff84ca967c3
[ "MIT" ]
null
null
null
main.py
Alenx58/python-mysql-elasticsearch
a5deb16dcfce6d37c9c4a076f7ec6ff84ca967c3
[ "MIT" ]
null
null
null
#!/usr/bin/env python3 # -*- coding:utf-8 -*- # Author : Alenx.Hai <alenx.hai@gmail.com> # created time: 2020/12/21-10:49 import asyncio from src.mysql_elastic import MySQLElasticSearch if __name__ == '__main__': loop = asyncio.get_event_loop() loop.run_until_complete(asyncio.wait([main()]))
21.263158
51
0.707921
e9fbb4ffd34a72b02bcdf9fee23d69719622bfd4
397
py
Python
PythonDesafios/d107/teste.py
adaatii/Python-Curso-em-Video-
30b37713b3685469558babb93b557b53210f010c
[ "MIT" ]
null
null
null
PythonDesafios/d107/teste.py
adaatii/Python-Curso-em-Video-
30b37713b3685469558babb93b557b53210f010c
[ "MIT" ]
null
null
null
PythonDesafios/d107/teste.py
adaatii/Python-Curso-em-Video-
30b37713b3685469558babb93b557b53210f010c
[ "MIT" ]
null
null
null
# Crie um mdulo chamado moeda.py que tenha as funes incorporadas aumentar(), diminuir(), dobro() e metade(). Faa # tambm um programa que importe esse mdulo e use algumas dessas funes. import moeda p = float(input('Digite o preo: ')) print(f'A metade do {p} R${moeda.metade(p)}') print(f'O dobro de {p} R${moeda.dobro(p)}') print(f'Aumentando 10%, temos R${moeda.aumentar(p, 10)}')
36.090909
116
0.702771
e9fc3b08d76230c48ce220e58abe719b3c7d3fe9
3,024
py
Python
homeassistant/components/homekit/covers.py
mfrueh/home-assistant
5d64628b5bf4713016883282fd54de9c7d5089d0
[ "Apache-2.0" ]
null
null
null
homeassistant/components/homekit/covers.py
mfrueh/home-assistant
5d64628b5bf4713016883282fd54de9c7d5089d0
[ "Apache-2.0" ]
null
null
null
homeassistant/components/homekit/covers.py
mfrueh/home-assistant
5d64628b5bf4713016883282fd54de9c7d5089d0
[ "Apache-2.0" ]
null
null
null
"""Class to hold all cover accessories.""" import logging from homeassistant.components.cover import ATTR_CURRENT_POSITION from homeassistant.helpers.event import async_track_state_change from . import TYPES from .accessories import HomeAccessory, add_preload_service from .const import ( SERV_WINDOW_COVERING, CHAR_CURRENT_POSITION, CHAR_TARGET_POSITION, CHAR_POSITION_STATE) _LOGGER = logging.getLogger(__name__)
36.878049
75
0.67791
e9fce1f0a0567c478c06135a1b26bb39e2c00202
5,888
py
Python
plotter/hysplit_reader_long.py
yosukefk/plotter
16127ee7fc3105c717e92875ee3d61477bd41533
[ "MIT" ]
null
null
null
plotter/hysplit_reader_long.py
yosukefk/plotter
16127ee7fc3105c717e92875ee3d61477bd41533
[ "MIT" ]
6
2021-05-25T15:51:27.000Z
2021-08-18T20:39:41.000Z
plotter/hysplit_reader_long.py
yosukefk/plotter
16127ee7fc3105c717e92875ee3d61477bd41533
[ "MIT" ]
null
null
null
import pandas as pd import numpy as np import datetime import pytz from pathlib import Path import warnings from io import IOBase from . import calpost_reader calpost_cat = calpost_reader.calpost_cat def hysplit_reader_long(f, tslice=slice(None, None), x=None, y=None, z=None, rdx_map=None): """reads hysplit output file, returns dict of numpy arrays :param FileIO f: either (1)opened hysplit output file, (2) hysplit output filename or (3) list of (1) or (2) :param slice tslice: slice of time index :param list x: list of x coords :param list y: list of y coords :return: dict, with ['v'] has data as 3d array (t, y, x) :rtype: dict """ print(type(f)) if isinstance(f, IOBase): raise ValueError('plese pass filename, not FileIO...') # assume file name passed if 'f' is string if isinstance(f, (str, Path)): df = pd.read_csv(f, sep=r'\s+') return hysplit_reader_long(df, tslice, x, y, z, rdx_map) # list of files may have different time period and locations. So # first they are grouped by time perod, then each chunk got read. # then they got joined with the time stiching routine aware of # spin-up time if isinstance(f, list): lines = [next(pd.read_csv(fn, sep=r'\s+', nrows=1).itertuples()) for fn in f] # Pandas(Index=0, JDAY=268.208, YR1=19, MO1=9, DA1=25, HR1=5, MN1=0, # YR2=19, MO2=9, DA2=25, HR2=5, MN2=1, Pol=1, Lev=1, Station=1, # Value=0.0) print(lines) dtes = [datetime.datetime(_.YR1, _.MO1, _.DA1, _.HR1, _.MN1).replace(tzinfo=pytz.utc).astimezone(pytz.timezone('Etc/GMT+6')) for _ in lines] df_fnames = pd.DataFrame({'fname': f, 'datetime': dtes}) df_fnames.to_csv('fnames.csv') # group the file names by the datetime dct_fnames = {} for fn,dte in zip(f, dtes): dct_fnames.setdefault(dte, []).append(fn) file_dates = list(dct_fnames.keys()) dat = [] for dte,fnames in dct_fnames.items(): dfs = [pd.read_csv(fn, sep=r'\s+') for fn in fnames] df = pd.concat(dfs) dat.append( hysplit_reader_long(df, tslice, x, y, z, rdx_map) ) dat = calpost_cat(dat, use_later_files=True) dat['ts'] = dat['ts'][tslice] dat['v'] = dat['v'][tslice] return dat # now i should be getting dataframe df = f units = '???' print('dt') # extremely slow! #df['Datetime'] = [datetime.datetime(_.YR1, _.MO1, _.DA1, _.HR1, # _.MN1).replace(tzinfo=pytz.utc).astimezone(pytz.timezone('Etc/GMT+6')) # for _ in df.itertuples()] df['Datetime'] = pd.to_datetime(df[['YR1', 'MO1', 'DA1', 'HR1', 'MN1']].assign( YR1= lambda df: df['YR1'] + 2000).rename( columns={'YR1':'year', 'MO1':'month', 'DA1': 'day', 'HR1': 'hour', 'MN1': 'minute'}), utc=True).dt.tz_convert('Etc/GMT+6') # bad idea! #df['Datetime_tup'] = [_ for _ in df[['YR1', 'MO1', 'DA1', 'HR1', # 'MN1']].itertuples(index=False)] df = df[['Datetime', 'Lev', 'Station', 'Value']] #grouped = df.groupby(['Datetime', 'Lev', 'Station']) nrec = len(df.index) print('set_index') df = df[['Datetime', 'Lev', 'Station', 'Value']].set_index( ['Datetime', 'Station', 'Lev'] ) print('dt') ts = df.index.levels[0] #xxx = pd.DataFrame(ts, columns=('year', 'month', 'day', 'hour', # 'minute')) #print(xxx) #xxx = xxx.assign(year=lambda x: x['year']+2000) #print(xxx) # #ts = pd.to_datetime( # pd.DataFrame( # ts, # columns=('year', 'month', 'day', 'hour', 'minute') # ).assign( # year=lambda x: x['year']+2000 # )) #print(ts) print('cont') stations = df.index.levels[1] nz = len(df.index.levels[2]) nsta = len(df.index.levels[1]) nt = len(df.index.levels[0]) print('nt,nz,nsta,nrec=', nt, nz, nsta, nrec) # ........ bad idea #assert nt * nz * nsta == nrec if not nt * nz * nsta == nrec: print(f'expected {nt*nz*nsta} rec, got {nrec}, short by {nt*nz*nsta-nrec}') print(' f:', f) print(' rng:', df.index.levels[0][0], df.index.levels[0][-1]) print('unstack') df = df.unstack().unstack() df.columns = df.columns.droplevel() if rdx_map: x = rdx_map.x y = rdx_map.y nx = len(x) ny = len(y) grid = rdx_map.grid v = df.to_numpy() if rdx_map.coverage == 'full, c-order' and nsta==nx*ny: v = v.reshape(nt, nz, ny, nx) elif rdx_map.coverage == 'full, f-order' and nsta==nx*ny: raise NotImplementedError( 'qa first! receptor def = "{}", '.format(rdx_map.coverage)) v = v.reshape(nt, nz, nx, ny) v = np.swapaxes(v, -1, -2) elif rdx_map.coverage in ('full, c-order', 'full, f-order', 'full, random', 'patial, random'): rdx = np.arange(nt*nz) + 1 mymap = rdx_map.get_index(stations).to_numpy() mymap = mymap[:, ::-1] vv = np.empty((nt, nz, ny, nx)) vv[...] = np.nan v = v.reshape(nt , nz, -1) for tt,t in zip(vv, v): for zz, z in zip(tt, t): for ji,p in zip(mymap,z): zz[tuple(ji)] = p v = vv else: raise ValueError('rdx_map is mandatory for now') #dct = {'v': v, 'ts': ts, 'units': units, 'df': f, 'name': None} dct = {'v': v, 'ts': ts, 'units': units, 'name': None} dct.update( {'x': x, 'y': y, 'grid': grid, }) del df return dct
33.078652
112
0.529552
e9fce58b8db982ac1059efc2000a44b8a6f0d6b6
1,094
py
Python
tests/UserTest/test_user_db.py
brijeshb42/flask-web
a859fb68fe0eedf5ee872767d107f95a4e6f4856
[ "MIT" ]
14
2015-02-20T18:31:33.000Z
2020-12-23T02:33:05.000Z
tests/UserTest/test_user_db.py
brijeshb42/flask-web
a859fb68fe0eedf5ee872767d107f95a4e6f4856
[ "MIT" ]
2
2015-02-21T18:49:12.000Z
2015-10-06T18:10:30.000Z
tests/UserTest/test_user_db.py
brijeshb42/yapper
a859fb68fe0eedf5ee872767d107f95a4e6f4856
[ "MIT" ]
10
2015-02-21T11:06:57.000Z
2022-02-21T01:25:34.000Z
import unittest from yapper import create_app, db from yapper.blueprints.user.models import User, Role
30.388889
69
0.637112
e9fd5e9401ba6d04c5d4bf4d42d343bc34357a32
2,880
py
Python
CIM16/IEC61970/Generation/Production/StartIgnFuelCurve.py
MaximeBaudette/PyCIM
d68ee5ccfc1d32d44c5cd09fb173142fb5ff4f14
[ "MIT" ]
null
null
null
CIM16/IEC61970/Generation/Production/StartIgnFuelCurve.py
MaximeBaudette/PyCIM
d68ee5ccfc1d32d44c5cd09fb173142fb5ff4f14
[ "MIT" ]
null
null
null
CIM16/IEC61970/Generation/Production/StartIgnFuelCurve.py
MaximeBaudette/PyCIM
d68ee5ccfc1d32d44c5cd09fb173142fb5ff4f14
[ "MIT" ]
1
2021-04-02T18:04:49.000Z
2021-04-02T18:04:49.000Z
# Copyright (C) 2010-2011 Richard Lincoln # # 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 CIM16.IEC61970.Core.Curve import Curve
45
309
0.720833
e9fe99f79d22866cd1c3d457b72379bf7128ed8c
122,030
py
Python
Line/beijing_aqi.py
Hansz00/pyecharts-gallery
a0a16d980e9d4f7d355c5ada938614579ee8d461
[ "MIT" ]
1
2020-08-25T07:41:10.000Z
2020-08-25T07:41:10.000Z
Line/beijing_aqi.py
Hansz00/pyecharts-gallery
a0a16d980e9d4f7d355c5ada938614579ee8d461
[ "MIT" ]
null
null
null
Line/beijing_aqi.py
Hansz00/pyecharts-gallery
a0a16d980e9d4f7d355c5ada938614579ee8d461
[ "MIT" ]
1
2022-03-10T09:05:44.000Z
2022-03-10T09:05:44.000Z
import pyecharts.options as opts from pyecharts.charts import Line """ Gallery pyecharts 1.1.0 : https://echarts.baidu.com/examples/editor.html?c=line-aqi : 1dataZoom Y """ all_data = [ ["2000-06-05", 116], ["2000-06-06", 129], ["2000-06-07", 135], ["2000-06-08", 86], ["2000-06-09", 73], ["2000-06-10", 85], ["2000-06-11", 73], ["2000-06-12", 68], ["2000-06-13", 92], ["2000-06-14", 130], ["2000-06-15", 245], ["2000-06-16", 139], ["2000-06-17", 115], ["2000-06-18", 111], ["2000-06-19", 309], ["2000-06-20", 206], ["2000-06-21", 137], ["2000-06-22", 128], ["2000-06-23", 85], ["2000-06-24", 94], ["2000-06-25", 71], ["2000-06-26", 106], ["2000-06-27", 84], ["2000-06-28", 93], ["2000-06-29", 85], ["2000-06-30", 73], ["2000-07-01", 83], ["2000-07-02", 125], ["2000-07-03", 107], ["2000-07-04", 82], ["2000-07-05", 44], ["2000-07-06", 72], ["2000-07-07", 106], ["2000-07-08", 107], ["2000-07-09", 66], ["2000-07-10", 91], ["2000-07-11", 92], ["2000-07-12", 113], ["2000-07-13", 107], ["2000-07-14", 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["2014-09-05", 140], ["2014-09-06", 186], ["2014-09-07", 200], ["2014-09-08", 61], ["2014-09-09", 109], ["2014-09-10", 111], ["2014-09-11", 114], ["2014-09-12", 97], ["2014-09-13", 94], ["2014-09-14", 66], ["2014-09-15", 54], ["2014-09-16", 87], ["2014-09-17", 80], ["2014-09-18", 84], ["2014-09-19", 117], ["2014-09-20", 168], ["2014-09-21", 129], ["2014-09-22", 127], ["2014-09-23", 64], ["2014-09-24", 60], ["2014-09-25", 144], ["2014-09-26", 170], ["2014-09-27", 58], ["2014-09-28", 87], ["2014-09-29", 70], ["2014-09-30", 53], ["2014-10-01", 92], ["2014-10-02", 78], ["2014-10-03", 123], ["2014-10-04", 95], ["2014-10-05", 54], ["2014-10-06", 68], ["2014-10-07", 200], ["2014-10-08", 314], ["2014-10-09", 379], ["2014-10-10", 346], ["2014-10-11", 233], ["2014-10-14", 80], ["2014-10-15", 73], ["2014-10-16", 76], ["2014-10-17", 132], ["2014-10-18", 211], ["2014-10-19", 289], ["2014-10-20", 250], ["2014-10-21", 82], ["2014-10-22", 99], ["2014-10-23", 163], ["2014-10-24", 267], ["2014-10-25", 353], ["2014-10-26", 78], ["2014-10-27", 72], ["2014-10-28", 88], ["2014-10-29", 140], ["2014-10-30", 206], ["2014-10-31", 204], ["2014-11-01", 65], ["2014-11-03", 59], ["2014-11-04", 150], ["2014-11-05", 79], ["2014-11-07", 63], ["2014-11-08", 93], ["2014-11-09", 80], ["2014-11-10", 95], ["2014-11-11", 59], ["2014-11-13", 65], ["2014-11-14", 77], ["2014-11-15", 143], ["2014-11-16", 98], ["2014-11-17", 64], ["2014-11-18", 93], ["2014-11-19", 282], ["2014-11-23", 155], ["2014-11-24", 94], ["2014-11-25", 196], ["2014-11-26", 293], ["2014-11-27", 83], ["2014-11-28", 114], ["2014-11-29", 276], ["2014-12-01", 54], ["2014-12-02", 65], ["2014-12-03", 51], ["2014-12-05", 62], ["2014-12-06", 89], ["2014-12-07", 65], ["2014-12-08", 82], ["2014-12-09", 276], ["2014-12-10", 153], ["2014-12-11", 52], ["2014-12-13", 69], ["2014-12-14", 113], ["2014-12-15", 82], ["2014-12-17", 99], ["2014-12-19", 53], ["2014-12-22", 103], ["2014-12-23", 100], ["2014-12-25", 73], ["2014-12-26", 155], ["2014-12-27", 243], ["2014-12-28", 155], ["2014-12-29", 125], ["2014-12-30", 65], ["2015-01-01", 65], ["2015-01-02", 79], ["2015-01-03", 200], ["2015-01-04", 226], ["2015-01-05", 122], ["2015-01-06", 60], ["2015-01-07", 85], ["2015-01-08", 190], ["2015-01-09", 105], ["2015-01-10", 208], ["2015-01-11", 59], ["2015-01-12", 160], ["2015-01-13", 211], ["2015-01-14", 265], ["2015-01-15", 386], ["2015-01-16", 118], ["2015-01-17", 89], ["2015-01-18", 94], ["2015-01-19", 77], ["2015-01-20", 113], ["2015-01-22", 143], ["2015-01-23", 257], ["2015-01-24", 117], ["2015-01-25", 185], ["2015-01-26", 119], ["2015-01-28", 65], ["2015-01-29", 87], ["2015-01-31", 60], ["2015-02-01", 108], ["2015-02-02", 188], ["2015-02-03", 143], ["2015-02-05", 62], ["2015-02-06", 100], ["2015-02-09", 152], ["2015-02-10", 166], ["2015-02-11", 55], ["2015-02-12", 59], ["2015-02-13", 175], ["2015-02-14", 293], ["2015-02-15", 326], ["2015-02-16", 153], ["2015-02-18", 73], ["2015-02-19", 267], ["2015-02-20", 183], ["2015-02-21", 394], ["2015-02-22", 158], ["2015-02-23", 86], ["2015-02-24", 207], ] ( Line() .add_xaxis(xaxis_data=[item[0] for item in all_data]) .add_yaxis( series_name="", y_axis=[item[1] for item in all_data], yaxis_index=0, is_smooth=True, is_symbol_show=False, ) .set_global_opts( title_opts=opts.TitleOpts(title="Beijing AQI"), tooltip_opts=opts.TooltipOpts(trigger="axis"), datazoom_opts=[ opts.DataZoomOpts(yaxis_index=0), opts.DataZoomOpts(type_="inside", yaxis_index=0), ], visualmap_opts=opts.VisualMapOpts( pos_top="10", pos_right="10", is_piecewise=True, pieces=[ {"gt": 0, "lte": 50, "color": "#096"}, {"gt": 50, "lte": 100, "color": "#ffde33"}, {"gt": 100, "lte": 150, "color": "#ff9933"}, {"gt": 150, "lte": 200, "color": "#cc0033"}, {"gt": 200, "lte": 300, "color": "#660099"}, {"gt": 300, "color": "#7e0023"}, ], out_of_range={"color": "#999"}, ), xaxis_opts=opts.AxisOpts(type_="category"), yaxis_opts=opts.AxisOpts( type_="value", name_location="start", min_=0, max_=500, is_scale=True, axistick_opts=opts.AxisTickOpts(is_inside=False), ), ) .set_series_opts( markline_opts=opts.MarkLineOpts( data=[ {"yAxis": 50}, {"yAxis": 100}, {"yAxis": 150}, {"yAxis": 200}, {"yAxis": 300}, ], label_opts=opts.LabelOpts(position="end"), ) ) .render("beijing_aqi.html") )
24.410882
63
0.426575
e9ff12848b4786dd9b5181f046c3b8596891ad5d
1,416
py
Python
test_scripts/test_stack_and_visualize.py
jakevdp/spheredb
e5e5ff8b8902459b3f38a1a413a712ac1695accc
[ "BSD-3-Clause" ]
1
2021-08-29T06:01:28.000Z
2021-08-29T06:01:28.000Z
test_scripts/test_stack_and_visualize.py
jakevdp/spheredb
e5e5ff8b8902459b3f38a1a413a712ac1695accc
[ "BSD-3-Clause" ]
null
null
null
test_scripts/test_stack_and_visualize.py
jakevdp/spheredb
e5e5ff8b8902459b3f38a1a413a712ac1695accc
[ "BSD-3-Clause" ]
2
2018-08-03T20:27:35.000Z
2021-08-29T06:01:30.000Z
""" Stacking and Visualizing ------------------------ This script does the following: 1. Input LSST images, warp to sparse matrix, store as scidb arrays. This tests the warping of a single LSST exposure into a sparse matrix representation of a HEALPix grid. """ import os import sys import glob import matplotlib.pyplot as plt import numpy as np sys.path.append(os.path.abspath('..')) from spheredb.scidb_tools import HPXPixels3D, find_index_bounds filenames = glob.glob("/home/jakevdp/research/LSST_IMGS/*/R*/S*.fits") print "total number of files:", len(filenames) HPX_data = HPXPixels3D(input_files=filenames[:20], name='LSSTdata', force_reload=False) times = HPX_data.unique_times() xlim, ylim, tlim = HPX_data.index_bounds() for time in times[:2]: tslice = HPX_data.time_slice(time) tslice_arr = tslice.arr[xlim[0]:xlim[1], ylim[0]:ylim[1]].toarray() fig, ax = plt.subplots() im = ax.imshow(np.log(tslice_arr), cmap=plt.cm.binary) ax.set_xlim(400, 440) ax.set_ylim(860, 820) fig.colorbar(im, ax=ax) ax.set_title("time = {0}".format(time)) coadd = HPX_data.coadd().arr[xlim[0]:xlim[1], ylim[0]:ylim[1]].toarray() fig, ax = plt.subplots() im = ax.imshow(np.log(coadd), cmap=plt.cm.binary) ax.set_xlim(400, 440) ax.set_ylim(860, 820) fig.colorbar(im, ax=ax) ax.set_title("coadd") plt.show()
27.230769
70
0.664548
18007f3ffa7e153ffa5c57f5301a0d773f024cb8
307
py
Python
Problem/PeopleFund/concatenate.py
yeojin-dev/coding-test
30ce8507838beaa9232c6fc6c62a7dcb62d51464
[ "MIT" ]
2
2018-07-11T08:13:06.000Z
2018-07-11T08:47:12.000Z
Problem/PeopleFund/concatenate.py
yeojin-dev/coding-test
30ce8507838beaa9232c6fc6c62a7dcb62d51464
[ "MIT" ]
null
null
null
Problem/PeopleFund/concatenate.py
yeojin-dev/coding-test
30ce8507838beaa9232c6fc6c62a7dcb62d51464
[ "MIT" ]
null
null
null
import numpy as np sizes = list(map(int, input().split())) arr1 = list() arr2 = list() for _ in range(sizes[0]): arr1.append(list(map(int, input().split()))) for _ in range(sizes[1]): arr2.append(list(map(int, input().split()))) print(np.concatenate((np.array(arr1), np.array(arr2)), axis=0))
19.1875
63
0.635179
18012d97d113307f75b71fea1cea0948b4e7a4b1
28,941
py
Python
tests/test_splitname.py
goerz/bibdeskparser
4f60f9960f6f0156c2f3c89033065c4e121800ab
[ "BSD-3-Clause" ]
null
null
null
tests/test_splitname.py
goerz/bibdeskparser
4f60f9960f6f0156c2f3c89033065c4e121800ab
[ "BSD-3-Clause" ]
null
null
null
tests/test_splitname.py
goerz/bibdeskparser
4f60f9960f6f0156c2f3c89033065c4e121800ab
[ "BSD-3-Clause" ]
null
null
null
#!/usr/bin/env python # -*- coding: utf-8 -*- import unittest from bibdeskparser.customization import InvalidName, splitname splitname_test_cases = ( ( r'Per Brinch Hansen', {'first': ['Per', 'Brinch'], 'von': [], 'last': ['Hansen'], 'jr': []}, ), ( r'Brinch Hansen, Per', {'first': ['Per'], 'von': [], 'last': ['Brinch', 'Hansen'], 'jr': []}, ), ( r'Brinch Hansen,, Per', {'first': ['Per'], 'von': [], 'last': ['Brinch', 'Hansen'], 'jr': []}, ), ( r"Charles Louis Xavier Joseph de la Vall{\'e}e Poussin", { 'first': ['Charles', 'Louis', 'Xavier', 'Joseph'], 'von': ['de', 'la'], 'last': [r'Vall{\'e}e', 'Poussin'], 'jr': [], }, ), ( r'D[onald] E. Knuth', {'first': ['D[onald]', 'E.'], 'von': [], 'last': ['Knuth'], 'jr': []}, ), ( r'A. {Delgado de Molina}', { 'first': ['A.'], 'von': [], 'last': ['{Delgado de Molina}'], 'jr': [], }, ), ( r"M. Vign{\'e}", {'first': ['M.'], 'von': [], 'last': [r"Vign{\'e}"], 'jr': []}, ), ############################################################################### # # Test cases from # http://maverick.inria.fr/~Xavier.Decoret/resources/xdkbibtex/bibtex_summary.html # ############################################################################### (r'AA BB', {'first': ['AA'], 'von': [], 'last': ['BB'], 'jr': []}), (r'AA', {'first': [], 'von': [], 'last': ['AA'], 'jr': []}), (r'AA bb', {'first': ['AA'], 'von': [], 'last': ['bb'], 'jr': []}), (r'aa', {'first': [], 'von': [], 'last': ['aa'], 'jr': []}), (r'AA bb CC', {'first': ['AA'], 'von': ['bb'], 'last': ['CC'], 'jr': []}), ( r'AA bb CC dd EE', {'first': ['AA'], 'von': ['bb', 'CC', 'dd'], 'last': ['EE'], 'jr': []}, ), ( r'AA 1B cc dd', {'first': ['AA', '1B'], 'von': ['cc'], 'last': ['dd'], 'jr': []}, ), ( r'AA 1b cc dd', {'first': ['AA'], 'von': ['1b', 'cc'], 'last': ['dd'], 'jr': []}, ), ( r'AA {b}B cc dd', {'first': ['AA', '{b}B'], 'von': ['cc'], 'last': ['dd'], 'jr': []}, ), ( r'AA {b}b cc dd', {'first': ['AA'], 'von': ['{b}b', 'cc'], 'last': ['dd'], 'jr': []}, ), ( r'AA {B}b cc dd', {'first': ['AA'], 'von': ['{B}b', 'cc'], 'last': ['dd'], 'jr': []}, ), ( r'AA {B}B cc dd', {'first': ['AA', '{B}B'], 'von': ['cc'], 'last': ['dd'], 'jr': []}, ), ( r'AA \BB{b} cc dd', {'first': ['AA', r'\BB{b}'], 'von': ['cc'], 'last': ['dd'], 'jr': []}, ), ( r'AA \bb{b} cc dd', {'first': ['AA'], 'von': [r'\bb{b}', 'cc'], 'last': ['dd'], 'jr': []}, ), ( r'AA {bb} cc DD', {'first': ['AA', '{bb}'], 'von': ['cc'], 'last': ['DD'], 'jr': []}, ), ( r'AA bb {cc} DD', {'first': ['AA'], 'von': ['bb'], 'last': ['{cc}', 'DD'], 'jr': []}, ), ( r'AA {bb} CC', {'first': ['AA', '{bb}'], 'von': [], 'last': ['CC'], 'jr': []}, ), (r'bb CC, AA', {'first': ['AA'], 'von': ['bb'], 'last': ['CC'], 'jr': []}), (r'bb CC, aa', {'first': ['aa'], 'von': ['bb'], 'last': ['CC'], 'jr': []}), ( r'bb CC dd EE, AA', {'first': ['AA'], 'von': ['bb', 'CC', 'dd'], 'last': ['EE'], 'jr': []}, ), (r'bb, AA', {'first': ['AA'], 'von': [], 'last': ['bb'], 'jr': []}), ( r'bb CC,XX, AA', {'first': ['AA'], 'von': ['bb'], 'last': ['CC'], 'jr': ['XX']}, ), ( r'bb CC,xx, AA', {'first': ['AA'], 'von': ['bb'], 'last': ['CC'], 'jr': ['xx']}, ), (r'BB,, AA', {'first': ['AA'], 'von': [], 'last': ['BB'], 'jr': []}), ( r"Paul \'Emile Victor", { 'first': ['Paul', r"\'Emile"], 'von': [], 'last': ['Victor'], 'jr': [], }, ), ( r"Paul {\'E}mile Victor", { 'first': ['Paul', r"{\'E}mile"], 'von': [], 'last': ['Victor'], 'jr': [], }, ), ( r"Paul \'emile Victor", {'first': ['Paul'], 'von': [r"\'emile"], 'last': ['Victor'], 'jr': []}, ), ( r"Paul {\'e}mile Victor", { 'first': ['Paul'], 'von': [r"{\'e}mile"], 'last': ['Victor'], 'jr': [], }, ), ( r"Victor, Paul \'Emile", { 'first': ['Paul', r"\'Emile"], 'von': [], 'last': ['Victor'], 'jr': [], }, ), ( r"Victor, Paul {\'E}mile", { 'first': ['Paul', r"{\'E}mile"], 'von': [], 'last': ['Victor'], 'jr': [], }, ), ( r"Victor, Paul \'emile", { 'first': ['Paul', r"\'emile"], 'von': [], 'last': ['Victor'], 'jr': [], }, ), ( r"Victor, Paul {\'e}mile", { 'first': ['Paul', r"{\'e}mile"], 'von': [], 'last': ['Victor'], 'jr': [], }, ), ( r'Dominique Galouzeau de Villepin', { 'first': ['Dominique', 'Galouzeau'], 'von': ['de'], 'last': ['Villepin'], 'jr': [], }, ), ( r'Dominique {G}alouzeau de Villepin', { 'first': ['Dominique'], 'von': ['{G}alouzeau', 'de'], 'last': ['Villepin'], 'jr': [], }, ), ( r'Galouzeau de Villepin, Dominique', { 'first': ['Dominique'], 'von': ['Galouzeau', 'de'], 'last': ['Villepin'], 'jr': [], }, ), ############################################################################### # # Test cases from pybtex # See file /pybtex/tests/parse_name_test.py in the pybtex source. # ############################################################################### ( r'A. E. Siegman', {'first': ['A.', 'E.'], 'von': [], 'last': ['Siegman'], 'jr': []}, ), ( r'A. G. W. Cameron', { 'first': ['A.', 'G.', 'W.'], 'von': [], 'last': ['Cameron'], 'jr': [], }, ), (r'A. Hoenig', {'first': ['A.'], 'von': [], 'last': ['Hoenig'], 'jr': []}), ( r'A. J. Van Haagen', { 'first': ['A.', 'J.', 'Van'], 'von': [], 'last': ['Haagen'], 'jr': [], }, ), ( r'A. S. Berdnikov', {'first': ['A.', 'S.'], 'von': [], 'last': ['Berdnikov'], 'jr': []}, ), ( r'A. Trevorrow', {'first': ['A.'], 'von': [], 'last': ['Trevorrow'], 'jr': []}, ), ( r'Adam H. Lewenberg', {'first': ['Adam', 'H.'], 'von': [], 'last': ['Lewenberg'], 'jr': []}, ), ( r'Addison-Wesley Publishing Company', { 'first': ['Addison-Wesley', 'Publishing'], 'von': [], 'last': ['Company'], 'jr': [], }, ), ( r'Advogato (Raph Levien)', { 'first': ['Advogato', '(Raph'], 'von': [], 'last': ['Levien)'], 'jr': [], }, ), ( r'Andrea de Leeuw van Weenen', { 'first': ['Andrea'], 'von': ['de', 'Leeuw', 'van'], 'last': ['Weenen'], 'jr': [], }, ), ( r'Andreas Geyer-Schulz', {'first': ['Andreas'], 'von': [], 'last': ['Geyer-Schulz'], 'jr': []}, ), ( r'Andr{\'e} Heck', {'first': [r'Andr{\'e}'], 'von': [], 'last': ['Heck'], 'jr': []}, ), ( r'Anne Br{\"u}ggemann-Klein', { 'first': ['Anne'], 'von': [], 'last': [r'Br{\"u}ggemann-Klein'], 'jr': [], }, ), (r'Anonymous', {'first': [], 'von': [], 'last': ['Anonymous'], 'jr': []}), (r'B. Beeton', {'first': ['B.'], 'von': [], 'last': ['Beeton'], 'jr': []}), ( r'B. Hamilton Kelly', {'first': ['B.', 'Hamilton'], 'von': [], 'last': ['Kelly'], 'jr': []}, ), ( r'B. V. Venkata Krishna Sastry', { 'first': ['B.', 'V.', 'Venkata', 'Krishna'], 'von': [], 'last': ['Sastry'], 'jr': [], }, ), ( r'Benedict L{\o}fstedt', {'first': ['Benedict'], 'von': [], 'last': [r'L{\o}fstedt'], 'jr': []}, ), ( r'Bogus{\l}aw Jackowski', {'first': ['Bogus{\l}aw'], 'von': [], 'last': ['Jackowski'], 'jr': []}, ), ( r'Christina A. L.\ Thiele', { 'first': ['Christina', 'A.', 'L.\\'], 'von': [], 'last': ['Thiele'], 'jr': [], }, ), ( r"D. Men'shikov", {'first': ['D.'], 'von': [], 'last': ["Men'shikov"], 'jr': []}, ), ( r'Darko \v{Z}ubrini{\'c}', { 'first': ['Darko'], 'von': [], 'last': [r'\v{Z}ubrini{\'c}'], 'jr': [], }, ), ( r'Dunja Mladeni{\'c}', {'first': ['Dunja'], 'von': [], 'last': [r'Mladeni{\'c}'], 'jr': []}, ), ( r'Edwin V. {Bell, II}', { 'first': ['Edwin', 'V.'], 'von': [], 'last': ['{Bell, II}'], 'jr': [], }, ), ( r'Frank G. {Bennett, Jr.}', { 'first': ['Frank', 'G.'], 'von': [], 'last': ['{Bennett, Jr.}'], 'jr': [], }, ), ( r'Fr{\'e}d{\'e}ric Boulanger', { 'first': [r'Fr{\'e}d{\'e}ric'], 'von': [], 'last': ['Boulanger'], 'jr': [], }, ), ( r'Ford, Jr., Henry', {'first': ['Henry'], 'von': [], 'last': ['Ford'], 'jr': ['Jr.']}, ), ( r'mr Ford, Jr., Henry', {'first': ['Henry'], 'von': ['mr'], 'last': ['Ford'], 'jr': ['Jr.']}, ), (r'Fukui Rei', {'first': ['Fukui'], 'von': [], 'last': ['Rei'], 'jr': []}), ( r'G. Gr{\"a}tzer', {'first': ['G.'], 'von': [], 'last': [r'Gr{\"a}tzer'], 'jr': []}, ), ( r'George Gr{\"a}tzer', {'first': ['George'], 'von': [], 'last': [r'Gr{\"a}tzer'], 'jr': []}, ), ( r'Georgia K. M. Tobin', { 'first': ['Georgia', 'K.', 'M.'], 'von': [], 'last': ['Tobin'], 'jr': [], }, ), ( r'Gilbert van den Dobbelsteen', { 'first': ['Gilbert'], 'von': ['van', 'den'], 'last': ['Dobbelsteen'], 'jr': [], }, ), ( r'Gy{\"o}ngyi Bujdos{\'o}', { 'first': [r'Gy{\"o}ngyi'], 'von': [], 'last': [r'Bujdos{\'o}'], 'jr': [], }, ), ( r'Helmut J{\"u}rgensen', {'first': ['Helmut'], 'von': [], 'last': [r'J{\"u}rgensen'], 'jr': []}, ), ( r'Herbert Vo{\ss}', {'first': ['Herbert'], 'von': [], 'last': ['Vo{\ss}'], 'jr': []}, ), ( r"H{\'a}n Th{\^e}\llap{\raise 0.5ex\hbox{\'{\relax}}} Th{\'a}nh", { 'first': [ r'H{\'a}n', r"Th{\^e}\llap{\raise 0.5ex\hbox{\'{\relax}}}", ], 'von': [], 'last': [r"Th{\'a}nh"], 'jr': [], }, ), ( r"H{\`a}n Th\^e\llap{\raise0.5ex\hbox{\'{\relax}}} Th{\`a}nh", { 'first': [r'H{\`a}n', r"Th\^e\llap{\raise0.5ex\hbox{\'{\relax}}}"], 'von': [], 'last': [r"Th{\`a}nh"], 'jr': [], }, ), ( r'J. Vesel{\'y}', {'first': ['J.'], 'von': [], 'last': [r'Vesel{\'y}'], 'jr': []}, ), ( r'Javier Rodr\'{\i}guez Laguna', { 'first': ['Javier', r'Rodr\'{\i}guez'], 'von': [], 'last': ['Laguna'], 'jr': [], }, ), ( r'Ji\v{r}\'{\i} Vesel{\'y}', { 'first': [r'Ji\v{r}\'{\i}'], 'von': [], 'last': [r'Vesel{\'y}'], 'jr': [], }, ), ( r'Ji\v{r}\'{\i} Zlatu{\v{s}}ka', { 'first': [r'Ji\v{r}\'{\i}'], 'von': [], 'last': [r'Zlatu{\v{s}}ka'], 'jr': [], }, ), ( r'Ji\v{r}{\'\i} Vesel{\'y}', { 'first': [r'Ji\v{r}{\'\i}'], 'von': [], 'last': [r'Vesel{\'y}'], 'jr': [], }, ), ( r'Ji\v{r}{\'{\i}}Zlatu{\v{s}}ka', { 'first': [], 'von': [], 'last': [r'Ji\v{r}{\'{\i}}Zlatu{\v{s}}ka'], 'jr': [], }, ), ( r'Jim Hef{}feron', {'first': ['Jim'], 'von': [], 'last': ['Hef{}feron'], 'jr': []}, ), ( r'J{\"o}rg Knappen', {'first': [r'J{\"o}rg'], 'von': [], 'last': ['Knappen'], 'jr': []}, ), ( r'J{\"o}rgen L. Pind', { 'first': [r'J{\"o}rgen', 'L.'], 'von': [], 'last': ['Pind'], 'jr': [], }, ), ( r'J{\'e}r\^ome Laurens', {'first': [r'J{\'e}r\^ome'], 'von': [], 'last': ['Laurens'], 'jr': []}, ), ( r'J{{\"o}}rg Knappen', {'first': [r'J{{\"o}}rg'], 'von': [], 'last': ['Knappen'], 'jr': []}, ), ( r'K. Anil Kumar', {'first': ['K.', 'Anil'], 'von': [], 'last': ['Kumar'], 'jr': []}, ), ( r'Karel Hor{\'a}k', {'first': ['Karel'], 'von': [], 'last': [r'Hor{\'a}k'], 'jr': []}, ), ( r'Karel P\'{\i}{\v{s}}ka', { 'first': ['Karel'], 'von': [], 'last': [r'P\'{\i}{\v{s}}ka'], 'jr': [], }, ), ( r'Karel P{\'\i}{\v{s}}ka', { 'first': ['Karel'], 'von': [], 'last': [r'P{\'\i}{\v{s}}ka'], 'jr': [], }, ), ( r'Karel Skoup\'{y}', {'first': ['Karel'], 'von': [], 'last': [r'Skoup\'{y}'], 'jr': []}, ), ( r'Karel Skoup{\'y}', {'first': ['Karel'], 'von': [], 'last': [r'Skoup{\'y}'], 'jr': []}, ), ( r'Kent McPherson', {'first': ['Kent'], 'von': [], 'last': ['McPherson'], 'jr': []}, ), ( r'Klaus H{\"o}ppner', {'first': ['Klaus'], 'von': [], 'last': [r'H{\"o}ppner'], 'jr': []}, ), ( r'Lars Hellstr{\"o}m', {'first': ['Lars'], 'von': [], 'last': [r'Hellstr{\"o}m'], 'jr': []}, ), ( r'Laura Elizabeth Jackson', { 'first': ['Laura', 'Elizabeth'], 'von': [], 'last': ['Jackson'], 'jr': [], }, ), ( r'M. D{\'{\i}}az', {'first': ['M.'], 'von': [], 'last': [r'D{\'{\i}}az'], 'jr': []}, ), ( r'M/iche/al /O Searc/oid', { 'first': [r'M/iche/al', r'/O'], 'von': [], 'last': [r'Searc/oid'], 'jr': [], }, ), ( r'Marek Ry{\'c}ko', {'first': ['Marek'], 'von': [], 'last': [r'Ry{\'c}ko'], 'jr': []}, ), ( r'Marina Yu. Nikulina', { 'first': ['Marina', 'Yu.'], 'von': [], 'last': ['Nikulina'], 'jr': [], }, ), ( r'Max D{\'{\i}}az', {'first': ['Max'], 'von': [], 'last': [r'D{\'{\i}}az'], 'jr': []}, ), ( r'Merry Obrecht Sawdey', { 'first': ['Merry', 'Obrecht'], 'von': [], 'last': ['Sawdey'], 'jr': [], }, ), ( r'Miroslava Mis{\'a}kov{\'a}', { 'first': ['Miroslava'], 'von': [], 'last': [r'Mis{\'a}kov{\'a}'], 'jr': [], }, ), ( r'N. A. F. M. Poppelier', { 'first': ['N.', 'A.', 'F.', 'M.'], 'von': [], 'last': ['Poppelier'], 'jr': [], }, ), ( r'Nico A. F. M. Poppelier', { 'first': ['Nico', 'A.', 'F.', 'M.'], 'von': [], 'last': ['Poppelier'], 'jr': [], }, ), ( r'Onofrio de Bari', {'first': ['Onofrio'], 'von': ['de'], 'last': ['Bari'], 'jr': []}, ), ( r'Pablo Rosell-Gonz{\'a}lez', { 'first': ['Pablo'], 'von': [], 'last': [r'Rosell-Gonz{\'a}lez'], 'jr': [], }, ), ( r'Paco La Bruna', {'first': ['Paco', 'La'], 'von': [], 'last': ['Bruna'], 'jr': []}, ), ( r'Paul Franchi-Zannettacci', { 'first': ['Paul'], 'von': [], 'last': ['Franchi-Zannettacci'], 'jr': [], }, ), ( r'Pavel \v{S}eve\v{c}ek', { 'first': ['Pavel'], 'von': [], 'last': [r'\v{S}eve\v{c}ek'], 'jr': [], }, ), ( r'Petr Ol{\v{s}}ak', {'first': ['Petr'], 'von': [], 'last': [r'Ol{\v{s}}ak'], 'jr': []}, ), ( r'Petr Ol{\v{s}}{\'a}k', {'first': ['Petr'], 'von': [], 'last': [r'Ol{\v{s}}{\'a}k'], 'jr': []}, ), ( r'Primo\v{z} Peterlin', {'first': [r'Primo\v{z}'], 'von': [], 'last': ['Peterlin'], 'jr': []}, ), ( r'Prof. Alban Grimm', {'first': ['Prof.', 'Alban'], 'von': [], 'last': ['Grimm'], 'jr': []}, ), ( r'P{\'e}ter Husz{\'a}r', { 'first': [r'P{\'e}ter'], 'von': [], 'last': [r'Husz{\'a}r'], 'jr': [], }, ), ( r'P{\'e}ter Szab{\'o}', {'first': [r'P{\'e}ter'], 'von': [], 'last': [r'Szab{\'o}'], 'jr': []}, ), ( r'Rafa{\l}\.Zbikowski', {'first': [], 'von': [], 'last': [r'Rafa{\l}\.Zbikowski'], 'jr': []}, ), ( r'Rainer Sch{\"o}pf', {'first': ['Rainer'], 'von': [], 'last': [r'Sch{\"o}pf'], 'jr': []}, ), ( r'T. L. (Frank) Pappas', { 'first': ['T.', 'L.', '(Frank)'], 'von': [], 'last': ['Pappas'], 'jr': [], }, ), ( r'TUG 2004 conference', { 'first': ['TUG', '2004'], 'von': [], 'last': ['conference'], 'jr': [], }, ), ( r'TUG {\sltt DVI} Driver Standards Committee', { 'first': ['TUG', '{\sltt DVI}', 'Driver', 'Standards'], 'von': [], 'last': ['Committee'], 'jr': [], }, ), ( r'TUG {\sltt xDVIx} Driver Standards Committee', { 'first': ['TUG'], 'von': ['{\sltt xDVIx}'], 'last': ['Driver', 'Standards', 'Committee'], 'jr': [], }, ), ( r'University of M{\"u}nster', { 'first': ['University'], 'von': ['of'], 'last': [r'M{\"u}nster'], 'jr': [], }, ), ( r'Walter van der Laan', { 'first': ['Walter'], 'von': ['van', 'der'], 'last': ['Laan'], 'jr': [], }, ), ( r'Wendy G. McKay', {'first': ['Wendy', 'G.'], 'von': [], 'last': ['McKay'], 'jr': []}, ), ( r'Wendy McKay', {'first': ['Wendy'], 'von': [], 'last': ['McKay'], 'jr': []}, ), ( r'W{\l}odek Bzyl', {'first': [r'W{\l}odek'], 'von': [], 'last': ['Bzyl'], 'jr': []}, ), ( r'\LaTeX Project Team', { 'first': [r'\LaTeX', 'Project'], 'von': [], 'last': ['Team'], 'jr': [], }, ), ( r'\rlap{Lutz Birkhahn}', {'first': [], 'von': [], 'last': [r'\rlap{Lutz Birkhahn}'], 'jr': []}, ), ( r'{Jim Hef{}feron}', {'first': [], 'von': [], 'last': ['{Jim Hef{}feron}'], 'jr': []}, ), ( r'{Kristoffer H\o{}gsbro Rose}', { 'first': [], 'von': [], 'last': ['{Kristoffer H\o{}gsbro Rose}'], 'jr': [], }, ), ( r'{TUG} {Working} {Group} on a {\TeX} {Directory} {Structure}', { 'first': ['{TUG}', '{Working}', '{Group}'], 'von': ['on', 'a'], 'last': [r'{\TeX}', '{Directory}', '{Structure}'], 'jr': [], }, ), ( r'{The \TUB{} Team}', {'first': [], 'von': [], 'last': [r'{The \TUB{} Team}'], 'jr': []}, ), ( r'{\LaTeX} project team', { 'first': [r'{\LaTeX}'], 'von': ['project'], 'last': ['team'], 'jr': [], }, ), ( r'{\NTG{} \TeX{} future working group}', { 'first': [], 'von': [], 'last': [r'{\NTG{} \TeX{} future working group}'], 'jr': [], }, ), ( r'{{\LaTeX\,3} Project Team}', { 'first': [], 'von': [], 'last': [r'{{\LaTeX\,3} Project Team}'], 'jr': [], }, ), ( r'Johansen Kyle, Derik Mamania M.', { 'first': ['Derik', 'Mamania', 'M.'], 'von': [], 'last': ['Johansen', 'Kyle'], 'jr': [], }, ), ( r"Johannes Adam Ferdinand Alois Josef Maria Marko d'Aviano Pius von und zu Liechtenstein", { 'first': [ 'Johannes', 'Adam', 'Ferdinand', 'Alois', 'Josef', 'Maria', 'Marko', ], 'von': ["d'Aviano", 'Pius', 'von', 'und', 'zu'], 'last': ['Liechtenstein'], 'jr': [], }, ), ( r"Brand\~{a}o, F", {'first': ['F'], 'von': [], 'last': ['Brand\\', '{a}o'], 'jr': []}, ), ) if __name__ == '__main__': unittest.main()
27.174648
98
0.338171
1801df02ecd58a8f78ca27f271870b89690c5eb0
1,349
py
Python
db_model.py
Build-Week-Saltiest-Hacker/machine-learning
1822e2ecdca8279bc49095f6da527152e298b95d
[ "MIT" ]
null
null
null
db_model.py
Build-Week-Saltiest-Hacker/machine-learning
1822e2ecdca8279bc49095f6da527152e298b95d
[ "MIT" ]
null
null
null
db_model.py
Build-Week-Saltiest-Hacker/machine-learning
1822e2ecdca8279bc49095f6da527152e298b95d
[ "MIT" ]
null
null
null
# schema for SQL database from data import app, db
31.372093
74
0.604151
1804da1fa980c8e71b8a65bd6282db015d7cd076
2,608
py
Python
acl/utils.py
stjordanis/aspect-document-similarity
ca17e0a8730caa224b0efe8909b1e5a87bb456ea
[ "MIT" ]
47
2020-10-14T09:28:39.000Z
2022-03-01T01:54:32.000Z
acl/utils.py
stjordanis/aspect-document-similarity
ca17e0a8730caa224b0efe8909b1e5a87bb456ea
[ "MIT" ]
2
2021-11-21T20:07:10.000Z
2022-02-10T09:25:40.000Z
acl/utils.py
stjordanis/aspect-document-similarity
ca17e0a8730caa224b0efe8909b1e5a87bb456ea
[ "MIT" ]
8
2020-11-07T08:43:01.000Z
2022-02-15T05:45:13.000Z
import re import logging logger = logging.getLogger(__name__) def get_text_from_doc(doc) -> str: """ Build document text from title + abstract :param doc: S2 paper :return: Document text """ text = '' if 'title' in doc: text += doc['title'] if doc['abstract']: text += '\n' + doc['abstract'] return text def get_text_from_doc_id(doc_id: str, doc_index) -> str: """ Build document text from title + abstract :param doc_id: S2-id :param doc_index: S2-id to S2-paper data :return: Document text """ if doc_id in doc_index: return get_text_from_doc(doc_index[doc_id]) else: raise ValueError(f'Document not found in index: {doc_id}') # resolve 'and' titles and filter for out-of-index docs
26.612245
84
0.536426
1805a8be23b715a568fd9d510dee5510be26a4d2
995
py
Python
build-a-django-content-aggregator/source_code_step_2/podcasts/tests.py
syberflea/materials
54f44725b40edf00c1b523d7a85b34a85014d7eb
[ "MIT" ]
3,682
2018-05-07T19:45:24.000Z
2022-03-31T15:19:10.000Z
build-a-django-content-aggregator/source_code_step_2/podcasts/tests.py
sribarrow/materials
c17c4a4d6f8487e59eac1df8c88ca92b73d6d2a5
[ "MIT" ]
148
2018-05-15T21:18:49.000Z
2022-03-21T11:25:39.000Z
build-a-django-content-aggregator/source_code_step_2/podcasts/tests.py
sribarrow/materials
c17c4a4d6f8487e59eac1df8c88ca92b73d6d2a5
[ "MIT" ]
5,535
2018-05-25T23:36:08.000Z
2022-03-31T16:55:52.000Z
from django.test import TestCase from django.utils import timezone from .models import Episode
34.310345
78
0.639196
18062b275cb72a752756840a4bbb8ef63a17377e
4,114
py
Python
superset/migrations/versions/070c043f2fdb_add_granularity_to_charts_where_missing.py
razzius/superset
93f59e055e8312fb28687bc9fc22342b4be68d0e
[ "Apache-2.0" ]
18,621
2017-06-19T09:57:44.000Z
2021-01-05T06:28:21.000Z
superset/migrations/versions/070c043f2fdb_add_granularity_to_charts_where_missing.py
changeiot/superset
299b5dc64448d04abe6b35ee85fbd2b938c781bc
[ "Apache-2.0" ]
9,043
2017-07-05T16:10:48.000Z
2021-01-05T17:58:01.000Z
superset/migrations/versions/070c043f2fdb_add_granularity_to_charts_where_missing.py
changeiot/superset
299b5dc64448d04abe6b35ee85fbd2b938c781bc
[ "Apache-2.0" ]
5,527
2017-07-06T01:39:43.000Z
2021-01-05T06:01:11.000Z
# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """add granularity to charts where missing Revision ID: 070c043f2fdb Revises: 41ce8799acc3 Create Date: 2021-02-04 09:34:13.608891 """ # revision identifiers, used by Alembic. revision = "070c043f2fdb" down_revision = "41ce8799acc3" import json from alembic import op from sqlalchemy import and_, Boolean, Column, Integer, String, Text from sqlalchemy.ext.declarative import declarative_base from superset import db Base = declarative_base() def upgrade(): """ Adds the granularity param to charts without it populated. This is required for time range filtering to work properly. Uses the following approach: - Find all charts without a granularity or granularity_sqla param. - Get the dataset that backs the chart. - If the dataset has the main dttm column set, use it. - Otherwise, find all the dttm columns in the dataset and use the first one (this matches the behavior of Explore view on the frontend) - If no dttm columns exist in the dataset, don't change the chart. """ bind = op.get_bind() session = db.Session(bind=bind) slices_changed = 0 for slc in ( session.query(Slice) .filter( and_( Slice.datasource_type == "table", Slice.params.notlike('%"granularity%') ) ) .all() ): try: params = json.loads(slc.params) if "granularity" in params or "granularity_sqla" in params: continue table = session.query(SqlaTable).get(slc.datasource_id) if not table: continue if table.main_dttm_col: params["granularity"] = table.main_dttm_col slc.params = json.dumps(params, sort_keys=True) print(f"Set granularity for slice {slc.id} to {table.main_dttm_col}") slices_changed += 1 continue table_columns = ( session.query(TableColumn) .filter(TableColumn.table_id == table.id) .filter(TableColumn.is_dttm == True) .all() ) if len(table_columns): params["granularity"] = table_columns[0].column_name slc.params = json.dumps(params, sort_keys=True) print( f"Set granularity for slice {slc.id} to {table_columns[0].column_name}" ) slices_changed += 1 except Exception as e: print(e) print(f"Parsing params for slice {slc.id} failed.") pass print(f"{slices_changed} slices altered") session.commit() session.close() def downgrade(): """ It's impossible to downgrade this migration. """ pass
30.474074
91
0.645357
1806f8b39a3aeab210ed874956e25e9bd4d01444
325
py
Python
AtCoder/ABC/B/page-13/090B.py
Nishi05/Competitive-programming
e59a6755b706d9d5c1f359f4511d92c114e6a94e
[ "MIT" ]
null
null
null
AtCoder/ABC/B/page-13/090B.py
Nishi05/Competitive-programming
e59a6755b706d9d5c1f359f4511d92c114e6a94e
[ "MIT" ]
null
null
null
AtCoder/ABC/B/page-13/090B.py
Nishi05/Competitive-programming
e59a6755b706d9d5c1f359f4511d92c114e6a94e
[ "MIT" ]
null
null
null
# A B # 0 10 # # a, b = map(int, input().split()) cnt = 0 for i in range(a, b+1): s = str(i) s_r = s[::-1] n = int(len(str(s))/2) if s[: n] == s_r[:n]: cnt += 1 print(cnt)
23.214286
42
0.609231
18070effada07af1c287eb2501ebc5c7848149ff
2,499
py
Python
__init__.py
kotn3l/blender-flver
3476d720337a6d7a28bd55f9b112524c0f61581d
[ "MIT" ]
11
2020-04-28T03:21:13.000Z
2022-03-23T13:18:33.000Z
__init__.py
kotn3l/blender-flver
3476d720337a6d7a28bd55f9b112524c0f61581d
[ "MIT" ]
2
2021-06-28T07:44:42.000Z
2022-03-18T00:47:42.000Z
__init__.py
elizagamedev/blender-flver
25cc152de19acb4028035d3ed389706df25e094a
[ "MIT" ]
2
2021-12-23T13:31:57.000Z
2022-03-16T06:30:13.000Z
bl_info = { "name": "Import Fromsoft FLVER models", "description": "Import models from various Fromsoft games such as Dark Souls", "author": "Eliza Velasquez", "version": (0, 1, 0), "blender": (2, 80, 0), "category": "Import-Export", "location": "File > Import", "warning": "", "support": "COMMUNITY", "wiki_url": "", # TODO: wiki url "tracker_url": "", # TODO: tracker url } _submodules = { "importer", "flver", "reader", } # Reload submodules on addon reload if "bpy" in locals(): import importlib for submodule in _submodules: if submodule in locals(): importlib.reload(locals()[submodule]) import bpy from . import importer from bpy_extras.io_utils import ImportHelper from bpy.props import StringProperty, BoolProperty
30.47561
74
0.620648
1809819c2d6283b15f8fc4c9f611ea65d6e320d3
32,193
py
Python
plugin.video.vstream/resources/lib/gui/gui.py
akuala/REPO.KUALA
ea9a157025530d2ce8fa0d88431c46c5352e89d4
[ "Apache-2.0" ]
2
2018-11-02T19:55:30.000Z
2020-08-14T02:22:20.000Z
plugin.video.vstream/resources/lib/gui/gui.py
akuala/REPO.KUALA
ea9a157025530d2ce8fa0d88431c46c5352e89d4
[ "Apache-2.0" ]
null
null
null
plugin.video.vstream/resources/lib/gui/gui.py
akuala/REPO.KUALA
ea9a157025530d2ce8fa0d88431c46c5352e89d4
[ "Apache-2.0" ]
3
2019-12-17T20:47:00.000Z
2021-02-11T19:03:59.000Z
# -*- coding: utf-8 -*- # https://github.com/Kodi-vStream/venom-xbmc-addons from resources.lib.gui.contextElement import cContextElement from resources.lib.gui.guiElement import cGuiElement from resources.lib.db import cDb from resources.lib.handler.outputParameterHandler import cOutputParameterHandler from resources.lib.handler.inputParameterHandler import cInputParameterHandler from resources.lib.handler.pluginHandler import cPluginHandler from resources.lib.parser import cParser from resources.lib.util import cUtil, QuotePlus from resources.lib.comaddon import listitem, addon, dialog, isKrypton, window, xbmc import re, xbmcplugin
42.192661
178
0.671916
1809e4f7973197265ce5a6a201169c2856659885
1,555
py
Python
src/jt/rubicon/java/_typemanager.py
karpierz/jtypes.rubicon
8f8196e47de93183eb9728fec0d08725fc368ee0
[ "BSD-3-Clause" ]
2
2018-11-29T06:19:05.000Z
2018-12-09T09:47:55.000Z
src/jt/rubicon/java/_typemanager.py
karpierz/jtypes.rubicon
8f8196e47de93183eb9728fec0d08725fc368ee0
[ "BSD-3-Clause" ]
null
null
null
src/jt/rubicon/java/_typemanager.py
karpierz/jtypes.rubicon
8f8196e47de93183eb9728fec0d08725fc368ee0
[ "BSD-3-Clause" ]
null
null
null
# Copyright (c) 2016-2019, Adam Karpierz # Licensed under the BSD license # http://opensource.org/licenses/BSD-3-Clause from ...jvm.lib.compat import * from ...jvm.lib import annotate from ...jvm.lib import public from ._typehandler import * # noqa
28.272727
82
0.652733
180ba7fe8e58c4e3cae590b1f061d367ca5c9d22
63,592
py
Python
rest/models.py
istarnes/restit
24d2805ab68696cab7718cc1164b7f716582ffb7
[ "0BSD" ]
null
null
null
rest/models.py
istarnes/restit
24d2805ab68696cab7718cc1164b7f716582ffb7
[ "0BSD" ]
null
null
null
rest/models.py
istarnes/restit
24d2805ab68696cab7718cc1164b7f716582ffb7
[ "0BSD" ]
null
null
null
import os from django.conf import settings from django.core.exceptions import FieldDoesNotExist from hashids import Hashids import hashlib import string from datetime import datetime, date, timedelta from decimal import Decimal TWOPLACES = Decimal(10) ** -2 from django.db import models from django.apps import apps get_model = apps.get_model from django.http import Http404 from django.core.exceptions import ValidationError import threading from rest import helpers as rest_helpers from rest.uberdict import UberDict from rest import search from rest.privpub import PrivatePublicEncryption import importlib GRAPH_HELPERS = UberDict() GRAPH_HELPERS.restGet = None GRAPH_HELPERS.get_request = None GRAPH_HELPERS.views = None ENCRYPTER_KEY_FILE = os.path.join(settings.ROOT, "config", "encrypt_key.pem") ENCRYPTER = None if os.path.exists(ENCRYPTER_KEY_FILE): ENCRYPTER = PrivatePublicEncryption(private_key_file=ENCRYPTER_KEY_FILE) def requestHasPerms(request, perms, group=None): if not request.user.is_authenticated: return False, "auth required", 401 if not hasattr(request, 'member'): request.member, request.group = request.user.__class__.getMemberGroup(request, False, False) if request.member.hasPerm(perms): return True, None, None if group is None and hasattr(request, "group"): group = request.group if group and request.member.hasGroupPerm(group, perms): return True, None, None return False, "permission denied", 402 def toGraph(self, request=None, graph="basic"): RestModel._setupGraphHelpers() if not request: request = GRAPH_HELPERS.get_request() return GRAPH_HELPERS.restGet(request, self, return_httpresponse=False, **self.getGraph(graph)) def restGetGenericModel(self, field): # called by the rest module to magically parse # a component that is marked genericrelation in a graph if not hasattr(self, field): rest_helpers.log_print("model has no field: {0}".format(field)) return None name = getattr(self, field) if not name or "." not in name: return None a_name, m_name = name.split(".") model = RestModel.getModel(a_name, m_name) if not model: rest_helpers.log_print("GENERIC MODEL DOES NOT EXIST: {0}".format(name)) return model def restGetGenericRelation(self, field): # called by the rest module to magically parse # a component that is marked genericrelation in a graph GenericModel = self.restGetGenericModel(field) if not GenericModel: return None key = getattr(self, "{0}_id".format(field)) return GenericModel.rw_objects().filter(pk=key).first() def saveFields(self, allow_null=True, **kwargs): """ Helper method to save a list of fields """ self._changed__ = UberDict() for key, value in list(kwargs.items()): if value is None and not allow_null: continue self.restSaveField(key, value) if len(self._changed__): self.save() def saveMediaFile(self, file, name, file_name=None, is_base64=False, group=None): """ Generic method to save a media file """ if file_name is None: file_name = name MediaItem = RestModel.getModel("medialib", "MediaItem") # make sure we set the name base64_data if is_base64: mi = MediaItem(name=file_name, base64_data=file, group=group) elif type(file) in [str, str] and (file.startswith("https:") or file.startswith("http:")): mi = MediaItem(name=name, downloadurl=file, group=group) else: mi = MediaItem(name=name, newfile=file, group=group) mi.save() setattr(self, name, mi) self.save() return mi def restStatus(self, request, status, **kwargs): RestModel._setupGraphHelpers() return GRAPH_HELPERS.restStatus(request, status, **kwargs) def restGet(self, request, graph=None, as_dict=False): RestModel._setupGraphHelpers() if not request: request = self.getActiveRequest() if not graph and request: graph = request.DATA.get("graph", "default") elif not graph: graph = "default" return_response = not as_dict return GRAPH_HELPERS.restGet(request, self, return_httpresponse=return_response, **self.getGraph(graph)) def toDict(self, graph=None): RestModel._setupGraphHelpers() return self.restGet(None, graph=graph, as_dict=True)
40.146465
152
0.544754
180d1820c70ce1e075a46251cae4f2ab29f2929f
803
py
Python
examples/rp_analytics.py
eirrgang/radical.pilot
ceccd1867dd172935d602ff4c33a5ed4467e0dc8
[ "MIT" ]
47
2015-03-16T01:08:11.000Z
2022-02-02T10:36:39.000Z
examples/rp_analytics.py
eirrgang/radical.pilot
ceccd1867dd172935d602ff4c33a5ed4467e0dc8
[ "MIT" ]
1,856
2015-01-02T09:32:20.000Z
2022-03-31T21:45:06.000Z
examples/rp_analytics.py
eirrgang/radical.pilot
ceccd1867dd172935d602ff4c33a5ed4467e0dc8
[ "MIT" ]
28
2015-06-10T18:15:14.000Z
2021-11-07T04:36:45.000Z
#!/usr/bin/env python3 __copyright__ = 'Copyright 2013-2016, http://radical.rutgers.edu' __license__ = 'MIT' import sys import radical.utils as ru import radical.pilot as rp rpu = rp.utils # ------------------------------------------------------------------------------ # if __name__ == '__main__': if len(sys.argv) <= 1: print("\n\tusage: %s <session_id>\n") sys.exit(1) sid = sys.argv[1] profiles = rpu.fetch_profiles(sid=sid, skip_existing=True) for p in profiles: print(p) profs = ru.read_profiles(profiles) for p in profs: print(type(p)) prof = ru.combine_profiles(profs) print(len(prof)) for entry in prof: print(entry) # ------------------------------------------------------------------------------
18.25
80
0.495641
180d3a3f60ca987d84a73cb66042ea85d5cffea9
758
py
Python
tests/contrib/django/testapp/middleware.py
mvas/apm-agent-python
f4582e90eb5308b915ca51e2e98620fc22af09ec
[ "BSD-3-Clause" ]
null
null
null
tests/contrib/django/testapp/middleware.py
mvas/apm-agent-python
f4582e90eb5308b915ca51e2e98620fc22af09ec
[ "BSD-3-Clause" ]
null
null
null
tests/contrib/django/testapp/middleware.py
mvas/apm-agent-python
f4582e90eb5308b915ca51e2e98620fc22af09ec
[ "BSD-3-Clause" ]
null
null
null
try: from django.utils.deprecation import MiddlewareMixin except ImportError: # no-op class for Django < 1.10
27.071429
56
0.740106
180dd0f316d9175e1decc0de1732de58c97bdcf4
3,874
py
Python
run.py
Yvonne-Ouma/Password-Locker
b16f8e9ee36d3cb70eefb58bf7be2de1bb1948fc
[ "MIT" ]
null
null
null
run.py
Yvonne-Ouma/Password-Locker
b16f8e9ee36d3cb70eefb58bf7be2de1bb1948fc
[ "MIT" ]
null
null
null
run.py
Yvonne-Ouma/Password-Locker
b16f8e9ee36d3cb70eefb58bf7be2de1bb1948fc
[ "MIT" ]
null
null
null
#!/usr/bin/env python3.6 from user import User from credential import Credential def createUser(userName,password): ''' Function to create a new user ''' newUser = User(userName,password) return newUser def saveUsers(user): ''' Function to save users ''' user.saveUser() def saveCredential(credential): ''' Function to save a new credential ''' Credential.saveCredential(credential) def delCredential(credential): ''' Function to delete a credential ''' credential.deleteCredential() def findCredential(name): ''' Function that finds a credential by name returns the credential ''' return Credential.find_by_name(name) def check_existingCredentials(name): ''' Function that checks if a credential exists with that name and return a boolean ''' return Credential.credential_exist(name) def displayCredentials(): ''' Function that returns all the saved credentials ''' return Credential.displayCredentials() if __name__ == '__main__': main()
27.28169
177
0.558596
180e054f46ac36903917c85a5ca1fbddc3d6ad0b
844
py
Python
soundrts/constants.py
ctoth/soundrts
1a1271182d53c16d3e29f5dc8f8e987415a9467b
[ "BSD-3-Clause" ]
null
null
null
soundrts/constants.py
ctoth/soundrts
1a1271182d53c16d3e29f5dc8f8e987415a9467b
[ "BSD-3-Clause" ]
null
null
null
soundrts/constants.py
ctoth/soundrts
1a1271182d53c16d3e29f5dc8f8e987415a9467b
[ "BSD-3-Clause" ]
null
null
null
# constants used in more than one module # Some of them might find a better home later. from lib.nofloat import PRECISION MAIN_METASERVER_URL = open("cfg/metaserver.txt").read().strip() # old value used by some features (stats, ...) METASERVER_URL = "http://jlpo.free.fr/soundrts/metaserver/" # simulation VIRTUAL_TIME_INTERVAL = 300 # milliseconds COLLISION_RADIUS = 175 # millimeters # 350 / 2 USE_RANGE_MARGIN = 175 # millimeters ORDERS_QUEUE_LIMIT = 10 MAX_NB_OF_RESOURCE_TYPES = 10 DEFAULT_MINIMAL_DAMAGE = int(.17 * PRECISION) # used for packing the orders NEWLINE_REPLACEMENT = ";" SPACE_REPLACEMENT = "," # minimal interval (in seconds) between 2 sounds ALERT_LIMIT = .5 FOOTSTEP_LIMIT = .1 # don't play events after this limit (in seconds) EVENT_LIMIT = 3 # use the profiler (warning: will slow down the game) PROFILE = False
25.575758
63
0.755924
180efba78897c0fa073f01ffc1050d72acb958e1
9,104
py
Python
Modules/Attention/Steps.py
ishine/GST_Tacotron
0c3d8e51042dc5d49abc842b59a13ea70f927f9d
[ "MIT" ]
21
2020-02-23T03:35:27.000Z
2021-11-01T11:08:18.000Z
Modules/Attention/Steps.py
ishine/GST_Tacotron
0c3d8e51042dc5d49abc842b59a13ea70f927f9d
[ "MIT" ]
6
2020-03-14T15:43:38.000Z
2021-07-06T09:06:57.000Z
Modules/Attention/Steps.py
ishine/GST_Tacotron
0c3d8e51042dc5d49abc842b59a13ea70f927f9d
[ "MIT" ]
7
2020-03-07T11:33:09.000Z
2021-11-28T16:19:01.000Z
import tensorflow as tf import numpy as np ''' TF 2.0's basic attention layers(Attention and AdditiveAttention) calculate parallelly. TO USE MONOTONIC FUNCTION, ATTENTION MUST KNOW 'n-1 ALIGNMENT'. Thus, this parallel versions do not support the monotonic function. '''
39.755459
161
0.611819
180f8229eeb538cba11111f51d0cfaabcfe979dc
14,002
py
Python
test.py
gmberton/deep-visual-geo-localization-benchmark
7ac395411b7eeff99da66675dedc5372839e5632
[ "MIT" ]
1
2022-03-25T06:48:16.000Z
2022-03-25T06:48:16.000Z
test.py
gmberton/deep-visual-geo-localization-benchmark
7ac395411b7eeff99da66675dedc5372839e5632
[ "MIT" ]
null
null
null
test.py
gmberton/deep-visual-geo-localization-benchmark
7ac395411b7eeff99da66675dedc5372839e5632
[ "MIT" ]
null
null
null
import faiss import torch import logging import numpy as np from tqdm import tqdm from torch.utils.data import DataLoader from torch.utils.data.dataset import Subset def test_efficient_ram_usage(args, eval_ds, model, test_method="hard_resize"): """This function gives the same output as test(), but uses much less RAM. This can be useful when testing with large descriptors (e.g. NetVLAD) on large datasets (e.g. San Francisco). Obviously it is slower than test(), and can't be used with PCA. """ model = model.eval() if test_method == 'nearest_crop' or test_method == "maj_voting": distances = np.empty([eval_ds.queries_num * 5, eval_ds.database_num], dtype=np.float32) else: distances = np.empty([eval_ds.queries_num, eval_ds.database_num], dtype=np.float32) with torch.no_grad(): if test_method == 'nearest_crop' or test_method == 'maj_voting': queries_features = np.ones((eval_ds.queries_num * 5, args.features_dim), dtype="float32") else: queries_features = np.ones((eval_ds.queries_num, args.features_dim), dtype="float32") logging.debug("Extracting queries features for evaluation/testing") queries_infer_batch_size = 1 if test_method == "single_query" else args.infer_batch_size eval_ds.test_method = test_method queries_subset_ds = Subset(eval_ds, list(range(eval_ds.database_num, eval_ds.database_num+eval_ds.queries_num))) queries_dataloader = DataLoader(dataset=queries_subset_ds, num_workers=args.num_workers, batch_size=queries_infer_batch_size, pin_memory=(args.device=="cuda")) for inputs, indices in tqdm(queries_dataloader, ncols=100): if test_method == "five_crops" or test_method == "nearest_crop" or test_method == 'maj_voting': inputs = torch.cat(tuple(inputs)) # shape = 5*bs x 3 x 480 x 480 features = model(inputs.to(args.device)) if test_method == "five_crops": # Compute mean along the 5 crops features = torch.stack(torch.split(features, 5)).mean(1) if test_method == "nearest_crop" or test_method == 'maj_voting': start_idx = (indices[0] - eval_ds.database_num) * 5 end_idx = start_idx + indices.shape[0] * 5 indices = np.arange(start_idx, end_idx) queries_features[indices, :] = features.cpu().numpy() else: queries_features[indices.numpy()-eval_ds.database_num, :] = features.cpu().numpy() queries_features = torch.tensor(queries_features).type(torch.float32).cuda() logging.debug("Extracting database features for evaluation/testing") # For database use "hard_resize", although it usually has no effect because database images have same resolution eval_ds.test_method = "hard_resize" database_subset_ds = Subset(eval_ds, list(range(eval_ds.database_num))) database_dataloader = DataLoader(dataset=database_subset_ds, num_workers=args.num_workers, batch_size=args.infer_batch_size, pin_memory=(args.device=="cuda")) for inputs, indices in tqdm(database_dataloader, ncols=100): inputs = inputs.to(args.device) features = model(inputs) for pn, (index, pred_feature) in enumerate(zip(indices, features)): distances[:, index] = ((queries_features-pred_feature)**2).sum(1).cpu().numpy() del features, queries_features, pred_feature predictions = distances.argsort(axis=1)[:, :max(args.recall_values)] if test_method == 'nearest_crop': distances = np.array([distances[row, index] for row, index in enumerate(predictions)]) distances = np.reshape(distances, (eval_ds.queries_num, 20 * 5)) predictions = np.reshape(predictions, (eval_ds.queries_num, 20 * 5)) for q in range(eval_ds.queries_num): # sort predictions by distance sort_idx = np.argsort(distances[q]) predictions[q] = predictions[q, sort_idx] # remove duplicated predictions, i.e. keep only the closest ones _, unique_idx = np.unique(predictions[q], return_index=True) # unique_idx is sorted based on the unique values, sort it again predictions[q, :20] = predictions[q, np.sort(unique_idx)][:20] predictions = predictions[:, :20] # keep only the closer 20 predictions for each elif test_method == 'maj_voting': distances = np.array([distances[row, index] for row, index in enumerate(predictions)]) distances = np.reshape(distances, (eval_ds.queries_num, 5, 20)) predictions = np.reshape(predictions, (eval_ds.queries_num, 5, 20)) for q in range(eval_ds.queries_num): # votings, modify distances in-place top_n_voting('top1', predictions[q], distances[q], args.majority_weight) top_n_voting('top5', predictions[q], distances[q], args.majority_weight) top_n_voting('top10', predictions[q], distances[q], args.majority_weight) # flatten dist and preds from 5, 20 -> 20*5 # and then proceed as usual to keep only first 20 dists = distances[q].flatten() preds = predictions[q].flatten() # sort predictions by distance sort_idx = np.argsort(dists) preds = preds[sort_idx] # remove duplicated predictions, i.e. keep only the closest ones _, unique_idx = np.unique(preds, return_index=True) # unique_idx is sorted based on the unique values, sort it again # here the row corresponding to the first crop is used as a # 'buffer' for each query, and in the end the dimension # relative to crops is eliminated predictions[q, 0, :20] = preds[np.sort(unique_idx)][:20] predictions = predictions[:, 0, :20] # keep only the closer 20 predictions for each query del distances #### For each query, check if the predictions are correct positives_per_query = eval_ds.get_positives() # args.recall_values by default is [1, 5, 10, 20] recalls = np.zeros(len(args.recall_values)) for query_index, pred in enumerate(predictions): for i, n in enumerate(args.recall_values): if np.any(np.in1d(pred[:n], positives_per_query[query_index])): recalls[i:] += 1 break recalls = recalls / eval_ds.queries_num * 100 recalls_str = ", ".join([f"R@{val}: {rec:.1f}" for val, rec in zip(args.recall_values, recalls)]) return recalls, recalls_str def test(args, eval_ds, model, test_method="hard_resize", pca=None): """Compute features of the given dataset and compute the recalls.""" assert test_method in ["hard_resize", "single_query", "central_crop", "five_crops", "nearest_crop", "maj_voting"], f"test_method can't be {test_method}" if args.efficient_ram_testing: return test_efficient_ram_usage(args, eval_ds, model, test_method) model = model.eval() with torch.no_grad(): logging.debug("Extracting database features for evaluation/testing") # For database use "hard_resize", although it usually has no effect because database images have same resolution eval_ds.test_method = "hard_resize" database_subset_ds = Subset(eval_ds, list(range(eval_ds.database_num))) database_dataloader = DataLoader(dataset=database_subset_ds, num_workers=args.num_workers, batch_size=args.infer_batch_size, pin_memory=(args.device=="cuda")) if test_method == "nearest_crop" or test_method == 'maj_voting': all_features = np.empty((5 * eval_ds.queries_num + eval_ds.database_num, args.features_dim), dtype="float32") else: all_features = np.empty((len(eval_ds), args.features_dim), dtype="float32") for inputs, indices in tqdm(database_dataloader, ncols=100): features = model(inputs.to(args.device)) features = features.cpu().numpy() if pca != None: features = pca.transform(features) all_features[indices.numpy(), :] = features logging.debug("Extracting queries features for evaluation/testing") queries_infer_batch_size = 1 if test_method == "single_query" else args.infer_batch_size eval_ds.test_method = test_method queries_subset_ds = Subset(eval_ds, list(range(eval_ds.database_num, eval_ds.database_num+eval_ds.queries_num))) queries_dataloader = DataLoader(dataset=queries_subset_ds, num_workers=args.num_workers, batch_size=queries_infer_batch_size, pin_memory=(args.device=="cuda")) for inputs, indices in tqdm(queries_dataloader, ncols=100): if test_method == "five_crops" or test_method == "nearest_crop" or test_method == 'maj_voting': inputs = torch.cat(tuple(inputs)) # shape = 5*bs x 3 x 480 x 480 features = model(inputs.to(args.device)) if test_method == "five_crops": # Compute mean along the 5 crops features = torch.stack(torch.split(features, 5)).mean(1) features = features.cpu().numpy() if pca != None: features = pca.transform(features) if test_method == "nearest_crop" or test_method == 'maj_voting': # store the features of all 5 crops start_idx = eval_ds.database_num + (indices[0] - eval_ds.database_num) * 5 end_idx = start_idx + indices.shape[0] * 5 indices = np.arange(start_idx, end_idx) all_features[indices, :] = features else: all_features[indices.numpy(), :] = features queries_features = all_features[eval_ds.database_num:] database_features = all_features[:eval_ds.database_num] faiss_index = faiss.IndexFlatL2(args.features_dim) faiss_index.add(database_features) del database_features, all_features logging.debug("Calculating recalls") distances, predictions = faiss_index.search(queries_features, max(args.recall_values)) if test_method == 'nearest_crop': distances = np.reshape(distances, (eval_ds.queries_num, 20 * 5)) predictions = np.reshape(predictions, (eval_ds.queries_num, 20 * 5)) for q in range(eval_ds.queries_num): # sort predictions by distance sort_idx = np.argsort(distances[q]) predictions[q] = predictions[q, sort_idx] # remove duplicated predictions, i.e. keep only the closest ones _, unique_idx = np.unique(predictions[q], return_index=True) # unique_idx is sorted based on the unique values, sort it again predictions[q, :20] = predictions[q, np.sort(unique_idx)][:20] predictions = predictions[:, :20] # keep only the closer 20 predictions for each query elif test_method == 'maj_voting': distances = np.reshape(distances, (eval_ds.queries_num, 5, 20)) predictions = np.reshape(predictions, (eval_ds.queries_num, 5, 20)) for q in range(eval_ds.queries_num): # votings, modify distances in-place top_n_voting('top1', predictions[q], distances[q], args.majority_weight) top_n_voting('top5', predictions[q], distances[q], args.majority_weight) top_n_voting('top10', predictions[q], distances[q], args.majority_weight) # flatten dist and preds from 5, 20 -> 20*5 # and then proceed as usual to keep only first 20 dists = distances[q].flatten() preds = predictions[q].flatten() # sort predictions by distance sort_idx = np.argsort(dists) preds = preds[sort_idx] # remove duplicated predictions, i.e. keep only the closest ones _, unique_idx = np.unique(preds, return_index=True) # unique_idx is sorted based on the unique values, sort it again # here the row corresponding to the first crop is used as a # 'buffer' for each query, and in the end the dimension # relative to crops is eliminated predictions[q, 0, :20] = preds[np.sort(unique_idx)][:20] predictions = predictions[:, 0, :20] # keep only the closer 20 predictions for each query #### For each query, check if the predictions are correct positives_per_query = eval_ds.get_positives() # args.recall_values by default is [1, 5, 10, 20] recalls = np.zeros(len(args.recall_values)) for query_index, pred in enumerate(predictions): for i, n in enumerate(args.recall_values): if np.any(np.in1d(pred[:n], positives_per_query[query_index])): recalls[i:] += 1 break # Divide by the number of queries*100, so the recalls are in percentages recalls = recalls / eval_ds.queries_num * 100 recalls_str = ", ".join([f"R@{val}: {rec:.1f}" for val, rec in zip(args.recall_values, recalls)]) return recalls, recalls_str
54.909804
121
0.644337
18107664baf7404f0465f06470c192a8803624ac
355
py
Python
insertionsort.py
emcd123/Matroids
f1ab7a5164a60b753ba429ef7ba9ce36517d4439
[ "MIT" ]
null
null
null
insertionsort.py
emcd123/Matroids
f1ab7a5164a60b753ba429ef7ba9ce36517d4439
[ "MIT" ]
null
null
null
insertionsort.py
emcd123/Matroids
f1ab7a5164a60b753ba429ef7ba9ce36517d4439
[ "MIT" ]
1
2021-11-21T18:03:07.000Z
2021-11-21T18:03:07.000Z
import random li=[] for i in range(10):#creating a random list using code from blackboard li=li+[random.randrange(0,50)] print(li) print(insertionSort(li)) #print(li)
18.684211
69
0.630986
1810948fff7ddb4956a7253f2de040223223f990
1,442
py
Python
python-packages/hyperopt-0.0.2/hyperopt/tests/test_fmin.py
rajegannathan/grasp-lift-eeg-cat-dog-solution-updated
ee45bee6f96cdb6d91184abc16f41bba1546c943
[ "BSD-3-Clause" ]
2
2017-08-13T14:09:32.000Z
2018-07-16T23:39:00.000Z
python-packages/hyperopt-0.0.2/hyperopt/tests/test_fmin.py
rajegannathan/grasp-lift-eeg-cat-dog-solution-updated
ee45bee6f96cdb6d91184abc16f41bba1546c943
[ "BSD-3-Clause" ]
null
null
null
python-packages/hyperopt-0.0.2/hyperopt/tests/test_fmin.py
rajegannathan/grasp-lift-eeg-cat-dog-solution-updated
ee45bee6f96cdb6d91184abc16f41bba1546c943
[ "BSD-3-Clause" ]
2
2018-04-02T06:45:11.000Z
2018-07-16T23:39:02.000Z
import nose.tools from hyperopt import fmin, rand, tpe, hp, Trials, exceptions, space_eval
23.258065
72
0.489598
1810ed3f25b77f5724cfa46b09080dd25d3ba89c
737
py
Python
aaweb/__init__.py
cpelite/astorian-airways
55498f308de7a4b8159519e191b492675ec5612a
[ "CC0-1.0" ]
null
null
null
aaweb/__init__.py
cpelite/astorian-airways
55498f308de7a4b8159519e191b492675ec5612a
[ "CC0-1.0" ]
null
null
null
aaweb/__init__.py
cpelite/astorian-airways
55498f308de7a4b8159519e191b492675ec5612a
[ "CC0-1.0" ]
3
2020-04-14T20:46:50.000Z
2021-03-11T19:07:20.000Z
# -*- coding: utf-8 -*- import os from datetime import timedelta from flask import Flask, session default_timezone = 'Europe/Berlin' app = Flask(__name__, static_folder='../static', static_url_path='/static', template_folder="../templates/") app.permanent_session_lifetime = timedelta(minutes=60) app.config.update( SESSION_COOKIE_NAME = "AAsession", ERROR_LOG_FILE = "%s/app.log" % os.environ.get('OPENSHIFT_LOG_DIR', 'logs') ) # VIEWS import aaweb.views import aaweb.forms # API import aaweb.api # additional functionalities import aaweb.error import aaweb.log
20.472222
108
0.738128
1812cc808e8b51d1262a39abd3b6e4c2337c6ac5
1,528
py
Python
Examples/Segmentation/WatershedSegmentation1.py
nalinimsingh/ITK_4D
95a2eacaeaffe572889832ef0894239f89e3f303
[ "Apache-2.0" ]
3
2018-10-01T20:46:17.000Z
2019-12-17T19:39:50.000Z
Examples/Segmentation/WatershedSegmentation1.py
nalinimsingh/ITK_4D
95a2eacaeaffe572889832ef0894239f89e3f303
[ "Apache-2.0" ]
null
null
null
Examples/Segmentation/WatershedSegmentation1.py
nalinimsingh/ITK_4D
95a2eacaeaffe572889832ef0894239f89e3f303
[ "Apache-2.0" ]
4
2018-05-17T16:34:54.000Z
2020-09-24T02:12:40.000Z
#========================================================================== # # Copyright Insight Software Consortium # # 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.txt # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # #==========================================================================*/ import InsightToolkit as itk import sys reader = itk.itkImageFileReaderF2_New() reader.SetFileName( sys.argv[1] ) diffusion = itk.itkGradientAnisotropicDiffusionImageFilterF2F2_New() diffusion.SetInput(reader.GetOutput()) diffusion.SetTimeStep(0.0625) diffusion.SetConductanceParameter(9.0) diffusion.SetNumberOfIterations( 5 ); gradient = itk.itkGradientMagnitudeImageFilterF2F2_New() gradient.SetInput(diffusion.GetOutput()) watershed = itk.itkWatershedImageFilterF2_New() watershed.SetInput(gradient.GetOutput()) watershed.SetThreshold(0.01) watershed.SetLevel(0.2) writer = itk.itkImageFileWriterUL2_New() writer.SetFileName( sys.argv[2] ) writer.SetInput( watershed.GetOutput() ) writer.Update()
33.217391
78
0.676702
1815ed2b6c358f6414fe0404d22b0c279e749b59
1,520
py
Python
study_roadmaps/python_sample_examples/gluon/3_aux_functions/train.py
Shreyashwaghe/monk_v1
4ee4d9483e8ffac9b73a41f3c378e5abf5fc799b
[ "Apache-2.0" ]
7
2020-07-26T08:37:29.000Z
2020-10-30T10:23:11.000Z
study_roadmaps/python_sample_examples/gluon/3_aux_functions/train.py
mursalfk/monk_v1
62f34a52f242772186ffff7e56764e958fbcd920
[ "Apache-2.0" ]
9
2020-01-28T21:40:39.000Z
2022-02-10T01:24:06.000Z
study_roadmaps/python_sample_examples/gluon/3_aux_functions/train.py
mursalfk/monk_v1
62f34a52f242772186ffff7e56764e958fbcd920
[ "Apache-2.0" ]
1
2020-10-07T12:57:44.000Z
2020-10-07T12:57:44.000Z
import os import sys sys.path.append("../../../monk/"); import psutil from gluon_prototype import prototype gtf = prototype(verbose=1); gtf.Prototype("sample-project-1", "sample-experiment-1"); gtf.Default(dataset_path="../../../monk/system_check_tests/datasets/dataset_cats_dogs_train", model_name="resnet18_v1", freeze_base_network=True, num_epochs=2); ######################################################## Summary ##################################################### gtf.Summary() ########################################################################################################################### ##################################################### EDA - Find Num images per class ##################################### gtf.EDA(show_img=True, save_img=True); ########################################################################################################################### ##################################################### EDA - Find Missing and corrupted images ##################################### gtf.EDA(check_missing=True, check_corrupt=True); ########################################################################################################################### ##################################################### Estimate Training Time ##################################### gtf.Estimate_Train_Time(num_epochs=50); ###########################################################################################################################
33.043478
131
0.309211
18173f17dd015c09e3b1cfc44c736b20bfea7170
126
py
Python
ppa-mirror/config.py
elprup/ppa-mirror
29e8a5027bbb698fcb36a250484b08ea945f65cf
[ "MIT" ]
null
null
null
ppa-mirror/config.py
elprup/ppa-mirror
29e8a5027bbb698fcb36a250484b08ea945f65cf
[ "MIT" ]
null
null
null
ppa-mirror/config.py
elprup/ppa-mirror
29e8a5027bbb698fcb36a250484b08ea945f65cf
[ "MIT" ]
1
2021-03-04T13:43:34.000Z
2021-03-04T13:43:34.000Z
cache_root = '/home/ubuntu/ppa-mirror/cache/' mirror_root = '/home/ubuntu/ppa-mirror/repo' http_proxy = "188.112.194.222:8080"
42
45
0.746032
181aa4e686c7e2eb75b68979882bfaab2af06de9
3,031
py
Python
downloader.py
tuxetuxe/downloader
76a1ac01189a6946b15ac6f58661931551dfc0ef
[ "Apache-2.0" ]
3
2016-11-09T13:02:46.000Z
2020-06-04T10:38:11.000Z
downloader.py
tuxetuxe/downloader
76a1ac01189a6946b15ac6f58661931551dfc0ef
[ "Apache-2.0" ]
null
null
null
downloader.py
tuxetuxe/downloader
76a1ac01189a6946b15ac6f58661931551dfc0ef
[ "Apache-2.0" ]
null
null
null
import sys, getopt import sched import time import csv from pprint import pprint import urllib, urllib2 from random import randint import threading proxies_file = "" targets_file = "" proxies = [] targets = [] scheduler = sched.scheduler(time.time, time.sleep) if __name__ == "__main__": main(sys.argv[1:])
25.470588
88
0.626856
181b018a34f9e83a9ca0468d516a71155390ba8b
1,799
py
Python
backend/api/views/utils.py
pm5/Disfactory
2cceec2544b1bd5bb624882be626494d54a08119
[ "MIT" ]
null
null
null
backend/api/views/utils.py
pm5/Disfactory
2cceec2544b1bd5bb624882be626494d54a08119
[ "MIT" ]
null
null
null
backend/api/views/utils.py
pm5/Disfactory
2cceec2544b1bd5bb624882be626494d54a08119
[ "MIT" ]
null
null
null
import random from django.conf import settings from django.db.models import Prefetch from django.db.models.functions.math import Radians, Cos, ACos, Sin from ..models import Factory, ReportRecord, Image, Document def _get_nearby_factories(latitude, longitude, radius): """Return nearby factories based on position and search range.""" # ref: https://stackoverflow.com/questions/574691/mysql-great-circle-distance-haversine-formula distance = 6371 * ACos( Cos(Radians(latitude)) * Cos(Radians("lat")) * Cos(Radians("lng") - Radians(longitude)) + Sin(Radians(latitude)) * Sin(Radians("lat")) ) radius_km = radius ids = Factory.objects.annotate(distance=distance).only("id").filter(distance__lt=radius_km).order_by("id") if len(ids) > settings.MAX_FACTORY_PER_GET: ids = _sample(ids, settings.MAX_FACTORY_PER_GET) return ( Factory.objects.filter(id__in=[obj.id for obj in ids]) .prefetch_related(Prefetch('report_records', queryset=ReportRecord.objects.only("created_at").all())) .prefetch_related(Prefetch('images', queryset=Image.objects.only("id").all())) .prefetch_related(Prefetch('documents', queryset=Document.objects.only('created_at', 'display_status').all())) .all() )
36.714286
125
0.692051
181cfdf188f95cef8715790def585eab0fdb4f44
886
py
Python
tests/test_pyros_schemas/test_decorators.py
pyros-dev/pyros-schemas
a460920260ee77a1b5b6d5c0b97df52f1572ff79
[ "MIT" ]
3
2018-01-01T17:10:16.000Z
2018-11-15T15:41:46.000Z
tests/test_pyros_schemas/test_decorators.py
pyros-dev/pyros-schemas
a460920260ee77a1b5b6d5c0b97df52f1572ff79
[ "MIT" ]
7
2018-02-02T10:05:55.000Z
2018-02-17T15:15:46.000Z
tests/test_pyros_schemas/test_decorators.py
pyros-dev/pyros-schemas
a460920260ee77a1b5b6d5c0b97df52f1572ff79
[ "MIT" ]
2
2017-09-27T09:46:31.000Z
2018-02-02T09:37:13.000Z
from __future__ import absolute_import from __future__ import print_function import pytest import std_srvs.srv as std_srvs # public decorators from pyros_schemas.ros import with_service_schemas # # Testing with_service_schemas decorator # # Just in case we run this directly if __name__ == '__main__': pytest.main([ 'test_decorators.py::test_decorated_service' ])
22.717949
70
0.72912
181dd4525734f8cc34fa28f835971bb355463f95
516
py
Python
src/removeElement.py
ianxin/algorithm
22214b6c81bee926f5a1c74c9417b2e7edd3ceed
[ "MIT" ]
2
2018-03-13T08:59:14.000Z
2018-03-13T08:59:25.000Z
src/removeElement.py
ianxin/Algorithm
22214b6c81bee926f5a1c74c9417b2e7edd3ceed
[ "MIT" ]
null
null
null
src/removeElement.py
ianxin/Algorithm
22214b6c81bee926f5a1c74c9417b2e7edd3ceed
[ "MIT" ]
null
null
null
""" @param: A: A list of integers @param: elem: An integer @return: The new length after remove """ #list #list list
23.454545
40
0.484496
181e8052c8ceced20aed0b9306fa76476c4461fb
2,057
py
Python
setup.py
codespider/flagon
d94a50844025ea88fd67dc7651c4a860c3be6d1a
[ "MIT" ]
3
2018-08-29T19:01:10.000Z
2018-09-14T16:07:30.000Z
setup.py
codespider/flagon
d94a50844025ea88fd67dc7651c4a860c3be6d1a
[ "MIT" ]
8
2018-08-24T08:56:09.000Z
2018-09-15T11:13:27.000Z
setup.py
codespider/flagon
d94a50844025ea88fd67dc7651c4a860c3be6d1a
[ "MIT" ]
null
null
null
from setuptools import setup, find_packages import io from collections import OrderedDict with io.open('README.rst', 'rt', encoding='utf8') as f: readme = f.read() setup( name='Flask-Wired', version=get_version(), license='MIT', author='Karthikkannan Maruthamuthu', author_email='karthikkannan@gmail.com', maintainer='Karthikkannan Maruthamuthu', maintainer_email='karthikkannan@gmail.com', description='Package for Flask wiring.', long_description=readme, url='https://github.com/treebohotels/Flask-Wired', project_urls=OrderedDict(( ('Documentation', 'https://github.com/treebohotels/Flask-Wired'), ('Code', 'https://github.com/treebohotels/Flask-Wired'), ('Issue tracker', 'https://github.com/treebohotels/Flask-Wired/issues'), )), package_dir={'': '.'}, packages=find_packages(".", exclude=['tests', 'sample_app']), include_package_data=True, zip_safe=False, platforms='any', python_requires='>=3.6', install_requires=[ 'Flask==1.0.2', 'Flask-Script==2.0.6', 'Flask-Migrate==2.2.1', 'flask-marshmallow==0.9.0', 'Flask-SQLAlchemy==2.3.2', 'marshmallow-sqlalchemy==0.14.1', 'psycopg2==2.7.5', ], entry_points={ }, test_suite="tests", classifiers=[ 'Development Status :: 1 - Planning', 'Environment :: Web Environment', 'Framework :: Flask', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Topic :: Internet :: WWW/HTTP :: Dynamic Content', 'Topic :: Internet :: WWW/HTTP :: WSGI :: Application', 'Topic :: Software Development :: Libraries :: Application Frameworks', 'Topic :: Software Development :: Libraries :: Python Modules', ], )
31.646154
80
0.616918
181ed57e3eb39153ad141aa8f03aeb15ee7f7127
510
py
Python
idManager/view/authentication_view.py
lgarciasbr/idm-api
3517d29d55eb2a06fb5b4b21359b6cf6d11529a0
[ "Apache-2.0" ]
2
2018-01-14T22:43:43.000Z
2018-01-14T22:43:48.000Z
idManager/view/authentication_view.py
lgarciasbr/idm-api
3517d29d55eb2a06fb5b4b21359b6cf6d11529a0
[ "Apache-2.0" ]
null
null
null
idManager/view/authentication_view.py
lgarciasbr/idm-api
3517d29d55eb2a06fb5b4b21359b6cf6d11529a0
[ "Apache-2.0" ]
null
null
null
from flask import jsonify
25.5
90
0.721569
181efed1a7997edb4c8e051cadb0058f5afd1105
604
py
Python
setup.py
TheSriram/deuce
9e8a7a342275aa02d0a59953b5a8c96ffb760b51
[ "Apache-2.0" ]
null
null
null
setup.py
TheSriram/deuce
9e8a7a342275aa02d0a59953b5a8c96ffb760b51
[ "Apache-2.0" ]
null
null
null
setup.py
TheSriram/deuce
9e8a7a342275aa02d0a59953b5a8c96ffb760b51
[ "Apache-2.0" ]
null
null
null
# -*- coding: utf-8 -*- try: from setuptools import setup, find_packages except ImportError: from ez_setup import use_setuptools use_setuptools() from setuptools import setup, find_packages REQUIRES = ['six', 'pecan', 'setuptools >= 1.1.6', 'cassandra-driver', 'pymongo'] setup( name='deuce', version='0.1', description='Deuce - Block-level de-duplication as-a-service', author='Rackspace', author_email='', install_requires=REQUIRES, test_suite='deuce', zip_safe=False, include_package_data=True, packages=find_packages(exclude=['tests']) )
25.166667
66
0.680464
1820ae4e6fd68c69f37f4266bffb6793e643a89a
6,580
py
Python
script.py
rahulkmr1/heroku-python-script
053be38dc8c6c6ab9929ca5af772d19c57f5e498
[ "MIT" ]
null
null
null
script.py
rahulkmr1/heroku-python-script
053be38dc8c6c6ab9929ca5af772d19c57f5e498
[ "MIT" ]
null
null
null
script.py
rahulkmr1/heroku-python-script
053be38dc8c6c6ab9929ca5af772d19c57f5e498
[ "MIT" ]
null
null
null
import telepot import time import requests from bs4 import BeautifulSoup as bs import cPickle import csv RAHUL_ID = 931906767 # You can leave this bit out if you're using a paid PythonAnywhere account # proxy_url = "http://proxy.server:3128" # telepot.api._pools = { # 'default': urllib3.ProxyManager(proxy_url=proxy_url, num_pools=3, maxsize=10, retries=False, timeout=30), # } # telepot.api._onetime_pool_spec = (urllib3.ProxyManager, dict(proxy_url=proxy_url, num_pools=1, maxsize=1, retries=False, timeout=30)) # end of the stuff that's only needed for free accounts ######################## login_url = 'https://www.placement.iitbhu.ac.in/accounts/login/' client = requests.session() login = client.get(login_url) login = bs(login.content, "html.parser") payload = { "login": "rahul.kumar.cse15@itbhu.ac.in", "password": "rahulkmr", "csrfmiddlewaretoken": login.input['value'] } result = client.post( login_url, data = payload, headers = dict(referer=login_url) ) forum = client.get("https://www.placement.iitbhu.ac.in/forum/c/notice-board/2019-20/") soup = bs(forum.content, "html.parser") #load last message delivred to users try: with open("posts", "rb") as f: posts = cPickle.load(f); except Exception as e: print e posts = soup.findAll("td", "topic-name") for i in range(len(posts)): posts[i] = posts[i].a posts.pop(0) posts.pop(0) updated = soup.findAll('td','topic-last-post') # updated.pop() # updated.pop(0) ######################### bot = telepot.Bot('940251504:AAG19YYQYtkiEOCrW0fZETvmYQSskElARcc') # chat_ids = {RAHUL_ID} with open("IDs", "rb") as f: chat_ids = cPickle.load(f) print '#################No of IDs loaded: ', len(chat_ids) ####### Commands ######## ######################### command = {'/add':add_cmd, '/remove':remove_cmd, '/all':allPosts, '/recent':top} bot.message_loop(handle) print ('Listening ...') # for chat_id in chat_ids: # bot.sendMessage(chat_id, text='Server started', parse_mode="HTML") bot.sendMessage(RAHUL_ID, text='Server started', parse_mode="HTML") # Keep the program running. if __name__ == '__main__': main()
25.019011
173
0.655623
1824cd98e77d7661e6eb7f082d5655ec1a45fa19
1,607
py
Python
examples/4-tensorflow-mnist/tensorflow_mnist/train.py
awcchungster/baklava
ad301afd7aa163ccf662efe08d00eeff68cdb667
[ "Apache-2.0" ]
3
2021-08-24T03:10:14.000Z
2022-01-07T20:53:37.000Z
examples/4-tensorflow-mnist/tensorflow_mnist/train.py
awcchungster/baklava
ad301afd7aa163ccf662efe08d00eeff68cdb667
[ "Apache-2.0" ]
5
2021-07-15T20:19:26.000Z
2021-08-18T23:26:46.000Z
examples/4-tensorflow-mnist/tensorflow_mnist/train.py
LaudateCorpus1/baklava
0e029097983db6cea00a7d779b887b149975fbc4
[ "Apache-2.0" ]
5
2021-07-03T17:46:15.000Z
2022-02-24T08:05:39.000Z
""" Train ===== Defines functions which train models and write model artifacts to disk. """ from __future__ import print_function import os import tempfile import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data from tensorflow_mnist import model, paths def train(path): """ Train a decision tree classifier using a floating point feature matrix and a categorical classification target. Arguments: path (str): The path indicating where to save the final model artifacts """ # Construct the model graph graph, x, y, step, initializer, accuracy, prediction = model.build() # Start a training session with tf.Session(graph=graph) as sess: # Initialize the graph sess.run(initializer) # Train the model for 1000 steps mnist = input_data.read_data_sets(tempfile.mkdtemp(), one_hot=True) for _ in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(step, feed_dict={x: batch_xs, y: batch_ys}) # Display accuracy measurement print(sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})) # Save the variable data to disk os.makedirs(path) saver = tf.train.Saver() saver.save(sess, path) print('Success!') def main(): """ Load features and labels, train the neural network, and serialize model artifact. Note: This is the training entrypoint used by baklava! """ path = paths.model('mnist') train(path)
26.344262
79
0.653391
18257b1e23725fb3440c7a7dd07da911552a0f1a
16,942
py
Python
google/cloud/binaryauthorization/v1/binaryauthorization-v1-py/google/cloud/binaryauthorization_v1/types/resources.py
googleapis/googleapis-gen
d84824c78563d59b0e58d5664bfaa430e9ad7e7a
[ "Apache-2.0" ]
7
2021-02-21T10:39:41.000Z
2021-12-07T07:31:28.000Z
google/cloud/binaryauthorization/v1/binaryauthorization-v1-py/google/cloud/binaryauthorization_v1/types/resources.py
googleapis/googleapis-gen
d84824c78563d59b0e58d5664bfaa430e9ad7e7a
[ "Apache-2.0" ]
6
2021-02-02T23:46:11.000Z
2021-11-15T01:46:02.000Z
google/cloud/binaryauthorization/v1/binaryauthorization-v1-py/google/cloud/binaryauthorization_v1/types/resources.py
googleapis/googleapis-gen
d84824c78563d59b0e58d5664bfaa430e9ad7e7a
[ "Apache-2.0" ]
4
2021-01-28T23:25:45.000Z
2021-08-30T01:55:16.000Z
# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import proto # type: ignore from google.protobuf import timestamp_pb2 # type: ignore __protobuf__ = proto.module( package='google.cloud.binaryauthorization.v1', manifest={ 'Policy', 'AdmissionWhitelistPattern', 'AdmissionRule', 'Attestor', 'UserOwnedGrafeasNote', 'PkixPublicKey', 'AttestorPublicKey', }, ) __all__ = tuple(sorted(__protobuf__.manifest))
37.986547
156
0.656416
1825d71ce3841cab87835439bc5331f28ba2643a
4,841
py
Python
builtinPlugins/plugin_spending.py
jscherer26/Icarra
5bc8b298ae21dcde7e8e2253b9ed9db95fd0d164
[ "BSD-3-Clause" ]
1
2021-11-09T04:36:57.000Z
2021-11-09T04:36:57.000Z
builtinPlugins/plugin_spending.py
jscherer26/Icarra
5bc8b298ae21dcde7e8e2253b9ed9db95fd0d164
[ "BSD-3-Clause" ]
null
null
null
builtinPlugins/plugin_spending.py
jscherer26/Icarra
5bc8b298ae21dcde7e8e2253b9ed9db95fd0d164
[ "BSD-3-Clause" ]
2
2020-03-28T02:55:19.000Z
2021-11-09T04:37:08.000Z
# Copyright (c) 2006-2010, Jesse Liesch # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the author nor the # names of its contributors may be used to endorse or promote products # derived from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE IMPLIED # DISCLAIMED. IN NO EVENT SHALL JESSE LIESCH BE LIABLE FOR ANY # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. from PyQt4.QtCore import * from PyQt4.QtGui import * import copy from editGrid import * from plugin import * from portfolio import * import appGlobal
33.157534
148
0.736625
18299c6187e63ee39b775b8ca8e59d659c576c75
5,913
py
Python
pyro_examples/dpgmm_full.py
hanyas/pyro_examples
7c8784bd9ac498cfaf2983da158a8209db21966e
[ "MIT" ]
1
2021-01-05T04:58:10.000Z
2021-01-05T04:58:10.000Z
pyro_examples/dpgmm_full.py
hanyas/pyro_examples
7c8784bd9ac498cfaf2983da158a8209db21966e
[ "MIT" ]
null
null
null
pyro_examples/dpgmm_full.py
hanyas/pyro_examples
7c8784bd9ac498cfaf2983da158a8209db21966e
[ "MIT" ]
null
null
null
import torch from torch.distributions import Gamma import torch.nn.functional as F import matplotlib.pyplot as plt from tqdm import tqdm from pyro.distributions import * import pyro from pyro.optim import Adam from pyro.infer import SVI, Trace_ELBO, Predictive assert pyro.__version__.startswith('1') pyro.enable_validation(True) pyro.set_rng_seed(1337) torch.set_num_threads(1) # device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") device = torch.device("cpu") data = torch.cat((MultivariateNormal(-2 * torch.ones(2), 0.1 * torch.eye(2)).sample([25]), MultivariateNormal(2 * torch.ones(2), 0.1 * torch.eye(2)).sample([25]), MultivariateNormal(torch.tensor([0., 0.]), 0.1 * torch.eye(2)).sample([25]))) data = data.to(device) N = data.shape[0] D = data.shape[1] T = 5 optim = Adam({"lr": 0.01}) svi = SVI(model, guide, optim, loss=Trace_ELBO(num_particles=35)) alpha = 0.1 * torch.ones(1, device=device) elbo = train(5000) plt.figure() plt.plot(elbo)
33.596591
121
0.641468
1829f18c9a4a6999de1f057e3d27520859bfe66b
539
py
Python
calplus/tests/unit/v1/test_utils.py
nghiadt16/CALplus
68c108e6abf7eeac4937b870dc7462dd6ee2fcc3
[ "Apache-2.0" ]
null
null
null
calplus/tests/unit/v1/test_utils.py
nghiadt16/CALplus
68c108e6abf7eeac4937b870dc7462dd6ee2fcc3
[ "Apache-2.0" ]
4
2017-04-05T16:14:07.000Z
2018-12-14T14:19:15.000Z
calplus/tests/unit/v1/test_utils.py
nghiadt16/CALplus
68c108e6abf7eeac4937b870dc7462dd6ee2fcc3
[ "Apache-2.0" ]
2
2017-04-18T16:53:58.000Z
2018-12-04T05:42:51.000Z
from calplus.tests import base from calplus.v1 import utils
22.458333
44
0.641929
182a6b769a1cd6d38014902642d94977a040e698
4,213
py
Python
luna_pathology/cli/load_slide.py
msk-mind-apps/luna-pathology
f0e17ccfeb3dc93de150aed5bbef9fcd7443d6d0
[ "Apache-2.0" ]
null
null
null
luna_pathology/cli/load_slide.py
msk-mind-apps/luna-pathology
f0e17ccfeb3dc93de150aed5bbef9fcd7443d6d0
[ "Apache-2.0" ]
3
2021-07-21T20:28:37.000Z
2021-08-02T18:52:32.000Z
luna_pathology/cli/load_slide.py
msk-mind-apps/luna-pathology
f0e17ccfeb3dc93de150aed5bbef9fcd7443d6d0
[ "Apache-2.0" ]
null
null
null
# General imports import os, json, logging import click from pathlib import Path import yaml # From common from luna_core.common.custom_logger import init_logger from luna_core.common.DataStore import DataStore_v2 from luna_core.common.Node import Node from luna_core.common.config import ConfigSet from luna_core.common.sparksession import SparkConfig def load_slide_with_datastore(app_config, datastore_id, method_data): """Load a slide to the datastore from the whole slide image table. Args: app_config (string): path to application configuration file. datastore_id (string): datastore name. usually a slide id. method_data (dict): method parameters including input, output details. Returns: None """ logger = logging.getLogger(f"[datastore={datastore_id}]") # Do some setup cfg = ConfigSet("APP_CFG", config_file=app_config) datastore = DataStore_v2(method_data["datastore_path"]) method_id = method_data["job_tag"] # fetch patient_id column patient_id_column = method_data.get("patient_id_column_name", None) if patient_id_column == "": patient_id_column = None try: spark = SparkConfig().spark_session("APP_CFG", "query_slide") slide_id = datastore_id if patient_id_column: # assumes if patient_id column, source is parquet from dremio # right now has nested row-type into dict, todo: account for map type representation of dict in dremio df = spark.read.parquet(method_data['table_path'])\ .where(f"UPPER(slide_id)='{slide_id}'")\ .select("path", "metadata", patient_id_column)\ .toPandas() if not len(df) == 1: print(df) raise ValueError(f"Resulting query record is not singular, multiple scan's exist given the container address {slide_id}") record = df.loc[0] properties = record['metadata'] properties['patient_id'] = str(record[patient_id_column]) else: df = spark.read.format("delta").load(method_data['table_path'])\ .where(f"UPPER(slide_id)='{slide_id}'")\ .select("path", "metadata")\ .toPandas() if not len(df) == 1: print(df) raise ValueError(f"Resulting query record is not singular, multiple scan's exist given the container address {slide_id}") record = df.loc[0] properties = record['metadata'] spark.stop() except Exception as e: logger.exception (f"{e}, stopping job execution...") raise e # Put results in the data store data_path = Path(record['path'].split(':')[-1]) print(data_path) datastore.put(data_path, datastore_id, method_id, "WholeSlideImage", symlink=True) with open(os.path.join(method_data["datastore_path"], datastore_id, method_id, "WholeSlideImage", "metadata.json"), "w") as fp: json.dump(properties, fp) if __name__ == "__main__": cli()
36.318966
137
0.657963
182ab8edcc4ae73b49deea3cf51426229fb8e5ad
442
py
Python
classifiers/CornerDetector.py
Vivek2018/OSM_Building-Detection-Custom-Repo
278b1f5a46e49cb547162d495979056c36945e43
[ "MIT" ]
null
null
null
classifiers/CornerDetector.py
Vivek2018/OSM_Building-Detection-Custom-Repo
278b1f5a46e49cb547162d495979056c36945e43
[ "MIT" ]
null
null
null
classifiers/CornerDetector.py
Vivek2018/OSM_Building-Detection-Custom-Repo
278b1f5a46e49cb547162d495979056c36945e43
[ "MIT" ]
null
null
null
import numpy as np import cv2 from matplotlib import pyplot as plt image = cv2.imread('champaigneditedcompressed.png') kernel = np.ones((20, 20), np.float32) / 25 img = cv2.filter2D(image, -1, kernel) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) corners = cv2.goodFeaturesToTrack(gray,10,0.01,10) corners = np.int0(corners) print(corners) for i in corners: x,y = i.ravel() cv2.circle(img,(x,y),3,255,-1) plt.imshow(img),plt.show()
23.263158
51
0.714932
182bb85b10503c8fb7bd8a2c09551b2160fe497c
25,581
py
Python
ECUSimulation/io_processing/surveillance_handler.py
arturmrowca/IVNS
8915142d16debe4af780a9eb6859e44dea2ca7e6
[ "MIT" ]
null
null
null
ECUSimulation/io_processing/surveillance_handler.py
arturmrowca/IVNS
8915142d16debe4af780a9eb6859e44dea2ca7e6
[ "MIT" ]
null
null
null
ECUSimulation/io_processing/surveillance_handler.py
arturmrowca/IVNS
8915142d16debe4af780a9eb6859e44dea2ca7e6
[ "MIT" ]
2
2018-08-04T07:43:51.000Z
2018-12-14T14:59:46.000Z
''' Created on 12 Jun, 2015 @author: artur.mrowca ''' from enum import Enum from PyQt5.Qt import QObject from PyQt5 import QtCore from tools.ecu_logging import ECULogger import copy
46.008993
176
0.692741
182bd0de90019e26f6a862933d6591b76c148320
1,994
py
Python
breadp/checks/pid.py
tgweber/breadp
12b97b9d2d997b1345a8e026690d57b3286a04d0
[ "Apache-2.0" ]
null
null
null
breadp/checks/pid.py
tgweber/breadp
12b97b9d2d997b1345a8e026690d57b3286a04d0
[ "Apache-2.0" ]
null
null
null
breadp/checks/pid.py
tgweber/breadp
12b97b9d2d997b1345a8e026690d57b3286a04d0
[ "Apache-2.0" ]
null
null
null
################################################################################ # Copyright: Tobias Weber 2019 # # Apache 2.0 License # # This file contains all code related to pid check objects # ################################################################################ import re import requests from breadp.checks import Check from breadp.checks.result import BooleanResult
28.898551
80
0.533099
182cba7e9952331f563ef145511a6c92d1f0f8eb
495
py
Python
tests/infrastructure/persistence/test_holiday_dynamo_repository.py
gabrielleandro0801/holidays-importer
4a698ded80048ee37161b1f1ff4b4af64f085ab7
[ "MIT" ]
null
null
null
tests/infrastructure/persistence/test_holiday_dynamo_repository.py
gabrielleandro0801/holidays-importer
4a698ded80048ee37161b1f1ff4b4af64f085ab7
[ "MIT" ]
null
null
null
tests/infrastructure/persistence/test_holiday_dynamo_repository.py
gabrielleandro0801/holidays-importer
4a698ded80048ee37161b1f1ff4b4af64f085ab7
[ "MIT" ]
null
null
null
from unittest import TestCase from src.domain.holiday import Holiday import src.infrastructure.persistence.holiday_dynamo_repository as repository HOLIDAY = Holiday( date='2021-12-25', name='Natal', category='NATIONAL' )
29.117647
83
0.779798
182e6f7b7c70dcc5da411a03395acac1d83ee9e9
3,136
py
Python
src/models/Models.py
nbrutti/uol-export
c79a1a6b5c68e61a85952a60b935943aec27cdda
[ "MIT" ]
null
null
null
src/models/Models.py
nbrutti/uol-export
c79a1a6b5c68e61a85952a60b935943aec27cdda
[ "MIT" ]
null
null
null
src/models/Models.py
nbrutti/uol-export
c79a1a6b5c68e61a85952a60b935943aec27cdda
[ "MIT" ]
null
null
null
from config.defs import * import peewee db = peewee.SqliteDatabase(DATABASE_NAME) ### Relacionamentos ### db.create_tables([Partida, Substituicao, Penalti, CartaoAmarelo, CartaoVermelho, GolContra, Gol, Time]) db.create_tables([PartidasSubstituicoes, PartidasPenaltis, PartidasCartoesAmarelos, PartidasCartoesVermelhos, PartidasGolsContra, PartidasGols])
24.888889
144
0.748724
182eadd7acbf4364e0c9b88cd120533f1ae8e1e3
1,165
py
Python
quantnn/__init__.py
simonpf/qrnn
1de11ce8cede6b4b3de0734bcc8c198c10226188
[ "MIT" ]
null
null
null
quantnn/__init__.py
simonpf/qrnn
1de11ce8cede6b4b3de0734bcc8c198c10226188
[ "MIT" ]
3
2022-01-11T08:41:03.000Z
2022-02-11T14:25:09.000Z
quantnn/__init__.py
simonpf/qrnn
1de11ce8cede6b4b3de0734bcc8c198c10226188
[ "MIT" ]
5
2020-12-11T03:18:32.000Z
2022-02-14T10:32:09.000Z
r""" ======= quantnn ======= The quantnn package provides functionality for probabilistic modeling and prediction using deep neural networks. The two main features of the quantnn package are implemented by the :py:class:`~quantnn.qrnn.QRNN` and :py:class:`~quantnn.qrnn.DRNN` classes, which implement quantile regression neural networks (QRNNs) and density regression neural networks (DRNNs), respectively. The modules :py:mod:`quantnn.quantiles` and :py:mod:`quantnn.density` provide generic (backend agnostic) functions to manipulate probabilistic predictions. """ import logging as _logging import os from rich.logging import RichHandler from quantnn.neural_network_model import set_default_backend, get_default_backend from quantnn.qrnn import QRNN from quantnn.drnn import DRNN from quantnn.quantiles import ( cdf, pdf, posterior_mean, probability_less_than, probability_larger_than, sample_posterior, sample_posterior_gaussian, quantile_loss, ) _LOG_LEVEL = os.environ.get("QUANTNN_LOG_LEVEL", "WARNING").upper() _logging.basicConfig( level=_LOG_LEVEL, format="%(message)s", datefmt="[%X]", handlers=[RichHandler()] )
29.871795
91
0.775107
182f0fecd4c6abc4561282446bbffe0f48f4cc60
805
py
Python
habitat_baselines/motion_planning/robot_target.py
srama2512/habitat-api
bc85d0961cef3b4a08bc9263869606109fb6ff0a
[ "MIT" ]
355
2020-08-18T03:48:26.000Z
2022-03-30T00:22:50.000Z
habitat_baselines/motion_planning/robot_target.py
srama2512/habitat-api
bc85d0961cef3b4a08bc9263869606109fb6ff0a
[ "MIT" ]
328
2020-08-12T21:25:09.000Z
2022-03-31T10:39:21.000Z
habitat_baselines/motion_planning/robot_target.py
srama2512/habitat-api
bc85d0961cef3b4a08bc9263869606109fb6ff0a
[ "MIT" ]
159
2020-08-12T22:23:36.000Z
2022-03-30T22:56:52.000Z
#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import attr import magnum as mn import numpy as np
23.676471
75
0.70559
18323906f8da6c858e162af77f828aa7dc3d5141
1,314
py
Python
leetcode/445.Add_Two_Numbers_II/python/add_two_numbers_v1.py
realXuJiang/research_algorithms
8f2876288cb607b9eddb2aa75f51a1d574b51ec4
[ "Apache-2.0" ]
1
2019-08-12T09:32:30.000Z
2019-08-12T09:32:30.000Z
leetcode/445.Add_Two_Numbers_II/python/add_two_numbers_v1.py
realXuJiang/research_algorithms
8f2876288cb607b9eddb2aa75f51a1d574b51ec4
[ "Apache-2.0" ]
null
null
null
leetcode/445.Add_Two_Numbers_II/python/add_two_numbers_v1.py
realXuJiang/research_algorithms
8f2876288cb607b9eddb2aa75f51a1d574b51ec4
[ "Apache-2.0" ]
null
null
null
#!/usr/bin/env python #-*- coding: utf-8 -*- if __name__ == "__main__": tn = TwoNumbers() l1 = tn.builderListNode(1234) l2 = tn.builderListNode(34) tn.printLS(tn.addTwoNumbers(l1, l2))
22.655172
42
0.47793
1833d1d97b94601d7c7672bd7240b57d03e2cddf
3,961
py
Python
recsys/util/feature_helper.py
manazhao/tf_recsys
6053712d11165c068e5d618989f716b2a0f60186
[ "Apache-2.0" ]
1
2019-04-20T15:05:37.000Z
2019-04-20T15:05:37.000Z
recsys/util/feature_helper.py
manazhao/tf_recsys
6053712d11165c068e5d618989f716b2a0f60186
[ "Apache-2.0" ]
null
null
null
recsys/util/feature_helper.py
manazhao/tf_recsys
6053712d11165c068e5d618989f716b2a0f60186
[ "Apache-2.0" ]
null
null
null
import logging import tensorflow as tf import recsys.util.proto.config_pb2 as config # Constructs a tf.Example with feature dictionary where key is feature name and # value is tf.train.Feature # Reads batched features and labels from given files, and consumes them through # callback function "consum_batch_fn". # feature_spec: dictionary specifying the type of each feature. # input_config: settings for generating batched features and labels. # consume_batch_fn: callback function that defines the consumption of the # batched features and labels.
39.61
124
0.789447
18343ff0759e4173734193d8fad780c280807cc1
1,894
py
Python
components/handlers/star_modules.py
nus-mtp/another-cs-study-planner
02b52871a34f580b779ede08750f2d4e887bcf65
[ "MIT" ]
1
2017-04-30T17:59:08.000Z
2017-04-30T17:59:08.000Z
components/handlers/star_modules.py
nus-mtp/another-cs-study-planner
02b52871a34f580b779ede08750f2d4e887bcf65
[ "MIT" ]
87
2017-02-13T09:06:13.000Z
2017-04-14T09:23:08.000Z
components/handlers/star_modules.py
nus-mtp/another-cs-study-planner
02b52871a34f580b779ede08750f2d4e887bcf65
[ "MIT" ]
1
2017-04-11T05:26:00.000Z
2017-04-11T05:26:00.000Z
''' This module handles starring of modules. ''' import web from app import RENDER from components import model, session
33.22807
90
0.594509
183882e7bff2e8589b66d5bada377b9d753cd440
27,362
py
Python
src/features/smarterdb.py
cnr-ibba/SMARTER-database
837f7d514c33e458ad0e39e26784c761df29e004
[ "MIT" ]
null
null
null
src/features/smarterdb.py
cnr-ibba/SMARTER-database
837f7d514c33e458ad0e39e26784c761df29e004
[ "MIT" ]
44
2021-05-25T16:00:34.000Z
2022-03-12T01:12:45.000Z
src/features/smarterdb.py
cnr-ibba/SMARTER-database
837f7d514c33e458ad0e39e26784c761df29e004
[ "MIT" ]
null
null
null
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Feb 23 16:21:35 2021 @author: Paolo Cozzi <paolo.cozzi@ibba.cnr.it> """ import os import logging import pathlib import pycountry import mongoengine from enum import Enum from typing import Union from pymongo import database, ReturnDocument from dotenv import find_dotenv, load_dotenv from .utils import get_project_dir SPECIES2CODE = { "Sheep": "OA", "Goat": "CH" } SMARTERDB = "smarter" DB_ALIAS = "smarterdb" # Get an instance of a logger logger = logging.getLogger(__name__) def get_or_create_breed( species: str, name: str, code: str, aliases: list = []): logger.debug(f"Checking: '{species}':'{name}':'{code}'") # get a breed object relying on parameters qs = Breed.objects(species=species, name=name, code=code) modified = False if qs.count() == 1: breed = qs.get() logger.debug(f"Got {breed}") for alias in aliases: if alias not in breed.aliases: # track for update modified = True logger.info(f"Adding '{alias}' to '{breed}' aliases") breed.aliases.append(alias) elif qs.count() == 0: logger.debug("Create a new breed object") modified = True breed = Breed( species=species, name=name, code=code, aliases=aliases, n_individuals=0 ) else: # should never see this relying on collection unique keys raise SmarterDBException( f"Got {qs.count()} results for '{species}':'{name}':'{code}'") if modified: logger.debug(f"Save '{breed}' to database") breed.save() return breed, modified def get_or_create_sample( SampleSpecies: Union[SampleGoat, SampleSheep], original_id: str, dataset: Dataset, type_: str, breed: Breed, country: str, chip_name: str = None, sex: SEX = None, alias: str = None) -> Union[SampleGoat, SampleSheep]: """Get or create a sample providing attributes (search for original_id in provided dataset Args: SampleSpecies: (Union[SampleGoat, SampleSheep]): the class required for insert/update original_id (str): The original_id in the dataset dataset (Dataset): the dataset instance used to register sample type_ (str): "background" or "foreground" breed (Breed): A breed instance country (str): Country as a string chip_name (str): the chip name sex (SEX): A SEX instance alias (str): an original_id alias Returns: Union[SampleGoat, SampleSheep]: a SampleSpecies instance """ created = False # search for sample in database qs = SampleSpecies.objects( original_id=original_id, dataset=dataset) if qs.count() == 1: logger.debug(f"Sample '{original_id}' found in database") sample = qs.get() elif qs.count() == 0: # insert sample into database logger.info(f"Registering sample '{original_id}' in database") sample = SampleSpecies( original_id=original_id, country=country, species=dataset.species, breed=breed.name, breed_code=breed.code, dataset=dataset, type_=type_, chip_name=chip_name, sex=sex, alias=alias ) sample.save() # incrementing breed n_individuals counter breed.n_individuals += 1 breed.save() created = True else: raise SmarterDBException( f"Got {qs.count()} results for '{original_id}'") return sample, created def get_sample_type(dataset: Dataset): """ test if foreground or background dataset Args: dataset (Dataset): the dataset instance used to register sample Returns: str: sample type ("background" or "foreground") """ type_ = None for sampletype in SAMPLETYPE: if sampletype.value in dataset.type_: logger.debug( f"Found {sampletype.value} in {dataset.type_}") type_ = sampletype.value break return type_ def __eq__(self, other): if super().__eq__(other): return True else: # check by positions for attribute in ["chrom", "position"]: if getattr(self, attribute) != getattr(other, attribute): return False # check genotype equality if self.illumina_top != other.illumina_top: return False return True def __check_coding(self, genotype: list, coding: str, missing: str): """Internal method to check genotype coding""" # get illumina data as an array data = getattr(self, coding).split("/") for allele in genotype: # mind to missing values. If missing can't be equal to illumina_top if allele in missing: continue if allele not in data: return False return True def is_top(self, genotype: list, missing: list = ["0", "-"]) -> bool: """Return True if genotype is compatible with illumina TOP coding Args: genotype (list): a list of two alleles (ex ['A','C']) missing (str): missing allele string (def "0") Returns: bool: True if in top coordinates """ return self.__check_coding(genotype, "illumina_top", missing) def is_forward(self, genotype: list, missing: list = ["0", "-"]) -> bool: """Return True if genotype is compatible with illumina FORWARD coding Args: genotype (list): a list of two alleles (ex ['A','C']) missing (str): missing allele string (def "0") Returns: bool: True if in top coordinates """ return self.__check_coding(genotype, "illumina_forward", missing) def is_ab(self, genotype: list, missing: list = ["0", "-"]) -> bool: """Return True if genotype is compatible with illumina AB coding Args: genotype (list): a list of two alleles (ex ['A','B']) missing (str): missing allele string (def "-") Returns: bool: True if in top coordinates """ for allele in genotype: # mind to missing valies if allele not in ["A", "B"] + missing: return False return True def is_affymetrix( self, genotype: list, missing: list = ["0", "-"]) -> bool: """Return True if genotype is compatible with affymetrix coding Args: genotype (list): a list of two alleles (ex ['A','C']) missing (str): missing allele string (def "0") Returns: bool: True if in top coordinates """ return self.__check_coding(genotype, "affymetrix_ab", missing) def forward2top(self, genotype: list, missing: list = ["0", "-"]) -> list: """Convert an illumina forward SNP in a illumina top snp Args: genotype (list): a list of two alleles (ex ['A','C']) missing (str): missing allele string (def "0") Returns: list: The genotype in top format """ # get illumina data as an array forward = self.illumina_forward.split("/") top = self.illumina_top.split("/") result = [] for allele in genotype: # mind to missing values if allele in missing: result.append("0") elif allele not in forward: raise SmarterDBException( f"{genotype} is not in forward coding") else: result.append(top[forward.index(allele)]) return result def ab2top(self, genotype: list, missing: list = ["0", "-"]) -> list: """Convert an illumina ab SNP in a illumina top snp Args: genotype (list): a list of two alleles (ex ['A','B']) missing (str): missing allele string (def "-") Returns: list: The genotype in top format """ # get illumina data as a dict top = self.illumina_top.split("/") top = {"A": top[0], "B": top[1]} result = [] for allele in genotype: # mind to missing values if allele in missing: result.append("0") elif allele not in ["A", "B"]: raise SmarterDBException( f"{genotype} is not in ab coding") else: result.append(top[allele]) return result def affy2top(self, genotype: list, missing: list = ["0", "-"]) -> list: """Convert an affymetrix SNP in a illumina top snp Args: genotype (list): a list of two alleles (ex ['A','C']) missing (str): missing allele string (def "0") Returns: list: The genotype in top format """ # get illumina data as an array affymetrix = self.affymetrix_ab.split("/") top = self.illumina_top.split("/") result = [] for allele in genotype: # mind to missing values if allele in missing: result.append("0") elif allele not in affymetrix: raise SmarterDBException( f"{genotype} is not in affymetrix coding") else: result.append(top[affymetrix.index(allele)]) return result class VariantSpecies(mongoengine.Document): rs_id = mongoengine.StringField() chip_name = mongoengine.ListField(mongoengine.StringField()) name = mongoengine.StringField(unique=True) # sequence should model both illumina or affymetrix sequences sequence = mongoengine.DictField() locations = mongoengine.ListField( mongoengine.EmbeddedDocumentField(Location)) # HINT: should sender be a Location attribute? sender = mongoengine.StringField() # Affymetryx specific fields # more probe could be assigned to the same SNP probeset_id = mongoengine.ListField(mongoengine.StringField()) affy_snp_id = mongoengine.StringField() cust_id = mongoengine.StringField() # abstract class with custom indexes # TODO: need a index for position (chrom, position, version) meta = { 'abstract': True, 'indexes': [ { 'fields': [ "locations.chrom", "locations.position" ], }, 'probeset_id', 'rs_id' ] } def save(self, *args, **kwargs): """Custom save method. Deal with variant name before save""" if not self.name and self.affy_snp_id: logger.debug(f"Set variant name to {self.affy_snp_id}") self.name = self.affy_snp_id # default save method super(VariantSpecies, self).save(*args, **kwargs) def get_location_index(self, version: str, imported_from='SNPchiMp v.3'): """Returns location index for assembly version and imported source Args: version (str): assembly version (ex: 'Oar_v3.1') imported_from (str): coordinates source (ex: 'SNPchiMp v.3') Returns: int: the index of the location requested """ for index, location in enumerate(self.locations): if (location.version == version and location.imported_from == imported_from): return index raise SmarterDBException( f"Location '{version}' '{imported_from}' is not in locations" ) def get_location(self, version: str, imported_from='SNPchiMp v.3'): """Returns location for assembly version and imported source Args: version (str): assembly version (ex: 'Oar_v3.1') imported_from (str): coordinates source (ex: 'SNPchiMp v.3') Returns: Location: the genomic coordinates """ locations = list(filter(custom_filter, self.locations)) if len(locations) != 1: raise SmarterDBException( "Couldn't determine a unique location for " f"'{self.name}' '{version}' '{imported_from}'") return locations[0] class VariantSheep(VariantSpecies): meta = { 'db_alias': DB_ALIAS, 'collection': 'variantSheep' } class VariantGoat(VariantSpecies): meta = { 'db_alias': DB_ALIAS, 'collection': 'variantGoat' }
28.472425
121
0.604853
1838c0e9c32271122443074ccc035f2557452781
6,143
py
Python
test/utils/multi_objective/test_box_decomposition.py
SamuelMarks/botorch
7801e2f56dc447322b2b6c92cab683d8900e4c7f
[ "MIT" ]
2
2021-01-11T18:16:27.000Z
2021-11-30T09:34:44.000Z
test/utils/multi_objective/test_box_decomposition.py
SamuelMarks/botorch
7801e2f56dc447322b2b6c92cab683d8900e4c7f
[ "MIT" ]
17
2020-12-11T20:07:22.000Z
2022-03-27T16:46:42.000Z
test/utils/multi_objective/test_box_decomposition.py
SamuelMarks/botorch
7801e2f56dc447322b2b6c92cab683d8900e4c7f
[ "MIT" ]
null
null
null
#! /usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from __future__ import annotations import torch from botorch.exceptions.errors import BotorchError, BotorchTensorDimensionError from botorch.utils.multi_objective.box_decomposition import NondominatedPartitioning from botorch.utils.testing import BotorchTestCase
40.414474
86
0.453524
183903f43cbf11f71276277d26afb62e4bb54ab6
34,139
py
Python
tests/pyupgrade_test.py
sloria/pyupgrade
18c625150c7118d05e6f15facf77a0423b764230
[ "MIT" ]
null
null
null
tests/pyupgrade_test.py
sloria/pyupgrade
18c625150c7118d05e6f15facf77a0423b764230
[ "MIT" ]
null
null
null
tests/pyupgrade_test.py
sloria/pyupgrade
18c625150c7118d05e6f15facf77a0423b764230
[ "MIT" ]
null
null
null
# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import unicode_literals import ast import sys import pytest from pyupgrade import _fix_dict_set from pyupgrade import _fix_escape_sequences from pyupgrade import _fix_format_literals from pyupgrade import _fix_fstrings from pyupgrade import _fix_new_style_classes from pyupgrade import _fix_percent_format from pyupgrade import _fix_six from pyupgrade import _fix_super from pyupgrade import _fix_tokens from pyupgrade import _fix_unicode_literals from pyupgrade import _imports_unicode_literals from pyupgrade import _is_bytestring from pyupgrade import _percent_to_format from pyupgrade import _simplify_conversion_flag from pyupgrade import main from pyupgrade import parse_format from pyupgrade import parse_percent_format from pyupgrade import unparse_parsed_string def _has_16806_bug(): # See https://bugs.python.org/issue16806 return ast.parse('"""\n"""').body[0].value.col_offset == -1 def test_main_trivial(): assert main(()) == 0
27.982787
79
0.42365
1839ffd1101b5584269c5f29639d17cc7d6a6e7c
194
py
Python
Preprocessing/preprocessing.py
nadineazhalia/CSH4H3-TEXT-MINING
77b2ffb862314d664f575757a40038cc69f86c60
[ "Apache-2.0" ]
null
null
null
Preprocessing/preprocessing.py
nadineazhalia/CSH4H3-TEXT-MINING
77b2ffb862314d664f575757a40038cc69f86c60
[ "Apache-2.0" ]
null
null
null
Preprocessing/preprocessing.py
nadineazhalia/CSH4H3-TEXT-MINING
77b2ffb862314d664f575757a40038cc69f86c60
[ "Apache-2.0" ]
null
null
null
file_berita = open("berita.txt", "r") berita = file_berita.read() berita = berita.split() berita = [x.lower() for x in berita] berita = list(set(berita)) berita = sorted(berita) print (berita)
21.555556
37
0.695876
183a36737605defc576589d45932fdf08d365a08
2,139
py
Python
demo_scripts/charts/bar_chart_index_translator_demo.py
webclinic017/qf-lib
96463876719bba8a76c8269cef76addf3a2d836d
[ "Apache-2.0" ]
198
2019-08-16T15:09:23.000Z
2022-03-30T12:44:00.000Z
demo_scripts/charts/bar_chart_index_translator_demo.py
webclinic017/qf-lib
96463876719bba8a76c8269cef76addf3a2d836d
[ "Apache-2.0" ]
13
2021-01-07T10:15:19.000Z
2022-03-29T13:01:47.000Z
demo_scripts/charts/bar_chart_index_translator_demo.py
webclinic017/qf-lib
96463876719bba8a76c8269cef76addf3a2d836d
[ "Apache-2.0" ]
29
2019-08-16T15:21:28.000Z
2022-02-23T09:53:49.000Z
# Copyright 2016-present CERN European Organization for Nuclear Research # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import matplotlib.pyplot as plt import pandas as pd from qf_lib.common.enums.orientation import Orientation from qf_lib.plotting.charts.bar_chart import BarChart from qf_lib.plotting.decorators.data_element_decorator import DataElementDecorator from qf_lib.plotting.helpers.index_translator import IndexTranslator index = ['constant', 'b', 'c', 'd'] # index = [0, 4, 5, 6] labels_to_locations_dict = { 'constant': 0, 'b': 4, 'c': 5, 'd': 6 } colors = ['orange'] + ['forestgreen'] * 3 if __name__ == '__main__': main()
38.196429
120
0.71482
183c49112552415248f084e0c358b6ea11192708
2,771
py
Python
tests/request/test_parameter_invalid.py
Colin-b/pyxelrest
5c8db40d1537d0f9c29acd928ec9519b6bb557ec
[ "MIT" ]
7
2018-12-07T10:08:53.000Z
2021-03-24T07:52:36.000Z
tests/request/test_parameter_invalid.py
Colin-b/pyxelrest
5c8db40d1537d0f9c29acd928ec9519b6bb557ec
[ "MIT" ]
76
2018-12-07T10:29:48.000Z
2021-11-17T00:54:24.000Z
tests/request/test_parameter_invalid.py
Colin-b/pyxelrest
5c8db40d1537d0f9c29acd928ec9519b6bb557ec
[ "MIT" ]
null
null
null
import pytest from responses import RequestsMock from tests import loader
30.119565
123
0.339589
183cd22d8adcd570cdd6c5eceb4ba00ee9152282
61
py
Python
src/yookassa_payout/domain/response/__init__.py
yoomoney/yookassa-payout-sdk-python
f6953e97573bb4a4ee6f830f726a6fcfdf504e2a
[ "MIT" ]
5
2021-03-11T14:38:25.000Z
2021-08-13T10:41:50.000Z
src/yookassa_payout/domain/common/__init__.py
yoomoney/yookassa-payout-sdk-python
f6953e97573bb4a4ee6f830f726a6fcfdf504e2a
[ "MIT" ]
2
2021-02-15T18:18:34.000Z
2021-08-13T13:49:46.000Z
src/yookassa_payout/domain/request/__init__.py
yoomoney/yookassa-payout-sdk-python
f6953e97573bb4a4ee6f830f726a6fcfdf504e2a
[ "MIT" ]
1
2022-01-29T08:47:02.000Z
2022-01-29T08:47:02.000Z
"""Package for YooKassa Payout API Python Client Library."""
30.5
60
0.754098
183d4dac8cfc4c8ac345fb08043e4248c6a0257b
467
py
Python
tests/integration/test_entry_point.py
jacksmith15/delfino
38972e0e0e610c2694462306250a51537a04b1e9
[ "MIT" ]
null
null
null
tests/integration/test_entry_point.py
jacksmith15/delfino
38972e0e0e610c2694462306250a51537a04b1e9
[ "MIT" ]
null
null
null
tests/integration/test_entry_point.py
jacksmith15/delfino
38972e0e0e610c2694462306250a51537a04b1e9
[ "MIT" ]
null
null
null
import toml from delfino.constants import ENTRY_POINT, PYPROJECT_TOML_FILENAME from delfino.models.pyproject_toml import PyprojectToml from tests.constants import PROJECT_ROOT
33.357143
66
0.807281
183ecccecd1a87d9ecdaf239b0b8acab5f9e8ed2
6,888
py
Python
gamble/gamble.py
lookma/simple-coin-gamble
8f1684e62b62f28a176458606ed193c812d97bc7
[ "MIT" ]
null
null
null
gamble/gamble.py
lookma/simple-coin-gamble
8f1684e62b62f28a176458606ed193c812d97bc7
[ "MIT" ]
null
null
null
gamble/gamble.py
lookma/simple-coin-gamble
8f1684e62b62f28a176458606ed193c812d97bc7
[ "MIT" ]
null
null
null
from random import randint from typing import Callable, List, Optional class RoundResults: def __init__(self, players: List[Player]) -> None: self.__total_amounts: List[float] = [] self.__number_of_winners: List[int] = [] self.__number_of_losers: List[int] = [] self.__number_of_total_losses: List[int] = [] self.__winner_percentages: List[float] = [] self.__min_amounts: List[float] = [] self.__max_amounts: List[float] = [] self.__avg_amounts: List[float] = [] self.add_round(players)
30.477876
99
0.620499
18412368254bcf43c33a2c706aa24bebe16b5a08
16
py
Python
roomai/games/__init__.py
tonyxxq/RoomAI
5f28e31e659dd7808127c3c3cc386e6892a93982
[ "MIT" ]
1
2018-11-29T01:57:18.000Z
2018-11-29T01:57:18.000Z
roomai/models/texasholdem/__init__.py
tonyxxq/RoomAI
5f28e31e659dd7808127c3c3cc386e6892a93982
[ "MIT" ]
null
null
null
roomai/models/texasholdem/__init__.py
tonyxxq/RoomAI
5f28e31e659dd7808127c3c3cc386e6892a93982
[ "MIT" ]
null
null
null
#!/bin/python
4
13
0.5625
1842a50616fbef1cfe0cb3f52da633c9ff6caecd
1,285
py
Python
config.py
SevenMoGod/movenet.pytorch
95ec8535245228aa4335243e68722810e50bcaf8
[ "MIT" ]
87
2021-11-13T11:05:55.000Z
2022-03-30T11:00:45.000Z
config.py
Dyian-snow/movenet.pytorch
95ec8535245228aa4335243e68722810e50bcaf8
[ "MIT" ]
18
2021-11-16T01:13:19.000Z
2022-03-31T16:04:31.000Z
config.py
Dyian-snow/movenet.pytorch
95ec8535245228aa4335243e68722810e50bcaf8
[ "MIT" ]
28
2021-11-13T11:22:05.000Z
2022-03-29T10:02:09.000Z
""" @Fire https://github.com/fire717 """ cfg = { ##### Global Setting 'GPU_ID': '0', "num_workers":8, "random_seed":42, "cfg_verbose":True, "save_dir": "output/", "num_classes": 17, "width_mult":1.0, "img_size": 192, ##### Train Setting 'img_path':"./data/croped/imgs", 'train_label_path':'./data/croped/train2017.json', 'val_label_path':'./data/croped/val2017.json', 'balance_data':False, 'log_interval':10, 'save_best_only': True, 'pin_memory': True, ##### Train Hyperparameters 'learning_rate':0.001,#1.25e-4 'batch_size':64, 'epochs':120, 'optimizer':'Adam', #Adam SGD 'scheduler':'MultiStepLR-70,100-0.1', #default SGDR-5-2 CVPR step-4-0.8 MultiStepLR 'weight_decay' : 5.e-4,#0.0001, 'class_weight': None,#[1., 1., 1., 1., 1., 1., 1., ] 'clip_gradient': 5,#1, ##### Test 'test_img_path':"./data/croped/imgs", #"../data/eval/imgs", #"../data/eval/imgs", #"../data/all/imgs" #"../data/true/mypc/crop_upper1" #../data/coco/small_dataset/imgs #"../data/testimg" 'exam_label_path':'../data/all/data_all_new.json', 'eval_img_path':'../data/eval/imgs', 'eval_label_path':'../data/eval/mypc.json', }
21.416667
91
0.568872
18433079856714742d377305353f6075edaf8a57
11,038
py
Python
uart.py
WRansohoff/nmigen_uart_test
d520d3b72698a901f63e3485aadca620f1444350
[ "MIT" ]
null
null
null
uart.py
WRansohoff/nmigen_uart_test
d520d3b72698a901f63e3485aadca620f1444350
[ "MIT" ]
null
null
null
uart.py
WRansohoff/nmigen_uart_test
d520d3b72698a901f63e3485aadca620f1444350
[ "MIT" ]
null
null
null
from nmigen import * from nmigen.back.pysim import * # Function to calculate a clock divider which creates the # desired output frequency from a given input frequency. # Verifies that the divider is a positive integer, and that # the resulting signal doesn't deviate more than expected. # Basic work-in-progress UART modules. # - TX / RX only, no flow control or USART. # - Samples during the middle of the clock period, no oversampling. # - 8-bit words, 1 stop bit, no parity bit. # - Receives bits LSB-first only. # - Configurable baud rate. # UART receiver. # UART transmitter # Combined UART interface with both TX and RX modules. # # Simple UART testbench. # # Helper UART test method to simulate receiving a byte. def uart_rx_byte( uart, val ): # Simulate a "start bit". yield uart.rx.eq( 0 ) # Wait one cycle. for i in range( uart.clk_div ): yield Tick() # Simulate the byte with one cycle between each bit. for i in range( 8 ): if val & ( 1 << i ): yield uart.rx.eq( 1 ) else: yield uart.rx.eq( 0 ) for j in range( uart.clk_div ): yield Tick() # Simulate the "stop bit", and wait one cycle. yield uart.rx.eq( 1 ) for i in range( uart.clk_div ): yield Tick() # Helper UART test method to simulate transmitting a buffered byte. # UART 'receive' testbench. # UART 'transmit' testbench. # Create a UART module and run tests on it. # (The baud rate is set to a high value to speed up the simulation.) if __name__ == "__main__": #uart_rx = UART_RX( 24000000, 9600 ) #uart_tx = UART_TX( 24000000, 9600 ) uart_rx = UART_RX( 24000000, 1000000 ) uart_tx = UART_TX( 24000000, 1000000 ) uart = UART( uart_rx, uart_tx ) # Run the UART tests. with Simulator( uart, vcd_file = open( 'test.vcd', 'w' ) ) as sim: # Run the UART test with a 24MHz clock. sim.add_clock( 24e-6 ) sim.add_sync_process( proc_rx ) sim.add_sync_process( proc_tx ) sim.run()
34.820189
74
0.603642
184359b6c6261d67915a09440ec8b6d1a0cc0927
5,853
py
Python
edk2basetools/FMMT/core/GuidTools.py
YuweiChen1110/edk2-basetools
cfd05c928492b7ffd1329634cfcb089db995eeca
[ "BSD-2-Clause-Patent" ]
null
null
null
edk2basetools/FMMT/core/GuidTools.py
YuweiChen1110/edk2-basetools
cfd05c928492b7ffd1329634cfcb089db995eeca
[ "BSD-2-Clause-Patent" ]
null
null
null
edk2basetools/FMMT/core/GuidTools.py
YuweiChen1110/edk2-basetools
cfd05c928492b7ffd1329634cfcb089db995eeca
[ "BSD-2-Clause-Patent" ]
null
null
null
## @file # This file is used to define the FMMT dependent external tool management class. # # Copyright (c) 2021-, Intel Corporation. All rights reserved.<BR> # SPDX-License-Identifier: BSD-2-Clause-Patent ## import glob import logging import os import shutil import sys import tempfile import uuid from edk2basetools.FMMT.PI.Common import * from edk2basetools.FMMT.utils.FmmtLogger import FmmtLogger as logger import subprocess guidtools = GUIDTools()
38.254902
160
0.571843
18444ea5a0cd3e04e2706a71502de539bb9fa0dc
1,709
py
Python
python/tests/test_tree_intersection.py
Yonatan1P/data-structures-and-algorithms
ddd647d52a3182ca01032bfdb72f94ea22a0e76b
[ "MIT" ]
1
2020-12-16T22:38:12.000Z
2020-12-16T22:38:12.000Z
python/tests/test_tree_intersection.py
Yonatan1P/data-structures-and-algorithms
ddd647d52a3182ca01032bfdb72f94ea22a0e76b
[ "MIT" ]
1
2020-11-14T05:37:48.000Z
2020-11-14T05:37:48.000Z
python/tests/test_tree_intersection.py
Yonatan1P/data-structures-and-algorithms
ddd647d52a3182ca01032bfdb72f94ea22a0e76b
[ "MIT" ]
null
null
null
from challenges.tree_intersection.tree_intersection import find_intersection from challenges.tree.tree import BinarySearchTree
21.632911
76
0.627853
1847f0a48843e1e83cb2f45be72c476d66e2ca39
562
py
Python
setup.py
rif/imgdup
fe59c6b4b8c06699d48f887bc7a90acea48aa8f2
[ "MIT" ]
14
2016-02-10T04:53:42.000Z
2021-08-08T17:39:55.000Z
setup.py
rif/imgdup
fe59c6b4b8c06699d48f887bc7a90acea48aa8f2
[ "MIT" ]
null
null
null
setup.py
rif/imgdup
fe59c6b4b8c06699d48f887bc7a90acea48aa8f2
[ "MIT" ]
2
2017-11-01T14:02:46.000Z
2019-02-20T10:55:52.000Z
from setuptools import setup, find_packages setup( name = "imgdup", version = "1.3", packages = find_packages(), scripts = ['imgdup.py'], install_requires = ['pillow>=2.8.1'], # metadata for upload to PyPI author = "Radu Ioan Fericean", author_email = "radu@fericean.ro", description = "Visual similarity image finder and cleaner (image deduplication tool)", license = "MIT", keywords = "deduplication duplicate images image visual finder", url = "https://github.com/rif/imgdup", # project home page, if any )
31.222222
90
0.663701
184a025720245d69fec4505befed933cb56ea1a7
178
py
Python
exercicio13.py
LuizHps18/infosatc-lp-avaliativo-01
0b891d74a98705182175a53e023b6cbbe8cc880a
[ "MIT" ]
null
null
null
exercicio13.py
LuizHps18/infosatc-lp-avaliativo-01
0b891d74a98705182175a53e023b6cbbe8cc880a
[ "MIT" ]
null
null
null
exercicio13.py
LuizHps18/infosatc-lp-avaliativo-01
0b891d74a98705182175a53e023b6cbbe8cc880a
[ "MIT" ]
null
null
null
k = float(input("Digite uma distncia em quilometros: ")) m = k / 1.61 print("A distncia digitada de {} quilometros, essa distncia convertida {:.2f} milhas" .format(k,m))
35.6
105
0.696629
184a7377a4969ebcc47ccb33cd2b9fb82e77a11d
660
py
Python
rcs/wiki/urls.py
ShuffleBox/django-rcsfield
dd8b5b22635bcdae9825e00b65887bb51171e76f
[ "BSD-3-Clause" ]
null
null
null
rcs/wiki/urls.py
ShuffleBox/django-rcsfield
dd8b5b22635bcdae9825e00b65887bb51171e76f
[ "BSD-3-Clause" ]
null
null
null
rcs/wiki/urls.py
ShuffleBox/django-rcsfield
dd8b5b22635bcdae9825e00b65887bb51171e76f
[ "BSD-3-Clause" ]
null
null
null
from django.conf.urls.defaults import * urlpatterns = patterns('rcs.wiki.views', url(r'^((?:[A-Z]+[a-z]+){2,})/$', 'page', {}, name="wiki_page"), url(r'^((?:[A-Z]+[a-z]+){2,})/edit/$', 'edit', {}, name="wiki_edit"), url(r'^((?:[A-Z]+[a-z]+){2,})/attachments/$', 'attachments', {}, name="wiki_attachments"), url(r'^((?:[A-Z]+[a-z]+){2,})/rev/([a-f0-9]+)/$', 'revision', {}, name="wiki_revision"), url(r'^((?:[A-Z]+[a-z]+){2,})/diff/([\w]+)/([\w]+)/$', 'diff', {}, name="wiki_diff"), url(r'^list/$', 'list', {}, name="wiki_list"), url(r'^recent/$', 'recent', {}, name="wiki_recent"), url(r'^$', 'index', {}, name="wiki_index"), )
55
94
0.487879
184a8a8a53eaf08a2a13054389bb04e1b3d15e28
3,359
py
Python
sample 1/main.py
RezaFirouzii/multi-choice_correction_opencv
31c777d6714216e0811947a1ceadc893c2c1d7c0
[ "MIT" ]
1
2022-03-04T15:55:20.000Z
2022-03-04T15:55:20.000Z
sample 1/main.py
RezaFirouzii/multi-choice_correction_opencv
31c777d6714216e0811947a1ceadc893c2c1d7c0
[ "MIT" ]
null
null
null
sample 1/main.py
RezaFirouzii/multi-choice_correction_opencv
31c777d6714216e0811947a1ceadc893c2c1d7c0
[ "MIT" ]
null
null
null
import cv2 as cv import numpy as np import pandas as pd import heapq if __name__ == "__main__": img = cv.imread('sample1_2.jpg') cop = img.copy() img = cv.cvtColor(img, cv.COLOR_BGR2GRAY) img = cv.adaptiveThreshold(img, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY_INV, 15, 10) kernel = cv.getStructuringElement(cv.MORPH_RECT, (4, 1)) img = cv.morphologyEx(img, cv.MORPH_CLOSE, kernel) contours, hierarchy = cv.findContours(img, cv.RETR_LIST, cv.CHAIN_APPROX_NONE) contours = list(filter(lambda x: 300 < cv.contourArea(x) < 450, contours)) contours = sort_contours(contours) answers = [] for i, contour in enumerate(contours): x, y, w, h = cv.boundingRect(contour) roi = cv.cvtColor(cop[y: y+h, x: x + w], cv.COLOR_BGR2GRAY) roi_cop = roi.copy() roi = cv.adaptiveThreshold(roi, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY_INV, 15, 10) kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (1, 3)) roi = cv.morphologyEx(roi, cv.MORPH_CLOSE, kernel) cnts, hierarchy = cv.findContours(roi, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE) cnts = list(filter(cv.contourArea, cnts)) cnts = sort_contours_horizontally(cnts) tests = list(map(cv.boundingRect, cnts)) coord = [(x, y)] for j, test in enumerate(tests): # each test is a contour coord.append(test) x, y, w, h = test area = w * h filled_area = np.count_nonzero(roi[y: y+h, x: x+w]) tests[j] = filled_area / area if is_valid_test(tests): choice = tests.index(max(tests)) + 1 answers.append(choice) X, Y = coord[0] x, y, w, h = coord[choice] pt1 = (X + x, Y + y) pt2 = (X + x + w, Y + y + h) cv.rectangle(cop, pt1, pt2, (0, 255, 0), 2) else: answers.append(-1) for i in range(len(answers)): print(i + 1, ":", answers[i]) data = { "Q": [i for i in range(1, len(answers) + 1)], "A": answers } data = pd.DataFrame(data) data.to_excel('./sample1.xlsx', 'Answer Sheet 1') cv.imwrite('output.jpg', cop) cv.imshow('Detected Choices', cop) cv.waitKey()
28.709402
101
0.567133
184b18ea17717fde23e6a6b62fed9b2b61f17cb3
704
py
Python
a-practical-introduction-to-python-programming-brian-heinold/chapter-08/exercise-07.py
elarabyelaidy19/awesome-reading
5c01a4272ba58e4f7ea665aab14b4c0aa252ea89
[ "MIT" ]
31
2021-11-02T19:51:13.000Z
2022-02-17T10:55:26.000Z
a-practical-introduction-to-python-programming-brian-heinold/chapter-08/exercise-07.py
MosTafaHoSamm/awesome-reading
469408fefc049d78ed53a2b2331b5d5cecdc6c06
[ "MIT" ]
1
2022-01-18T12:27:54.000Z
2022-01-18T12:27:54.000Z
a-practical-introduction-to-python-programming-brian-heinold/chapter-08/exercise-07.py
MosTafaHoSamm/awesome-reading
469408fefc049d78ed53a2b2331b5d5cecdc6c06
[ "MIT" ]
3
2022-01-11T05:01:34.000Z
2022-02-05T14:36:29.000Z
# 7. Write a program that estimates the average number of drawings it takes before the users # numbers are picked in a lottery that consists of correctly picking six different numbers that # are between 1 and 10. To do this, run a loop 1000 times that randomly generates a set of # user numbers and simulates drawings until the users numbers are drawn. Find the average # number of drawings needed over the 1000 times the loop runs. import random lottery_numbers = [i for i in range(1, 11)] avg = 0 for i in range(1000): user = random.randint(1, 10) lott = random.choice(lottery_numbers) if lott == user: avg = avg + 1 print('Average number of drawings:', round(1000 / avg, 4))
37.052632
95
0.728693
184bf76e800fcea4dae223c4ac96db64613fb1ae
709
py
Python
humfrey/update/utils.py
ox-it/humfrey
c92e46a24a9bf28aa9638a612f166d209315e76b
[ "BSD-3-Clause" ]
6
2015-01-09T15:53:07.000Z
2020-02-13T14:00:53.000Z
humfrey/update/utils.py
ox-it/humfrey
c92e46a24a9bf28aa9638a612f166d209315e76b
[ "BSD-3-Clause" ]
null
null
null
humfrey/update/utils.py
ox-it/humfrey
c92e46a24a9bf28aa9638a612f166d209315e76b
[ "BSD-3-Clause" ]
1
2017-05-12T20:46:15.000Z
2017-05-12T20:46:15.000Z
from django.conf import settings from django.utils.importlib import import_module from humfrey.update.transform.base import Transform
29.541667
67
0.723554
184dce967a4de0cb71723aecd6ec63f6783befa6
2,448
py
Python
flask/model/device_model.py
Dev-Jahn/cms
84ea115bdb865daff83d069502f6f0dd105fc4f0
[ "RSA-MD" ]
null
null
null
flask/model/device_model.py
Dev-Jahn/cms
84ea115bdb865daff83d069502f6f0dd105fc4f0
[ "RSA-MD" ]
9
2021-01-05T07:48:28.000Z
2021-05-14T06:38:27.000Z
flask/model/device_model.py
Dev-Jahn/cms
84ea115bdb865daff83d069502f6f0dd105fc4f0
[ "RSA-MD" ]
4
2021-01-05T06:46:09.000Z
2021-05-06T01:44:28.000Z
from .db_base import db, env
48
112
0.624183
184e8888d3aeff144a6fa7390d4e574c4fcd9c17
18,542
py
Python
pytests/tuqquery/tuq_tokens.py
ramalingam-cb/testrunner
81cea7a5a493cf0c67fca7f97c667cd3c6ad2142
[ "Apache-2.0" ]
null
null
null
pytests/tuqquery/tuq_tokens.py
ramalingam-cb/testrunner
81cea7a5a493cf0c67fca7f97c667cd3c6ad2142
[ "Apache-2.0" ]
null
null
null
pytests/tuqquery/tuq_tokens.py
ramalingam-cb/testrunner
81cea7a5a493cf0c67fca7f97c667cd3c6ad2142
[ "Apache-2.0" ]
null
null
null
from lib.remote.remote_util import RemoteMachineShellConnection from pytests.tuqquery.tuq import QueryTests
65.059649
367
0.642595
184fa55d99eb6ba4a36992ee508941f13328275f
1,074
py
Python
src/python/autotransform/input/empty.py
nathro/AutoTransform
04ef5458bc8401121e33370ceda6ef638e535e9a
[ "MIT" ]
11
2022-01-02T00:50:24.000Z
2022-02-22T00:30:09.000Z
src/python/autotransform/input/empty.py
nathro/AutoTransform
04ef5458bc8401121e33370ceda6ef638e535e9a
[ "MIT" ]
6
2022-01-06T01:45:34.000Z
2022-02-03T21:49:52.000Z
src/python/autotransform/input/empty.py
nathro/AutoTransform
04ef5458bc8401121e33370ceda6ef638e535e9a
[ "MIT" ]
null
null
null
# AutoTransform # Large scale, component based code modification library # # Licensed under the MIT License <http://opensource.org/licenses/MIT> # SPDX-License-Identifier: MIT # Copyright (c) 2022-present Nathan Rockenbach <http://github.com/nathro> # @black_format """The implementation for the DirectoryInput.""" from __future__ import annotations from typing import ClassVar, Sequence from autotransform.input.base import Input, InputName from autotransform.item.base import Item
28.263158
83
0.712291
1851692534eb7b89ed5ce5f0fcea30358bb3c381
27,790
py
Python
snowplow_tracker/tracker.py
jackwilliamson/snowplow-python-tracker
b4ee5192bde044f406182bef848b51bd21646f12
[ "Apache-2.0" ]
null
null
null
snowplow_tracker/tracker.py
jackwilliamson/snowplow-python-tracker
b4ee5192bde044f406182bef848b51bd21646f12
[ "Apache-2.0" ]
1
2019-01-08T17:09:11.000Z
2019-01-08T17:09:11.000Z
snowplow_tracker/tracker.py
jackwilliamson/snowplow-python-tracker
b4ee5192bde044f406182bef848b51bd21646f12
[ "Apache-2.0" ]
1
2017-05-30T20:49:24.000Z
2017-05-30T20:49:24.000Z
""" tracker.py Copyright (c) 2013-2014 Snowplow Analytics Ltd. All rights reserved. This program is licensed to you under the Apache License Version 2.0, and you may not use this file except in compliance with the Apache License Version 2.0. You may obtain a copy of the Apache License Version 2.0 at http://www.apache.org/licenses/LICENSE-2.0. Unless required by applicable law or agreed to in writing, software distributed under the Apache License Version 2.0 is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the Apache License Version 2.0 for the specific language governing permissions and limitations there under. Authors: Anuj More, Alex Dean, Fred Blundun Copyright: Copyright (c) 2013-2014 Snowplow Analytics Ltd License: Apache License Version 2.0 """ import time import uuid import six from contracts import contract, new_contract from snowplow_tracker import payload, _version, SelfDescribingJson from snowplow_tracker import subject as _subject from snowplow_tracker.timestamp import Timestamp, TrueTimestamp, DeviceTimestamp """ Constants & config """ VERSION = "py-%s" % _version.__version__ DEFAULT_ENCODE_BASE64 = True BASE_SCHEMA_PATH = "iglu:com.snowplowanalytics.snowplow" SCHEMA_TAG = "jsonschema" CONTEXT_SCHEMA = "%s/contexts/%s/1-0-1" % (BASE_SCHEMA_PATH, SCHEMA_TAG) UNSTRUCT_EVENT_SCHEMA = "%s/unstruct_event/%s/1-0-0" % (BASE_SCHEMA_PATH, SCHEMA_TAG) FORM_NODE_NAMES = ("INPUT", "TEXTAREA", "SELECT") FORM_TYPES = ( "button", "checkbox", "color", "date", "datetime", "datetime-local", "email", "file", "hidden", "image", "month", "number", "password", "radio", "range", "reset", "search", "submit", "tel", "text", "time", "url", "week" ) """ Tracker class """
40.688141
147
0.556747
185308de027ac2681bc3f8d490477023a29fcb44
6,597
py
Python
src/oic/oauth2/util.py
alanbuxey/pyoidc
5f2d9ac468aaad599260f70481062c9d31273da2
[ "Apache-2.0" ]
290
2015-01-02T20:14:53.000Z
2022-01-24T11:39:10.000Z
src/oic/oauth2/util.py
peppelinux/pyoidc
2e751ed84039259a2b138148eae204c877518950
[ "Apache-2.0" ]
103
2015-02-03T13:20:59.000Z
2017-09-19T20:01:08.000Z
src/oic/oauth2/util.py
peppelinux/pyoidc
2e751ed84039259a2b138148eae204c877518950
[ "Apache-2.0" ]
128
2015-01-02T20:14:19.000Z
2021-11-07T14:28:03.000Z
import logging from http import cookiejar as http_cookiejar from http.cookiejar import http2time # type: ignore from typing import Any # noqa from typing import Dict # noqa from urllib.parse import parse_qs from urllib.parse import urlsplit from urllib.parse import urlunsplit from oic.exception import UnSupported from oic.oauth2.exception import TimeFormatError from oic.utils.sanitize import sanitize logger = logging.getLogger(__name__) __author__ = "roland" URL_ENCODED = "application/x-www-form-urlencoded" JSON_ENCODED = "application/json" DEFAULT_POST_CONTENT_TYPE = URL_ENCODED PAIRS = { "port": "port_specified", "domain": "domain_specified", "path": "path_specified", } ATTRS = { "version": None, "name": "", "value": None, "port": None, "port_specified": False, "domain": "", "domain_specified": False, "domain_initial_dot": False, "path": "", "path_specified": False, "secure": False, "expires": None, "discard": True, "comment": None, "comment_url": None, "rest": "", "rfc2109": True, } # type: Dict[str, Any] def get_or_post( uri, method, req, content_type=DEFAULT_POST_CONTENT_TYPE, accept=None, **kwargs ): """ Construct HTTP request. :param uri: :param method: :param req: :param content_type: :param accept: :param kwargs: :return: """ if method in ["GET", "DELETE"]: if req.keys(): _req = req.copy() comp = urlsplit(str(uri)) if comp.query: _req.update(parse_qs(comp.query)) _query = str(_req.to_urlencoded()) path = urlunsplit( (comp.scheme, comp.netloc, comp.path, _query, comp.fragment) ) else: path = uri body = None elif method in ["POST", "PUT"]: path = uri if content_type == URL_ENCODED: body = req.to_urlencoded() elif content_type == JSON_ENCODED: body = req.to_json() else: raise UnSupported("Unsupported content type: '%s'" % content_type) header_ext = {"Content-Type": content_type} if accept: header_ext = {"Accept": accept} if "headers" in kwargs.keys(): kwargs["headers"].update(header_ext) else: kwargs["headers"] = header_ext else: raise UnSupported("Unsupported HTTP method: '%s'" % method) return path, body, kwargs def set_cookie(cookiejar, kaka): """ Place a cookie (a http_cookielib.Cookie based on a set-cookie header line) in the cookie jar. Always chose the shortest expires time. :param cookiejar: :param kaka: Cookie """ # default rfc2109=False # max-age, httponly for cookie_name, morsel in kaka.items(): std_attr = ATTRS.copy() std_attr["name"] = cookie_name _tmp = morsel.coded_value if _tmp.startswith('"') and _tmp.endswith('"'): std_attr["value"] = _tmp[1:-1] else: std_attr["value"] = _tmp std_attr["version"] = 0 attr = "" # copy attributes that have values try: for attr in morsel.keys(): if attr in ATTRS: if morsel[attr]: if attr == "expires": std_attr[attr] = http2time(morsel[attr]) else: std_attr[attr] = morsel[attr] elif attr == "max-age": if morsel[attr]: std_attr["expires"] = http2time(morsel[attr]) except TimeFormatError: # Ignore cookie logger.info( "Time format error on %s parameter in received cookie" % (sanitize(attr),) ) continue for att, spec in PAIRS.items(): if std_attr[att]: std_attr[spec] = True if std_attr["domain"] and std_attr["domain"].startswith("."): std_attr["domain_initial_dot"] = True if morsel["max-age"] == 0: try: cookiejar.clear( domain=std_attr["domain"], path=std_attr["path"], name=std_attr["name"], ) except ValueError: pass else: # Fix for Microsoft cookie error if "version" in std_attr: try: std_attr["version"] = std_attr["version"].split(",")[0] except (TypeError, AttributeError): pass new_cookie = http_cookiejar.Cookie(**std_attr) # type: ignore cookiejar.set_cookie(new_cookie)
30.123288
97
0.555404
1853550d01976a79c3f2f5631cb3c4c7ae9f5fcf
5,890
py
Python
main.py
aditya02acharya/TypingAgent
34c5230be72c3878942457a6e44b7078fbd08ea0
[ "MIT" ]
5
2020-09-07T16:40:34.000Z
2022-01-18T15:50:57.000Z
main.py
aditya02acharya/TypingAgent
34c5230be72c3878942457a6e44b7078fbd08ea0
[ "MIT" ]
1
2020-10-06T13:14:46.000Z
2020-10-06T13:14:46.000Z
main.py
aditya02acharya/TypingAgent
34c5230be72c3878942457a6e44b7078fbd08ea0
[ "MIT" ]
null
null
null
import sys import yaml import numpy import random import logging import argparse from os import path, makedirs from datetime import datetime from src.finger_proxy.proxy_agent import ProxyAgent from src.utilities.logging_config_manager import setup_logging from src.display.touchscreendevice import TouchScreenDevice from src.vision.vision_agent import VisionAgent from src.finger.finger_agent import FingerAgent from src.proofread.proofread_agent import ProofreadAgent from src.supervisor.supervisor_agent import SupervisorAgent parser = argparse.ArgumentParser() # General parameters parser.add_argument("--all", action="store_true", default=False, help="train/test all the agents [vision, finger, proofread, supervisor]") parser.add_argument("--vision", action="store_true", default=False, help="train/test only the vision agent") parser.add_argument("--finger", action="store_true", default=False, help="train/test only the finger agent") parser.add_argument("--proofread", action="store_true", default=False, help="train/test only the proofread agent") parser.add_argument("--supervisor", action="store_true", default=False, help="train/test only the supervisor agent") parser.add_argument("--train", action="store_true", default=False, help="run model in train mode") parser.add_argument("--config", required=True, help="name of the configuration file (REQUIRED)") parser.add_argument("--seed", type=int, default=datetime.now().microsecond, help="random seed default: current time") parser.add_argument("--type", default=">", help="sentence to type for the agent.") parser.add_argument("--batch", action="store_true", default=False, help="evaluate a batch of sentences.") parser.add_argument("--users", type=int, default=1, help="number of users to simulate") parser.add_argument("--twofinger", action="store_true", default=False, help="enable typing with two finger.") parser.add_argument("--verbose", action="store_true", default=False, help="print tqdm step in new line.") # get user command line arguments. args = parser.parse_args() # Initialise random seed. numpy.random.seed(args.seed) random.seed(args.seed) # Setup Logger. if not path.isdir("logs"): # if logs folder doesn't exist create one. makedirs("logs") setup_logging(default_path=path.join("configs", "logging.yml")) logger = logging.getLogger(__name__) logger.info("logger is set.") # load app config. if path.exists(path.join("configs", args.config)): with open(path.join("configs", args.config), 'r') as file: config_file = yaml.load(file, Loader=yaml.FullLoader) logger.info("App Configurations loaded.") else: logger.error("File doesn't exist: Failed to load %s file under configs folder." % str(args.config)) sys.exit(0) if args.train: if path.exists(path.join("configs", config_file['training_config'])): with open(path.join("configs", config_file['training_config']), 'r') as file: train_config = yaml.load(file, Loader=yaml.FullLoader) logger.info("Training Configurations loaded.") else: logger.error("File doesn't exist: Failed to load %s file under configs folder." % config_file['training_config']) sys.exit(0) if args.vision or args.all: logger.info("Initiating Vision Agent Training.") vision_agent = VisionAgent(config_file['device_config'], train_config['vision'], args.verbose) vision_agent.train(vision_agent.episodes) if args.finger or args.all: logger.info("Initiating Finger Agent Training.") finger_agent = FingerAgent(config_file['device_config'], train_config['finger'], 0, True, args.verbose) finger_agent.train(finger_agent.episodes) if args.proofread or args.all: logger.info("Initiating Proofread Agent Training.") proofread_agent = ProofreadAgent(config_file['device_config'], train_config['proofread'], args.verbose) proofread_agent.train(proofread_agent.episodes) if args.supervisor or args.all: logger.info("Initiating Supervisor Agent Training.") if args.twofinger: supervisor_agent = SupervisorAgent(config_file['device_config'], train_config, True, True, args.verbose) else: supervisor_agent = SupervisorAgent(config_file['device_config'], train_config, True, False, args.verbose) print(type(supervisor_agent.episodes)) supervisor_agent.train(supervisor_agent.episodes) else: if path.exists(path.join("configs", config_file['testing_config'])): with open(path.join("configs", config_file['testing_config']), 'r') as file: test_config = yaml.load(file, Loader=yaml.FullLoader) logger.info("Training Configurations loaded.") else: logger.error("File doesn't exist: Failed to load %s file under configs folder." % config_file['testing_config']) sys.exit(0) if args.vision or args.all: logger.info("Initiating Vision Agent Evaluation.") vision_agent = VisionAgent(config_file['device_config'], test_config['vision']) vision_agent.evaluate(args.type) if args.finger or args.all: logger.info("Initiating Finger Agent Evaluation.") finger_agent = FingerAgent(config_file['device_config'], test_config['finger'], 0, False) finger_agent.evaluate(args.type, sat_desired=test_config['finger']['typing_accuracy']) if args.supervisor or args.all: logger.info("Initiating Supervisor Agent Evaluation.") if args.twofinger: supervisor_agent = SupervisorAgent(config_file['device_config'], test_config, False, True, args.verbose) else: supervisor_agent = SupervisorAgent(config_file['device_config'], test_config, False, False, args.verbose) supervisor_agent.evaluate(args.type, args.batch, args.users)
47.5
117
0.720204
185491bbcdadc1f460e3cbb3e31ce90f8c3eb65e
1,854
py
Python
examples/chain.py
yeeliu01/pyrfa
536c94f1bcff232415495cbe04b8897ad91e0c76
[ "MIT" ]
33
2016-11-29T08:18:28.000Z
2021-11-11T15:40:19.000Z
examples/chain.py
yeeliu01/pyrfa
536c94f1bcff232415495cbe04b8897ad91e0c76
[ "MIT" ]
41
2016-09-20T10:15:11.000Z
2021-10-20T01:14:22.000Z
examples/chain.py
devcartel/thomsonreuters
536c94f1bcff232415495cbe04b8897ad91e0c76
[ "MIT" ]
9
2016-10-19T00:09:22.000Z
2020-08-03T03:02:15.000Z
#!/usr/bin/python # # Decoding a legacy chain ric # import pyrfa p = pyrfa.Pyrfa() p.createConfigDb("./pyrfa.cfg") p.acquireSession("Session1") p.createOMMConsumer() p.login() p.directoryRequest() p.dictionaryRequest() p.setInteractionType("snapshot") fids = ['LINK_1', 'LINK_2', 'LINK_3', 'LINK_4', 'LINK_5', 'LINK_6', 'LINK_7', 'LINK_8', 'LINK_9', 'LINK_10', 'LINK_11', 'LINK_12', 'LINK_13', 'LINK_14', 'LONGLINK1', 'LONGLINK2', 'LONGLINK3', 'LONGLINK4', 'LONGLINK5', 'LONGLINK6', 'LONGLINK7', 'LONGLINK8', 'LONGLINK9', 'LONGLINK10', 'LONGLINK11', 'LONGLINK12', 'LONGLINK13', 'LONGLINK14', 'BR_LINK1', 'BR_LINK2', 'BR_LINK3', 'BR_LINK4', 'BR_LINK5', 'BR_LINK6', 'BR_LINK7', 'BR_LINK8', 'BR_LINK9', 'BR_LINK10', 'BR_LINK11', 'BR_LINK12', 'BR_LINK13', 'BR_LINK14'] rics = expandChainRIC("0#.FTSE") print(rics)
34.981132
103
0.635922
185637d8cc3eb01cc46a55e5e9f5b84f8e7f9e79
1,746
py
Python
hard-gists/749857/snippet.py
jjhenkel/dockerizeme
eaa4fe5366f6b9adf74399eab01c712cacaeb279
[ "Apache-2.0" ]
21
2019-07-08T08:26:45.000Z
2022-01-24T23:53:25.000Z
hard-gists/749857/snippet.py
jjhenkel/dockerizeme
eaa4fe5366f6b9adf74399eab01c712cacaeb279
[ "Apache-2.0" ]
5
2019-06-15T14:47:47.000Z
2022-02-26T05:02:56.000Z
hard-gists/749857/snippet.py
jjhenkel/dockerizeme
eaa4fe5366f6b9adf74399eab01c712cacaeb279
[ "Apache-2.0" ]
17
2019-05-16T03:50:34.000Z
2021-01-14T14:35:12.000Z
#!/usr/bin/python # -*- coding: utf-8 -*- # launchctl unload /System/Library/LaunchDaemons/com.apple.syslogd.plist # launchctl load /System/Library/LaunchDaemons/com.apple.syslogd.plist from twisted.internet import reactor, stdio, defer from twisted.internet.protocol import Protocol, Factory from twisted.protocols.basic import LineReceiver import time, re, math, json #<22>Nov 1 00:12:04 gleicon-vm1 postfix/smtpd[4880]: connect from localhost[127.0.0.1] severity = ['emerg', 'alert', 'crit', 'err', 'warn', 'notice', 'info', 'debug', ] facility = ['kern', 'user', 'mail', 'daemon', 'auth', 'syslog', 'lpr', 'news', 'uucp', 'cron', 'authpriv', 'ftp', 'ntp', 'audit', 'alert', 'at', 'local0', 'local1', 'local2', 'local3', 'local4', 'local5', 'local6', 'local7',] fs_match = re.compile("<(.+)>(.*)", re.I) def main(): factory = SyslogdFactory() reactor.listenTCP(25000, factory, 10) reactor.run() if __name__ == '__main__': main()
31.178571
87
0.605956
185646f6d47cb9be2bd7e09abafec85a18497f07
11,371
py
Python
research/Issue2/utils.py
johnklee/ff_crawler
53b056bd94ccf55388d12c7f70460d280964f45f
[ "MIT" ]
null
null
null
research/Issue2/utils.py
johnklee/ff_crawler
53b056bd94ccf55388d12c7f70460d280964f45f
[ "MIT" ]
4
2021-04-09T02:05:42.000Z
2021-07-04T07:42:15.000Z
research/Issue2/utils.py
johnklee/ff_crawler
53b056bd94ccf55388d12c7f70460d280964f45f
[ "MIT" ]
null
null
null
import requests as reqlib import os import re import random import time import pickle import abc import hashlib import threading from urllib.parse import urlparse from purifier import TEAgent from purifier.logb import getLogger from enum import IntEnum from typing import Tuple, List, Dict, Optional
38.157718
141
0.540322
1856d318d47ce3e4786a9a38b7674ba6814094a5
1,554
py
Python
Python-CPU/monitor.py
cwd0204/Python
35413d0cfab0d659d710fd3f752dacef00f4a713
[ "MIT" ]
1
2022-01-05T05:49:59.000Z
2022-01-05T05:49:59.000Z
Python-CPU/monitor.py
cwd0204/Python
35413d0cfab0d659d710fd3f752dacef00f4a713
[ "MIT" ]
null
null
null
Python-CPU/monitor.py
cwd0204/Python
35413d0cfab0d659d710fd3f752dacef00f4a713
[ "MIT" ]
null
null
null
# CPU # Charles # Charles import matplotlib.pyplot as plt import matplotlib.font_manager as font_manager import psutil as p POINTS = 300 fig, ax = plt.subplots() ax.set_ylim([0, 100]) ax.set_xlim([0, POINTS]) ax.set_autoscale_on(False) ax.set_xticks([]) ax.set_yticks(range(0, 101, 10)) ax.grid(True) # user = [None] * POINTS # sys = [None] * POINTS # CPU idle = [None] * POINTS l_user, = ax.plot(range(POINTS), user, label='User %') l_sys, = ax.plot(range(POINTS), sys, label='Sys %') l_idle, = ax.plot(range(POINTS), idle, label='Idle %') ax.legend(loc='upper center', ncol=4, prop=font_manager.FontProperties(size=10)) bg = fig.canvas.copy_from_bbox(ax.bbox) before = cpu_usage() if __name__ == '__main__': start_monitor()
20.72
80
0.689189
185a8ab47b8d277c20020394a96aac3365fae3e8
8,128
py
Python
leaderboards/api_views.py
bfrederix/django-improv
23ae4b2cc3b7d38aa2a4d6872ea084247a1e34f6
[ "Apache-2.0" ]
1
2020-08-07T18:46:19.000Z
2020-08-07T18:46:19.000Z
leaderboards/api_views.py
bfrederix/django-improv
23ae4b2cc3b7d38aa2a4d6872ea084247a1e34f6
[ "Apache-2.0" ]
null
null
null
leaderboards/api_views.py
bfrederix/django-improv
23ae4b2cc3b7d38aa2a4d6872ea084247a1e34f6
[ "Apache-2.0" ]
null
null
null
import datetime from rest_framework import viewsets from rest_framework.response import Response from leaderboards import LEADERBOARD_MAX_PER_PAGE from leaderboards.models import LeaderboardEntry, Medal, LeaderboardSpan from leaderboards.serializers import (LeaderboardEntrySerializer, MedalSerializer, LeaderboardSerializer, LeaderboardSpanSerializer, LeaderboardEntrySpanSerializer) from leaderboards import service as leaderboards_service from users import service as users_service from channels import service as channels_service from shows import service as shows_service from utilities.api import APIObject
42.333333
118
0.664001
185ab66623ac277ebae7a53438dfbee88f107a07
4,450
py
Python
pyaz/sql/instance_pool/__init__.py
py-az-cli/py-az-cli
9a7dc44e360c096a5a2f15595353e9dad88a9792
[ "MIT" ]
null
null
null
pyaz/sql/instance_pool/__init__.py
py-az-cli/py-az-cli
9a7dc44e360c096a5a2f15595353e9dad88a9792
[ "MIT" ]
null
null
null
pyaz/sql/instance_pool/__init__.py
py-az-cli/py-az-cli
9a7dc44e360c096a5a2f15595353e9dad88a9792
[ "MIT" ]
1
2022-02-03T09:12:01.000Z
2022-02-03T09:12:01.000Z
from ... pyaz_utils import _call_az def show(name, resource_group): ''' Get the details for an instance pool. Required Parameters: - name -- Instance Pool Name - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` ''' return _call_az("az sql instance-pool show", locals()) def list(resource_group=None): ''' List available instance pools. Optional Parameters: - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` ''' return _call_az("az sql instance-pool list", locals()) def update(name, resource_group, add=None, force_string=None, remove=None, set=None, tags=None): ''' Update an instance pool. Required Parameters: - name -- Instance Pool Name - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` Optional Parameters: - add -- Add an object to a list of objects by specifying a path and key value pairs. Example: --add property.listProperty <key=value, string or JSON string> - force_string -- When using 'set' or 'add', preserve string literals instead of attempting to convert to JSON. - remove -- Remove a property or an element from a list. Example: --remove property.list <indexToRemove> OR --remove propertyToRemove - set -- Update an object by specifying a property path and value to set. Example: --set property1.property2=<value> - tags -- space-separated tags: key[=value] [key[=value] ...]. Use '' to clear existing tags. ''' return _call_az("az sql instance-pool update", locals()) def delete(name, resource_group, no_wait=None, yes=None): ''' Delete an instance pool. Required Parameters: - name -- Instance Pool Name - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` Optional Parameters: - no_wait -- Do not wait for the long-running operation to finish. - yes -- Do not prompt for confirmation. ''' return _call_az("az sql instance-pool delete", locals()) def create(capacity, family, location, name, resource_group, subnet, tier, license_type=None, no_wait=None, tags=None, vnet_name=None): ''' Create an instance pool. Required Parameters: - capacity -- Capacity of the instance pool in vcores. - family -- The compute generation component of the sku. Allowed value: Gen5 - location -- Location. Values from: `az account list-locations`. You can configure the default location using `az configure --defaults location=<location>`. - name -- Instance Pool Name - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` - subnet -- Name or ID of the subnet that allows access to an Instance Pool. If subnet name is provided, --vnet-name must be provided. - tier -- The edition component of the sku. Allowed value: GeneralPurpose. Optional Parameters: - license_type -- The license type to apply for this instance pool. - no_wait -- Do not wait for the long-running operation to finish. - tags -- space-separated tags: key[=value] [key[=value] ...]. Use '' to clear existing tags. - vnet_name -- The virtual network name ''' return _call_az("az sql instance-pool create", locals()) def wait(name, resource_group, created=None, custom=None, deleted=None, exists=None, interval=None, timeout=None, updated=None): ''' Wait for an instance pool to reach a desired state. Required Parameters: - name -- Instance Pool Name - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` Optional Parameters: - created -- wait until created with 'provisioningState' at 'Succeeded' - custom -- Wait until the condition satisfies a custom JMESPath query. E.g. provisioningState!='InProgress', instanceView.statuses[?code=='PowerState/running'] - deleted -- wait until deleted - exists -- wait until the resource exists - interval -- polling interval in seconds - timeout -- maximum wait in seconds - updated -- wait until updated with provisioningState at 'Succeeded' ''' return _call_az("az sql instance-pool wait", locals())
45.408163
164
0.702472
185b8c2212dd3b144fbc0efeca4d07970b4b5805
316
py
Python
exercicios/ex090.py
Siqueira-Vinicius/Python
bd1f7e2bcdfd5481724d32db387f51636bb4ad60
[ "MIT" ]
null
null
null
exercicios/ex090.py
Siqueira-Vinicius/Python
bd1f7e2bcdfd5481724d32db387f51636bb4ad60
[ "MIT" ]
null
null
null
exercicios/ex090.py
Siqueira-Vinicius/Python
bd1f7e2bcdfd5481724d32db387f51636bb4ad60
[ "MIT" ]
null
null
null
aluno = {} aluno['nome'] = str(input('Digite o nome do aluno: ')) aluno['media'] = float(input('Digite a mdia desse aluno: ')) if aluno['media'] >= 5: aluno['situao'] = '\033[32mAprovado\033[m' else: aluno['situao'] = '\033[31mReprovado\033[m' for k, v in aluno.items(): print(f'{k} do aluno {v}')
35.111111
61
0.617089
185c355337e2e9938d29808ca0f7b31c79694a3f
813
py
Python
cntr_div_train_test_images.py
globalgood-ag/treecover
ecab0ac2cef622b5f72054d5a234237a34c0bd4d
[ "MIT" ]
null
null
null
cntr_div_train_test_images.py
globalgood-ag/treecover
ecab0ac2cef622b5f72054d5a234237a34c0bd4d
[ "MIT" ]
null
null
null
cntr_div_train_test_images.py
globalgood-ag/treecover
ecab0ac2cef622b5f72054d5a234237a34c0bd4d
[ "MIT" ]
null
null
null
# -*- coding: utf-8 -*- """ Created on Tue Aug 6 10:57:41 2019 Creates train and test splits at the IMAGE LEVEL to prep for thumbnail extraction in countr_cnn_1 @author: smcguire """ import pandas as pd import numpy as np import matplotlib.pyplot as plt ## read dataframe of unique images with annotation info df_unique = pd.read_pickle('./df_unique.pkl') # create df_test from every 4th image df_test = df_unique[df_unique.index % 4 == 0] # create df_train_val from every image not in df_test df_train_val = df_unique[df_unique.index % 4 != 0] # reset indexes df_test = df_test.reset_index(drop=True) df_train_val = df_train_val.reset_index(drop=True) # pickle dataframes df_test.to_pickle('./df_test.pkl') df_train_val.to_pickle('./df_train_val.pkl')
26.225806
98
0.710947
185c491ee371d020cd3b4bc449367e92f4f7af90
1,144
py
Python
SUAVE/SUAVE-2.5.0/trunk/SUAVE/Attributes/Propellants/Aviation_Gasoline.py
Vinicius-Tanigawa/Undergraduate-Research-Project
e92372f07882484b127d7affe305eeec2238b8a9
[ "MIT" ]
null
null
null
SUAVE/SUAVE-2.5.0/trunk/SUAVE/Attributes/Propellants/Aviation_Gasoline.py
Vinicius-Tanigawa/Undergraduate-Research-Project
e92372f07882484b127d7affe305eeec2238b8a9
[ "MIT" ]
null
null
null
SUAVE/SUAVE-2.5.0/trunk/SUAVE/Attributes/Propellants/Aviation_Gasoline.py
Vinicius-Tanigawa/Undergraduate-Research-Project
e92372f07882484b127d7affe305eeec2238b8a9
[ "MIT" ]
null
null
null
## @ingroup Attributes-Propellants # Aviation_Gasoline.py # # Created: Unk 2013, SUAVE TEAM # Modified: Apr 2015, SUAVE TEAM # ---------------------------------------------------------------------- # Imports # ---------------------------------------------------------------------- from .Propellant import Propellant # ---------------------------------------------------------------------- # Aviation_Gasoline Propellant Class # ---------------------------------------------------------------------- ## @ingroup Attributes-Propellants
23.346939
72
0.436189
185eea51530d25c06bcb22494c22d6c4640df3ce
4,108
py
Python
write_grok/write_grok.py
namedyangfan/Python_practice
7f7394d82bb5afc13b039eec286b9485a775ae39
[ "MIT" ]
null
null
null
write_grok/write_grok.py
namedyangfan/Python_practice
7f7394d82bb5afc13b039eec286b9485a775ae39
[ "MIT" ]
null
null
null
write_grok/write_grok.py
namedyangfan/Python_practice
7f7394d82bb5afc13b039eec286b9485a775ae39
[ "MIT" ]
null
null
null
import os, glob, shutil
45.644444
96
0.529211
185f0bca3ed3085aa387bfdbe9104d5218249f4a
5,752
py
Python
src/tfi/publish.py
ajbouh/tfi
6e89e8c8f1ca3b285c788cc6b802fc44f9001290
[ "MIT" ]
160
2017-09-13T00:32:05.000Z
2018-05-21T18:17:32.000Z
src/tfi/publish.py
tesserai/tfi
6e89e8c8f1ca3b285c788cc6b802fc44f9001290
[ "MIT" ]
6
2017-09-14T17:54:21.000Z
2018-01-27T19:31:18.000Z
src/tfi/publish.py
ajbouh/tfi
6e89e8c8f1ca3b285c788cc6b802fc44f9001290
[ "MIT" ]
11
2017-09-13T00:37:08.000Z
2018-03-05T08:03:34.000Z
import decimal import hashlib import json import requests import tempfile import uuid import os from tqdm import tqdm from requests_toolbelt import MultipartEncoder, MultipartEncoderMonitor namespace = "default" fission_url = os.environ["FISSION_URL"]
30.433862
96
0.577712